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update mutligpu readme and MllamaForConditionalGeneration import #681

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update mutligpu readme and MllamaForConditionalGeneration import #681

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2 changes: 1 addition & 1 deletion recipes/quickstart/finetuning/multigpu_finetuning.md
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Expand Up @@ -23,7 +23,7 @@ Get access to a machine with multiple GPUs (in this case we tested with 4 A100 a
This has been tested on 4 H100s GPUs.

```bash
FSDP_CPU_RAM_EFFICIENT_LOADING=1 ACCELERATE_USE_FSDP=1 torchrun --nnodes 1 --nproc_per_node 4 finetuning.py --enable_fsdp --quantization int4 --model_name /path_of_model_folder/70B --mixed_precision False --low_cpu_fsdp --use_peft --peft_method lora --output_dir Path/to/save/PEFT/model
FSDP_CPU_RAM_EFFICIENT_LOADING=1 ACCELERATE_USE_FSDP=1 torchrun --nnodes 1 --nproc_per_node 4 finetuning.py --enable_fsdp --quantization 4bit --model_name /path_of_model_folder/70B --mixed_precision False --low_cpu_fsdp --use_peft --peft_method lora --output_dir Path/to/save/PEFT/model
```

### With FSDP + PEFT
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10 changes: 7 additions & 3 deletions src/llama_recipes/finetuning.py
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Expand Up @@ -21,11 +21,15 @@
AutoTokenizer,
BitsAndBytesConfig,
AutoProcessor,
MllamaForConditionalGeneration,
AutoModel,
AutoModel
)
from transformers.models.llama.modeling_llama import LlamaDecoderLayer
from transformers.models.mllama.modeling_mllama import MllamaSelfAttentionDecoderLayer,MllamaCrossAttentionDecoderLayer,MllamaVisionEncoderLayer
from transformers.models.mllama.modeling_mllama import (
MllamaSelfAttentionDecoderLayer,
MllamaCrossAttentionDecoderLayer,
MllamaVisionEncoderLayer,
MllamaForConditionalGeneration
)

from llama_recipes.configs import fsdp_config as FSDP_CONFIG
from llama_recipes.configs import train_config as TRAIN_CONFIG
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