diff --git a/.gitignore b/.gitignore
index a2d3412..de4bc5d 100755
--- a/.gitignore
+++ b/.gitignore
@@ -177,3 +177,4 @@ cython_debug/
 /playground/art_assert/
 /playground/unpack/
 /playground/boom-train/
+/frontend/
diff --git a/README.md b/README.md
index 2a413c5..f57f9ac 100755
--- a/README.md
+++ b/README.md
@@ -16,6 +16,7 @@ The goal of this repository is to use Pydantic to build legitimate requests to a
 - [x] tool.random_prompt
 - [x] tool.paint_mask
 - [x] tool.image_metadata
+- [x] tokenizer
 - [x] /ai/generate-image
 - [x] /user/subscription
 - [x] /user/login
@@ -85,7 +86,7 @@ from dotenv import load_dotenv
 from pydantic import SecretStr
 
 from novelai_python import APIError, LoginCredential
-from novelai_python.sdk.ai.generate import TextLLMModel, LLM
+from novelai_python.sdk.ai.generate import TextLLMModel, LLM, get_default_preset
 
 load_dotenv()
 username = os.getenv("NOVELAI_USER", None)
@@ -99,7 +100,13 @@ login_credential = LoginCredential(
 
 async def chat(prompt: str):
     try:
-        agent = LLM.build(prompt=prompt, model=TextLLMModel.Kayra)
+        model = TextLLMModel.ERATO  # llama3
+        parameters = get_default_preset(model).parameters
+        agent = LLM.build(
+            prompt=prompt,
+            model=model,
+            parameters=None  # Auto Select or get from preset
+        )
         result = await agent.request(session=login_credential)
     except APIError as e:
         raise Exception(f"Error: {e.message}")
@@ -126,6 +133,21 @@ pip install novelai_python
 python3 -m novelai_python.server -h '127.0.0.1' -p 7888
 ```
 
+#### Tokenizer
+
+```python
+from novelai_python._enum import get_tokenizer_model, TextLLMModel
+from novelai_python.tokenizer import NaiTokenizer
+
+tokenizer_package = NaiTokenizer(get_tokenizer_model(TextLLMModel.ERATO))
+t_text = "a fox jumped over the lazy dog"
+encode_tokens = tokenizer_package.encode(t_text)
+print(tokenizer_package.tokenize_text(t_text))
+print(f"Tokenized text: {encode_tokens}")
+print(tokenizer_package.decode(tokenizer_package.encode(t_text)))
+
+```
+
 ## Acknowledgements 🙏
 
 [BackEnd](https://api.novelai.net/docs)
diff --git a/pdm.lock b/pdm.lock
index a83bdd8..c231b7e 100755
--- a/pdm.lock
+++ b/pdm.lock
@@ -5,7 +5,7 @@
 groups = ["default", "dev", "testing"]
 strategy = ["cross_platform", "inherit_metadata"]
 lock_version = "4.5.0"
-content_hash = "sha256:441ca939f9a590f5c4832a750e5375bd23f58a546b392106094e6da5bd21ffb3"
+content_hash = "sha256:d97e0fe3e9659cc78992a3fbe8cc33307067e47ad4b074c9c5e48f72289208a6"
 
 [[metadata.targets]]
 requires_python = ">=3.9"
@@ -288,6 +288,20 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "colorlog"
+version = "6.8.2"
+requires_python = ">=3.6"
+summary = "Add colours to the output of Python's logging module."
+groups = ["default"]
+dependencies = [
+    "colorama; sys_platform == \"win32\"",
+]
+files = [
+    {file = "colorlog-6.8.2-py3-none-any.whl", hash = "sha256:4dcbb62368e2800cb3c5abd348da7e53f6c362dda502ec27c560b2e58a66bd33"},
+    {file = "colorlog-6.8.2.tar.gz", hash = "sha256:3e3e079a41feb5a1b64f978b5ea4f46040a94f11f0e8bbb8261e3dbbeca64d44"},
+]
+
 [[package]]
 name = "cryptography"
 version = "42.0.5"
@@ -552,7 +566,7 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.22.2"
+version = "0.25.1"
 requires_python = ">=3.8.0"
 summary = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 groups = ["default"]
@@ -566,8 +580,8 @@ dependencies = [
     "typing-extensions>=3.7.4.3",
 ]
 files = [
-    {file = "huggingface_hub-0.22.2-py3-none-any.whl", hash = "sha256:3429e25f38ccb834d310804a3b711e7e4953db5a9e420cc147a5e194ca90fd17"},
-    {file = "huggingface_hub-0.22.2.tar.gz", hash = "sha256:32e9a9a6843c92f253ff9ca16b9985def4d80a93fb357af5353f770ef74a81be"},
+    {file = "huggingface_hub-0.25.1-py3-none-any.whl", hash = "sha256:a5158ded931b3188f54ea9028097312cb0acd50bffaaa2612014c3c526b44972"},
+    {file = "huggingface_hub-0.25.1.tar.gz", hash = "sha256:9ff7cb327343211fbd06e2b149b8f362fd1e389454f3f14c6db75a4999ee20ff"},
 ]
 
 [[package]]
@@ -618,6 +632,17 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "json-repair"
+version = "0.29.4"
+requires_python = ">=3.8"
+summary = "A package to repair broken json strings"
+groups = ["default"]
+files = [
+    {file = "json_repair-0.29.4-py3-none-any.whl", hash = "sha256:2d7addfa01e3b4c295c4ebabd5f393127adae0d345616d3a2517df8260429dae"},
+    {file = "json_repair-0.29.4.tar.gz", hash = "sha256:2921760e707ac0d0b63478402fd6ea3162d4191adf873b396becb31c47a1ac30"},
+]
+
 [[package]]
 name = "loguru"
 version = "0.7.2"
@@ -1276,6 +1301,21 @@ files = [
     {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
 ]
 
+[[package]]
+name = "robust-downloader"
+version = "0.0.2"
+summary = "A Simple Robust Downloader written in Python"
+groups = ["default"]
+dependencies = [
+    "colorlog",
+    "requests",
+    "tqdm",
+]
+files = [
+    {file = "robust-downloader-0.0.2.tar.gz", hash = "sha256:08c938b96e317abe6b037e34230a91bda9b5d613f009bca4a47664997c61de90"},
+    {file = "robust_downloader-0.0.2-py3-none-any.whl", hash = "sha256:8fe08bfb64d714fd1a048a7df6eb7b413eb4e624309a49db2c16fbb80a62869d"},
+]
+
 [[package]]
 name = "safer"
 version = "4.12.3"
@@ -1389,7 +1429,7 @@ files = [
 
 [[package]]
 name = "tokenizers"
-version = "0.20.0"
+version = "0.19.1"
 requires_python = ">=3.7"
 summary = ""
 groups = ["default"]
@@ -1397,69 +1437,69 @@ dependencies = [
     "huggingface-hub<1.0,>=0.16.4",
 ]
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 ]
 
 [[package]]
diff --git a/playground/generate.py b/playground/generate.py
index 21ea21e..248a307 100644
--- a/playground/generate.py
+++ b/playground/generate.py
@@ -27,9 +27,9 @@ async def chat(prompt="Hello"):
     try:
         agent = LLM.build(
             prompt=prompt,
-            model=TextLLMModel.Kayra,
+            model=TextLLMModel.ERATO,
         )
-        result = await agent.request(session=credential)
+        result = await agent.request(session=_login_credential)
     except APIError as e:
         logger.exception(e)
         print(f"Error: {e.message}")
@@ -37,8 +37,10 @@ async def chat(prompt="Hello"):
     except Exception as e:
         logger.exception(e)
     else:
-        print(f"Result: \n{result.text}")
+        print(f"Result:\n{result.text}")
 
 
-loop = asyncio.get_event_loop()
-loop.run_until_complete(chat())
+loop = asyncio.new_event_loop()
+loop.run_until_complete(chat(
+    prompt="a fox jumped over the lazy dog, and the dog barked at the fox. The fox ran away."
+))
diff --git a/playground/generate_stream.py b/playground/generate_stream.py
index 3b03fd6..9ffc68c 100644
--- a/playground/generate_stream.py
+++ b/playground/generate_stream.py
@@ -34,14 +34,12 @@ async def stream(prompt="Hello"):
     try:
         agent = LLMStream.build(
             prompt=prompt,
-            model=TextLLMModel.Kayra,
+            model=TextLLMModel.ERATO,
         )
         _data = []
-        # 现在，你可以使用异步for循环来处理每一部分数据
-        generator = agent.request(session=credential)
+        generator = agent.request(session=_login_credential)
         async for data in generator:
             data: LLMStreamResp
-            print(data.text)  # 或者做其他需要的处理
             _data.append(data)
     except APIError as e:
         print(f"Error: {e.message}")
diff --git a/playground/tokenizer/tokenizer_demo.py b/playground/tokenizer/tokenizer_demo.py
new file mode 100644
index 0000000..d48b3f0
--- /dev/null
+++ b/playground/tokenizer/tokenizer_demo.py
@@ -0,0 +1,94 @@
+import json
+import os
+import pathlib
+import zlib
+from typing import Dict, List, Optional
+
+import requests
+from json_repair import repair_json
+from pydantic import BaseModel, model_validator
+from tokenizers import Tokenizer, pre_tokenizers, Regex, decoders
+from tokenizers.models import BPE
+
+# https://novelai.net/tokenizer/compressed/llama3nai_tokenizer.def?v=2&static=true
+
+model_name = "clip_tokenizer"
+model_full_name = f"{model_name}.def"
+url = f"https://novelai.net/tokenizer/compressed/{model_full_name}?v=2&static=true"
+if not os.path.exists(model_full_name):
+    print(f"Downloading {model_full_name} from {url}")
+    response = requests.get(url)
+    response.raise_for_status()
+    # write down
+    with open(model_full_name, "wb") as f:
+        f.write(response.content)
+
+
+class TokenizerSetting(BaseModel):
+    class TokenizerConfig(BaseModel):
+        splitRegex: str
+        maxEncodeChars: Optional[int] = None
+        maxNoWhitespaceChars: Optional[int] = None
+        ignoreMerges: Optional[bool] = False
+
+    config: TokenizerConfig
+    specialTokens: List[str]
+    vocab: Dict[str, int]
+    merges: list
+
+    @model_validator(mode="after")
+    def ensure(self):
+        self.merges = [tuple(merge) for merge in self.merges]
+        return self
+
+
+# 读取和解压文件
+file = pathlib.Path(__file__).parent.joinpath(model_full_name)
+encoded_data = file.read_bytes()
+decompress_obj = zlib.decompressobj(-zlib.MAX_WBITS)
+decode = decompress_obj.decompress(encoded_data)
+
+# 修复和解析 JSON
+repaired_json = repair_json(decode.decode('utf-8'), return_objects=True)
+json.dump(repaired_json, open(f"{model_name}.json", "w"), indent=2)
+tokenizer_setting = TokenizerSetting.model_validate(repaired_json)
+
+# 创建 tokenizer
+tokenizer = Tokenizer(BPE(
+    vocab=tokenizer_setting.vocab,
+    merges=tokenizer_setting.merges,
+    ignore_merges=tokenizer_setting.config.ignoreMerges
+))
+
+# 设置特殊 tokens
+tokenizer.add_special_tokens(tokenizer_setting.specialTokens)
+print(tokenizer.token_to_id(" "))
+if tokenizer_setting.config.maxEncodeChars:
+    tokenizer.enable_truncation(max_length=tokenizer_setting.config.maxEncodeChars)
+# 设置 normalizer
+# tokenizer.normalizer = normalizers.Sequence([])
+
+# 设置 pre_tokenizer
+pre_zus = [
+    pre_tokenizers.Split(
+        behavior="merged_with_next",
+        pattern=Regex(tokenizer_setting.config.splitRegex)
+    ),
+]
+if tokenizer.token_to_id(" ") is None:
+    pre_zus.append(pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False))
+pre_tokenizer = pre_tokenizers.Sequence(pre_zus)
+
+tokenizer.pre_tokenizer = pre_tokenizer
+tokenizer.decoder = decoders.ByteLevel()
+
+# 使用 tokenizer
+text = "Hello, World! This is a test."
+encoded = tokenizer.encode(text, add_special_tokens=True)
+print(f"Pre-tokenized text: {pre_tokenizer.pre_tokenize_str(text)}")
+print(f"Encoded tokens: {encoded.tokens}")
+print(f"Token IDs: {encoded.ids}")
+
+# 解码
+decoded = tokenizer.decode(encoded.ids)
+print(f"Decoded text:{decoded}")
diff --git a/playground/tokenizer/usage.py b/playground/tokenizer/usage.py
index 0d96bea..f7bf328 100644
--- a/playground/tokenizer/usage.py
+++ b/playground/tokenizer/usage.py
@@ -1,16 +1,22 @@
-from novelai_python.tokenizer import ImagePromptTokenizer
+from novelai_python._enum import TextTokenizerGroup, get_tokenizer_model, TextLLMModel
+from novelai_python.tokenizer import NaiTokenizer
+from novelai_python.utils.encode import b64_to_tokens
 
-tokenizer_util = ImagePromptTokenizer(ImagePromptTokenizer.MODEL_V2_PATH)
-text = "The quick brown fox jumps over the goblin."
-token_id = tokenizer_util.encode(text)
-print("Token IDs:", token_id)
-decoded_text = tokenizer_util.decode(token_id)
-print("Decoded text:", decoded_text)
+tokenizer_package = NaiTokenizer(get_tokenizer_model(TextLLMModel.ERATO))
+t_text = "a fox jumped over the lazy dog"
+encode_tokens = tokenizer_package.encode(t_text)
+print(tokenizer_package.tokenize_text(t_text))
+print(f"Tokenized text: {encode_tokens}")
+print(tokenizer_package.decode(tokenizer_package.encode(t_text)))
+
+b64 = "UfQBADoAAABIAQAAGQAAANwAAAATAAAAexQAAEAAAAD/mwAA2GkAAJ8DAAAXAQAAtT4AAC8WAAA="
+oks = b64_to_tokens(b64)
+print(oks)
 
 
 def limit_prompt_shown(raw_text: str, token_limit=225):
     assert isinstance(raw_text, str), "raw_text must be a string"
-    tokenizer = ImagePromptTokenizer(ImagePromptTokenizer.MODEL_V2_PATH)
+    tokenizer = NaiTokenizer(TextTokenizerGroup.NERDSTASH_V2)
     token_array = tokenizer.encode(raw_text)
     used_tokens_len = len(token_array)
     if used_tokens_len > token_limit:
diff --git a/pyproject.toml b/pyproject.toml
index 67e9c59..0db095c 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "novelai-python"
-version = "0.4.17"
+version = "0.5.0"
 description = "NovelAI Python Binding With Pydantic"
 authors = [
     { name = "sudoskys", email = "coldlando@hotmail.com" },
@@ -26,6 +26,8 @@ dependencies = [
     "ftfy>=6.2.0",
     "regex>=2023.12.25",
     "tokenizers>=0.15.2",
+    "json-repair>=0.29.4",
+    "robust-downloader>=0.0.2",
 ]
 requires-python = ">=3.9"
 readme = "README.md"
diff --git a/src/novelai_python/__init__.py b/src/novelai_python/__init__.py
index c44c489..1ea162b 100755
--- a/src/novelai_python/__init__.py
+++ b/src/novelai_python/__init__.py
@@ -2,7 +2,6 @@
 # @Time    : 2023/11/18 上午12:18
 # @Author  : sudoskys
 # @File    : __init__.py
-
 from ._exceptions import (
     NovelAiError,
     APIError,
diff --git a/src/novelai_python/_enum.py b/src/novelai_python/_enum.py
new file mode 100644
index 0000000..991909f
--- /dev/null
+++ b/src/novelai_python/_enum.py
@@ -0,0 +1,94 @@
+from enum import Enum
+from typing import Optional, Union
+
+
+class TextLLMModel(Enum):
+    NEO_2B = "2.7B"
+    J_6B = "6B"
+    J_6B_V3 = "6B-v3"
+    J_6B_V4 = "6B-v4"
+    GENJI_PYTHON_6B = "genji-python-6b"
+    GENJI_JP_6B = "genji-jp-6b"
+    GENJI_JP_6B_V2 = "genji-jp-6b-v2"
+    EUTERPE_V0 = "euterpe-v0"
+    EUTERPE_V2 = "euterpe-v2"
+    KRAKE_V1 = "krake-v1"
+    KRAKE_V2 = "krake-v2"
+    BLUE = "blue"
+    RED = "red"
+    GREEN = "green"
+    PURPLE = "purple"
+    PINK = "pink"
+    YELLOW = "yellow"
+    WHITE = "white"
+    BLACK = "black"
+    CASSANDRA = "cassandra"
+    COMMENT_BOT = "hypebot"
+    INFILL = "infillmodel"
+    CLIO = "clio-v1"
+    KAYRA = "kayra-v1"
+    ERATO = "llama-3-erato-v1"
+
+
+class TextTokenizerGroup(object):
+    GENJI = "genji_tokenizer.def"
+    PILE = "pile_tokenizer.def"
+    PILE_NAI = "pile_tokenizer.def"
+    NAI_INLINE = "gpt2_tokenizer.def"
+    NERDSTASH_V2 = "nerdstash_tokenizer_v2.def"
+    NERDSTASH = "nerdstash_tokenizer.def"
+    LLAMA3 = "llama3_tokenizer.def"
+    GPT2 = "gpt2_tokenizer.def"
+    CLIP = "clip_tokenizer.def"
+
+
+TextLLMModelTypeAlias = Union[TextLLMModel, str]
+
+TOKENIZER_MODEL_MAP = {
+    TextLLMModel.GENJI_JP_6B_V2: TextTokenizerGroup.GENJI,
+    TextLLMModel.CASSANDRA: TextTokenizerGroup.PILE,
+    TextLLMModel.KRAKE_V2: TextTokenizerGroup.PILE,
+    TextLLMModel.INFILL: TextTokenizerGroup.NAI_INLINE,
+    TextLLMModel.KAYRA: TextTokenizerGroup.NERDSTASH_V2,
+    TextLLMModel.BLUE: TextTokenizerGroup.NERDSTASH_V2,
+    TextLLMModel.PINK: TextTokenizerGroup.NERDSTASH_V2,
+    TextLLMModel.YELLOW: TextTokenizerGroup.NERDSTASH_V2,
+    TextLLMModel.RED: TextTokenizerGroup.NERDSTASH_V2,
+    TextLLMModel.GREEN: TextTokenizerGroup.NERDSTASH_V2,
+    TextLLMModel.BLACK: TextTokenizerGroup.NERDSTASH_V2,
+    TextLLMModel.CLIO: TextTokenizerGroup.NERDSTASH,
+    TextLLMModel.PURPLE: TextTokenizerGroup.LLAMA3,
+    TextLLMModel.WHITE: TextTokenizerGroup.LLAMA3,
+    TextLLMModel.ERATO: TextTokenizerGroup.LLAMA3,
+}
+
+COLORS_LLM = [
+    TextLLMModel.BLUE,
+    TextLLMModel.RED,
+    TextLLMModel.GREEN,
+    TextLLMModel.PURPLE,
+    TextLLMModel.PINK,
+    TextLLMModel.YELLOW,
+    TextLLMModel.WHITE,
+    TextLLMModel.BLACK,
+]
+
+
+def get_llm_group(model: TextLLMModel) -> Optional[TextTokenizerGroup]:
+    if isinstance(model, str):
+        model = TextLLMModel(model)
+    return TOKENIZER_MODEL_MAP.get(model, None)
+
+
+def get_tokenizer_model(model: TextLLMModel) -> str:
+    if isinstance(model, str):
+        model = TextLLMModel(model)
+    group = TOKENIZER_MODEL_MAP.get(model, TextTokenizerGroup.GPT2)
+    return group
+
+
+def get_tokenizer_model_url(model: TextLLMModel) -> str:
+    model_name = get_tokenizer_model(model)
+    if not model_name.endswith(".def"):
+        model_name = f"{model_name}.def"
+    return f"https://novelai.net/tokenizer/compressed/{model_name}?v=2&static=true"
diff --git a/src/novelai_python/_response/ai/generate.py b/src/novelai_python/_response/ai/generate.py
index 6fc1a53..0cbdaea 100644
--- a/src/novelai_python/_response/ai/generate.py
+++ b/src/novelai_python/_response/ai/generate.py
@@ -2,8 +2,8 @@
 
 from pydantic import BaseModel, ConfigDict
 
-from novelai_python.sdk.ai.generate._enum import TOKENIZER, TextLLMModel  # noqa
-from novelai_python.tokenizer import LLMTokenizer
+from novelai_python._enum import get_tokenizer_model, TextLLMModel
+from novelai_python.tokenizer import NaiTokenizer
 from novelai_python.utils.encode import b64_to_tokens
 
 if TYPE_CHECKING:
@@ -20,4 +20,7 @@ class LLMResp(BaseModel):
 
     @staticmethod
     def decode_token(token_str, model: TextLLMModel) -> str:
-        return LLMTokenizer().decode(b64_to_tokens(token_str), tokenizer_name=TOKENIZER.get(model))
+        dtype = 'uint32' if model in [TextLLMModel.ERATO] else 'uint16'
+        return NaiTokenizer(model=get_tokenizer_model(model)).decode(
+            b64_to_tokens(token_str, dtype=dtype)
+        )
diff --git a/src/novelai_python/_response/ai/generate_stream.py b/src/novelai_python/_response/ai/generate_stream.py
index 7a2e77c..3b71343 100644
--- a/src/novelai_python/_response/ai/generate_stream.py
+++ b/src/novelai_python/_response/ai/generate_stream.py
@@ -2,8 +2,8 @@
 
 from pydantic import BaseModel, ConfigDict
 
-from novelai_python.sdk.ai.generate._enum import TOKENIZER, TextLLMModel  # noqa
-from novelai_python.tokenizer import LLMTokenizer
+from novelai_python._enum import get_tokenizer_model, TextLLMModel
+from novelai_python.tokenizer import NaiTokenizer
 from novelai_python.utils.encode import b64_to_tokens
 
 
@@ -20,4 +20,7 @@ class LLMStreamResp(BaseModel):
 
     @staticmethod
     def decode(token_str, model: TextLLMModel) -> str:
-        return LLMTokenizer().decode(b64_to_tokens(token_str), tokenizer_name=TOKENIZER.get(model))
+        dtype = 'uint32' if model in [TextLLMModel.ERATO] else 'uint16'
