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South Park Fanatic Mistral-7B Fine-Tuning on Multiple GPUs

corresponding blog post here

This repository contains scripts for

  • generating data with GPT-4 via rq
  • Mistral-7B (and other LLMs) fine-tuning on multiple GPUs with peft for LoRA and bitsandbytes for quantization

Instructions

Installation

  1. Install the project dependencies by running the following command:
pip install -r requirements.txt
  1. To begin training, create configuration YAML file ( below example_config.yaml from this repo)
base_model_id: "mistralai/Mistral-7B-v0.1"
training_dataset_jsonl_path: /path/to/your/train_dataset.jsonl
eval_dataset_jsonl_path: /path/to/your/eval_dataset.jsonl
prompt_template: "### question: {question}\n ### answer: {answer} </s>"
tokenizer_max_length: 256
bnb_config:
  load_in_4bit: true
  bnb_4bit_use_double_quant: true
  bnb_4bit_quant_type: "nf4"
lora_config:
  r: 64
  target_modules: [
    "q_proj",
    "k_proj",
    "v_proj",
    "o_proj",
    "gate_proj",
    "up_proj",
    "down_proj",
    "lm_head",
  ]
  bias: none
  lora_dropout: 0.05
  task_type: CAUSAL_LM
output_dir: /path/to/your/output/model
training:
  optim: "paged_adamw_8bit"
  warmup_steps: 1
  per_device_train_batch_size: 1
  gradient_accumulation_steps: 1
  max_steps: 500
  logging_steps: 25
  logging_dir: /path/to/logging/dir,  # Directory for storing logs
  save_strategy: "steps"
  save_steps: 100
  evaluation_strategy: "steps"
  eval_steps: 100
  do_eval: true
  learning_rate: 2.5e-5

and run

accelerate config 
accelerate launch train.py config.yaml # example_config.yaml is a good start

Dataset Format

Input data should be in the jsonl format, with each line containing an question and answer field pair. Here are few example lines from the dataset:

{"question": "What is the capital of France?", "answer": "The capital of France is Paris."}
{"question": "Who wrote 'Pride and Prejudice'?", "answer": "Pride and Prejudice was written by Jane Austen."}
{"question": "When was the Declaration of Independence signed?", "answer": "The Declaration of Independence was signed on July 4, 1776."}

Configuration Details

  • base_model_id: The base model to be fine-tuned.

  • training_dataset_jsonl_path & eval_dataset_jsonl_path: Path of the training and evaluation datasets respectively.

  • prompt_template: How the input and output pairs should be formatted.

  • tokenizer_max_length: The maximum length for the tokenizer.

  • bnb_config: Configuration for the bitsandbytes library.

  • lora_config: Configuration for the low-rank approximation.

  • output_dir: The directory where you want the trained model to be saved.

  • training: Configuration for the training steps, including the optimizer to be used, warmup steps, maximum steps, batch size, logging steps, where to save logs, save strategy, evaluation strategy, and learning rate.

License

This project is licensed under the MIT License.