Here's how to fine-tune llama-3 8b. ♾️

#37
by Ateeqq - opened

Here's a theoretical explanation of how to Fine-Tune Llama-3 8B:

For Practical Tutorial - Check: https://exnrt.com/blog/ai/finetune-llama3-8b/

1. Preparation:

  • Data Acquisition:
    • Identify your specific task for fine-tuning.
    • Gather a high-quality dataset relevant to your task. This dataset should be large enough and well-structured for effective training.
  • Environment Setup:
    • Install necessary libraries like transformers, datasets, and potentially unsloth for integration with Llama-3.
    • Ensure you have access to a powerful computing environment with GPUs for faster training.

2. Model Selection and Preprocessing:

  • Choose the Model:
    • Select the Llama-3 8B model from the Hugging Face Hub or a similar repository.
    • Consider using the 4-bit version (load_in_4bit=True) for memory efficiency if supported by your hardware.
  • Data Preprocessing:
    • Preprocess your dataset according to the model's requirements. This might involve cleaning, tokenizing, and formatting the data appropriately.

3. Fine-tuning Process:

  • Define Training Arguments:
    • Set hyperparameters like learning rate, batch size, and number of training epochs using TrainingArguments from transformers.
  • Fine-tuning Technique:
    • Choose a fine-tuning technique:
      • Supervised Fine-tuning (SFT): Train the model on your dataset using labeled examples where the desired outputs are provided. This is a common approach for tasks like text classification or question answering.
      • Reinforcement Learning with Human Feedback (RLHF): Provide human feedback to guide the model's learning process. This can be helpful for tasks where defining clear labels is difficult.
  • Training Loop:
    • Implement a training loop that feeds your preprocessed data to the model and optimizes its parameters based on the chosen fine-tuning technique. Utilize libraries like SFTTrainer for streamlined training.

4. Evaluation and Refinement:

  • Evaluate Performance:
    • After training, assess the model's performance on a separate validation dataset relevant to your task. Metrics used for evaluation will depend on the specific task (e.g., accuracy for classification, BLEU score for machine translation).
  • Refine the Model:
    • Analyze the evaluation results. If performance is unsatisfactory, consider:
      • Adjusting hyperparameters.
      • Collecting more data.
      • Trying a different fine-tuning technique.

5. Deployment:

  • Once satisfied with the model's performance, you can deploy it for real-world use in your application. This might involve integrating it into a web service or mobile app.

Additional Considerations:

  • Computational Resources: Fine-tuning large models like Llama-3 8B can be computationally expensive. Ensure you have access to sufficient resources (GPUs, memory) for training.
  • Data Quality: The quality and relevance of your dataset significantly impact the fine-tuning outcome. Focus on gathering high-quality data specific to your task.
  • Ethical Considerations: Be mindful of potential biases in your data and the model's outputs. Consider implementing safeguards to mitigate bias and ensure responsible use of the fine-tuned model.

Thanks, didn't expect to randomly learn this looking at the community posts.

Ateeqq changed discussion title from Here's how to fine-tune. to Here's how to fine-tune llama-3 8b. ♾️

What happened with the plain HF transformers.Trainer() API ? all I see now is with the TRL library.

What happened with the plain HF transformers.Trainer() API ? all I see now is with the TRL library.

I am currently working on it (with the health dataset) but facing a CUDA error. Hopefully, it will be resolved soon.

Here's the Colab Notebook: https://colab.research.google.com/drive/1TUa9J2J_1Sj-G7mQHX45fKzZtnW3s1vj?usp=sharing

RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Anyone know what structure I should be making my dataset to best finetune the model?

Sign up or log in to comment