+        return NaiTokenizer(model=get_tokenizer_model(model)).decode(
+            b64_to_tokens(token_str, dtype=dtype)
+        )
diff --git a/src/novelai_python/sdk/ai/generate/__init__.py b/src/novelai_python/sdk/ai/generate/__init__.py
index a8ef1b2..866b7b8 100644
--- a/src/novelai_python/sdk/ai/generate/__init__.py
+++ b/src/novelai_python/sdk/ai/generate/__init__.py
@@ -1,24 +1,30 @@
 # -*- coding: utf-8 -*-
 # @Author  : sudoskys
-from typing import Optional, Union, List
+import itertools
+import json
+from typing import Optional, Union
 from urllib.parse import urlparse
 
 import curl_cffi
 import httpx
-import pydantic
 from curl_cffi.requests import AsyncSession
 from loguru import logger
 from pydantic import ConfigDict, PrivateAttr, model_validator, Field
 from tenacity import wait_random, retry, stop_after_attempt, retry_if_exception
 
-from ._const import d_repetition_penalty_whitelist, d_bad_words_ids
-from ._enum import TextLLMModel, PenStyle, generate_order, TOKENIZER
-from ._preset import Params, PRESET
+from ._enum import get_bad_words_ids, \
+    get_eos_token_id, get_end_exclude_sequences, get_logit_bias_group, \
+    pickup_reverse_bias_model, get_default_preset, get_repetition_penalty_whitelist
+from ._schema import PenStyle, Key, KeyOrderEntry, LogitBiasGroup, AdvanceLLMSetting, \
+    LLMGenerationParams
 from ...schema import ApiBaseModel
+from ...._enum import TextLLMModelTypeAlias, TextLLMModel, get_tokenizer_model, get_llm_group, COLORS_LLM, \
+    TextTokenizerGroup
 from ...._exceptions import APIError, SessionHttpError
 from ...._response.ai.generate import LLMResp
 from ....credential import CredentialBase
-from ....tokenizer import LLMTokenizer
+from ....tokenizer import NaiTokenizer
+from ....utils import try_jsonfy
 from ....utils.encode import tokens_to_b64
 
 
@@ -26,12 +32,25 @@ class LLM(ApiBaseModel):
     """
     LLM for /ai/generate
     """
-    _endpoint: str = PrivateAttr("https://api.novelai.net")
+    _endpoint: str = PrivateAttr("https://text.novelai.net")
     input: str = Field(..., description="Base64 encoded token text")
-    model: Union[TextLLMModel, str] = TextLLMModel.Kayra
+    model: TextLLMModelTypeAlias = TextLLMModel.ERATO
     """Model to use"""
-    parameters: Params = Params()
 
+    advanced_setting: AdvanceLLMSetting = AdvanceLLMSetting()
+    """Advanced Setting"""
+
+    parameters: Optional[LLMGenerationParams] = None
+    """Optional: Generation Parameters"""
+
+    default_bias: Optional[bool] = True
+    """Default Bias"""
+
+    logprobs_count: Optional[int] = None
+    """Logprobs Count"""
+
+    bias_dinkus_asterism: Optional[bool] = False
+    prefix: Optional[str] = "vanilla"
     model_config = ConfigDict(extra="ignore")
 
     @property
@@ -63,77 +82,232 @@ async def necessary_headers(self, request_data) -> dict:
         }
 
     @model_validator(mode="after")
-    def validate_model(self):
-        tokenizer = LLMTokenizer()
+    def normalize_model(self):
+        tokenizer = NaiTokenizer(get_tokenizer_model(self.model))
+        model_group = get_llm_group(self.model)
+        total_tokens = len(tokenizer.encode(self.input))
         if isinstance(self.input, str):
-            prompt = tokenizer.encode(self.input, tokenizer_name=TOKENIZER.get(self.model))
-            self.input = tokens_to_b64(prompt)
-        if self.parameters.stop_sequences:
-            if not isinstance(self.parameters.stop_sequences, list):
-                raise pydantic.ValidationError("stop_sequences must be a list")
-            stop_sequences = []
-            for i, obj in enumerate(self.parameters.stop_sequences):
-                if isinstance(obj, str):
-                    stop_sequences.append(tokenizer.encode(obj, tokenizer_name=TOKENIZER.get(self.model)))
-                elif not isinstance(obj, list):
-                    raise ValueError(
-                        f"Expected type 'str' or 'list' for item #{i} of 'stop_sequences', " f"but got '{type(obj)}'"
+            prompt = tokenizer.encode(self.input)
+            dtype = "uint32" if self.model in [TextLLMModel.ERATO] else "uint16"
+            self.input = tokens_to_b64(prompt, dtype=dtype)
+        if not self.parameters:
+            default_preset = get_default_preset(self.model)
+            self.parameters = default_preset.parameters
+
+        # repetition_penalty_default_whitelist
+        if self.parameters.repetition_penalty_default_whitelist and self.model in [TextLLMModel.CLIO,
+                                                                                   TextLLMModel.KAYRA,
+                                                                                   TextLLMModel.ERATO] + COLORS_LLM:
+            if not isinstance(self.parameters.repetition_penalty_whitelist, list):
+                self.parameters.repetition_penalty_whitelist = []
+            self.parameters.repetition_penalty_whitelist += list(itertools.chain.from_iterable([
+                tokenizer.encode(tok)
+                for tok in get_repetition_penalty_whitelist(model_group)
+            ]))
+            self.parameters.repetition_penalty_default_whitelist = None
+
+        # bad_words_ids
+        bad_words_ids = [[16067]] if self.model == TextLLMModel.ERATO else []
+        if self.advanced_setting.bracket_ban:
+            bad_words_ids = bad_words_ids + get_bad_words_ids(model_group, use_banBrackets=True)
+        if self.bias_dinkus_asterism:
+            bad_words_ids = bad_words_ids + get_bad_words_ids(model_group, use_banBrackets=False)
+            if self.model in [
+                TextLLMModel.CLIO, TextLLMModel.KAYRA, TextLLMModel.ERATO,
+            ] + COLORS_LLM:
+                self.parameters.eos_token_id = get_eos_token_id(model_group)
+                if self.parameters.min_length < 5:
+                    self.parameters.min_length = 5
+                """
+                endExcludeSequences = get_end_exclude_sequences(model_group)
+                """
+                # context.length < 30
+                if len(self.input) < 3000:
+                    bad_words_ids = bad_words_ids + [[self.parameters.eos_token_id]]
+
+        # 有效的 logit_bias_group_exp
+        self.parameters.logit_bias_groups = get_logit_bias_group(model_group)
+        logit_bias_group_exp = []
+        if self.default_bias:
+            bias = -0.12
+            sequence_list = []
+            if self.model not in [TextLLMModel.NEO_2B, TextLLMModel.INFILL]:
+                if model_group in [TextTokenizerGroup.GPT2]:
+                    sequence_list = [[8162], [46256, 224]]
+                elif model_group in [TextTokenizerGroup.PILE]:
+                    sequence_list = [[9264]]
+                elif model_group in [TextTokenizerGroup.PILE_NAI]:
+                    sequence_list = [[9264], [50260]]
+                elif model_group in [TextTokenizerGroup.NERDSTASH, TextTokenizerGroup.NERDSTASH_V2]:
+                    sequence_list = [[23], [21]]
+                    bias = -0.08
+            for sequence in sequence_list:
+                # 如果不重复
+                if not any(
+                        cell.sequence == sequence and cell.bias == bias
+                        for cell in logit_bias_group_exp
+                ):
+                    logit_bias_group_exp.append(
+                        LogitBiasGroup(
+                            sequence=sequence,
+                            bias=bias,
+                            ensure_sequence_finish=False,
+                            generate_once=False
+                        )
                     )
-            self.parameters.stop_sequences = stop_sequences
-        if self.parameters.repetition_penalty:
-            # adjust repetition penalty value for Sigurd and Euterpe
-            if self.model in (TextLLMModel.Sigurd, TextLLMModel.Euterpe):
-                rep_pen = self.parameters.repetition_penalty
-                self.parameters.repetition_penalty = (0.525 * (rep_pen - 1) / 7) + 1
+        if self.model in [
+            TextLLMModel.J_6B,
+            TextLLMModel.J_6B_V4,
+            TextLLMModel.J_6B_V3,
+            TextLLMModel.J_6B_V4,
+            TextLLMModel.EUTERPE_V0,
+            TextLLMModel.EUTERPE_V2, TextLLMModel.GENJI_PYTHON_6B,
+            TextLLMModel.GENJI_JP_6B,
+            TextLLMModel.GENJI_JP_6B_V2
+        ]:
+            self.parameters.repetition_penalty = (self.parameters.repetition_penalty - 1) * -0.5249999999999999 / -7 + 1
+        if self.prefix == "" or self.model in [
+            TextLLMModel.J_6B, TextLLMModel.J_6B_V3, TextLLMModel.J_6B_V4,
+            TextLLMModel.EUTERPE_V0, TextLLMModel.EUTERPE_V2,
+            TextLLMModel.KRAKE_V1, TextLLMModel.KRAKE_V2,
+            TextLLMModel.CLIO,
+            TextLLMModel.KAYRA,
+        ] + COLORS_LLM:
+            self.prefix = "vanilla"
+        if pickup_reverse_bias_model(self.model) == TextLLMModel.GENJI_JP_6B_V2:
+            # 反转 logit_bias_group_exp 的 bias
+            for group in logit_bias_group_exp:
+                group.bias = -group.bias
+        order_queue = []
+        for order in self.parameters.order:
+            if isinstance(order, KeyOrderEntry):
+                if order.enabled:
+                    if order.id[0] == Key.TopG[0]:
+                        self.parameters.top_g = None
+                    order_queue.append(order.id[1])
+                else:
+                    if order.id[0] == Key.Temperature[0]:
+                        self.parameters.temperature = None
+                    elif order.id[0] == Key.TopK[0]:
+                        self.parameters.top_k = None
+                    elif order.id[0] == Key.TopP[0]:
+                        self.parameters.top_p = None
+                    elif order.id[0] == Key.TFS[0]:
+                        self.parameters.tail_free_sampling = None
+                    elif order.id[0] == Key.TopA[0]:
+                        self.parameters.top_a = None
+                    elif order.id[0] == Key.TypicalP[0]:
+                        self.parameters.typical_p = None
+                    elif order.id[0] == Key.Cfg[0]:
+                        self.parameters.cfg_scale = None
+                        self.parameters.cfg_uc = None
+                    elif order.id[0] == Key.TopG[0]:
+                        self.parameters.top_g = None
+                    elif order.id[0] == Key.Mirostat[0]:
+                        self.parameters.mirostat_tau = None
+                        self.parameters.mirostat_lr = None
+                    elif order.id[0] == Key.Math1[0]:
+                        self.parameters.math1_temp = None
+                        self.parameters.math1_quad = None
+                        self.parameters.math1_quad_entropy_scale = None
+                    elif order.id[0] == Key.MinP[0]:
+                        self.parameters.min_p = None
+        if self.model not in [TextLLMModel.CLIO, TextLLMModel.KAYRA] + COLORS_LLM:
+            self.parameters.top_g = None
+            if Key.TopG[1] in order_queue:
+                order_queue.remove(Key.TopG[1])
+        if self.model not in [TextLLMModel.CLIO, TextLLMModel.KAYRA, TextLLMModel.ERATO] + COLORS_LLM:
+            self.parameters.mirostat_tau = None
+            self.parameters.mirostat_lr = None
+            if Key.Mirostat[1] in order_queue:
+                order_queue.remove(Key.Mirostat[1])
+        if self.model not in [TextLLMModel.KAYRA, TextLLMModel.ERATO] + COLORS_LLM:
+            self.parameters.math1_temp = None
+            self.parameters.math1_quad = None
+            self.parameters.math1_quad_entropy_scale = None
+            if Key.Math1[1] in order_queue:
+                order_queue.remove(Key.Math1[1])
+        if self.model not in [TextLLMModel.KAYRA, TextLLMModel.ERATO] + COLORS_LLM:
+            self.parameters.min_p = None
+            if Key.MinP[1] in order_queue:
+                order_queue.remove(Key.MinP[1])
+        self.parameters.order = order_queue
+        for order in self.parameters.order:
+            if isinstance(order, KeyOrderEntry):
+                raise ValueError(f"type:KeyOrderEntry list not fully converted into type:int: {order}")
 
+        valid_sequences = []
+        for cell in logit_bias_group_exp:
+            if not any(token < 0 or token >= total_tokens for token in cell.sequence):
+                # 超出范围的 Token
+                logger.debug(
+                    f"Bias {cell} contains tokens that are out of range and will be ignored."
+                )
+            else:
+                # 将有效偏置组添加到列表
+                valid_sequences.append(cell)
+        if len(valid_sequences) == 0:
+            self.advanced_setting.logit_bias_exp = None
+        else:
+            self.advanced_setting.logit_bias_exp = valid_sequences
+
+        # Prefix
+        if self.prefix == "special_pedia":
+            bad_words_ids += [[24, 24], [24, 85, 24], [24, 1629], [24, 49287], [24, 49255]]
+
+        valid_bad_words_ids = []
+        self.parameters.bad_words_ids = bad_words_ids
+        for ban_word in self.parameters.bad_words_ids:
+            if not any(token < 0 or token >= total_tokens for token in ban_word):
+                logger.warning(
+                    f"Bad word {ban_word} contains tokens that are out of range and will be ignored."
+                )
+            else:
+                valid_bad_words_ids.append(ban_word)
+        if len(valid_bad_words_ids) > 0:
+            self.parameters.bad_words_ids = valid_bad_words_ids
+        else:
+            self.parameters.bad_words_ids = None
+
+        # Set
+        if not self.parameters.logit_bias_groups:
+            self.parameters.logit_bias_groups = None
+        self.advanced_setting.prefix = self.prefix
+        self.advanced_setting.order = order_queue
+        self.advanced_setting.num_logprobs = self.logprobs_count
+        if not self.advanced_setting.max_length:
+            self.advanced_setting.max_length = 40
         return self
 
     @classmethod
     def build(cls,
               prompt: str,
-              model: TextLLMModel = TextLLMModel.Kayra,
-              min_length: Optional[int] = None,
-              max_length: Optional[int] = None,
-              temperature: Optional[float] = None,
-              top_k: Optional[int] = None,
-              top_p: Optional[float] = None,
-              frequency_penalty: Optional[float] = None,
-              presence_penalty: Optional[float] = None,
-              stop_sequences: Optional[Union[List[str]]] = None,
-              repetition_penalty: Optional[float] = None,
-              pen_style: Optional[PenStyle] = None,
+              model: TextLLMModelTypeAlias = TextLLMModel.ERATO,
+              phrase_rep_pen: PenStyle = PenStyle.Off,
+              *,
+              advanced_setting: AdvanceLLMSetting = AdvanceLLMSetting(),
+              parameters: Optional[LLMGenerationParams] = None,
+              default_bias: Optional[bool] = True,
+              logprobs_count: Optional[int] = None,
+              bias_dinkus_asterism: Optional[bool] = False,
+              prefix: Optional[str] = "vanilla",
               **kwargs
               ) -> "LLM":
         """
         Generate Prompt
         """
         assert isinstance(prompt, str), "Prompt must be a string"
-        # 清理空值
-        kwarg = {
-            "min_length": min_length,
-            "max_length": max_length,
-            "temperature": temperature,
-            "top_k": top_k,
-            "top_p": top_p,
-            "frequency_penalty": frequency_penalty,
-            "presence_penalty": presence_penalty,
-            "stop_sequences": stop_sequences,
-            "repetition_penalty": repetition_penalty,
-            "pen_style": pen_style
-        }
-        kwargs = {**kwarg, **kwargs}
-        param = {k: v for k, v in kwargs.items() if v is not None}
-        _preset_model = PRESET.get(model.name, {})
-        if not _preset_model:
-            _build_prop = Params.model_validate(param)
-        else:
-            _build_prop = _preset_model.model_copy(deep=True, update=param)
-        assert _build_prop is not None, "Params validate failed"
-        return cls(
+        created = cls(
             input=prompt,
             model=model,
-            parameters=_build_prop
+            advanced_setting=advanced_setting,
+            parameters=parameters,
+            default_bias=default_bias,
+            logprobs_count=logprobs_count,
+            bias_dinkus_asterism=bias_dinkus_asterism,
+            prefix=prefix,
         )
+        return created
 
     @retry(
         wait=wait_random(min=1, max=3),
@@ -153,9 +327,17 @@ async def request(self,
         :return: LLMStreamResp
         """
         # Data Build
-        request_data = self.model_dump(mode="json", exclude_none=True)
-        request_data["order"] = generate_order(request_data) or []
-        assert request_data.get("parameters"), "Parameters is required"
+        base_map = self.parameters.get_base_map()
+        parameters = {
+            **base_map,
+            **self.advanced_setting.model_dump(mode="json", exclude_none=True),
+        }
+        parameters.update(self.parameters.model_dump(mode="json", exclude_none=True).items())
+        # Override
+        request_data = {
+            **self.model_dump(mode="json", include={"input", "model"}),
+            "parameters": parameters,
+        }
         async with session if isinstance(session, AsyncSession) else await session.get_session() as sess:
             # Header
             sess.headers.update(await self.necessary_headers(request_data))
@@ -163,7 +345,7 @@ async def request(self,
                 sess.headers.clear()
                 sess.headers.update(override_headers)
             # Log
-            logger.debug(f"LLM request data: {request_data}")
+            logger.debug(f"LLM request data: {json.dumps(request_data)}")
             # Request
             try:
                 assert hasattr(sess, "post"), "session must have post method."
