Phyton Replica of a model costing hundreds of billions of dollars later (GPT-4, Gemini, Llama, etc.)

[dEEpEst]

Summary of the Code and Its Purpose​

This script trains a language model using reward-based optimization (GRPO). Its goal is to improve the quality of generated responses for mathematical problems using the gsm8k dataset (a benchmark of grade-school math problems by OpenAI).

It supports two models:

• Meta-Llama-3.2-1B (by Meta)
• Qwen-2.5-1.5B (by Alibaba)

The training process focuses on ensuring that responses follow a structured XML format and are factually correct.

Code summary​

1. Dataset loading: Uses the "gsm8k" dataset (an OpenAI math problem benchmark).
2. Response format: Defines an XML-structured format (<reasoning> and <answer>) for the model's responses.
3. Response extraction: Implements functions to extract responses in XML and hash format (####).
4. Reward functions:
   • Evaluate the correctness of the model's response.
   • Check if it follows the XML format.
   • Penalize deviations from the expected format.
5. Model selection:
   • Uses Meta-Llama-3.2-1B or Qwen-2.5-1.5B (by default it uses Qwen).
6. Training configuration:
   • Uses GRPO optimization.
   • Sets Lora (Low-Rank Adaptation) for efficient tuning.
   • Uses multi-GPU with Flash Attention to improve speed.
7. Training:
   • Uses GRPOTrainer to train with the defined reward functions.

How to Use It​

1. Install dependencies:
   
   

   pip install torch transformers datasets trl peft

2. Run the script:
   

   python train_grpo.py

3. Code Breakdown:
   
   • Loads the gsm8k dataset to train the model with math-related questions.
   • Formats responses in XML, ensuring consistency and structured outputs.
   • Defines reward functions that penalize incorrect or poorly formatted answers.
   • Configures the model (Qwen or Llama) and loads it with LoRA for efficient fine-tuning.
   • Starts training using GRPOTrainer.

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Use Cases​

• Train a math chatbot that provides structured answers.
• Optimize smaller models to produce more reliable responses without requiring high-end GPUs.
• Experiment with GRPO fine-tuning to enhance language models efficiently.

This script allows training an AI model at a much lower cost than companies like OpenAI or Meta, making advanced AI capabilities more accessible.

Source Code​

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