This ONE TRICK Turns your LLM like DeepSeek R1💥 Train your own DeepLlama for Free! 💥

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AI Summary

Video Summary

• Objective: Train a reasoning model using a Google Colab notebook, based on the Quin 3 billion parameter model, to create a model that can understand, contemplate, and answer questions with reasoning tags.
• Resources: A tutorial by unslot, a Google Colab notebook, and the need for either patience (1-2 hours for free training) or a small budget ($3-$5 for faster training).
• Model Training:
  • Install unslot and VM for the necessary environment.
  • Load the Quin 2.53 billion parameter model (or a larger model if available).
  • Adjust sequence length and lower rank for better reasoning capabilities, depending on available compute.
  • Enable VM for fast inference if supported by the machine.
  • Load the model with quantization (4bit or 16bit).
  • Manage GPU memory by adjusting the max_new_tokens parameter.
  • Restart Google Colab runtime if encountering memory issues.
• Data Preparation:
  • Use the GSM 8K dataset from Hugging Face for math reasoning improvement.
  • Format the dataset with system prompts, reasoning tags, and answers.
• Reward Functions:
  • Use six reward functions to guide the model’s learning, focusing on correctness and format.
  • Monitor the KL Divergence and reward metrics during training.
• Training Process:
  • Define training parameters, including batch size and number of generations.
  • Use the GRPO trainer with the specified model, tokenizer, reward functions, training arguments, and dataset.
  • Save checkpoints and monitor training progress.
• Post-Training:
  • Apply the trained Lura to the model and test with questions to observe reasoning capabilities.
  • Save the trained Lura for future use.
  • Optionally, save the model to Hugging Face and convert it for use with other platforms.
• Conclusion: The process is an accessible way to train a reasoning model using free resources or with minimal cost for faster results.

Detailed Instructions and Tips (if any were provided)

• No specific CLI commands, website URLs, or detailed tips were provided in the transcript.