@@ -172,49 +354,46 @@ async def request(self,
                     json=request_data,
                     stream=False
                 )
-                header_type = response.headers.get('Content-Type')
-                if "application/json" not in header_type:
-                    try:
-                        _msg = response.json()
-                    except Exception as e:
-                        _msg = {"statusCode": response.status_code, "message": response.content}
-                    status_code = _msg.get("statusCode", response.status_code)
-                    message = _msg.get("message", "Unknown error")
-                    if status_code == 400:
-                        raise APIError(
-                            f"A validation error occured. {message}",
-                            request=request_data, code=status_code, response=_msg
-                        )
-
-                    elif status_code == 401:
-                        raise APIError(
-                            f"Access Token is incorrect. {message}",
-                            request=request_data, code=status_code, response=_msg
-                        )
-                    elif status_code == 402:
-                        raise APIError(
-                            f"An active subscription is required to access this endpoint. {message}",
-                            request=request_data, code=status_code, response=_msg
-                        )
-                    elif status_code == 409:
+                response_data = try_jsonfy(response.content, default_when_error={})
+                message = response_data.get("error") or response_data.get(
+                    "message") or f"Server error with status_code {response.status_code}"
+                status_code = response_data.get("statusCode", response.status_code)
+                if status_code == 200:
+                    output = response_data.get("output", None)
+                    if not output:
                         raise APIError(
-                            f"A conflict error occured. {message}",
-                            request=request_data, code=status_code, response=_msg
+                            f"No Content Returned because {message}",
+                            request=request_data, code=response.status_code, response=response.content
                         )
-                    else:
-                        raise APIError(
-                            f"An unknown error occured. {response.status_code} {message}",
-                            request=request_data, code=status_code, response=_msg
-                        )
-                else:
-                    output = response.json().get("output", None)
-                    assert output, APIError("No Content Returned",
-                                            request=request_data, code=response.status_code, response=response.content
-                                            )
                     return LLMResp(
                         output=output,
                         text=LLMResp.decode_token(model=self.model, token_str=output)
                     )
+                elif status_code == 400:
+                    raise APIError(
+                        f"A validation error occured. {message}",
+                        request=request_data, code=status_code, response=message
+                    )
+                elif status_code == 401:
+                    raise APIError(
+                        f"Access Token is incorrect. {message}",
+                        request=request_data, code=status_code, response=message
+                    )
+                elif status_code == 402:
+                    raise APIError(
+                        f"An active subscription is required to access this endpoint. {message}",
+                        request=request_data, code=status_code, response=message
+                    )
+                elif status_code == 409:
+                    raise APIError(
+                        f"A conflict error occured. {message}",
+                        request=request_data, code=status_code, response=message
+                    )
+                else:
+                    raise APIError(
+                        f"An unknown error occured. {response.status_code} {message}",
+                        request=request_data, code=status_code, response=message
+                    )
             except curl_cffi.requests.errors.RequestsError as exc:
                 logger.exception(exc)
                 raise SessionHttpError(
diff --git a/src/novelai_python/sdk/ai/generate/_const.py b/src/novelai_python/sdk/ai/generate/_const.py
index c59df87..3988d31 100644
--- a/src/novelai_python/sdk/ai/generate/_const.py
+++ b/src/novelai_python/sdk/ai/generate/_const.py
@@ -1,136 +1,128 @@
-d_repetition_penalty_whitelist = [
-    49256,
-    49264,
-    49231,
-    49230,
-    49287,
-    85,
-    49255,
-    49399,
-    49262,
-    336,
-    333,
-    432,
-    363,
-    468,
-    492,
-    745,
-    401,
-    426,
-    623,
-    794,
-    1096,
-    2919,
-    2072,
-    7379,
-    1259,
-    2110,
-    620,
-    526,
-    487,
-    16562,
-    603,
-    805,
-    761,
-    2681,
-    942,
-    8917,
-    653,
-    3513,
-    506,
-    5301,
-    562,
-    5010,
-    614,
-    10942,
-    539,
-    2976,
-    462,
-    5189,
-    567,
-    2032,
-    123,
-    124,
-    125,
-    126,
-    127,
-    128,
-    129,
-    130,
-    131,
-    132,
-    588,
-    803,
-    1040,
-    49209,
-    4,
-    5,
-    6,
-    7,
-    8,
-    9,
-    10,
-    11,
-    12
-]
+class RepetitionPenaltyWhitelist(object):
+    _NERDSTASH_COMMON = [
+        "'", "\"", ",", ".", ":", "\n", "-", "*", ")", " the", " a", " an", " and", " or", " not",
+        " no",
+        " is",
+        " was", " were", " did", " does", " isn", " wasn", " weren", " didn", " doesn", " him", " her",
+        " his",
+        " hers", " their", " its", " could", " couldn", " should", " shouldn", " would", " wouldn",
+        " have", " haven",
+        " had", " hadn", " has", " hasn", " can", " cannot", " are", " aren", " will", " won", "0", "1",
+        "2", "3", "4",
+        "5", "6", "7", "8", "9", ".\"", ",\"", "====", " "
+    ]
+    NERDSTASH = _NERDSTASH_COMMON + ["ve", "s", "t", "n", "d", "ll", "re", "m"]
+    NERDSTASH_V2 = _NERDSTASH_COMMON + ["'t've", "'s", "'t", "'ve", "'n", "'d", "'ll", "'re", "'m"]
+    LLAMA3 = [
+        "'", "\"", ",", ".", ":", "\n", "-", "*", ")", " the", " a", " an", " and", " or", " not", " no", " is",
+        " was", " were", " did", " does", " isn", " wasn", " weren", " didn", " doesn", " him", " her", " his",
+        " hers", " their", " its", " could", " couldn", " should", " shouldn", " would", " wouldn", " have",
+        " haven",
+        " had", " hadn", " has", " hasn", " can", " cannot", " are", " aren", " will", " won", "0", "1", "2", "3",
+        "4",
+        "5", "6", "7", "8", "9", ".\"", ",\"", "====", " ", "'s", "'t", "'ve", "'n", "'d", "'ll", "'re", "'m",
+        " to",
+        " on", " of", " in", " at", " from", " that"
+    ]
+
 
-d_bad_words_ids = [
-    [
-        3
-    ],
-    [
-        49356
-    ],
-    [
-        1431
-    ],
-    [
-        31715
-    ],
-    [
-        34387
-    ],
-    [
-        20765
-    ],
-    [
-        30702
-    ],
-    [
-        10691
-    ],
-    [
-        49333
-    ],
-    [
-        1266
-    ],
-    [
-        19438
-    ],
-    [
-        43145
-    ],
-    [
-        26523
-    ],
-    [
-        41471
-    ],
-    [
-        2936
-    ],
-    [
-        85,
-        85
-    ],
-    [
-        49332
-    ],
-    [
-        7286
-    ],
-    [
-        1115
+class BanWords(object):
+    PILE = [
+        [60], [62], [544], [683], [696], [880], [905], [1008], [1019], [1084], [1092], [1181], [1184], [1254],
+        [1447], [1570], [1656], [2194], [2470], [2479], [2498], [2947], [3138], [3291], [3455], [3725], [3851],
+        [3891], [3921], [3951], [4207], [4299], [4622], [4681], [5013], [5032], [5180], [5218], [5290], [5413],
+        [5456], [5709], [5749], [5774], [6038], [6257], [6334], [6660], [6904], [7082], [7086], [7254], [7444],
+        [7748], [8001], [8088], [8168], [8562], [8605], [8795], [8850], [9014], [9102], [9259], [9318], [9336],
+        [9502], [9686], [9793], [9855], [9899], [9955], [10148], [10174], [10943], [11326], [11337], [11661],
+        [12004], [12084], [12159], [12520], [12977], [13380], [13488], [13663], [13811], [13976], [14412],
+        [14598], [14767], [15640], [15707], [15775], [15830], [16079], [16354], [16369], [16445], [16595],
+        [16614], [16731], [16943], [17278], [17281], [17548], [17555], [17981], [18022], [18095], [18297],
+        [18413], [18736], [18772], [18990], [19181], [20095], [20197], [20481], [20629], [20871], [20879],
+        [20924], [20977], [21375], [21382], [21391], [21687], [21810], [21828], [21938], [22367], [22372],
+        [22734], [23405], [23505], [23734], [23741], [23781], [24237], [24254], [24345], [24430], [25416],
+        [25896], [26119], [26635], [26842], [26991], [26997], [27075], [27114], [27468], [27501], [27618],
+        [27655], [27720], [27829], [28052], [28118], [28231], [28532], [28571], [28591], [28653], [29013],
+        [29547], [29650], [29925], [30522], [30537], [30996], [31011], [31053], [31096], [31148], [31258],
+        [31350], [31379], [31422], [31789], [31830], [32214], [32666], [32871], [33094], [33376], [33440],
+        [33805], [34368], [34398], [34417], [34418], [34419], [34476], [34494], [34607], [34758], [34761],
+        [34904], [34993], [35117], [35138], [35237], [35487], [35830], [35869], [36033], [36134], [36320],
+        [36399], [36487], [36586], [36676], [36692], [36786], [37077], [37594], [37596], [37786], [37982],
+        [38475], [38791], [39083], [39258], [39487], [39822], [40116], [40125], [41000], [41018], [41256],
+        [41305], [41361], [41447], [41449], [41512], [41604], [42041], [42274], [42368], [42696], [42767],
+        [42804], [42854], [42944], [42989], [43134], [43144], [43189], [43521], [43782], [44082], [44162],
+        [44270], [44308], [44479], [44524], [44965], [45114], [45301], [45382], [45443], [45472], [45488],
+        [45507], [45564], [45662], [46265], [46267], [46275], [46295], [46462], [46468], [46576], [46694],
+        [47093], [47384], [47389], [47446], [47552], [47686], [47744], [47916], [48064], [48167], [48392],
+        [48471], [48664], [48701], [49021], [49193], [49236], [49550], [49694], [49806], [49824], [50001],
+        [50256], [0], [1]
+    ]
+    PILE_NAI = PILE + [[50259], [50257], [50260]]
+    GPT2 = [
+        [58], [60], [90], [92], [685], [1391], [1782], [2361], [3693], [4083], [4357], [4895], [5512], [5974],
+        [7131], [8183], [8351], [8762], [8964], [8973], [9063], [11208], [11709], [11907], [11919], [12878],
+        [12962], [13018], [13412], [14631], [14692], [14980], [15090], [15437], [16151], [16410], [16589], [17241],
+        [17414], [17635], [17816], [17912], [18083], [18161], [18477], [19629], [19779], [19953], [20520], [20598],
+        [20662], [20740], [21476], [21737], [22133], [22241], [22345], [22935], [23330], [23785], [23834], [23884],
+        [25295], [25597], [25719], [25787], [25915], [26076], [26358], [26398], [26894], [26933], [27007], [27422],
+        [28013], [29164], [29225], [29342], [29565], [29795], [30072], [30109], [30138], [30866], [31161], [31478],
+        [32092], [32239], [32509], [33116], [33250], [33761], [34171], [34758], [34949], [35944], [36338], [36463],
+        [36563], [36786], [36796], [36937], [37250], [37913], [37981], [38165], [38362], [38381], [38430], [38892],
+        [39850], [39893], [41832], [41888], [42535], [42669], [42785], [42924], [43839], [44438], [44587], [44926],
+        [45144], [45297], [46110], [46570], [46581], [46956], [47175], [47182], [47527], [47715], [48600], [48683],
+        [48688], [48874], [48999], [49074], [49082], [49146], [49946], [10221], [4841], [1427], [2602, 834],
+        [29343], [37405], [35780], [2602], [50256]
+    ]
+    GENJI = [
+        [58], [60], [90], [92], [685], [1391], [1782], [2361], [3693], [4083], [4357], [4895], [5512], [5974],
+        [7131], [8183], [8964], [8973], [11208], [11709], [11907], [12878], [12962], [13412], [14692], [14980],
+        [15090], [15437], [16151], [16589], [17241], [17414], [17635], [17816], [17912], [18083], [18161],
+        [18477], [19629], [19953], [20520], [20662], [20740], [21737], [22241], [23330], [23834], [26076],
+        [26398], [26894], [26933], [27007], [27422], [29164], [29795], [30072], [30109], [30866], [31478],
+        [32239], [33250], [34758], [34949], [36463], [36786], [36796], [36937], [37981], [38165], [38362],
+        [38381], [38430], [38892], [39850], [41832], [41888], [42535], [42669], [42924], [43839], [44587],
+        [45297], [46956], [47527], [47715], [48688], [48874], [48999], [49146], [49946], [50256], [8162],
+        [198, 198, 198], [49564, 198, 49564], [37605], [22522], [40265], [5099], [39752], [32368],
+        [49564, 49564, 49564, 49564, 49564, 49564], [31857, 31857, 31857, 31857, 31857, 31857], [17992],
+        [39187], [40367], [15790], [47571], [27032], [628, 198], [628, 628], [49564, 198, 49564], [28156],
+        [30298], [34650]
+    ]
+    NERDSTASH = [
+        [3], [49356], [1431], [31715], [34387], [20765], [30702], [10691], [49333], [1266], [19438], [43145],
+        [26523], [41471], [2936], [85, 85], [49332], [7286], [1115]
+    ]
+    NERDSTASH_V2 = NERDSTASH
+    LLAMA3 = [
+        [933, 11144], [25106, 11144], [58, 106901, 16073, 33710, 25, 109933], [933, 58, 11144], [128030],
+        [58, 30591, 33503, 17663, 100204, 25, 11144]
+    ]
+
+
+class DefaultBanWords(object):
+    GENJI = []
+    PILE = [
+        [29], [31], [654], [870], [1138], [1168], [2033], [2239], [2730], [3001], [3073], [4357], [4398],
+        [4713], [4725], [5064], [5204], [5264], [5291], [5647], [6750], [6781], [8239], [8743], [8994], [9884],
+        [9983], [10122], [11049], [11380], [11396], [11899], [12399], [12630], [13011], [13143], [13208],
+        [13544], [14157], [14193], [14219], [14277], [14490], [15104], [16160], [16262], [16375], [16616],
+        [17266], [17636], [17778], [17987], [18257], [18331], [18474], [19282], [19884], [20322], [20499],
+        [21083], [21431], [21760], [21966], [22158], [22226], [22747], [23983], [24576], [24815], [26077],
+        [26786], [27482], [27988], [28120], [28365], [28379], [28391], [28739], [28905], [29972], [30119],
+        [30189], [30803], [30863], [30952], [31422], [31547], [31759], [31841], [32044], [32056], [33094],
+        [34616], [35402], [36784], [36893], [37014], [37322], [37387], [37471], [37916], [37944], [38270],
+        [38271], [38329], [38363], [38709], [38958], [39334], [39822], [40000], [40354], [40473], [41533],
+        [42854], [43467], [43521], [43600], [43736], [43781], [43856], [44072], [44153], [44342], [44679],
+        [44740], [45635], [46145], [46267], [46294], [46410], [46762], [46768], [46802], [46880], [47007],
+        [47269], [47384], [47940], [48074], [48744], [49384], [49651], [49821], [49884]
+    ]
+    PILE_NAI = PILE
+    GPT2 = [
+        [11037], [12240], [1279], [13163], [14610], [14804], [15913], [16471], [16791], [18189], [1875],
+        [19841], [22039], [22330], [23785], [23984], [24293], [24618], [25970], [26700], [27], [28401], [28725],
+        [29], [29342], [29847], [31175], [32406], [32756], [33409], [33490], [33717], [33994], [34516], [3556],
+        [36475], [36937], [3784], [37922], [37981], [38155], [38214], [4211], [42496], [43734], [44167],
+        [45160], [4613], [46904], [47934], [49029], [49778], [5218], [5320], [6927], [7359], [9609], [9959],
+        [8162]
     ]
-]
+    NERDSTASH = [[24]]
+    NERDSTASH_V2 = NERDSTASH
+    LLAMA3 = []
diff --git a/src/novelai_python/sdk/ai/generate/_enum.py b/src/novelai_python/sdk/ai/generate/_enum.py
index 625f2ce..2a6b4f6 100644
--- a/src/novelai_python/sdk/ai/generate/_enum.py
+++ b/src/novelai_python/sdk/ai/generate/_enum.py
@@ -1,77 +1,2281 @@
-# Description: Enum class for TextLLMModel
-from enum import Enum
-from typing import List
+import json
+import pathlib
+from functools import lru_cache
+from typing import List, Dict, Optional
 
+from novelai_python._enum import TextLLMModelTypeAlias, TextTokenizerGroup, TextLLMModel, \
+    COLORS_LLM
+from novelai_python.sdk.ai.generate._const import BanWords, DefaultBanWords, RepetitionPenaltyWhitelist
+from novelai_python.sdk.ai.generate._schema import Theme, Preset, StoryDefault, ContextConfig, LoreDefaults, \
+    EphemeralDefaults, ContextDefaults, ContextPresets, KeyOrderEntry, Key, PenStyle, LLMGenerationParams, \
+    LogitBiasGroup
 
-class TextLLMModel(Enum):
+themes_json = pathlib.Path(__file__).parent / "themes.json"
+
+
+@lru_cache
+def get_themes() -> List[Theme]:
+    theme_data = json.loads(themes_json.read_text())
+    assert isinstance(theme_data, list)
+    return [Theme.model_validate(data) for data in theme_data]
+
+
+def get_bad_words_ids(group: TextTokenizerGroup, use_banBrackets: bool):
+    if use_banBrackets:
+        ban_words = BanWords()
+    else:
+        ban_words = DefaultBanWords()
+    if group == TextTokenizerGroup.GENJI:
+        return ban_words.GENJI
+    elif group == TextTokenizerGroup.PILE:
+        return ban_words.PILE
+    elif group == TextTokenizerGroup.PILE_NAI:
+        if use_banBrackets:
+            return ban_words.PILE_NAI
+        else:
+            return ban_words.PILE
+    elif group == TextTokenizerGroup.GPT2:
+        return ban_words.GPT2
+    elif group in [TextTokenizerGroup.NERDSTASH, TextTokenizerGroup.NERDSTASH_V2]:
+        return ban_words.NERDSTASH
+    elif group == TextTokenizerGroup.LLAMA3 and use_banBrackets:
+        return ban_words.LLAMA3
+    return []
+
+
+def get_eos_token_id(group: TextTokenizerGroup):
+    if group in [TextTokenizerGroup.PILE, TextTokenizerGroup.PILE_NAI]:
+        return 31
+    elif group == TextTokenizerGroup.GPT2:
+        return 29
+    elif group in [TextTokenizerGroup.NERDSTASH, TextTokenizerGroup.NERDSTASH_V2]:
+        return 49405
+    elif group == TextTokenizerGroup.LLAMA3:
+        return 29
+    raise NotImplementedError(f"Invalid tokenizer for adventure EOS token")
+
+
+def get_end_exclude_sequences(group: TextTokenizerGroup):
+    if group in [TextTokenizerGroup.PILE, TextTokenizerGroup.PILE_NAI]:
+        return [[187, 31]]
+    elif group == TextTokenizerGroup.GPT2:
+        return [[198, 29]]
+    elif group in [TextTokenizerGroup.NERDSTASH, TextTokenizerGroup.NERDSTASH_V2]:
+        return [[85, 49405]]
+    elif group == TextTokenizerGroup.LLAMA3:
+        return [[29]]
+    raise NotImplementedError(f"Invalid tokenizer for adventure end sequences")
+
+
+def get_logit_bias_group(group: TextTokenizerGroup):
+    """
+    Get logit bias group
+    :param group:
+    :return:
+    """
+    if group == TextTokenizerGroup.GENJI:
+        return [
+            LogitBiasGroup(sequence=[7398], bias=-0.25, ensure_sequence_finish=False, generate_once=False),
+            LogitBiasGroup(sequence=[15864], bias=-0.25, ensure_sequence_finish=False, generate_once=False),
+            LogitBiasGroup(sequence=[29146], bias=-0.25, ensure_sequence_finish=False, generate_once=False),
+            LogitBiasGroup(sequence=[4707], bias=-0.25, ensure_sequence_finish=False, generate_once=False)
+        ]
+    return []
+
+
+def pickup_reverse_bias_model(model: TextLLMModelTypeAlias):
+    """
+    :param model: model
+    :return:
     """
-    Enum class for TextLLMModel
+    if model in [TextLLMModel.J_6B, TextLLMModel.J_6B_V3, TextLLMModel.J_6B_V4]:
+        return TextLLMModel.J_6B_V4
+    elif model == TextLLMModel.NEO_2B:
+        return TextLLMModel.NEO_2B
+    elif model in [TextLLMModel.GENJI_JP_6B, TextLLMModel.GENJI_JP_6B_V2]:
+        return TextLLMModel.GENJI_JP_6B_V2
+    elif model == TextLLMModel.GENJI_PYTHON_6B:
+        return TextLLMModel.GENJI_PYTHON_6B
+    elif model in [TextLLMModel.EUTERPE_V0, TextLLMModel.EUTERPE_V2]:
+        return TextLLMModel.EUTERPE_V2
+    elif model in [TextLLMModel.KRAKE_V1, TextLLMModel.KRAKE_V2]:
+        return TextLLMModel.KRAKE_V2
+    return model
+
+
+def get_default_prefix(model: TextLLMModel) -> List[str]:
     """
-    Sigurd = "6B-v4"
-    Euterpe = "euterpe-v2"
-    Krake = "krake-v2"
-
-    Clio = "clio-v1"
-    Kayra = "kayra-v1"
-    """Default model for TextLLMModel"""
-
-    Genji = "genji-jp-6b-v2"
-    Snek = "genji-python-6b"
-    HypeBot = "hypebot"
-    """Default comment model for TextLLMModel"""
-    Inline = "infillmodel"
-
-    neo2b = "2.7B"
-    j6b = "6B"
-    j6bv3 = "6B-v3"
-    genjijp6b = "genji-jp-6b"
-    euterpev0 = "euterpe-v0"
-    blue = "blue"
-    red = "red"
-    green = "green"
-    purple = "purple"
-    cassandra = "cassandra"
-
-
-TOKENIZER = {
-    TextLLMModel.Sigurd: "gpt2",
-    TextLLMModel.Euterpe: "gpt2",
-    TextLLMModel.Krake: "pile",
-    TextLLMModel.Snek: "gpt2",
-    TextLLMModel.Genji: "gpt2-genji",
-    TextLLMModel.HypeBot: "gpt2",
-    TextLLMModel.Inline: "gpt2",
-    TextLLMModel.Clio: "nerdstash_v1",
-    TextLLMModel.Kayra: "nerdstash_v2",
-}
-
-
-class PenStyle(Enum):
-    Off = "off"
-    VeryLight = "very_light"
-    Light = "light"
-    Default = "medium"
-    Aggressive = "aggressive"
-    VeryAggressive = "very_aggressive"
-
-
-class Order(Enum):
-    Temperature = ("temperature", 0)
-    TopK = ("top_k", 1)
-    TopP = ("top_p", 2)
-    TFS = ("tfs", 3)
-    TopA = ("top_a", 4)
-    TypicalP = ("typical_p", 5)
-    Cfg = ("cfg", 6)
-    TopG = ("top_g", 7)
-    Mirostat = ("mirostat", 8)
-
-
-def generate_order(request_body: dict) -> List[int]:
-    order_list = []
-    for member in Order:
-        key, order = member.value
-        if key in request_body and request_body[key] is None:
-            order_list.append(order)
-    order_list.sort()  # ensures the order list is properly sorted according to the true order
-    return order_list
+    Get prefix for model
+    :param model:
+    :return:
+    """
+    if model in [TextLLMModel.J_6B_V4, TextLLMModel.EUTERPE_V2]:
+        return [
+            theme.label for theme in get_themes()
+            if theme.label not in ["special_instruct", "special_openings"]
+        ]
+    elif model == TextLLMModel.KRAKE_V2:
+        return [
+            "vanilla", "general_crossgenre", "theme_textadventure", "style_algernonblackwood",
+            "style_arthurconandoyle", "style_edgarallanpoe", "style_hplovecraft", "style_shridanlefanu",
+            "style_julesverne", "theme_19thcenturyromance", "theme_actionarcheology", "theme_ai",
+            "theme_animalfiction", "theme_childrens", "theme_christmas", "theme_comedicfantasy", "theme_cyberpunk",
+            "theme_darkfantasy", "theme_dragons", "theme_egypt", "theme_feudaljapan", "theme_generalfantasy",
+            "theme_history", "theme_horror", "theme_huntergatherer", "theme_litrpg", "theme_magicacademy",
+            "theme_libraries", "theme_mars", "theme_medieval", "theme_militaryscifi", "theme_mystery",
+            "theme_naval", "theme_philosophy", "theme_pirates", "theme_poeticfantasy", "theme_postapocalyptic",
+            "theme_rats", "theme_romanempire", "theme_sciencefantasy", "theme_spaceopera", "theme_superheroes",
+            "theme_airships", "theme_travel", "theme_urbanfantasy", "theme_valentines", "theme_vikings",
+            "theme_westernromance", "inspiration_crabsnailandmonkey", "inspiration_mercantilewolfgirlromance",
+            "inspiration_nervegear", "theme_romanceofthreekingdoms", "inspiration_thronewars",
+            "inspiration_witchatlevelcap"
+        ]
+    elif model in [TextLLMModel.CASSANDRA] + COLORS_LLM:
+        return ["vanilla", "special_instruct", "special_proseaugmenter", "theme_textadventure"]
+    elif model == TextLLMModel.KAYRA:
+        return ["vanilla", "special_instruct", "special_proseaugmenter", "theme_textadventure", "theme_christmas"]
+    return []
+
+
+def get_repetition_penalty_whitelist(group: TextTokenizerGroup) -> List[str]:
+    whitelist = RepetitionPenaltyWhitelist()
+    if group == TextTokenizerGroup.NERDSTASH:
+        return whitelist.NERDSTASH
+    elif group == TextTokenizerGroup.NERDSTASH_V2:
+        return whitelist.NERDSTASH_V2
+    elif group == TextTokenizerGroup.LLAMA3:
+        return whitelist.LLAMA3
+    return []
+
+
+def get_default_preset(model: TextLLMModel):
+    if model == TextLLMModel.J_6B_V4:
+        return J6bv4.get_all_presets()[0]
+    elif model == TextLLMModel.NEO_2B:
+        return Neo2b.get_all_presets()[0]
+    elif model == TextLLMModel.GENJI_PYTHON_6B:
+        return Genjipython6b.get_all_presets()[0]
+    elif model == TextLLMModel.GENJI_JP_6B_V2:
+        return Genjijp6bv2.get_all_presets()[0]
+    elif model == TextLLMModel.EUTERPE_V2:
+        return Euterpev2.get_all_presets()[0]
+    elif model == TextLLMModel.KRAKE_V1:
+        return Krakev1.get_all_presets()[0]
+    elif model == TextLLMModel.KRAKE_V2:
+        return Krakev2.get_all_presets()[0]
+    elif model in COLORS_LLM:
+        return Colorful.get_all_presets()[0]
+    elif model == TextLLMModel.CLIO:
+        return Clio.get_all_presets()[0]
+    elif model == TextLLMModel.KAYRA:
+        return Kayra.get_all_presets()[0]
+    elif model == TextLLMModel.ERATO:
+        return Erato.get_all_presets()[0]
+    return Default.get_all_presets()[0]
+
+
+class ModelPresetManager:
+    def __init__(self):
+        self.presets: Dict[TextLLMModel, List[Preset]] = {}
+
+    def register_preset(self, model: TextLLMModel, preset: Preset) -> None:
+        if model not in self.presets:
+            self.presets[model] = []
+        self.presets[model].append(preset)
+
+    def get_presets(self, model: TextLLMModel) -> list[Preset]:
+        return self.presets.get(model, [])
+
+    def get_all_presets(self) -> List[Preset]:
+        return [preset for presets in self.presets.values() for preset in presets]
+
+    def get_preset_by_id(self, model: TextLLMModel, preset_id: str) -> Optional[Preset]:
+        for preset in self.get_presets(model):
+            if preset.id == preset_id:
+                return preset
+        return None
+
+
+Default = ModelPresetManager()
+Default.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Sigurd Lore Generation",
+        id="default-sigurdloregen",
+        parameters=LLMGenerationParams(
+            temperature=0.34,
+            top_k=1,
+            top_p=1,
+            top_a=1,
+            typical_p=0.8,
+            tail_free_sampling=1,
+            repetition_penalty=2.725,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="The default Lore Generation preset for Sigurd."
+    )
+)
+Default.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Euterpe Lore Generation",
+        id="default-euterpeloregen",
+        parameters=LLMGenerationParams(
+            temperature=0.34,
+            top_k=1,
+            top_p=1,
+            top_a=1,
+            typical_p=0.8,
+            tail_free_sampling=1,
+            repetition_penalty=2.725,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="The default Lore Generation preset for Euterpe."
+    )
+)
+
+Default.register_preset(
+    TextLLMModel.CLIO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Fresh Coffee",
+        id="default-freshcoffee",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            top_k=25,
+            top_p=1,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.925,
+            repetition_penalty=1.9,
+            repetition_penalty_range=768,
+            repetition_penalty_slope=3.33,
+            repetition_penalty_frequency=0.0025,
+            repetition_penalty_presence=0.001,
+            repetition_penalty_default_whitelist=True,
+            phrase_rep_pen=PenStyle.VeryLight,  # Assuming PenStyle is an Enum or similar
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description=""
+    )
+)
+
+J6bv4 = ModelPresetManager()
+J6bv4.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Storywriter",
+        id="default-trueoptimalnucleus",
+        parameters=LLMGenerationParams(
+            temperature=0.72,
+            max_length=40,
+            min_length=1,
+            top_k=0,
+            top_p=0.725,
+            tail_free_sampling=1,
+            repetition_penalty=2.75,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.18,
+            bad_words_ids=[]
+        ),
+        description="Optimized settings for relevant output."
