Concept-Aware Fine-Tuning (CAFT)

arXiv Paper

Concept-aware fine-tuning (CAFT) encourages stronger conceptual understanding by incorporating multi-token prediction into fine-tuning.

Installation

git clone https://github.com/michaelchen-lab/caft-llm.git
cd caft-llm
pip install -e .

Setup

1. Create .env file with HUGGINGFACE_TOKEN=<token> and optionally WANDB_TOKEN=<token>
2. Add train_set.jsonl and eval_set.jsonl files to scripts/datasets/. Each instance should be of the format:

{
    "id": "<int/str>", "status": "OK", 
    "conversation": [
        {"role": "human", "content": "(prompt)"}, 
        {"role": "assistant", "content": "(ground truth answer)"},
    ]
}

Fine-tune a model using CAFT

Currently, only the auxiliary heads of meta-llama/Llama-3.1-8B-Instruct have been pretrained.

Method 1: Use the provided training script scripts/train.py

torchrun --nprod-per-node 1 scripts/train.py -ftm lora 
torchrun --nprod-per-node 1 scripts/train.py -ftm lora -ft-heads -hpretrain
torchrun --nprod-per-node 1 scripts/train.py -ftm sft -lr 5e-6 -fr-unembed
torchrun --nprod-per-node 1 scripts/train.py -ftm sft -lr 5e-6 -fr-unembed -ft-heads -hpretrain

Selected Arguments:

• --model-name-or-path -model: Currently only meta-llama/Llama-3.1-8B-Instruct is supported.
• --model-max-length -maxlen
• --finetune-method -ftm: lora or sft (full finetuning)
• --learning-rate -lr
• --epochs -e
• --freeze-unembedding -fr-unembed: Only applicable for full fine-tuning. Recommended: True
• --per-device-batch-size -micro-bs
• --gradient-accumulation-steps -grad-acc
• --heads-pretraining -hpretrain: Train auxiliary heads on your dataset for 1 epoch before apply CAFT to your model. -ft-heads must also be set to True.

The full list of arguments can be found using this command:

python scripts/train.py --help

Method 2: Integrate CAFT into your existing Transformers fine-tuning pipeline

import transformers
from caft import *

# Import your pretrained Transformers model, tokenizer, TrainingArguments, and data_module

add_auxiliary_heads(model)
add_caft_loss(transformers)

trainer = transformers.trainer.Trainer( # The additional CAFT functions track and save the auxiliary losses
    model=model, tokenizer=tokenizer, args=model_training_args,
    callbacks=[CAFTSaveLogging], 
    compute_metrics=caft_compute_metrics, 
    preprocess_logits_for_metrics=preprocess_logits_for_metrics, 
    **data_module
)

Please refer to scripts/train.py for a complete implementation example.

(Optional) Train Auxiliary Heads

1. Download the train and validation dataset from this Huggingface repo and save to scripts/datasets
2. Run the following command

torchrun nproc-per-node 4 scripts/train_aux_heads.py

Contributing

We welcome community contributions and feature requests for caft-llm. Feel free to open an issue or submit a pull request. If you have any questions or wish to collaborate, please contact [email protected].

Roadmap

• Support all model architectures.
  Description

  Currently, the `LlamaDecoderLayer` is used to create auxiliary heads; in other words, only Llama-based models are supported. Edit `core.py` to copy the last hidden layer of the given model instead of inserting `LlamaDecoderLayer`, then reinitialize the weights.
• Support speculative decoding.
  Description

  Speculative decoding can be implemented using the same method as Gloeckle et al. (2024) and Stern et al. (2018).
• Support FSDP and DeepSpeed

Acknowledgements

This codebase adapts code from several amazing projects, including Medusa and Facebook Multi-Token.