+    )
+)
+
+J6bv4.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Coherent Creativity",
+        id="default-coherentcreativity",
+        parameters=LLMGenerationParams(
+            temperature=0.51,
+            max_length=40,
+            min_length=1,
+            top_k=0,
+            top_p=1,
+            tail_free_sampling=0.992,
+            repetition_penalty=3.875,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            bad_words_ids=[]
+        ),
+        description="A good balance between coherence, creativity, and quality of prose."
+    )
+)
+
+J6bv4.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Luna Moth",
+        id="default-moth-luna",
+        parameters=LLMGenerationParams(
+            temperature=2,
+            max_length=40,
+            min_length=1,
+            top_k=85,
+            top_p=0.235,
+            tail_free_sampling=1,
+            repetition_penalty=2.975,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            bad_words_ids=[]
+        ),
+        description="A great degree of creativity without losing coherency."
+    )
+)
+
+J6bv4.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Sphinx Moth",
+        id="default-moth-sphinx",
+        parameters=LLMGenerationParams(
+            temperature=2.5,
+            max_length=40,
+            min_length=1,
+            top_k=30,
+            top_p=0.175,
+            tail_free_sampling=1,
+            repetition_penalty=3,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            bad_words_ids=[]
+        ),
+        description="Maximum randomness while still being plot relevant. Like Sphinx riddles!"
+    )
+)
+
+J6bv4.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Emperor Moth",
+        id="default-moth-emperor",
+        parameters=LLMGenerationParams(
+            temperature=1.25,
+            max_length=40,
+            min_length=1,
+            top_k=0,
+            top_p=0.235,
+            tail_free_sampling=1,
+            repetition_penalty=2.75,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            bad_words_ids=[]
+        ),
+        description="Medium randomness with a decent bit of creative writing.",
+        contextPresets=None
+    )
+)
+
+J6bv4.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Best Guess",
+        id="default-bestguess",
+        parameters=LLMGenerationParams(
+            temperature=0.8,
+            max_length=40,
+            min_length=1,
+            top_k=110,
+            top_p=0.9,
+            tail_free_sampling=1,
+            repetition_penalty=3.1,
+            repetition_penalty_range=512,
+            repetition_penalty_slope=3.33,
+            bad_words_ids=[]
+        ),
+        description="A subtle change with alternative context settings.",
+        contextPresets=ContextPresets(
+            contextDefaults=[
+                ContextDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="[",
+                        suffix="]\n",
+                        tokenBudget=1,
+                        reservedTokens=1,
+                        budgetPriority=-100,
+                        trimDirection="doNotTrim",
+                        insertionType="newline",
+                        maximumTrimType="sentence",
+                        insertionPosition=-12
+                    )
+                ),
+                ContextDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="[",
+                        suffix="]\n",
+                        tokenBudget=1,
+                        reservedTokens=1,
+                        budgetPriority=-800,
+                        trimDirection="doNotTrim",
+                        insertionType="newline",
+                        maximumTrimType="sentence",
+                        insertionPosition=-4
+                    )
+                )
+            ],
+            ephemeralDefaults=[
+                EphemeralDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="",
+                        suffix="\n",
+                        tokenBudget=1,
+                        reservedTokens=1,
+                        budgetPriority=-100,
+                        trimDirection="doNotTrim",
+                        insertionType="newline",
+                        maximumTrimType="newline",
+                        insertionPosition=-2
+                    ),
+                    startingStep=1,
+                    delay=0,
+                    duration=1,
+                    repeat=False,
+                    reverse=False
+                )
+            ],
+            loreDefaults=[
+                LoreDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="[",
+                        suffix="]\n",
+                        tokenBudget=1,
+                        reservedTokens=1,
+                        budgetPriority=-200,
+                        trimDirection="trimBottom",
+                        insertionType="newline",
+                        maximumTrimType="sentence",
+                        insertionPosition=-10
+                    ),
+                    lastUpdatedAt="2021-07-19T01:16:17.425Z",
+                    displayName="New Lorebook Entry",
+                    keys=[],
+                    searchRange=1000,
+                    enabled=True,
+                    forceActivation=False,
+                    keyRelative=False,
+                    nonStoryActivatable=False
+                )
+            ],
+            storyDefault=StoryDefault(
+                prefix="",
+                suffix="",
+                tokenBudget=1,
+                reservedTokens=512,
+                budgetPriority=0,
+                trimDirection="trimTop",
+                insertionType="newline",
+                maximumTrimType="sentence",
+                insertionPosition=-1
+            )
+        )
+    )
+)
+J6bv4.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Pleasing Results",
+        id="default-pleasingresults",
+        parameters=LLMGenerationParams(
+            temperature=0.44,
+            max_length=40,
+            min_length=1,
+            top_k=0,
+            top_p=1,
+            tail_free_sampling=0.9,
+            repetition_penalty=3.35,
+            repetition_penalty_range=1024,
+            repetition_penalty_slope=6.75,
+            bad_words_ids=[]
+        ),
+        description="Expectable output with alternative context settings.",
+        contextPresets=ContextPresets(
+            contextDefaults=[
+                ContextDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="",
+                        suffix="\n",
+                        tokenBudget=1,
+                        reservedTokens=206,
+                        budgetPriority=-200,
+                        trimDirection="trimBottom",
+                        insertionType="newline",
+                        maximumTrimType="sentence",
+                        insertionPosition=-11
+                    )
+                ),
+                ContextDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="",
+                        suffix="\n",
+                        tokenBudget=1,
+                        reservedTokens=1,
+                        budgetPriority=-400,
+                        trimDirection="trimBottom",
+                        insertionType="newline",
+                        maximumTrimType="sentence",
+                        insertionPosition=-4
+                    )
+                )
+            ],
+            ephemeralDefaults=[
+                EphemeralDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="",
+                        suffix="\n",
+                        tokenBudget=1,
+                        reservedTokens=1,
+                        budgetPriority=-10000,
+                        trimDirection="doNotTrim",
+                        insertionType="newline",
+                        maximumTrimType="newline",
+                        insertionPosition=-2,
+                    ),
+                    startingStep=1,
+                    delay=0,
+                    duration=1,
+                    repeat=False,
+                    reverse=False
+                )
+            ],
+            loreDefaults=[
+                LoreDefaults(
+                    text="",
+                    contextConfig=ContextConfig(
+                        prefix="",
+                        suffix="\n",
+                        tokenBudget=1,
+                        reservedTokens=0,
+                        budgetPriority=400,
+                        trimDirection="trimBottom",
+                        insertionType="newline",
+                        maximumTrimType="sentence",
+                        insertionPosition=-1
+                    ),
+                    lastUpdatedAt="2021-07-19T00:36:55.977Z",
+                    displayName="New Lorebook Entry",
+                    keys=[],
+                    searchRange=1000,
+                    enabled=True,
+                    forceActivation=False,
+                    keyRelative=False,
+                    nonStoryActivatable=False
+                )
+            ],
+            storyDefault=StoryDefault(
+                prefix="",
+                suffix="",
+                tokenBudget=1,
+                reservedTokens=512,
+                budgetPriority=0,
+                trimDirection="trimTop",
+                insertionType="newline",
+                maximumTrimType="sentence",
+                insertionPosition=-1
+            )
+        )
+    )
+)
+Neo2b = ModelPresetManager()
+
+Neo2b.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Storywriter (Calliope)",
+        id="defaultcalliope-trueoptimalnucleus",
+        parameters=LLMGenerationParams(
+            temperature=0.72,
+            top_k=0,
+            top_p=0.725,
+            tail_free_sampling=1,
+            repetition_penalty=2.75,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.18,
+            bad_words_ids=[]
+        ),
+        description="Optimized settings for relevant output."
+    )
+)
+
+Genjipython6b = ModelPresetManager()
+Genjipython6b.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Storywriter (Snek)",
+        id="defaultsnek-trueoptimalnucleus",
+        parameters=LLMGenerationParams(
+            temperature=0.72,
+            top_k=0,
+            top_p=0.725,
+            tail_free_sampling=1,
+            repetition_penalty=2.75,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.18,
+            bad_words_ids=[]
+        ),
+        description="Optimized settings for relevant output."
+    )
+)
+
+Genjijp6bv2 = ModelPresetManager()
+
+Genjijp6bv2.register_preset(
+    TextLLMModel.J_6B_V4,
+    Preset(
+        presetVersion=2,
+        remoteId="",
+        name="Genji Default",
+        id="defaultgenji-genji",
+        parameters=LLMGenerationParams(
+            temperature=0.86,
+            top_k=0,
+            top_p=1,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.927,
+            repetition_penalty=2.9,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=3.33,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0
+        ),
+        description="Default Settings",
+    )
+)
+
+Euterpev2 = ModelPresetManager()
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Genesis",
+        id="default-genesis",
+        parameters=LLMGenerationParams(
+            temperature=0.63,
+            top_k=0,
+            top_p=0.975,
+            tail_free_sampling=0.975,
+            repetition_penalty=2.975,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Stable and logical, but with scattered creativity."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Basic Coherence",
+        id="default-basic-coherence",
+        parameters=LLMGenerationParams(
+            temperature=0.585,
+            top_k=0,
+            top_p=1,
+            tail_free_sampling=0.87,
+            repetition_penalty=3.05,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.33,
+        ),
+        description="Keeps things on track."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Ouroboros",
+        id="default-ouroboros",
+        parameters=LLMGenerationParams(
+            temperature=1.07,
+            top_k=264,
+            top_p=1,
+            tail_free_sampling=0.925,
+            repetition_penalty=2.165,
+            repetition_penalty_range=404,
+            repetition_penalty_slope=0.84,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Versatile, conforms well to poems, lists, chat, etc."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Ace of Spades",
+        id="default-ace-of-spades",
+        parameters=LLMGenerationParams(
+            temperature=1.15,
+            max_length=30,
+            top_k=0,
+            top_p=0.95,
+            tail_free_sampling=0.8,
+            repetition_penalty=2.75,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=7.02,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Expressive, while still staying focused."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Moonlit Chronicler",
+        id="default-moonlit-chronicler",
+        parameters=LLMGenerationParams(
+            temperature=1.25,
+            top_k=300,
+            top_p=1,
+            top_a=0.782,
+            typical_p=0.95,
+            tail_free_sampling=0.802,
+            repetition_penalty=2.075,
+            repetition_penalty_range=512,
+            repetition_penalty_slope=0.36,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Tells a tale with confidence, but variety where it matters."
+    )
+)
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Fandango",
+        id="default-fandango",
+        parameters=LLMGenerationParams(
+            temperature=0.86,
+            top_k=20,
+            top_p=0.95,
+            tail_free_sampling=1,
+            repetition_penalty=2.25,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            # Assuming Key is a correct mapping for the order items
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+            ]
+        ),
+        description="A rhythmic dance of prose, whoever takes the lead."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="All-Nighter",
+        id="default-all-nighter",
+        parameters=LLMGenerationParams(
+            temperature=1.33,
+            top_k=13,
+            top_p=1,
+            tail_free_sampling=0.836,
+            repetition_penalty=2.366,
+            repetition_penalty_range=400,
+            repetition_penalty_slope=0.33,
+            repetition_penalty_frequency=0.01,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Creative diction with room for embellishments."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Low Rider",
+        id="default-low-rider-2",
+        parameters=LLMGenerationParams(
+            temperature=0.94,
+            top_k=12,
+            top_p=1,
+            tail_free_sampling=0.94,
+            repetition_penalty=2.66,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.18,
+            repetition_penalty_frequency=0.013,
+            repetition_penalty_presence=0,
+            # Assuming a mapping for the order as per your schema
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Reliable, aimed at story development."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Morpho",
+        id="default-morpho",
+        parameters=LLMGenerationParams(
+            temperature=0.6889,
+            max_length=25,
+            top_k=0,
+            top_p=1,
+            tail_free_sampling=1,
+            repetition_penalty=1,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0.1,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Let the AI generate without constraints."
+    )
+)
+
+Euterpev2.register_preset(
+    TextLLMModel.EUTERPE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Pro Writer",
+        id="default-prowriter20",
+        parameters=LLMGenerationParams(
+            temperature=1.348,
+            top_k=64,
+            top_p=0.909,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.688,
+            repetition_penalty=4.967,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Optimal settings for readability, based on AI-powered mass statistical analysis of Euterpe output."
+    )
+)
+
+Krakev1 = ModelPresetManager()
+Krakev1.register_preset(
+    TextLLMModel.KRAKE_V1,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Astraea",
+        id="default-astreacreative",
+        parameters=LLMGenerationParams(
+            temperature=0.9,
+            top_k=22,
+            top_p=0.85,
+            top_a=1,
+            typical_p=0.9,
+            tail_free_sampling=0.92,
+            repetition_penalty=1.024,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Follows your style with precision."
+    )
+)
+
+Krakev1.register_preset(
+    TextLLMModel.KRAKE_V1,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Blackjack",
+        id="default-blackjack",
+        parameters=LLMGenerationParams(
+            temperature=1.3,
+            top_k=0,
+            top_p=0.98,
+            top_a=0.98,
+            typical_p=1,
+            tail_free_sampling=0.95,
+            repetition_penalty=1.005,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=4,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Balanced probability with the chance for less obvious outcomes."
+    )
+)
+
+Krakev1.register_preset(
+    TextLLMModel.KRAKE_V1,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Adder",
+        id="default-adder04",
+        parameters=LLMGenerationParams(
+            temperature=1.06,
+            max_length=17,
+            top_k=0,
+            top_p=0.85,
+            top_a=0.086,
+            typical_p=0.986,
+            tail_free_sampling=0.961,
+            repetition_penalty=1.016,
+            repetition_penalty_range=720,
+            repetition_penalty_slope=0.77,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="For logic and narrative progression with the right amount of spice."
+    )
+)
+Krakev1.register_preset(
+    TextLLMModel.KRAKE_V1,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="20BC",
+        id="default-20bc",
+        parameters=LLMGenerationParams(
+            temperature=0.55,
+            top_k=0,
+            top_p=1,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.872,
+            repetition_penalty=1.03,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=3.33,
+            bad_words_ids=[],
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Keeps things on track."
+    )
+)
+Krakev1.register_preset(
+    TextLLMModel.KRAKE_V1,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Ptah",
+        id="default-ptah",
+        parameters=LLMGenerationParams(
+            temperature=0.98,
+            top_k=150,
+            top_p=0.825,
+            top_a=1,
+            typical_p=0.95,
+            tail_free_sampling=0.84,
+            repetition_penalty=1.032,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Gives you the tools to build your worlds."
+    )
+)
+Krakev1.register_preset(
+    TextLLMModel.KRAKE_V1,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Iris",
+        id="default-iris",
+        parameters=LLMGenerationParams(
+            temperature=2.5,
+            top_k=0,
+            top_p=1,
+            top_a=1,
+            typical_p=0.799,
+            tail_free_sampling=0.9,
+            repetition_penalty=1,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="A wide array of ideas from beyond to shake up your writing."
+    )
+)
+Krakev1.register_preset(
+    TextLLMModel.KRAKE_V1,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Calypso",
+        id="default-calypso",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            top_k=20,
+            top_p=1,
+            top_a=0.2,
+            typical_p=1,
+            tail_free_sampling=1,
+            repetition_penalty=1.04,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Creatively elaborates upon an existing scene."
+    )
+)
+Krakev2 = ModelPresetManager()
+
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Blue Lighter",
+        id="default-blue-lighter",
+        parameters=LLMGenerationParams(
+            temperature=1.33,
+            top_k=81,
+            top_p=0.88,
+            top_a=0.085,
+            typical_p=0.965,
+            tail_free_sampling=0.937,
+            repetition_penalty=1.05,
+            repetition_penalty_range=560,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Easy to steer, imaginative, and fast-paced."
+    )
+)
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Redjack",
+        id="default-redjack",
+        parameters=LLMGenerationParams(
+            temperature=1.1,
+            top_k=0,
+            top_p=0.96,
+            top_a=0.98,
+            typical_p=1,
+            tail_free_sampling=0.92,
+            repetition_penalty=1.0075,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=4,
+            repetition_penalty_frequency=0.025,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Focuses on staying on track, only rarely deviating from what's already written."
+    )
+)
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Reverie",
+        id="default-reverie",
+        parameters=LLMGenerationParams(
+            temperature=0.925,
+            top_k=85,
+            top_p=0.985,
+            top_a=0.12,
+            typical_p=0.85,
+            tail_free_sampling=0.925,
+            repetition_penalty=1.0225,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Narrative consistency with a focus on paying attention to memory and module content."
+    )
+)
+
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Top Gun Beta",
+        id="default-topgun-beta",
+        parameters=LLMGenerationParams(
+            temperature=0.58,
+            top_k=10,
+            top_p=0.675,
+            top_a=0.1,
+            typical_p=0.985,
+            tail_free_sampling=0.919,
+            repetition_penalty=1.05,
+            repetition_penalty_range=880,
+            repetition_penalty_slope=0.2,
+            repetition_penalty_frequency=0.02,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Creative and fast-paced. Go into less expected directions while staying coherent and avoiding repetitive loops."
+    )
+)
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Calypso",
+        id="default-calypso",
+        parameters=LLMGenerationParams(
+            temperature=1.1,
+            top_k=10,
+            top_p=0.95,
+            top_a=0.15,
+            typical_p=0.95,
+            tail_free_sampling=0.95,
+            repetition_penalty=1.075,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Weave an enrapturing endless tale, custom fit for 2nd person perspective but highly flexible for other uses."
+    )
+)
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Blue Adder",
+        id="default-blue-adder",
+        parameters=LLMGenerationParams(
+            temperature=1.01,
+            top_k=0,
+            top_p=0.7,
+            top_a=0.06,
+            typical_p=0.996,
+            tail_free_sampling=0.991,
+            repetition_penalty=1.02325,
+            repetition_penalty_range=496,
+            repetition_penalty_slope=0.72,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Jumps from one idea to the next, logical but unpredictable."
+    )
+)
+
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="20BC+",
+        id="default-20bcplus",
+        parameters=LLMGenerationParams(
+            temperature=0.58,
+            max_length=75,
+            min_length=1,
+            top_k=20,
+            top_p=1,
+            top_a=0.05,
+            typical_p=0.985,
+            tail_free_sampling=0.879,
+            repetition_penalty=1.055,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=3.33,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Keeps things on track."
+    )
+)
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Calibrated",
+        id="default-calibrated",
+        parameters=LLMGenerationParams(
+            temperature=0.34,
+            top_k=0,
+            top_p=1,
+            top_a=1,
+            typical_p=0.975,
+            tail_free_sampling=1,
+            repetition_penalty=1.036,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=3.33,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Adjusted for highly consistent output."
+    )
+)
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Iris",
+        id="default-iris",
+        parameters=LLMGenerationParams(
+            temperature=2.5,
+            top_k=0,
+            top_p=1,
+            top_a=1,
+            typical_p=0.9566,
+            tail_free_sampling=0.97,
+            repetition_penalty=1,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Imaginative and original, a kaleidoscope of unpredictability."
+    )
+)
+
+Krakev2.register_preset(
+    TextLLMModel.KRAKE_V2,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Krait",
+        id="default-krait",
+        parameters=LLMGenerationParams(
+            temperature=0.9,
+            top_k=1000,
+            top_p=0.992,
+            top_a=0.072,
+            typical_p=0.98,
+            tail_free_sampling=0.997,
+            repetition_penalty=1.0236,
+            repetition_penalty_range=610,
+            repetition_penalty_slope=0.85,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Controlled chaos, for random generation and chat style."
+    )
+)
+Colorful = ModelPresetManager()
+Colorful.register_preset(
+    TextLLMModel.BLUE,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="⬜️",
+        id="default-na",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            top_k=0,
+            top_p=1,
+            tail_free_sampling=0.87,
+            repetition_penalty=1,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0
+        ),
+        description=""
+    )
+)
+Clio = ModelPresetManager()
+Clio.register_preset(
+    TextLLMModel.CLIO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Vingt-Un",
+        id="default-vigntun",
+        parameters=LLMGenerationParams(
+            temperature=1.21,
+            top_k=0,
+            top_p=0.912,
+            top_a=1,
+            typical_p=0.912,
+            tail_free_sampling=0.921,
+            repetition_penalty=1.21,
+            repetition_penalty_range=321,
+            repetition_penalty_slope=2.1,
+            repetition_penalty_frequency=0.00621,
+            repetition_penalty_presence=0,
+            phrase_rep_pen=PenStyle.VeryLight,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="A good all-around default with a bent towards prose."
+    )
+)
+
+Clio.register_preset(
+    TextLLMModel.CLIO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Long Press",
+        id="default-longpress",
+        parameters=LLMGenerationParams(
+            temperature=1.155,
+            top_k=25,
+            top_p=1,
+            top_a=0.265,
+            typical_p=0.985,
+            tail_free_sampling=0.88,
+            repetition_penalty=1.65,
+            repetition_penalty_range=8192,
+            repetition_penalty_slope=1.5,
+            repetition_penalty_frequency=0.0085,
+            repetition_penalty_presence=0.0125,
+            repetition_penalty_default_whitelist=True,
+            phrase_rep_pen=PenStyle.VeryLight,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Intended for creative prose."
+    )
+)
+Clio.register_preset(
+    TextLLMModel.CLIO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Edgewise",
+        id="default-edgewise",
+        parameters=LLMGenerationParams(
+            temperature=1.09,
+            top_k=0,
+            top_p=0.969,
+            top_a=0.09,
+            typical_p=0.99,
+            tail_free_sampling=0.969,
+            repetition_penalty=1.09,
+            repetition_penalty_range=8192,
+            repetition_penalty_slope=0.069,
+            repetition_penalty_frequency=0.006,
+            repetition_penalty_presence=0.009,
+            repetition_penalty_default_whitelist=True,
+            phrase_rep_pen=PenStyle.VeryLight,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Handles a variety of generation styles well."
+    )
+)
+Clio.register_preset(
+    TextLLMModel.CLIO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Fresh Coffee",
+        id="default-freshcoffee",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            top_k=25,
+            top_p=1,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.925,
+            repetition_penalty=1.9,
+            repetition_penalty_range=768,
+            repetition_penalty_slope=3.33,
+            repetition_penalty_frequency=0.0025,
+            repetition_penalty_presence=0.001,
+            repetition_penalty_default_whitelist=True,
+            phrase_rep_pen=PenStyle.VeryLight,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Keeps things on track."
+    )
+)
+Clio.register_preset(
+    TextLLMModel.CLIO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Talker C",
+        id="default-talkerc",
+        parameters=LLMGenerationParams(
+            temperature=1.5,
+            top_k=10,
+            top_p=0.75,
+            top_a=0.08,
+            typical_p=0.975,
+            tail_free_sampling=0.967,
+            repetition_penalty=2.25,
+            repetition_penalty_range=8192,
+            repetition_penalty_slope=0.09,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0.005,
+            repetition_penalty_default_whitelist=True,
+            phrase_rep_pen=PenStyle.VeryLight,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Designed for chat style generation."
+    )
+)
+Kayra = ModelPresetManager()
+Kayra.register_preset(
+    TextLLMModel.KAYRA,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Carefree",
+        id="default-carefree",
+        parameters=LLMGenerationParams(
+            temperature=1.35,
+            top_k=15,
+            top_p=0.85,
+            top_a=0.1,
+            typical_p=1,
+            tail_free_sampling=0.915,
+            repetition_penalty=2.8,
+            repetition_penalty_range=2048,
+            repetition_penalty_slope=0.02,
+            repetition_penalty_frequency=0.02,
+            repetition_penalty_presence=0,
+            repetition_penalty_default_whitelist=True,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Aggressive,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=False),
+                KeyOrderEntry(id=Key.MinP, enabled=False)
+            ]
+        ),
+        description="A good all-rounder."
+    )
+)
+Kayra.register_preset(
+    TextLLMModel.KAYRA,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Stelenes",
+        id="default-stelenes",
+        parameters=LLMGenerationParams(
+            temperature=2.5,
+            top_k=0,
+            top_p=1,
+            top_a=1,
+            typical_p=0.969,
+            tail_free_sampling=0.941,
+            repetition_penalty=1,
+            repetition_penalty_range=1024,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            repetition_penalty_default_whitelist=True,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Default,
+            top_g=0,
+            mirostat_tau=0,
+            mirostat_lr=1,
+            order=[
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=False),
+                KeyOrderEntry(id=Key.MinP, enabled=False)
+            ]
+        ),
+        description="More likely to choose reasonable alternatives. Variety on retries."
+    )
+)
+Kayra.register_preset(
+    TextLLMModel.KAYRA,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Fresh Coffee",
+        id="default-freshcoffeek",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            max_length=60,
+            top_k=25,
+            top_p=1,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.925,
+            repetition_penalty=1.9,
+            repetition_penalty_range=768,
+            repetition_penalty_slope=1,
+            repetition_penalty_frequency=0.0025,
+            repetition_penalty_presence=0.001,
+            repetition_penalty_default_whitelist=False,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Off,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=False),
+                KeyOrderEntry(id=Key.MinP, enabled=False)
+            ]
+        ),
+        description="Keeps things on track. Handles instruct well."
+    )
+)
+Kayra.register_preset(
+    TextLLMModel.KAYRA,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Asper",
+        id="default-asper",
+        parameters=LLMGenerationParams(
+            temperature=1.16,
+            top_k=175,
+            top_p=0.998,
+            top_a=0.004,
+            typical_p=0.96,
+            tail_free_sampling=0.994,
+            repetition_penalty=1.68,
+            repetition_penalty_range=2240,
+            repetition_penalty_slope=1.5,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0.005,
+            repetition_penalty_default_whitelist=True,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Default,
+            top_g=8,
+            mirostat_tau=0,
+            mirostat_lr=1,
+            order=[
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=False),
+                KeyOrderEntry(id=Key.MinP, enabled=False)
+            ]
+        ),
+        description="For creative writing. Expect unexpected twists."
+    )
+)
+
+Kayra.register_preset(
+    TextLLMModel.KAYRA,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Writer's Daemon",
+        id="default-writersdaemon",
+        parameters=LLMGenerationParams(
+            temperature=1.5,
+            top_k=70,
+            top_p=0.95,
+            top_a=0.02,
+            typical_p=0.95,
+            tail_free_sampling=0.95,
+            repetition_penalty=1.625,
+            repetition_penalty_range=2016,
+            repetition_penalty_slope=0,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.VeryAggressive,
+            top_g=0,
+            mirostat_tau=5,
+            mirostat_lr=0.25,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.TFS, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=False),
+                KeyOrderEntry(id=Key.MinP, enabled=False)
+            ]
+        ),
+        description="Extremely imaginative, sometimes too much."
+    )
+)
+
+Erato = ModelPresetManager()
+Erato.register_preset(
+    TextLLMModel.ERATO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Golden Arrow",
+        id="default-erato-goldenarrow",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            top_k=0,
+            top_p=0.995,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.87,
+            repetition_penalty=1.5,
+            repetition_penalty_range=2240,
+            repetition_penalty_slope=1,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            repetition_penalty_default_whitelist=True,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Light,  # Mapping "light" to PenStyle.Light
+            top_g=0,
+            mirostat_tau=0,
+            mirostat_lr=1,
+            math1_temp=0.3,
+            math1_quad=0.19,
+            math1_quad_entropy_scale=0,
+            min_p=0,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+                KeyOrderEntry(id=Key.MinP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False)
+            ]
+        ),
+        description="A good all-rounder."
+    )
+)
+Erato.register_preset(
+    TextLLMModel.ERATO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Wilder",
+        id="default-erato-wilder",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            top_k=300,
+            top_p=0.98,
+            top_a=0.004,
+            typical_p=0.96,
+            tail_free_sampling=0.96,
+            repetition_penalty=1.48,
+            repetition_penalty_range=2240,
+            repetition_penalty_slope=0.64,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            repetition_penalty_default_whitelist=True,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Default,
+            top_g=0,
+            mirostat_tau=0,
+            mirostat_lr=1,
+            math1_temp=-0.0485,
+            math1_quad=0.145,
+            math1_quad_entropy_scale=0,
+            min_p=0.02,
+            order=[
+                KeyOrderEntry(id=Key.Math1, enabled=True),
+                KeyOrderEntry(id=Key.MinP, enabled=True),
+                KeyOrderEntry(id=Key.Temperature, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Cfg, enabled=False)
+            ]
+        ),
+        description=(
+            "Higher variety of word choice, more differences between rerolls, "
+            "more prone to mistakes."
+        )
+    )
+)
+Erato.register_preset(
+    TextLLMModel.ERATO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Zany Scribe",
+        id="default-erato-zanyscribe",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            top_k=0,
+            top_p=0.98,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.99,
+            repetition_penalty=1,
+            repetition_penalty_range=3024,
+            repetition_penalty_slope=1.1,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0.25,
+            repetition_penalty_default_whitelist=True,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Default,
+            top_g=0,
+            mirostat_tau=1,
+            mirostat_lr=1,
+            math1_temp=-0.275,
+            math1_quad=0.35,
+            math1_quad_entropy_scale=-0.2,
+            min_p=0.08,
+            order=[
+                KeyOrderEntry(id=Key.Temperature, enabled=False),
+                KeyOrderEntry(id=Key.MinP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=True),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False)
+            ]
+        ),
+        description="Avoids mistakes and repetition. Prioritizes more complex words."
+    )
+)
+Erato.register_preset(
+    TextLLMModel.ERATO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="Dragonfruit",
+        id="default-erato-dragonfruit",
+        parameters=LLMGenerationParams(
+            temperature=1.37,
+            top_k=0,
+            top_p=1,
+            top_a=0.1,
+            typical_p=0.875,
+            tail_free_sampling=0.87,
+            repetition_penalty=3.25,
+            repetition_penalty_range=6000,
+            repetition_penalty_slope=3.25,
+            repetition_penalty_frequency=0,
+            repetition_penalty_presence=0,
+            repetition_penalty_default_whitelist=False,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Off,
+            top_g=0,
+            mirostat_tau=4,
+            mirostat_lr=0.2,
+            math1_temp=0.9,
+            math1_quad=0.07,
+            math1_quad_entropy_scale=-0.05,
+            min_p=0.035,
+            order=[
+                KeyOrderEntry(id=Key.Temperature, enabled=True),
+                KeyOrderEntry(id=Key.TypicalP, enabled=True),
+                KeyOrderEntry(id=Key.Math1, enabled=True),
+                KeyOrderEntry(id=Key.MinP, enabled=True),
+                KeyOrderEntry(id=Key.Mirostat, enabled=True),
+                KeyOrderEntry(id=Key.TopA, enabled=True),
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False)
+            ]
+        ),
+        description="Varied and complex language with little repetition. More frequent mistakes and contradictions."
+    )
+)
+Erato.register_preset(
+    TextLLMModel.ERATO,
+    Preset(
+        presetVersion=3,
+        remoteId="",
+        name="小説家",
+        id="default-erato-shousetsuka",
+        parameters=LLMGenerationParams(
+            temperature=1,
+            max_length=60,
+            min_length=1,
+            top_k=50,
+            top_p=0.85,
+            top_a=1,
+            typical_p=1,
+            tail_free_sampling=0.895,
+            repetition_penalty=1.63,
+            repetition_penalty_range=1024,
+            repetition_penalty_slope=3.33,
+            repetition_penalty_frequency=0.0035,
+            repetition_penalty_presence=0,
+            repetition_penalty_default_whitelist=False,
+            cfg_scale=1,
+            cfg_uc="",
+            phrase_rep_pen=PenStyle.Default,
+            top_g=0,
+            mirostat_tau=0,
+            mirostat_lr=1,
+            math1_temp=0.3,
+            math1_quad=0.0645,
+            math1_quad_entropy_scale=0.05,
+            min_p=0.05,
+            order=[
+                KeyOrderEntry(id=Key.Cfg, enabled=False),
+                KeyOrderEntry(id=Key.Temperature, enabled=False),
+                KeyOrderEntry(id=Key.Math1, enabled=True),
+                KeyOrderEntry(id=Key.MinP, enabled=True),
+                KeyOrderEntry(id=Key.TopP, enabled=False),
+                KeyOrderEntry(id=Key.TopA, enabled=False),
+                KeyOrderEntry(id=Key.TopK, enabled=False),
+                KeyOrderEntry(id=Key.TFS, enabled=False),
+                KeyOrderEntry(id=Key.TypicalP, enabled=False),
+                KeyOrderEntry(id=Key.TopG, enabled=False),
+                KeyOrderEntry(id=Key.Mirostat, enabled=False)
+            ]
+        ),
+        description="Designed for writing in Japanese. Works fine for English too."
+    )
+)
diff --git a/src/novelai_python/sdk/ai/generate/_preset.py b/src/novelai_python/sdk/ai/generate/_preset.py
deleted file mode 100644
index 99309be..0000000
--- a/src/novelai_python/sdk/ai/generate/_preset.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# MIT: https://github.com/Aedial/novelai-api/blob/main/novelai_api/
-from typing import Optional, Union, List
-
-import pydantic
-from pydantic import model_validator, Field, BaseModel
-
-from ._enum import PenStyle
-
-
-class LogitBiasExp(BaseModel):
-    sequence: List[int]
-    bias: float = Field(default=None, gt=-1, lt=1)
-    ensure_sequence_finish: bool
-    generate_once: bool
-
-
-class Params(BaseModel):
-    bad_words_ids: List[List[int]] = []
-    logit_bias_exp: Optional[List[LogitBiasExp]] = []
-    max_length: int = Field(default=40, ge=1, le=100)  # Max 150(vip3),100(vip2)
-    """Default 30"""
-    min_length: int = Field(default=1, ge=1, le=100)  # Max 150(vip3),100(vip2)
-    """Default 10"""
-    eos_token_id: int = Field(default=None, ge=0)  # 50528
-    typical_p: float = Field(default=None, gt=0, le=1)
-    num_logprobs: Optional[int] = None
-    order: Optional[List[int]] = None
-    phrase_rep_pen: PenStyle = PenStyle.Off
-    prefix: str = "vanilla"
-    repetition_penalty: float = Field(default=1, gt=0)
-    repetition_penalty_frequency: float = Field(default=0, ge=0)
-    repetition_penalty_presence: float = Field(default=0, ge=0)
-    repetition_penalty_range: int = Field(default=None, ge=0)  # 560 2048
-    repetition_penalty_slope: float = Field(default=None, ge=0)
-    repetition_penalty_whitelist: List[int] = []
-    return_full_text: bool = False
-    stop_sequences: Optional[Union[List[List[int]], List[str]]] = []
-    generate_until_sentence: bool = True
-
-    tail_free_sampling: float = Field(default=1.0, gt=0, le=1)
-    temperature: float = Field(default=1, gt=0)
-    """Default 1"""
-    top_a: Optional[float] = Field(default=0.1, gt=0, le=1)
-    top_k: int = Field(default=None, ge=0)
-    top_p: float = Field(default=None, gt=0, le=1)
-    use_cache: bool = False
-    use_string: bool = False
-    """Default False"""
-    cfg_uc: str = None
-    cfg_scale: float = None
-    top_g: float = None
-    mirostat_tau: float = None
-    mirostat_lr: float = None
-
-    @model_validator(mode="after")
-    def validate_model(self):
-        if self.min_length > self.max_length:
-            raise pydantic.ValidationError("min_length must be less than or equal to max_length")
-        return self
-
-
-_HyperBot = {
-    "stop_sequences": [[48585]],
-    "temperature": 1,
-    "max_length": 80,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 1,
-    "tail_free_sampling": 0.95,
-    "repetition_penalty": 1,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 0.18,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Storywriter = {
-    "temperature": 0.72,
-    "max_length": 40,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 0.725,
-    "top_a": 1,
-    "typical_p": 1,
-    "tail_free_sampling": 1,
-    "repetition_penalty": 2.75,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 0.18,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Sigurd = {
-    "temperature": 0.72,
-    "max_length": 40,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 0.725,
-    "top_a": 1,
-    "typical_p": 1,
-    "tail_free_sampling": 1,
-    "repetition_penalty": 2.75,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 0.18,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Euterpe = {
-    "temperature": 0.86,
-    "max_length": 40,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 0.925,
-    "top_a": 1,
-    "typical_p": 1,
-    "tail_free_sampling": 0.925,
-    "repetition_penalty": 2.25,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 0.09,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Genji = {
-    "temperature": 0.86,
-    "max_length": 40,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 1,
-    "top_a": 1,
-    "typical_p": 1,
-    "tail_free_sampling": 0.927,
-    "repetition_penalty": 2.9,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 3.33,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Snek = {
-    "temperature": 0.72,
-    "max_length": 40,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 0.725,
-    "top_a": 1,
-    "typical_p": 1,
-    "tail_free_sampling": 1,
-    "repetition_penalty": 2.75,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 0.18,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Inline = {
-    "eos_token_id": 50258,
-    "temperature": 0.8,
-    "max_length": 100,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 1,
-    "tail_free_sampling": 0.859,
-    "repetition_penalty": 1.084375,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 0.18,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Kayra = {
-    "temperature": 1.35,
-    "max_length": 40,
-    "min_length": 1,
-    "top_k": 15,
-    "top_p": 0.85,
-    "top_a": 0.1,
-    "typical_p": 1,
-    "tail_free_sampling": 0.915,
-    "repetition_penalty": 2.8,
-    "repetition_penalty_range": 2048,
-    "repetition_penalty_slope": 0.02,
-    "repetition_penalty_frequency": 0.02,
-    "repetition_penalty_presence": 0,
-    "repetition_penalty_default_whitelist": True,
-    "cfg_scale": 1,
-    "cfg_uc": "",
-    "phrase_rep_pen": "aggressive",
-    "top_g": 0
-}
-_Krake = {
-    "temperature": 1.33,
-    "max_length": 40,
-    "min_length": 1,
-    "top_k": 81,
-    "top_p": 0.88,
-    "top_a": 0.085,
-    "typical_p": 0.965,
-    "tail_free_sampling": 0.937,
-    "repetition_penalty": 1.05,
-    "repetition_penalty_range": 560,
-    "repetition_penalty_slope": 0,
-    "repetition_penalty_frequency": 0,
-    "repetition_penalty_presence": 0,
-}
-_Clio = {
-    "temperature": 1.21,
-    "max_length": 75,
-    "min_length": 1,
-    "top_k": 0,
-    "top_p": 0.912,
-    "top_a": 1,
-    "typical_p": 0.912,
-    "tail_free_sampling": 0.921,
-    "repetition_penalty": 1.21,
-    "repetition_penalty_range": 321,
-    "repetition_penalty_slope": 2.1,
-    "repetition_penalty_frequency": 0.00621,
-    "repetition_penalty_presence": 0,
-}
-PRESET = {
-    "Sigurd": Params.model_validate(_Sigurd),
-    "Euterpe": Params.model_validate(_Euterpe),
-    "Krake": Params.model_validate(_Krake),
-    "Clio": Params.model_validate(_Clio),
-    "Kayra": Params.model_validate(_Kayra),
-    "Genji": Params.model_validate(_Genji),
-    "Snek": Params.model_validate(_Snek),
-    "HypeBot": Params.model_validate(_HyperBot),
-    "Inline": Params.model_validate(_Inline),
-}
diff --git a/src/novelai_python/sdk/ai/generate/_schema.py b/src/novelai_python/sdk/ai/generate/_schema.py
new file mode 100644
index 0000000..2d18786
--- /dev/null
+++ b/src/novelai_python/sdk/ai/generate/_schema.py
@@ -0,0 +1,233 @@
+from dataclasses import dataclass
+from enum import Enum
+from typing import List, Optional, Tuple, Union
+
+from pydantic import BaseModel, Field, model_validator, ConfigDict
+
+"""
+class StorySettings(BaseModel):
+    parameters: Optional[dict] = {}
+    preset: Optional[str] = ''
+    trimResponses: Optional[bool] = True
+    banBrackets: Optional[bool] = True
+    prefix: Optional[str] = 'vanilla'
+    dynamicPenaltyRange: Optional[bool] = False
+    prefixMode: Optional[int] = 0
+    mode: Optional[int] = 0
+    model: Optional[TextLLMModel] = TextLLMModel.J_6B_V4
+"""
+"""
+def is_theme_need_model(model: TextLLMModel, entity: StorySettings):
+    # 定义一个集合包含允许的模型
+    valid_models = [TextLLMModel.CLIO, TextLLMModel.KAYRA, TextLLMModel.ERATO] + COLORS_LLM
+    if model is None:
+        model = TextLLMModel.KAYRA
+    # 检查模型是否在允许列表中
+    if model in valid_models:
+        return entity.mode == 1
+    else:
+        mode_from_prefix = entity.prefix
+        prefix_mode = 0
+        for theme in get_themes():
+            if theme.label == mode_from_prefix:
+                prefix_mode = theme.mode
+                break
+        return prefix_mode == 1 or entity.prefixMode == 1
+"""
+
+
+class Theme(BaseModel):
+    label: str
+    mode: int
+    description: str
+
+
+class PenStyle(Enum):
+    Off = "off"
+    VeryLight = "very_light"
+    Light = "light"
+    Default = "medium"
+    Aggressive = "aggressive"
+    VeryAggressive = "very_aggressive"
+
+
+@dataclass
+class Key(object):
+    Temperature = ("temperature", 0)
+    TopK = ("top_k", 1)
+    TopP = ("top_p", 2)
+    TFS = ("tfs", 3)
+    TopA = ("top_a", 4)
+    TypicalP = ("typical_p", 5)
+    Cfg = ("cfg", 6)
+    TopG = ("top_g", 7)
+    Mirostat = ("mirostat", 8)
+    Math1 = ("math1", 9)
+    MinP = ("min_p", 10)
+
+
+class KeyOrderEntry(BaseModel):
+    id: Tuple[str, int]
+    enabled: bool
+
+
+class LogitBiasGroup(BaseModel):
+    sequence: List[int]
+    bias: float = Field(default=None, gt=-1, lt=1)
+    ensure_sequence_finish: bool
+    generate_once: bool
+
+    @model_validator(mode="before")
+    def validate_sequence(cls, value):
+        print(value)
+        return value
+
+
+class AdvanceLLMSetting(BaseModel):
+    """
+    LLM Generation Request Parameters
+    """
+    min_length: Optional[int] = 1
+    max_length: Optional[int] = None
+    repetition_penalty: Optional[float] = None
+    generate_until_sentence: Optional[bool] = True
+    use_cache: Optional[bool] = False
+    use_string: Optional[bool] = False
+    return_full_text: Optional[bool] = False
+    prefix: Optional[str] = ""
+    logit_bias_exp: Optional[List[LogitBiasGroup]] = None
+    num_logprobs: Optional[int] = None
+    order: List[int] = []
+    bracket_ban: Optional[bool] = True
+    model_config = ConfigDict(extra="allow")
+
+
+class LLMGenerationParams(BaseModel):
+    """
+    LLM Generation Settings
+    """
+    textGenerationSettingsVersion: Optional[int] = None
+    temperature: Optional[float] = None
+    max_length: Optional[int] = Field(default=None, ge=1, le=100)  # Max 150(vip3),100(vip2)
+    min_length: Optional[int] = Field(default=None, ge=1, le=100)  # Max 150(vip3),100(vip2)
+    top_k: Optional[float] = Field(default=None, ge=0)
+    top_p: Optional[float] = Field(default=None, gt=0, le=1)
+    top_a: Optional[float] = Field(default=None, ge=0)
+    typical_p: Optional[float] = Field(default=None, ge=0, le=1)
+    tail_free_sampling: Optional[float] = Field(default=None, ge=0)
+    repetition_penalty: Optional[float] = Field(default=None, gt=0)
+    repetition_penalty_range: Optional[int] = Field(default=None, ge=0)
+    repetition_penalty_slope: Optional[float] = Field(default=None, ge=0)
+
+    eos_token_id: int = None
+    bad_words_ids: List[List[int]] = None
+    logit_bias_groups: Optional[List[LogitBiasGroup]] = []
+
+    repetition_penalty_frequency: Optional[float] = None
+    repetition_penalty_presence: Optional[float] = None
+    repetition_penalty_whitelist: Optional[List[int]] = None
+    repetition_penalty_default_whitelist: Optional[bool] = None
+    cfg_scale: Optional[float] = None
+    cfg_uc: Optional[str] = None
+    phrase_rep_pen: PenStyle = PenStyle.Off
+    top_g: Optional[float] = None
+    mirostat_tau: Optional[float] = None
+    mirostat_lr: Optional[float] = None
+    math1_temp: Optional[float] = None
+    math1_quad: Optional[float] = None
+    math1_quad_entropy_scale: Optional[float] = None
+    min_p: Optional[float] = None
+
+    order: Union[List[int], List[KeyOrderEntry]] = [
+        KeyOrderEntry(id=Key.Cfg, enabled=False),
+        KeyOrderEntry(id=Key.Temperature, enabled=True),
+        KeyOrderEntry(id=Key.TopK, enabled=True),
+        KeyOrderEntry(id=Key.TopP, enabled=True),
+        KeyOrderEntry(id=Key.TFS, enabled=True),
+        KeyOrderEntry(id=Key.TopA, enabled=False),
+        KeyOrderEntry(id=Key.TypicalP, enabled=False),
+        KeyOrderEntry(id=Key.TopG, enabled=False),
+        KeyOrderEntry(id=Key.Mirostat, enabled=False),
+        KeyOrderEntry(id=Key.Math1, enabled=False),
+        KeyOrderEntry(id=Key.MinP, enabled=False),
+    ]
+
+    def get_base_map(self):
+        parameters = self.model_dump(mode="json", exclude_none=True)
+        skipped_params = {
+            "textGenerationSettingsVersion",
+            "eos_token_id",
+            "bad_words_ids",
+            "logit_bias_groups",
+            "order"
+        }
+        return {key: value for key, value in parameters.items() if key not in skipped_params}
+
+
+class ContextConfig(BaseModel):
+    prefix: str
+    suffix: str
+    tokenBudget: int
+    reservedTokens: int
+    budgetPriority: int
+    trimDirection: str
+    insertionType: str
+    maximumTrimType: str
+    insertionPosition: int
+
+
+class EphemeralDefaults(BaseModel):
+    text: str
+    contextConfig: ContextConfig
+    startingStep: int
+    delay: int
+    duration: int
+    repeat: bool
+    reverse: bool
+
+
+class LoreDefaults(BaseModel):
+    text: str
+    contextConfig: ContextConfig
+    lastUpdatedAt: str
+    displayName: str
+    keys: List[str]
+    searchRange: int
+    enabled: bool
+    forceActivation: bool
+    keyRelative: bool
+    nonStoryActivatable: bool
+
+
+class StoryDefault(BaseModel):
+    prefix: str
+    suffix: str
+    tokenBudget: int
+    reservedTokens: int
+    budgetPriority: int
+    trimDirection: str
+    insertionType: str
+    maximumTrimType: str
+    insertionPosition: int
+
+
+class ContextDefaults(BaseModel):
+    text: str
+    contextConfig: ContextConfig
+
+
+class ContextPresets(BaseModel):
+    contextDefaults: Optional[List[ContextDefaults]]
+    ephemeralDefaults: Optional[List[EphemeralDefaults]]
+    loreDefaults: Optional[List[LoreDefaults]]
+    storyDefault: Optional[StoryDefault]
+
+
+class Preset(BaseModel):
+    presetVersion: int
+    remoteId: str
+    name: str
+    id: str
+    parameters: LLMGenerationParams
+    description: str
+    contextPresets: Optional[ContextPresets] = None
diff --git a/src/novelai_python/sdk/ai/generate/themes.json b/src/novelai_python/sdk/ai/generate/themes.json
new file mode 100644
index 0000000..e09b9d5
--- /dev/null
+++ b/src/novelai_python/sdk/ai/generate/themes.json
@@ -0,0 +1,362 @@
+[
+  {
+    "label": "No Module",
+    "mode": 0,
+    "description": ""
+  },
+  {
+    "label": "Special: Openings",
+    "mode": 0,
+    "description": ""
+  },
+  {
+    "label": "General: Instruct (Experimental)",
+    "mode": 0,
+    "description": "Use Instruct commands to give the AI a specific direction."
+  },
+  {
+    "label": "General: Cross-Genre",
+    "mode": 0,
+    "description": "A general module for many genres."
+  },
+  {
+    "label": "Special: Text Adventure",
+    "mode": 1,
+    "description": "It's up to you whether to change your course of action. Do you proceed?"
+  },
+  {
+    "label": "General: Prose Augmenter",
+    "mode": 0,
+    "description": "A general module for verbose prose. If you find the AI struggling with short sentences and other writing quality pitfalls, Prose Augmenter can help."
+  },
+  {
+    "label": "Style: Algernon Blackwood",
+    "mode": 0,
+    "description": "The unquestioned master of weird atmosphere, unusual fiction and ghost stories."
+  },
+  {
+    "label": "Style: Arthur Conan Doyle",
+    "mode": 0,
+    "description": "A master detective seeks the truth in every case, solving crimes with logic, wit, and a keen mind. It's elementary, my dear."
+  },
+  {
+    "label": "Style: Edgar Allan Poe",
+    "mode": 0,
+    "description": "The darkness in man's soul, the twisted will of a madman. Influenced by the works of one of literature's greatest authors."
+  },
+  {
+    "label": "Style: H.P. Lovecraft",
+    "mode": 0,
+    "description": "In a forgotten corner of the universe, beings stretch out ethereal tendrils. Who knows what unknown horrors lurk in the shadows...?"
+  },
+  {
+    "label": "Style: Sheridan Le Fanu",
+    "mode": 0,
+    "description": "It's a restless and scrupulous passion, curiosity. What whispers slip between the veil of light and shadow?"
+  },
+  {
+    "label": "Style: Jane Austen",
+    "mode": 0,
+    "description": "Experience a Regency world of wit, romance, and social politics."
+  },
+  {
+    "label": "Style: Jules Verne",
+    "mode": 0,
+    "description": "Journey to distant lands to explore strange cultures. Travel to a world of tribal folk, mermaids, and dinosaurs."
+  },
+  {
+    "label": "Style: William Shakespeare",
+    "mode": 0,
+    "description": "Beguiling language - in script form, tragedy, all-powerful characters, and a world of romantic fantasy and adventure."
+  },
+  {
+    "label": "Theme: 19th Century Romance",
+    "mode": 0,
+    "description": "Take a trip down memory lane to an era when chivalry was king. Witness love affairs bloom, and scandalous trysts be exposed!"
+  },
+  {
+    "label": "Theme: Action Archeology",
+    "mode": 0,
+    "description": "Rumors of great treasures abound throughout history. Follow clues through mystical ruins, avoiding devious traps along the way."
+  },
+  {
+    "label": "Theme: Artificial Intelligence",
+    "mode": 0,
+    "description": "In this world of silicon, the line between machine and human blurs. Dream of electric sheep, or starships off a distant world."
+  },
+  {
+    "label": "Theme: Ancient China",
+    "mode": 0,
+    "description": "Ancient China's turbulent history comes alive, featuring the rise of an Empire, celestial bureaucracy, and heroes. Claim your own destiny, or serve others' whims!"
+  },
+  {
+    "label": "Theme: Ancient Greece",
+    "mode": 0,
+    "description": "Wander through the streets of Athens, drink wine in tavernas, listen to the songs of the rhapsodists while you befriend the gods—or betray them."
+  },
+  {
+    "label": "Theme: Ancient India",
+    "mode": 0,
+    "description": "Find your place in ancient Indian society: Ancient Indian mythology, magic, demons, yadavs, rakshas, and more! A vast universe of myth and legend awaits!"
+  },
+  {
+    "label": "Theme: Animal Fiction",
+    "mode": 0,
+    "description": "Drawing from fiction written from the perspective of animals or about animals, be a fierce lion, cunning crow, or tall giraffe. Be whatever animal you desire!"
+  },
+  {
+    "label": "Theme: Anthropomorphic Animals",
+    "mode": 0,
+    "description": "Begone, hands! It's time for paws, tails, wings, and claws! These anthro animals have fur, and a lot of it."
+  },
+  {
+    "label": "Theme: Children's Fiction",
+    "mode": 0,
+    "description": "Evoke your inner child and engage in an adventure that will take you back to earlier days."
+  },
+  {
+    "label": "Theme: Christmas",
+    "mode": 0,
+    "description": "A joyful thematic wildcard to instantly transport you into the merriest season of the year!"
+  },
+  {
+    "label": "Theme: Comedic Fantasy",
+    "mode": 0,
+    "description": "Fantasy of a satirical and humorous bent, with witty narrators, bumbling wizards, and heroic but yet inept warriors!"
+  },
+  {
+    "label": "Theme: Contemporary",
+    "mode": 0,
+    "description": "Relatively relatable: portraying modern everyday life."
+  },
+  {
+    "label": "Theme: Cyberpunk",
+    "mode": 0,
+    "description": "Jack into the Grid, and conduct cybernetic warfare against other sentients. Enhance your body with various cybernetic enhancements and perform espionage in a dystopia."
+  },
+  {
+    "label": "Theme: Dark Fantasy",
+    "mode": 0,
+    "description": "The gifted few harness strange powers. When fortresses are besieged, this ancient world will be pushed to the brink."
+  },
+  {
+    "label": "Theme: Dragons",
+    "mode": 0,
+    "description": "Crimson skies are dominated by wings like hurricanes and teeth like daggers. This realm is ruled over by fire breathers of legend."
+  },
+  {
+    "label": "Theme: Egypt",
+    "mode": 0,
+    "description": "Mummies stir in the desert, seeking freedom from their curse. Explore ancient pyramids, and discover why the pharaohs were revered beyond their time."
+  },
+  {
+    "label": "Theme: Feudal Japan",
+    "mode": 0,
+    "description": "Go back to Feudal Japan, where samurai epics abound, along with detective stories, and tales of love conveyed through poetry."
+  },
+  {
+    "label": "Theme: Gaming",
+    "mode": 0,
+    "description": "Experience the crunch! Take part in a gaming company's office life, or play old-school games with your friends."
+  },
+  {
+    "label": "Theme: General Fantasy",
+    "mode": 0,
+    "description": "Behold a land where men wield magic swords, and elves sing songs of valor. Experience epic battles, and legendary adventures."
+  },
+  {
+    "label": "Theme: Golden Age Scifi",
+    "mode": 0,
+    "description": "No computers or long distance communications, just spaceships, robots, lasers, and (mostly) men surviving on their wits against the galaxy."
+  },
+  {
+    "label": "Theme: Hard SF",
+    "mode": 0,
+    "description": "Simply put, sci-fi that tries to be realistic. A lot of semi-plausible tech here!"
+  },
+  {
+    "label": "Theme: History",
+    "mode": 0,
+    "description": "A pen is stronger than a sword. Rewrite history however you desire."
+  },
+  {
+    "label": "Theme: Horror",
+    "mode": 0,
+    "description": "Experience nightmares like never before. Indulge in the deepest depths of the human mind, and beyond..."
+  },
+  {
+    "label": "Theme: Hunter Gatherer",
+    "mode": 0,
+    "description": "When man discovered fire, a new age dawned upon the world. Enter the paleolithic, and discover the secrets of survival in our primitive past."
+  },
+  {
+    "label": "Theme: LitRPG",
+    "mode": 0,
+    "description": "Stat blocks and status conditions, monsters and magic spells; Level up your roleplaying experience!"
+  },
+  {
+    "label": "Theme: Magic Academy",
+    "mode": 0,
+    "description": "Learn spells from wizards old and young. One must choose their path wisely—and never stop learning."
+  },
+  {
+    "label": "Theme: Magic Library",
+    "mode": 0,
+    "description": "They say a book is a gateway to another world. In this magical collection, the adage may be more literal than expected."
+  },
+  {
+    "label": "Theme: Light Novels",
+    "mode": 0,
+    "description": "Get your dose of otaku medicine, with a dash of isekai, fantasy, slice of life and some romance!"
+  },
+  {
+    "label": "Theme: Mars Colonization",
+    "mode": 0,
+    "description": "Brave pioneers set foot on another world. What would it take to tame the red planet?"
+  },
+  {
+    "label": "Theme: Medieval",
+    "mode": 0,
+    "description": "A new age dawns as knights rise and empires fall. Take up sword and shield, or cloak and dagger in this archaic setting."
+  },
+  {
+    "label": "Theme: Military SciFi",
+    "mode": 0,
+    "description": "Though the weapons changed, war remained the same. From futuristic trenches to intergalactic starfighters, experience life at the front lines."
+  },
+  {
+    "label": "Theme: Music",
+    "mode": 0,
+    "description": "The world of touring bands, drugs, and groupies! Can you resist temptation or stay focused on your art?"
+  },
+  {
+    "label": "Theme: Mystery",
+    "mode": 0,
+    "description": "Whodunit? Conduct investigations into mysteries that abound. Poke your nose into places where it doesn't belong."
+  },
+  {
+    "label": "Theme: Nature",
+    "mode": 0,
+    "description": "Survive the elements of rugged terrain, climb sheer mountains, and forge a path through the wilderness."
+  },
+  {
+    "label": "Theme: Naval Age of Discovery",
+    "mode": 0,
+    "description": "Sail the seven seas aboard mighty ships, while exploring exotic ports and uncharted waters."
+  },
+  {
+    "label": "Theme: Noir",
+    "mode": 0,
+    "description": "Cynical detectives, hard-boiled dialogue, and black and white morality."
+  },
+  {
+    "label": "Theme: Philosophy",
+    "mode": 0,
+    "description": "Let philosophical and political figures from old and new guide your stories."
+  },
+  {
+    "label": "Theme: Pirates",
+    "mode": 0,
+    "description": "There be loot to plunder on the high seas! 'Course, ye best be ready when the galleons come calling..."
+  },
+  {
+    "label": "Theme: Poetic Fantasy",
+    "mode": 0,
+    "description": "Craft beautiful poetry and stories, set in a curious land of magic and wonder, with delicate prose. Fairy dust not included."
+  },
+  {
+    "label": "Theme: Post-Apocalyptic",
+    "mode": 0,
+    "description": "The world has ended, but the story continues. Scavenge for supplies and face fearsome mutated beasts."
+  },
+  {
+    "label": "Theme: Rats",
+    "mode": 0,
+    "description": "Preying and stalking through the night, these little squeakers seek food and shelter wherever they find it. Eeek!"
+  },
+  {
+    "label": "Theme: Roman Empire",
+    "mode": 0,
+    "description": "When in Rome, do as the Romans do."
+  },
+  {
+    "label": "Theme: Science Fantasy",
+    "mode": 0,
+    "description": "Each step within these storied lands whispers tales of civilizations long forgotten where great technological works were once regarded as witchcraft."
+  },
+  {
+    "label": "Theme: Space Opera",
+    "mode": 0,
+    "description": "Soar between asteroids, explode planets, find your alien love, or maybe even start an intergalactic war. Infinite universe, infinite possibilities."
+  },
+  {
+    "label": "Theme: Superheroes",
+    "mode": 0,
+    "description": "When heroes clash with villains, only one can emerge victorious. Stand for justice, or join forces against it."
+  },
+  {
+    "label": "Theme: Steampunk",
+    "mode": 0,
+    "description": "Soar above the clouds and into a world of adventure, clockwork engines and flying machines."
+  },
+  {
+    "label": "Theme: Travel",
+    "mode": 0,
+    "description": "Travel around the world on years-long journeys to the darkest jungles, highest peaks, deepest seas, driest deserts, and see the myriad Wonders of the World!"
+  },
+  {
+    "label": "Theme: Urban Fantasy",
+    "mode": 0,
+    "description": "Interact with elves, fairies and other fantasy creatures in a modern urban environment where magic exists!"
+  },
+  {
+    "label": "Theme: Valentines",
+    "mode": 0,
+    "description": "This module is sure to make your heart flutter! Keep the sparks flying with this module trained on a collection of romantic literature!"
+  },
+  {
+    "label": "Theme: Vikings",
+    "mode": 0,
+    "description": "Raise your swords and shields, screaming on the way to Valhalla!"
+  },
+  {
+    "label": "Theme: Weird West",
+    "mode": 0,
+    "description": "Take to the dusty trail and encounter magical natives, werewolf cowboys, and undead creatures of the wild west."
+  },
+  {
+    "label": "Theme: Western Romance",
+    "mode": 0,
+    "description": "Blaze the open trails with a big iron on your hip, and maybe a pretty pardner as well."
+  },
+  {
+    "label": "Inspiration: Crab, Snail, and Monkey",
+    "mode": 0,
+    "description": "High school gets weird in this unique mixture of slice of life, romance, and supernatural mystery."
+  },
+  {
+    "label": "Inspiration: Mercantile Wolfgirl Romance",
+    "mode": 0,
+    "description": "A merchant and a wolf girl goddess travelling the lands."
+  },
+  {
+    "label": "Inspiration: Nervegear",
+    "mode": 0,
+    "description": "It's all fun and games, until the users of a VR MMORPG find they can't log out."
+  },
+  {
+    "label": "Inspiration: Romance of the Three Kingdoms",
+    "mode": 0,
+    "description": "China's greatest warlords vie for supremacy. Experience the art of war on a grand scale, in one of the most renowned tales ever told."
+  },
+  {
+    "label": "Inspiration: Throne Wars",
+    "mode": 0,
+    "description": "As the kingdom faces invasion from within and without, one must make difficult choices; to fight with honor, or or rule by fear?"
+  },
+  {
+    "label": "Inspiration: Witch at Level Cap",
+    "mode": 0,
+    "description": "Having gone to another world and maxed out her level cap, a witch lives a laid back life."
+  }
+]
\ No newline at end of file
diff --git a/src/novelai_python/sdk/ai/generate_stream.py b/src/novelai_python/sdk/ai/generate_stream.py
index d539664..dbb8a96 100644
--- a/src/novelai_python/sdk/ai/generate_stream.py
+++ b/src/novelai_python/sdk/ai/generate_stream.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 # @Author  : sudoskys
 import json
-from typing import Optional, Union, List, AsyncIterable
+from typing import Optional, Union, AsyncIterable
 
 import curl_cffi
 import httpx
@@ -9,11 +9,12 @@
 from loguru import logger
 from tenacity import wait_random, retry, stop_after_attempt, retry_if_exception
 
-from .generate import LLM, generate_order, TextLLMModel, PenStyle, PRESET
-from .generate_image import Params
+from .generate import LLM, TextLLMModel, PenStyle, AdvanceLLMSetting, LLMGenerationParams
+from ..._enum import TextLLMModelTypeAlias
 from ..._exceptions import APIError, SessionHttpError
 from ..._response.ai.generate_stream import LLMStreamResp
 from ...credential import CredentialBase
+from ...utils import try_jsonfy
 
 
 class LLMStream(LLM):
@@ -28,49 +29,32 @@ def base_url(self):
     @classmethod
     def build(cls,
               prompt: str,
-              model: TextLLMModel = TextLLMModel.Kayra,
-              min_length: Optional[int] = None,
-              max_length: Optional[int] = None,
-              temperature: Optional[float] = None,
-              top_k: Optional[int] = None,
-              top_p: Optional[float] = None,
-              frequency_penalty: Optional[float] = None,
-              presence_penalty: Optional[float] = None,
-              stop_sequences: Optional[Union[List[str]]] = None,
-              repetition_penalty: Optional[float] = None,
-              pen_style: Optional[PenStyle] = None,
+              model: TextLLMModelTypeAlias = TextLLMModel.ERATO,
+              phrase_rep_pen: PenStyle = PenStyle.Off,
+              *,
+              advanced_setting: AdvanceLLMSetting = AdvanceLLMSetting(),
+              parameters: Optional[LLMGenerationParams] = None,
+              default_bias: Optional[bool] = True,
+              logprobs_count: Optional[int] = 10,
+              bias_dinkus_asterism: Optional[bool] = False,
+              prefix: Optional[str] = "vanilla",
               **kwargs
               ) -> "LLMStream":
         """
         Generate Prompt
         """
         assert isinstance(prompt, str), "Prompt must be a string"
-        # 清理空值
-        kwarg = {
-            "min_length": min_length,
-            "max_length": max_length,
-            "temperature": temperature,
-            "top_k": top_k,
-            "top_p": top_p,
-            "frequency_penalty": frequency_penalty,
-            "presence_penalty": presence_penalty,
-            "stop_sequences": stop_sequences,
-            "repetition_penalty": repetition_penalty,
-            "pen_style": pen_style
-        }
-        kwargs = {**kwarg, **kwargs}
-        param = {k: v for k, v in kwargs.items() if v is not None}
-        _preset_model = PRESET.get(model.name, {})
-        if not _preset_model:
-            _build_prop = Params.model_validate(param)
-        else:
-            _build_prop = _preset_model.model_copy(deep=True, update=param)
-        assert _build_prop is not None, "Params validate failed"
-        return cls(
+        created = cls(
             input=prompt,
             model=model,
-            parameters=_build_prop
+            advanced_setting=advanced_setting,
+            parameters=parameters,
+            default_bias=default_bias,
+            logprobs_count=logprobs_count,
+            bias_dinkus_asterism=bias_dinkus_asterism,
+            prefix=prefix,
         )
+        return created
 
     @retry(
         wait=wait_random(min=1, max=3),
@@ -90,9 +74,17 @@ async def request(self,
         :return: LLMStreamResp
         """
         # Data Build
-        request_data = self.model_dump(mode="json", exclude_none=True)
-        request_data["order"] = generate_order(request_data) or []
-        assert request_data.get("parameters"), "Parameters is required"
+        base_map = self.parameters.get_base_map()
+        parameters = {
+            **base_map,
+            **self.advanced_setting.model_dump(mode="json", exclude_none=True),
+        }
+        parameters.update(self.parameters.model_dump(mode="json", exclude_none=True).items())
+        # Override
+        request_data = {
+            **self.model_dump(mode="json", include={"input", "model"}),
+            "parameters": parameters,
+        }
         async with session if isinstance(session, AsyncSession) else await session.get_session() as sess:
             # Header
             sess.headers.update(await self.necessary_headers(request_data))
@@ -109,42 +101,44 @@ async def request(self,
                     json=request_data,
                     stream=True  # 使用 stream=True 参数
                 )
+                response_data = try_jsonfy(response.content, default_when_error={})
+                message = response_data.get("error") or response_data.get(
+                    "message") or f"Server error with status_code {response.status_code}"
+                status_code = response_data.get("statusCode", response.status_code)
                 header_type = response.headers.get('Content-Type')
                 # 检查 'Content-Type' 是否为 'text/event-stream'
                 if header_type not in ['text/event-stream']:
-                    try:
-                        _msg = response.json()
-                    except Exception as e:
-                        _msg = {"statusCode": response.status_code, "message": response.content}
-                    status_code = _msg.get("statusCode", response.status_code)
-                    message = _msg.get("message", "Unknown error")
                     if status_code == 400:
                         raise APIError(
                             "A validation error occured.",
-                            request=request_data, code=status_code, response=_msg
+                            request=request_data, code=status_code, response=message
                         )
-
                     elif status_code == 401:
                         raise APIError(
                             "Access Token is incorrect.",
-                            request=request_data, code=status_code, response=_msg
+                            request=request_data, code=status_code, response=message
                         )
                     elif status_code == 402:
                         raise APIError(
                             "An active subscription is required to access this endpoint.",
-                            request=request_data, code=status_code, response=_msg
+                            request=request_data, code=status_code, response=message
                         )
                     elif status_code == 409:
                         raise APIError(
                             "A conflict error occured.",
-                            request=request_data, code=status_code, response=_msg
+                            request=request_data, code=status_code, response=message
                         )
                     else:
                         raise APIError(
-                            "An unknown error occured.",
-                            request=request_data, code=status_code, response=_msg
+                            f"Server did not send text/event-stream, status code {status_code}",
+                            request=request_data, code=status_code, response=message
                         )
                 else:
+                    if response.status_code != 200:
+                        raise APIError(
+                            f"Server error with status code {response.status_code}",
+                            request=request_data, code=response.status_code, response=response.content
+                        )
                     # Streaming data processing
                     async for line in response.aiter_lines():
                         event = line.decode().strip().split(":", 1)
diff --git a/src/novelai_python/tokenizer/NOTICE.MD b/src/novelai_python/tokenizer/NOTICE.MD
deleted file mode 100644
index 37bfedd..0000000
--- a/src/novelai_python/tokenizer/NOTICE.MD
+++ /dev/null
@@ -1,12 +0,0 @@
-# NOTICE OF THIS FOLDER
-
-## MIT License
-
-The MIT license applies to the files in:
-
-    file: pile_tokenizer.json from https://github.com/Aedial/novelai-api
-    file: nerdstash_v2.model from https://github.com/Aedial/novelai-api
-    file: nerdstash_v1.model from https://github.com/Aedial/novelai-api
-    file: gpt2-genji_tokenizer.json from https://github.com/Aedial/novelai-api
-    file: gpt2_tokenizer.json from https://github.com/Aedial/novelai-api
-    file: bpe_simple_vocab_16e6.txt.gz from https://github.com/Aedial/novelai-api
diff --git a/src/novelai_python/tokenizer/__init__.py b/src/novelai_python/tokenizer/__init__.py
index 3b7116c..fc88a30 100644
--- a/src/novelai_python/tokenizer/__init__.py
+++ b/src/novelai_python/tokenizer/__init__.py
@@ -1,226 +1,211 @@
-# MIT:https://github.com/Aedial/novelai-api/blob/794c4f3d89cc86df3c7d2c401b320f1822822ac0/novelai_api/Tokenizer.py
-# -*- coding: utf-8 -*-
-import itertools
+import gzip
+import io
 import pathlib
-from pathlib import Path
-from typing import Dict, List
+import zlib
+from typing import Dict, List, Optional
 
-import regex as re
-import sentencepiece
-import sentencepiece as spm
-import tokenizers
+import httpx
+from json_repair import repair_json
 from loguru import logger
+from pydantic import BaseModel, model_validator
+from tokenizers import Tokenizer, pre_tokenizers, Regex, decoders
+from tokenizers.models import BPE
 
-from novelai_python.tokenizer.simple_tokenizer import SimpleTokenizer
+from .clip_simple_tokenizer import SimpleTokenizer
 
-tokenizers_path = Path(__file__).parent
 
-
-# MIT:https://github.com/Aedial/novelai-api/blob/794c4f3d89cc86df3c7d2c401b320f1822822ac0/novelai_api/Tokenizer.py
-class SentencePiece(sentencepiece.SentencePieceProcessor):
-    """
-    Wrapper around sentencepiece.SentencePieceProcessor that adds the encode and decode methods
+def download_file(url, destination_path, session):
     """
+    Download a file from the specified URL and save it to the destination path.
 
-    trans_table_ids: Dict[int, str]
-    trans_table_str: Dict[str, int]
-    trans_regex_str: re.Pattern
-
-    def __init__(self, model_path: str):
-        super().__init__()
-        self.Load(model_path)
-
-        self.trans_table_ids = {
-            self.unk_id(): "<|unk|>",
-            self.pad_id(): "<|pad|>",
-            self.bos_id(): "<|startoftext|>",
-            self.eos_id(): "<|endoftext|>",
-        }
-
-        self.trans_table_str = {
-            "<|unk|>": self.unk_id(),
-            "<|pad|>": self.pad_id(),
-            "<|startoftext|>": self.bos_id(),
-            "<|endoftext|>": self.eos_id(),
-        }
-
-        trans_regex_keys = "|".join(re.escape(e) for e in self.trans_table_str)
-        self.trans_regex_str = re.compile(trans_regex_keys)
-
-    def encode(self, s: str) -> List[int]:
+    :param url: The URL to download the file from.
+    :param destination_path: The path to save the downloaded file.
+    :param session: An httpx.Client instance to use for the download.
+    :return: None
+    :raises ValueError: If the downloaded file size doesn't match the Content-Length header.
+    """
+    response = session.get(url, headers={"Content-Type": "application/json"})
+    response.raise_for_status()
+    with open(destination_path, "wb") as f:
+        f.write(response.content)
+
+
+class TokenizerSetting(BaseModel):
+    pass
+
+
+class ClipTokenizerSetting(TokenizerSetting):
+    text: str
+
+
+class LLMTokenizerSetting(TokenizerSetting):
+    class TokenizerConfig(BaseModel):
+        splitRegex: str
+        maxEncodeChars: Optional[int] = None
+        maxNoWhitespaceChars: Optional[int] = None
+        ignoreMerges: Optional[bool] = False
+
+    config: TokenizerConfig
+    specialTokens: List[str]
+    vocab: Dict[str, int]
+    merges: list
+
+    @model_validator(mode="after")
+    def ensure(self):
+        self.merges = [tuple(merge) for merge in self.merges]
+        return self
+
+
+class NaiTokenizer:
+    def __init__(self,
+                 model: str,
+                 tokenizer_model_path: Optional[pathlib.Path] = None,
+                 download_session: Optional[httpx.Client] = None,
+                 ):
+        if not model.endswith(".def"):
+            model = f"{model}.def"
+        self.model_full_name = model
+        self.tokenizer_setting = None
+        self.tokenizer = None
+        if download_session is None:
+            download_session = httpx.Client()
+        self._download_session = download_session
+        if tokenizer_model_path is None:
+            tokenizer_model_path = pathlib.Path(__file__).parent
+        self.tokenizer_model_path = tokenizer_model_path
+        self._load_tokenizer_settings()
+        self._create_tokenizer()
+
+    @staticmethod
+    def get_model_download_url(model_full_name):
+        return f"https://novelai.net/tokenizer/compressed/{model_full_name}?v=2&static=true"
+
+    @staticmethod
+    def _read_compressed_def(file_data: bytes) -> str:
         """
-        Encode the provided text using the SentencePiece tokenizer.
-        This workaround is needed because sentencepiece cannot handle some tokens
-
-        :param s: Text to encode
+        Attempt to read and decompress compressed data, trying gzip first, followed by zlib and raw deflate.
 
-        :return: List of tokens the provided text encodes into
+        :param file_data: Compressed byte data.
+        :return: Decompressed file content as a string.
+        :raises RuntimeError: If decompression fails for all methods.
         """
+        try:
+            decompress_obj = zlib.decompressobj(-zlib.MAX_WBITS)
+            return decompress_obj.decompress(file_data).decode('utf-8')
+        except Exception as e:
+            logger.debug(f"Error decompressing with raw deflate: {e}, trying gzip next.")
 
-        trans_table = self.trans_table_str
-
-        # find the indexes of the string that need to be replaced
-        indexes = list(self.trans_regex_str.finditer(s))
-
-        # fast path, no translation needed
-        if not indexes:
-            return self.EncodeAsIds(s)
-
-        # split the tokens into parts, using the indexes as separators and decode them
-        parts = [
-            s[0: indexes[0].start()],
-            *[s[i.end() + 1: j.start()] for i, j in zip(indexes, indexes[1:])],
-            s[indexes[-1].end() + 1:],
-        ]
-        encoded_parts: List[List[int]] = [self.EncodeAsIds(part) for part in parts]
+        try:
+            with gzip.GzipFile(fileobj=io.BytesIO(file_data)) as gzip_file:
+                return gzip_file.read().decode('utf-8')
+        except Exception as e:
+            logger.debug(f"Error decompressing with gzip: {e}, trying zlib next.")
 
-        # translate the junctions
-        junctions = [trans_table[i.group(0)] for i in indexes]
+        try:
+            return zlib.decompress(file_data).decode('utf-8')
+        except Exception as e:
+            logger.debug(f"Error decompressing with zlib: {e}, trying raw deflate next.")
 
-        # join the parts with the translated tokens
-        return [
-            *encoded_parts[0],
-            *itertools.chain.from_iterable((j, *p) for j, p in zip(junctions, encoded_parts[1:])),
-        ]
+        raise RuntimeError("Failed to decompress the data using gzip, zlib, or raw deflate.")
 
-    def decode(self, t: List[int]):
+    def _load_tokenizer_settings(self):
         """
-        Decode the provided tokens using the SentencePiece tokenizer.
-        This workaround is needed because sentencepiece cannot handle some tokens
-
-        :param t: Tokens to decode
-
-        :return: Text the provided tokens decode into
+        Load tokenizer settings from the model file.
+        :return: None
+        :raises FileNotFoundError: If the model file is not found.
+        :raises ValueError: If the tokenizer settings are invalid.
+        :raises LookupError: If the model file download fails.
         """
-
-        trans_table = self.trans_table_ids
-
-        # find the indexes of the string that need to be replaced
-        indexes = [i for i, token in enumerate(t) if token in trans_table]
-
-        # fast path, no translation needed
-        if not indexes:
-            return self.DecodeIds(t)
-
-        # split the tokens into parts, using the indexes as separators and decode them
-        parts = [
-            t[0: indexes[0]],
-            *[t[i + 1: j] for i, j in zip(indexes, indexes[1:])],
-            t[indexes[-1] + 1:],
-        ]
-        decoded_parts = [self.DecodeIds(part) for part in parts]
-
-        # translate the junctions
-        junctions = [trans_table[t[i]] for i in indexes]
-
-        # join the parts with the translated tokens
-        return "".join((decoded_parts[0], *itertools.chain.from_iterable(zip(junctions, decoded_parts[1:]))))
-
-
-def singleton(cls):
-    instances = {}
-
-    def wrapper(*args, **kwargs):
-        if cls not in instances:
-            instances[cls] = cls(*args, **kwargs)
-        return instances[cls]
-
-    return wrapper
-
-
-# MIT: https://github.com/Aedial/novelai-api/blob/794c4f3d89cc86df3c7d2c401b320f1822822ac0/novelai_api/Tokenizer.py#L118
-@singleton
-class LLMTokenizer:
-    """
-    Abstraction of the tokenizer behind each Model
-    """
-
-    _GPT2_PATH = "gpt2_tokenizer.json"
-    _GENJI_PATH = "gpt2-genji_tokenizer.json"
-    _PILE_PATH = "pile_tokenizer.json"
-    # ORIGIN TODO: check differences from NAI tokenizer (from my limited testing, there is None)
-    _NERDSTASH_TOKENIZER_v1_PATH = "nerdstash_v1.model"
-    _NERDSTASH_TOKENIZER_v2_PATH = "nerdstash_v2.model"
-
-    def __init__(self, tokenizer_path=tokenizers_path):
-        self._GPT2_TOKENIZER = tokenizers.Tokenizer.from_file(str(tokenizer_path / self._GPT2_PATH))
-        self._GENJI_TOKENIZER = tokenizers.Tokenizer.from_file(str(tokenizer_path / self._GENJI_PATH))
-        self._PILE_TOKENIZER = tokenizers.Tokenizer.from_file(str(tokenizer_path / self._PILE_PATH))
-        self._CLIP_TOKENIZER = SimpleTokenizer()
+        model_path = self.tokenizer_model_path.joinpath(self.model_full_name)
+        url = self.get_model_download_url(self.model_full_name)
+        try:
+            if not model_path.exists():
+                download_file(url, model_path, self._download_session)
+                logger.info(f"Tokenizer {self.model_full_name} downloaded.")
+        except Exception as e:
+            model_path.unlink(missing_ok=True)
+            raise LookupError(f"Failed to download model {self.model_full_name} from {url}: {e}")
+        model_bytes = model_path.read_bytes()
+        decoded_str = self._read_compressed_def(model_bytes)
+        repaired_data = repair_json(decoded_str, return_objects=True)
         try:
-            self._NERDSTASH_TOKENIZER_v1 = SentencePiece(str(tokenizer_path / self._NERDSTASH_TOKENIZER_v1_PATH))
-            self._NERDSTASH_TOKENIZER_v2 = SentencePiece(str(tokenizer_path / self._NERDSTASH_TOKENIZER_v2_PATH))
-        except FileNotFoundError as exc:
-            logger.warning(
-                "Nerdstash tokenizers not found, please check model path or "
-                "there cannot be special text such as foreign languages, spaces, etc. in your model path."
+            if "clip" in self.model_full_name.lower():
+                self.tokenizer_setting = ClipTokenizerSetting.model_validate(repaired_data)
+            else:
+                self.tokenizer_setting = LLMTokenizerSetting.model_validate(repaired_data)
+        except Exception as e:
+            raise ValueError(
+                f"Failed to load tokenizer settings: {e} for {model_path}",
+                "report this issue to https://github.com/LlmKira/novelai-python/issues"
             )
-            raise exc
-        self._tokenizers = {
-            "gpt2": self._GPT2_TOKENIZER,
-            "gpt2-genji": self._GENJI_TOKENIZER,
-            "pile": self._PILE_TOKENIZER,
-            "clip": self._CLIP_TOKENIZER,
-            "nerdstash_v1": self._NERDSTASH_TOKENIZER_v1,
-            "nerdstash_v2": self._NERDSTASH_TOKENIZER_v2,
-        }
-
-    def decode(self, o: List[int], tokenizer_name: str) -> str:
-        if tokenizer_name not in self._tokenizers:
-            raise NotImplementedError(f"Tokenizer {tokenizer_name} not recognized")
-        tokenizer = self._tokenizers[tokenizer_name]
-
-        return tokenizer.decode(o)
-
-    def encode(self, o: str, tokenizer_name: str) -> List[int]:
-        if tokenizer_name not in self._tokenizers:
-            raise NotImplementedError(f"Tokenizer {tokenizer_name} not recognized")
-
-        tokenizer = self._tokenizers[tokenizer_name]
-        if isinstance(tokenizer, tokenizers.Tokenizer):
-            return tokenizer.encode(o).ids
-        if isinstance(tokenizer, (SimpleTokenizer, sentencepiece.SentencePieceProcessor)):
-            return tokenizer.encode(o)
-        raise ValueError(f"Tokenizer {tokenizer} ({tokenizer_name}) not recognized")
-
-
-class ImagePromptTokenizer:
-    MODEL_V1_PATH = pathlib.Path(__file__).parent.joinpath("novelai.model").absolute().as_posix()
-    MODEL_V2_PATH = pathlib.Path(__file__).parent.joinpath("novelai_v2.model").absolute().as_posix()
-
-    def __init__(self, model_path):
-        self.tokenizer = spm.SentencePieceProcessor(model_file=model_path)  # noqa 401
-
-    def encode(self, raw_text, readable: bool = False):
-        if readable:
-            return self.tokenizer.encode(raw_text, out_type=str)  # noqa 401
-        return self.tokenizer.encode(raw_text)  # noqa 401
-
-    def decode(self, token_ids: List[int]):
-        return self.tokenizer.decode(token_ids)  # noqa 401
-
-
-class TokenizerUtil(ImagePromptTokenizer):
-    """
-    Deprecated, use ImagePromptTokenizer instead
-    """
-
-    def __init__(self, model_path):
-        super().__init__(model_path)
-
-    def encode(self, raw_text, readable: bool = False):
-        logger.warning("`TokenizerUtil` will deprecated, use `ImagePromptTokenizer` instead")
-        return super().encode(raw_text, readable)
-
-    def decode(self, token_ids: List[int]):
-        logger.warning("`TokenizerUtil` will deprecated, use `ImagePromptTokenizer` instead")
-        return super().decode(token_ids)
 
+    def _create_tokenizer(self):
+        if "clip" in self.model_full_name.lower():
+            self._create_clip_tokenizer()
+        else:
+            self._create_bpe_tokenizer()
+
+    def _create_clip_tokenizer(self):
+        setting = self.tokenizer_setting
+        if not isinstance(setting, ClipTokenizerSetting):
+            raise ValueError(f"Invalid tokenizer setting: {setting}")
+        self.tokenizer = SimpleTokenizer(bpe_model_content=setting.text)
+
+    def _create_bpe_tokenizer(self):
+        setting = self.tokenizer_setting
+        self.tokenizer = Tokenizer(BPE(
+            vocab=setting.vocab,
+            merges=setting.merges,
+            ignore_merges=setting.config.ignoreMerges
+        ))
+
+        self.tokenizer.add_special_tokens(setting.specialTokens)
+        if setting.config.maxEncodeChars:
+            self.tokenizer.enable_truncation(max_length=setting.config.maxEncodeChars)
+
+        pre_zus = [
+            pre_tokenizers.Split(
+                behavior="merged_with_next",
+                pattern=Regex(setting.config.splitRegex)
+            ),
+        ]
 
+        if self.tokenizer.token_to_id(" ") is None:
+            pre_zus.append(pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False))
+
+        pre_tokenizer = pre_tokenizers.Sequence(pre_zus)
+        self.tokenizer.pre_tokenizer = pre_tokenizer  # noqa
+        self.tokenizer.decoder = decoders.ByteLevel()  # noqa
+
+    def tokenize_text(self, text: str) -> List[int]:
+        if isinstance(self.tokenizer, Tokenizer):
+            return [
+                token.replace("Ġ", " ")  # .replace("Ċ", " ").replace("ċ", " ")
+                for token in self.tokenizer.encode(text).tokens
+            ]
+        if isinstance(self.tokenizer, SimpleTokenizer):
+            return self.tokenizer.encode(text).tokens
+        raise NotImplementedError("Tokenizer does not support token encoding")
+
+    def encode(self, sentence: str) -> List[int]:
+        if isinstance(self.tokenizer, SimpleTokenizer):
+            return self.tokenizer.encode(sentence).ids
+        if isinstance(self.tokenizer, Tokenizer):
+            return self.tokenizer.encode(sentence, add_special_tokens=True).ids
+        raise NotImplementedError("Tokenizer does not support token encoding")
+
+    def decode(self, token_ids: List[int]) -> str:
+        if isinstance(self.tokenizer, SimpleTokenizer):
+            return self.tokenizer.decode(token_ids)
+        if isinstance(self.tokenizer, Tokenizer):
+            return self.tokenizer.decode(token_ids)
+        raise NotImplementedError("Tokenizer does not support token decoding")
+
+
+# Example usage:
 if __name__ == "__main__":
-    tokenizer_util = ImagePromptTokenizer(ImagePromptTokenizer.MODEL_V1_PATH)
-    text = "The quick brown fox jumps over the goblin."
-    token_id = tokenizer_util.encode(text)
-    print("Token IDs:", token_id)
-    decoded_text = tokenizer_util.decode(token_id)
-    print("Decoded text:", decoded_text)
+    tokenizer_package = NaiTokenizer("pile_tokenizer")
+    t_text = "Hello, World! This is a   test."
+    print(tokenizer_package.tokenize_text(t_text))
+    print(tokenizer_package.encode(t_text))
+    print(tokenizer_package.decode(tokenizer_package.encode(t_text)))
diff --git a/src/novelai_python/tokenizer/bpe_simple_vocab_16e6.txt.gz b/src/novelai_python/tokenizer/bpe_simple_vocab_16e6.txt.gz
deleted file mode 100644
index 7b5088a..0000000
Binary files a/src/novelai_python/tokenizer/bpe_simple_vocab_16e6.txt.gz and /dev/null differ
diff --git a/src/novelai_python/tokenizer/simple_tokenizer.py b/src/novelai_python/tokenizer/clip_simple_tokenizer.py
similarity index 73%
rename from src/novelai_python/tokenizer/simple_tokenizer.py
rename to src/novelai_python/tokenizer/clip_simple_tokenizer.py
index 0c00a0d..f0a9760 100644
--- a/src/novelai_python/tokenizer/simple_tokenizer.py
+++ b/src/novelai_python/tokenizer/clip_simple_tokenizer.py
@@ -1,13 +1,20 @@
-# MIT:https://github.com/Aedial/novelai-api/blob/794c4f3d89cc86df3c7d2c401b320f1822822ac0/novelai_api/simple_tokenizer.py
-import gzip
+# MIT:https://github.com/openai/CLIP
 import html
 import os
+from dataclasses import dataclass
 from functools import lru_cache
+from typing import List
 
 import ftfy
 import regex as re
 
 
+@dataclass
+class EncodingResult:
+    ids: List[int]
+    tokens: List[str]
+
+
 @lru_cache()
 def default_bpe():
     return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@@ -55,46 +62,43 @@ def basic_clean(text):
 
 
 def whitespace_clean(text):
-    text = re.sub(r"\s+", " ", text)
+    text = re.sub(r'\s+', ' ', text)
     text = text.strip()
     return text
 
 
 class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
+    def __init__(self, bpe_model_content: str = default_bpe()):
         self.byte_encoder = bytes_to_unicode()
         self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
-        merges = merges[1: 49152 - 256 - 2 + 1]
+        # merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
+        merges = bpe_model_content.split('\n')
+        merges = merges[1:49152 - 256 - 2 + 1]
         merges = [tuple(merge.split()) for merge in merges]
         vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v + "</w>" for v in vocab]
+        vocab = vocab + [v + '</w>' for v in vocab]
         for merge in merges:
-            vocab.append("".join(merge))
-        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
+            vocab.append(''.join(merge))
+        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
         self.encoder = dict(zip(vocab, range(len(vocab))))
         self.decoder = {v: k for k, v in self.encoder.items()}
         self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {
-            "<|startoftext|>": "<|startoftext|>",
-            "<|endoftext|>": "<|endoftext|>",
-        }
+        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
         self.pat = re.compile(
             r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
-            re.IGNORECASE,
-        )
+            re.IGNORECASE)
 
     def bpe(self, token):
         if token in self.cache:
             return self.cache[token]
-        word = tuple(token[:-1]) + (token[-1] + "</w>",)
+        word = tuple(token[:-1]) + (token[-1] + '</w>',)
         pairs = get_pairs(word)
 
         if not pairs:
-            return token + "</w>"
+            return token + '</w>'
 
         while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
             if bigram not in self.bpe_ranks:
                 break
             first, second = bigram
@@ -121,19 +125,22 @@ def bpe(self, token):
                 break
             else:
                 pairs = get_pairs(word)
-        word = " ".join(word)
+        word = ' '.join(word)
         self.cache[token] = word
         return word
 
     def encode(self, text):
         bpe_tokens = []
+        ids = []
         text = whitespace_clean(basic_clean(text)).lower()
         for token in re.findall(self.pat, text):
-            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
-        return bpe_tokens
+            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+            bpe_result = self.bpe(token).split(' ')
+            bpe_tokens.extend(bpe_result)
+            ids.extend(self.encoder[bpe_token] for bpe_token in bpe_result)
+        return EncodingResult(ids=ids, tokens=[token.replace('</w>', ' ') for token in bpe_tokens])
 
     def decode(self, tokens):
-        text = "".join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors="replace").replace("</w>", " ")
+        text = ''.join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
         return text
diff --git a/src/novelai_python/tokenizer/gpt2-genji_tokenizer.json b/src/novelai_python/tokenizer/gpt2-genji_tokenizer.json
deleted file mode 100644
index dd7e443..0000000
--- a/src/novelai_python/tokenizer/gpt2-genji_tokenizer.json
+++ /dev/null
@@ -1 +0,0 @@
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Hz","Ġscrew s","Ġbra kes","Ġhell o","ĠS IM","rim p","Ġo re","24 7","Ġterr ific","Ġt ones","14 1","ĠMin utes","Ġcur ves","Ġbat ting","L ay","ĠC ock","ĠS as","Ġconstruct or","et z","G ov","Ġant agon","Ġde eds","ĠMc Cl","Ġu m","Ġsurf aced","Ġsh ades","oy a","Ġmer ged","Up on","Any way","Ġsl ogan","Ġdis respect","I ran","ĠT B","act ed","OO OO","Ġwa iver","ĠHuff ington","J osh","ĠEll en","K now","x on","it ems","22 8","Ġf ills","ĠN ike","I r","Ġ ì","Ġfr iction","ig ator","Ġsc ans","ĠVi enna","Ġperform ers","P rim","Ġb idding","M ur","Ġlean ed","ĠPri x","al ks","ĠTw itch","ĠDevelop er","ĠG ir","Ġcall back","Ġfreed oms","ĠP G","ur acy","Ġl ump","is man",",, ,,","19 92","ĠR ED","I J","com pl","Ġnew born","Ġ19 59","Ġoutput s","K evin","Ġfav ors","Ġbur ial","Ġ15 6","te chn","M aking","Ġcon quer","ad ic","æ ĸ","Ġel f","ĠSt ack","Ġâ ĺħ","ĠS aid","ĠFall out","Ġkind ness","ĠCustom s","com ment","Ġcritic ised","Ġpol ished","Ġdo i","at em","Sc reen","Ġbar red","Com ments","ĠGener ally","Ġsl ap","7 20","V ari","Ġh ats","ĠPlay ing","l ab","form s","Ġcan s","ĠD ON","ĠSom alia","E ver","mod ern","Ġsur geon","3 000","Ġrandom ized","================================ ================================","Ġpro claim","Ġto es","Ġam ple","Ġpres erving","gr and","Ġsil k","h m","ĠRel igious","psy ch","ĠField s","aw ays","ĠNight mare","Ġv ampire","n ar","Ġn avy","Ġvac ant","Ġhall way","Ġface book","ident ally","ĠN RA","ĠKir by","Ġsk irt","ou st","ĠN V","ĠPe ak","ĠMe yer","Ġappoint ments","Ġal ley","Ġchar ities","Ġund o","Ġdest inations","ĠTest ing","Ġdest ined","Ġimp lements","ĠHar old","Ġoptim ization","Ġswift ly","ĠSh ares","ĠAl bum","ãĥ ĸ","ĠF ut","Ġbl oc","ĠN FC","ĠC W","Ġneg lected","Ġannounce ments","com b","Ġrot ating","Ġrest s","ĠT icket","ched el","L ou","ĠV ic","Ġrepl aces","Ar c",":: ::","ĠGil bert","Ġillness es","Ġheter osexual","L INE","Ġdecre asing","Ġweigh s","Al ong","' );","70 7","Ġjoint s","ver ts","Ġ14 9","na ire","Ġsimpl est","Ġl ore","10 80","f iction","Ġs ou","Ġsan 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ion","Ġvar iance","Ġ --------","Ġmel ting","ĠT we","ĠB il","Ġsy rup","qu ick","ĠHold er","Ġinnoc ence","U LE","ĠM ight","99 99","Ġf al","Ġ19 53","ĠB S","st ill","ĠAb use","Ġun supported","xxxx xxxx","Ġinst itute","Ġfrag ment","ĠC ause","ĠFr ag","ĠAr s","à ¥","ast ics","Ġcross es","Ġ15 4","ĠUp grade","Ġmit igate","ĠRay mond","Ġtom ato","Ġst umbled","Ġdiff ers","Ġt ant","à ł","Ġrel ay","Ġconf ession","in as","ĠHe ather","dri ving","Det ails","Ġdr ift","ĠW ald","ĠLuck ily","or f","Ġexp ire","ĠP unch","g old","Ġunp aid","ĠT rent","Ġun armed","Ġill icit","Ġsm ash","Ġst ing","Ġtruth s","b irth","Ġso ber","ĠN it","Ġ ib","Ġus able","Ġst acks","Ġdom ination","EN SE","ĠMe asure","Ġprogram mer","Ġ< -","18 2","ĠCond ition","Ġback yard","ĠJ eb","ĠCre ed","ĠH ang","ĠCOM P","F ER","ĠIs h","Ġdetect ives","------------ ---","ĠMess enger","Ġgate way","15 1","ĠMaterial s","ĠD T","Ġdo omed","od o","Ġemail ed","ĠPer l","UT H","ĠSouth west","ĠT PP","Ġoptim ism","ĠT ow","ul ators"," «","Ġex ile","en v","P rop","ĠZimmer man","Ù İ","C a","om aly","ãĥ Ĩ","Ġrail road","L ee","23 2","Ġrepl icate","Ġcomfort ably","act ly","Ġr av","Ġhonest y","ĠPe pper","ĠBr ing","Ġh alls","Ġcont end","IS E","Ġna ive","Ġ14 3","Ġpo ised","Ġsoc ks","Quest ion","Ġdiscover ies","ĠWire less","ĠRe venge","Ġce ased","29 0","ĠCon sole","Ġbo iling","Ġded uction","XX XX","ĠSh ane","Ġpos ing","ĠACL U","Ġthe ology","qu arter","ĠH ank","Co in","ĠL v","ĠAv oid","Ġt enth","ĠKn icks","Ġinv oked","AR A","22 7","ĠJ ab","b ay","Ġbud dy","f an","23 4","ĠH il","ad h","ĠT Y","ĠIN D","Ġ19 39","Ġiter ation","ĠI O","em b","en ch","ĠW ins","h ours","\" âĢ¦","M arg","ĠD one","Ġwas ting","ing ed","Ġw ishing","Ġt apping","Ġnational ism","Ġsqu ares","ãĥ ¥","In side","Ġapp end","T ell","Ġt earing","ĠRe y","or c","ĠDay ton","ĠN H","Ch arl","ĠMor rison","Ġacc use","Ġtor rent","Ġdecl ines","M ine","ĠBash ar","op ia","19 93","ĠNo vel","ĠInst ant","Ġroll er","A round","ĠElect ions","G ames","Ġin expensive","ĠH ole","Ġn ause","bo at","Ġbus h","Ġw o","Ġpa used","l ived","Ġsc out","c ool","id as","ĠP am","udd le","ĠLet ters","Gu ard","Ġwand ering","Ġque er","ĠBl ind","ĠMar riage","D om","Ġpadd ing","Ġfold ers","Ġmeaning less","Ġcandid acy","Ġh id","ĠDub ai","Ġpost ure","Ġh iking","Legend ary","ĠT P","ĠAT K","ĠR iot","19 91","ĠWy oming","ĠRock y","Ġsal mon","Ġmet ro","Ġb ilateral","b illion","D T","ĠRh ode","ple tion","Ġtun ing","ĠNOT E","ĠRe bel","ic us","F und","Ġcock tail","Ġ5 01","Ġsp oon","Ġun ite","ĠRe ich","pos itive","Ġam azed","ĠN T","D esc","ECT ION","Ġfalse ly","ĠC rist","ĠVictor ian","the ir","Ġp od","Ġhe ap","ĠR anch","Ġre app","ĠAub urn","ĠThrough out","â ĺ","k ick","B eg","Ġmer ger","Ġl aps","2 17","un ion","Ġdeb ated","Ġsil ently","B al","0000 000","F ood","ĠSe qu","More over","Ġtang ible","ĠMine craft","o an","Ġrepresent ations","M ade","l oe","> \"","* .","Ġgest ures","gener al","Le ague","Ġpack ets","ĠBer g","Ġfraud ulent","Ġcritic ize","F un","Ġbl aming","Ġsl ash","Ġwh ales","Ġsub set","EL D","ĠAct ivity","sh 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6","ĠBre aking","19 3","ĠSub st","t ier","Ġcr ushing","Further more","keep ers","Ġsex ism","x d","Ġcall er","ĠS ense","Ġfuel ed","ur st","Ġup hold","ĠF ans","Ġâ Ĺ","ĠB ath","25 4","Ġpol es","Ġgrad ual","ĠS uff","Ġlik ing","Ġ19 49","ĠO mar","ĠL V","ĠG IF","Ġp ains","Ġinteract ing","u ously","inc re","Ġrum or","19 7","Ġspe lling","Ġg h","Ġremark ed","ĠM ON","å ¥","g ae","ĠWH AT","E ric","ĠCamp us","Ġmeth ane","Ġimag in","J UST","X T","i q","ĠR SS","att a","Ġdemonstr ators","ĠCal vin","ĠV illa","ĠAw esome","26 8","ĠL ots","G reg","ĠBor is","Ġdetain ees","ĠH erman","Ġa we","fund ing","Ġlim b","Ġmanip ulated","Ġbub bles","ĠK ell","ĠInterest ingly","Ġdrag ging","Ġec ological","ĠF it","Ġg ent","Ġbenef ited","Ġpoly g","ãĥ İ","Ġ20 30","Ġpsych ic","Hand ler","16 2","ĠP ulse","Ġsy s","per cent","Ġesc aping","Ġg inger","Ġwith stand","ĠD ew","ser ies","Ġinject ed","Ġturn over","he at","Ļ Ĥ","H appy","ĠSil ent","ãĤ Ń","iv ism","AM A","Ġre ef","r ub","Ġ16 2","Ġbank ers","ĠEth ics","v v","Ġcritic 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i","ĠH Q","Ġ\" #","ign ant","Ġtra umatic","ĠL ing","H un","Ġsab ot","on line","r andom","Ġren amed","ra red","K A","d ead","é t","ĠAss istance","Ġse af","++++ ++++","Ġse ldom","ĠWeb b","Ġbo olean","u let","Ġref rain","ĠDI Y","ru le","Ġshut ting","Ġutil izing","load ing","ĠPar am","co al","oot er","Ġattract ing","ĠD ol","Ġher s","ag netic","ĠRe ach","im o","Ġdisc arded","ĠP ip","01 5","ü r","Ġm ug","Im agine","C OL","Ġcurs ed","ĠSh ows","ĠCurt is","ĠSach s","spe aking","ĠV ista","ĠFram ework","ong o","Ġsub reddit","Ġcr us","ĠO val","R ow","g rowing","Ġinstall ment","Ġgl ac","ĠAdv ance","EC K","ĠLGBT Q","LE Y","Ġac et","Ġsuccess ive","ĠNic ole","Ġ19 57","Qu ote","Ġcircumst ance","ack ets","Ġ14 2","ort ium","Ġguess ed","ĠFr ame","Ġperpet rators","ĠAv iation","ĠBen ch","Ġhand c","A p","Ġ19 56","25 9","r and","Net Message","d in","urt les","h ig","ĠV III","ff iti","ĠSw ords","b ial","Ġkidn apping","dev ice","Ġb arn","ĠEl i","auc as","S end","Con structed","Ġ ½","Ġneed les","Ġad 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clamation","Ġgood ies","Ġ........ ..","pro claimed","P olit","Ġtop ical","Ġspecial ize","ĠA min","g m","Ġanch ored","Ġbear ings","s ample","ĠHigh land","ĠAut ism","Ġmerc enary","Ġinterview er","L ER","ĠSom ers","Ġembry o","ĠAss y","Ġ28 1","ĠEd iting","ĠCh osen","6 60","Ġp ci","ĠThunder bolt","BI LL","Ġchuck led","jri wal","h of","Ġearth ly","() {","ind ependence","Ġdisp ers","ĠV endor","ĠG areth","Ġp als","P enn","ĠSub mit","ic um","Th u","Ġcl andestine","Ġcann ibal","ĠCl erk","E Stream","gal itarian","âĻ ¥","g ew","Ġhor rend","ĠL ov","ĠRe action","ocr in","Class ic","Ġecho ing","Ġdiscl osing","ĠIns ight","og un","ĠInc arn","upload s","pp erc","guy en","Ġ19 01","ĠB ars","68 7","Ġb ribes","ĠFres no","ur at","ĠRe ese","Ġintr usive","Ġgri pping","ĠBlue print","ĠR asm","un ia","man aged","ĠHeb do","Ġ3 45","Ġdec oding","Ġpo ets","Ġj aws","ĠF IGHT","am eless","ĠMead ows","ĠHar baugh","Inter view","ĠH osp","ĠB RA","Ġdelet ion","m ob","W alker","ĠMoon light","ĠJ ed","ĠSoph ia","Ġus 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deleted file mode 100644
index 2bbe1b7..0000000
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fer","Ġins ulin","Ġ& \\","Ġn urs","Ġ اÙĦ","Ġrecogn ize","Ġmor al","oid s","ĠChrist mas","l oc","Ġcopy right","Ġman ip","w hen","Ġdep ression","Ġjour ney","m ond","ren e","b ound","Ġreg ime","OO ST","Ġro se","ĠCan adian","ĠE V","Ġtrans cription","EX T","OC K","ãĥ ³","d em","Ġpat ent","Ġext ensive","\"} ),","ĠCol or","| |","Ġcontro vers","Ġpres um","atter ing","sc ill","Ġinj uries","Ġsh arp","Ġg el","le ep","AR R","Ġcomp rehens","Ġsymb ol","ĠO S","p rivate","xi ety","Ġab andon","Ġprolif eration","rol og","ĠServ ices","r h","Ġhistor ical","H el","Ġtransfer red","}} ^{","ĠL o","% ),","K e","Ġd os","math sf","] ]","Ġro les","å ľ","St atus","Ġmom ents","C OM","Ġvers ions","Ġfac ility","Ġbo ost","h ome","em ia","Ġbeg ins","ĠÐ ±","g ence","ĠHam ilton","ĠApp ellant","og ether","25 0","f ortunately","Ġrequest ed","olog ic","Ġ6 6","ĠI g","ru ption","ĠSecret ary","Ġd as","ol id","Ġ Ïĥ","Ġwhe el","ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ","Ġac ids","ĠA ri","Ġsitu ations","ĠIn terest","NA ME","ĠCor p","Ġf 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PECT","Ġsp iral","Ġcomment ary","à ±","r itten","Ġtr ash","Ġuncon scious","w y","Sc ott","Ġquarter back","ab ol","Ġdiv isions","Arg ument","ĠLie utenant","Ġdraft ed","32 6","Ġinsect s","ĠG it","Ġcrypt ocur","S PE","US A","ĠLim ited","N OS","os al","PR ESS","f m","iss ues","Ġde form","ĠPut in","ĠM oney","oc arcin","Ġref ined","th row","hem atic","M RI","Ġper sec","de ep","Ġdepos its","Ġpen alties","n é","inter pret","Ċĉ Ċ","? !","ĠG H","Ġ19 42","k b","gen es","Jan uary","h over","te es",": '","N or","ĠNE W","Ġnorm s","stant ial","Ġcoast al","ev idence","gu ide","L uc","Ġimp art","Ġlit eral","ĠEgypt ian","ot omy","0 38","Ġuser name","Ġfo am","ch ant","Ġtouch down","A pi","Ġwh isk","ĠP ub","ĠPC I","ãĥ »","ĠMult i","Ġpat ience","phen yl","con sole","Ġsequ ential","ĠT ABLE","ĠM ason","ĠRog ers","down arrow","Ġno v","Ġneg lected","ĠTyp ically","Ġro de","Ġsal ad","ĠRob in","ĠDet ect","ĠT S","ai ro","Ġid i","ph ot","ĠOb viously","æ ı","Ġgly col","Ġex on","bl ank","Ġenh ancing","es ar","Ġcat 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ments","Ġorche stra","Ġpres criptions","4 99","Ġv é","ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ","ĠвÑģ е","ĠThe resa","Ġappl aud","Ġlegitim acy","T aylor","ÃIJµ ÃIJ","Ġexam iner","![ \\[","AR RAY","Ġpave ment","ĠB MP","Ġreact ing","ĠTem plate","ãĥĥ ãĤ¯","Ġb t","ferr oni","K s","ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ","ar ious","Ġ' @","Ġà ľ","Ġhold ings","Ġm osaic","ĠK B","Ġhal ves","Ġaqu atic","éĩ ij","ĠG lor","Mat the","ĠD um","ĠMar ian","\\ ~","ĠPar ish","ĠOrche stra","it et","Ġall oys","æĦ Ł","ĠM NRAS","p mb","ism ic","Ġconstit uting","ĠOpt ional","ra il","De velopment","Ġpoly gon","ĠB end","Ġvar char","AM A","Offic ial","Ġ3 96","ĠH ipp","ĠR AD","ĠPro sec","å® ī","Ġtouchdown s","S ock","Sh ader","55 7","ÙĦ Ùī","Found ation","N OR","ĠF uj","Ġel usive","ĠTH ERE",".\" _","ĠU TC","Ġreprodu cibility","Ġair ports","4 23","Ġes e","n ings","ass ing","Ġpost natal","ĠJud y","9 60","Ġd és","yn n","öd inger","è ¢","Ġmoment a","Ġal c","ĠSur ve","h b","Ġsoci 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us","pos er","ĠAp ost","Ġuniform ity","pret ty","Pop ulated","on yl","Ġp ng","ĠApp lic","Ġcirc adian","â ĭ","ml in","ĠAdvent ure","re cht","ĠAll an","rs quo","ĠL IN","Ġol factory","< -","> /","do ctor","ĠO ber","IB LE","Ad just","Ġtext ures","Ġneut rinos","Ġst ing","co ins","6 30","A IDS","ĠC RT","IT CH","Ġassay ed","ent ary","conf irm","Ġhack ers","H ung","T our","Ġ3 46","Ġstruct urally","ĠEvery body","oc last","Ġqu bit","ĠTor res","rele ased","ĠSe vent","ĠCon cept","M ULT","ĠB oss","Ġk ol","Ġhard ship","ĠCorn ell","Ġobject ively","Aut hentication","M ot","Ġgeomet rical","ĠH earing","Ġr hs","ĠDavid son","Ġasympt otically","ĠSurv ival","ĠAD D","ev olution","ĠAv iation","Ġ3 78","Ġport raits","Ġinvari ants","Ġadj o","Ġ¶ ¶","ĠMons ieur","Ġo ps","Ġdecor ative","ag land","sp ark","Ġpel lets",")) /(-","èĢ Į","Ġ\" >","lass es","} ).","op o","OUT PUT","ust a","t umor","ĠD EL","co res","Supp lemental","ĠSche me","I o","Ġ($ (","Ġdoubt ful","ĠHamb urg","Ġcommod ities","Ġann ular","s ac","all 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esta","Ġsp aring","é ric","Ġgl ances","Ġint ending","Ġdil ated","ĠINS ERT","CL OCK","ĠMin ist","ĠSan ct","ÙĪ د","P ed","ot ec","Cl osed","igraph y","- âĤ¬","d iscussion","Ġun conditional","Ġcorticoster oids","od el","Ïģ Ïĩ","Ġuncon ventional","6 88","Ġcre eping","ç as","ĠSign ature","f rey","Abs olute","] $$","ves ter","ance ment","ĠCL I",". %","de re","19 33","ĠKel ley","polar ized","agul ant","Ġadj unct","ĠE SC","sk ins","h skip","Ġ5 62","Ġ( >","per o","w anted","Ġ[ (\\[","Ġlif eless","Ġfuck in","opter a","Ġche fs","Ġbre thren","8 47","im prove","mark down","Ġse ñ","Ġstress ors","ç ¡","present ation","Ġcon fluence","ĠAss et","ĠT ale","Ġwe aving","ĠB CG","hig hest","Ġholog raphic","re ject","ob t","Ġheter ozyg","ĠA IR","prop ylene","ĠOl son","ĠProv incial","ri ot","Ġdis ple","ĠÑģ м","h ler","ĠD rain","he ated","bl ur","Ġnav igating","M ann","Ġenum erate","Ġapprehens ion","mu x","Ġsem is","J ur","Ġcont ender","Ġre const","ĠEll iot","æĹ¥ æľ¬","Ġ---- ,","Ġ ,\"","ĠT ours","ap ters","æķ Ļ","ĠOpt im","widget s","Ġro ared","CAC HE","ĠS ears","Ġsty led","ig on","ĠF ritz","Ġch ast","Leg al","> {{","Ġanalges ic","G ra","Ġprompt s","doctor al","Ġo missions","ĠL ange","8 13","ĠC oca","ĠInd y","ĠS iem","5 42","f amilies","ost atin","Ġsp ines","icar bon","Ġcontin ents","ĠS ear","Ġpred icated","B ab","T rain","ĠN ass","v ist","Ġcontrace ption","Ġsignal ed","7 18","Ġfl aps","Aust in","o of","Ġstr ata","Ġread out","================ ===========","trans lated","Ġdetr iment","ĠS ays","ĠAb el","ĠPerson ally","Ġ6 36","Ġmind fulness","res ident","Ġwhere abouts","A ctor","T OD","Ġst ator","Ġ5 99","ĠεÏĢ ι","ceed ed","Ġauthor izing","Ġdisgu ised","Ġhor rors","k id","ÃŃ st","Ġextr usion","ĠChev rolet","Ġ$ +","Ġsc rape","use um","ar ie","Ġnan oc","Ġshr oud","Ġsh ader","byter ian","Ġm illing","Ġsum ming","ik awa","N GC","ĠCON CLUSION","Ġmanage able","Ġempower ment","pe e","ĠSix ty","end ra","Ġmotiv ational","é ration","Ġsurg ically","s uffix","Ġ$ (-","Ġconj unct","Ġf b","Ġcl inging","C 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diff --git a/src/novelai_python/utils/__init__.py b/src/novelai_python/utils/__init__.py
index 9275a46..788d268 100755
--- a/src/novelai_python/utils/__init__.py
+++ b/src/novelai_python/utils/__init__.py
@@ -11,14 +11,18 @@
 from .encode import encode_access_key  # noqa 401
 
 
-def try_jsonfy(obj: Union[str, dict, list, tuple]):
+def try_jsonfy(obj: Union[str, dict, list, tuple], default_when_error=None):
     """
-    try to jsonfy a object
+    try to jsonfy object
     :param obj:
+    :param default_when_error:
     :return:
     """
     try:
         return json.loads(obj)
     except Exception as e:
-        logger.error(f"Decode Error {obj}")
-        return f"Decode Error {type(obj)}"
+        logger.trace(f"Decode Error {obj}")
+        if default_when_error is None:
+            return f"Decode Error {type(obj)}"
+        else:
+            return default_when_error
diff --git a/src/novelai_python/utils/encode.py b/src/novelai_python/utils/encode.py
index 3200200..606e352 100755
--- a/src/novelai_python/utils/encode.py
+++ b/src/novelai_python/utils/encode.py
@@ -1,13 +1,11 @@
 # -*- coding: utf-8 -*-
 # @Time    : 2024/2/7 上午11:41
-# @Author  : sudoskys
-# @File    : encode.py
-# @Software: PyCharm
+import base64
 from base64 import urlsafe_b64encode
 from hashlib import blake2b
-from typing import Iterable, List
 
 import argon2
+import numpy as np
 
 
 # https://github.com/HanaokaYuzu/NovelAI-API/blob/master/src/novelai/utils.py#L12
@@ -38,7 +36,6 @@ def encode_access_key(username: str, password: str) -> str:
     return hashed[:64]
 
 
-import base64
 import hashlib
 import hmac
 
@@ -63,21 +60,40 @@ def decode_base64(encoded_data):
     return decoded_data.decode("UTF-8")
 
 
-# MIT: https://github.com/Aedial/novelai-api/blob/794c4f3d89cc86df3c7d2c401b320f1822822ac0/novelai_api/utils.py
-def tokens_to_b64(tokens: Iterable[int]) -> str:
+def b64_to_tokens(encoded_str, dtype='uint32'):
     """
-    Encode a list of tokens into a base64 string that can be sent to the API
+    big-endian
+    将 Base64 编码的字符串解码为 tokens 数组。
+    :param encoded_str: 从 Base64 解码的字符串
+    :param dtype: 解码的数组类型，可以是 'uint32' 或 'uint16'
+    :return: 解码后的整数数组
     """
-
-    return base64.b64encode(b"".join(t.to_bytes(2, "little") for t in tokens)).decode()
-
-
-# MIT:https://github.com/Aedial/novelai-api/blob/794c4f3d89cc86df3c7d2c401b320f1822822ac0/novelai_api/utils.py#L332
-def b64_to_tokens(b64: str) -> List[int]:
+    # NOTE:
+    byte_data = base64.b64decode(encoded_str)
+    if dtype == 'uint32':
+        array_data = np.frombuffer(byte_data, dtype=np.uint32)
+    elif dtype == 'uint16':
+        array_data = np.frombuffer(byte_data, dtype=np.uint16)
+    else:
+        raise ValueError('Unsupported dtype')
+    return array_data.tolist()
+
+
+def tokens_to_b64(tokens, dtype='uint32'):
     """
-    Decode a base64 string returned by the API into a list of tokens
+    big-endian
+    将给定的 token 数组编码为 Base64 字符串。
+    :param tokens: 输入的整数数组
+    :param dtype: 输出的数组类型，可以是 'uint32' 或 'uint16'
+    :return: base64 编码字符串
     """
-
-    b = base64.b64decode(b64)
-
-    return [int.from_bytes(b[i: i + 2], "little") for i in range(0, len(b), 2)]
+    # 根据 dtype 确定 numpy 数组数据类型
+    if dtype == 'uint32':
+        array_data = np.array(tokens, dtype=np.uint32)
+    elif dtype == 'uint16':
+        array_data = np.array(tokens, dtype=np.uint16)
+    else:
+        raise ValueError('Unsupported dtype')
+    byte_data = array_data.tobytes()
+    base64_str = base64.b64encode(byte_data).decode('utf-8')
+    return base64_str