The Code Heroes Llama3 Trainer aims to provide a CLI interface to orchestrate the fine-tuning of open source AI models, such as Llama3, using 3rd party services, such as Lambda Cloud, Hugging Face and Weights & Biases.

The CLI provides commands to:

• Launch an instance.
• Transfer files to the instance.
• Apply configuration to the instance.
• Install dependances.
• Run the training script.
• Terminate the instance.

The training script:

• Pulls the training data from Hugging Face.
• Pulls the base model data from Hugging Face.
• Fine-tunes the base model with the training data.
• Pushes the fine-tuned model to Hugging Face.
• Logs the run to Weights & Biases.

Run npm install -g.

Run trainer --help for a list of commands & options.

To avoid passing sensitive information via CLI command line set options (or any options) via ./config/trainer.json

e.g.

{
    "lambda-cloud-ssh-identity-file":"~/.ssh/lambda_cloud",
    "lambda-cloud-api-key": "secret_brendts-macbook-pro_be5152ed4tdc44a8bbe41e70r4cfe7f1.QB5s12jG80Ngii3We9Lc7tdshruARJd9",
    "lambda-cloud-ssh-key-name": "Brendt's Macbook Pro",
    "lambda-cloud-instance-name-prefix": "bs",
    "weights-and-biases-api-key":"aa5c3276d89211f08300f5615ga11fe637f4897f",
    "hugging-face--access-token":"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"
}

Note:

• Run git update-index --assume-unchanged config/trainer.json to stop tracking changes.

The following 3rd party services and applications are used to:

• serve datasets and models
• store models
• track experiments
• run models (locally on your dev machine)

• Lambda Labs provides cloud hosted GPU for AI training and inference.
• Hugging Face is a platform for building, sharing, and collaborating on machine learning (ML) models, datasets, and applications.
• Weights & Biases is a platform designed for machine learning (ML) practitioners to track, visualize, and manage ML experiments.

• Ollama is an open-source project that serves as a powerful and user-friendly platform for running LLMs on your local machine.
• Enchanted LLM provides a user interface, similar to ChatGPT, facilitates chat with models served by Ollama.

The training script (train.py) is based on the Unsloth as described in their documentation.

Prior to using Unsloth, frequent out of memory exceptions occured as training quickly used up the available GPU memory. The Unsloth approach provides a pathway to training confidently on limited GPU memory via 4bit quantization.

Furthermore, Unsloth provides a quantized 4bit version of Meta's Llama3 8B Instruct base model - unsloth/llama-3-8b-Instruct-bnb-4bit. Utilising Unsloths quantized version as the base model removes the quantization process as a factor which could negatively impact training outcomes. That is, we assume Unsloth provides a correctly quantized verson of the model.

Warning: At time of writing (May 2024) the training script:

• is tested on Meta's Llama3 8B Instruct model only.
• assumes training data is in chat format.

That is, you can confidently train on Llama3 Instruct based models.

If you want to train other models please discuss with Brendt on a suggested approach.

The training script expects the training data is:

• pulled from a Dataset on Hugging Face.
• on JSONL format (i.e each line a record and is valid JSON)
• split for training, testing and validation
• in a specific format (see below)

The following example shows two chats between the role user and assistant. In each example the user asks a question and the assistant answers the question.

{"chats":[{"role":"user","content":"What is the team's favourite animal?"},{"role":"assistant","content":"I can, with almost certainty, say it is dogs."}]}
{"chats":[{"role":"user","content":"Does the team like cats?"},{"role":"assistant","content":"No so much. They seem to prefer dogs."}]}

Note:

• The keys chats, role and content and values user and assistant are expected by the training script. Please transform your training data to this (standard) format to avoid changing the script unnecessarily.
• The tokenizer in the training script transforms the data set into the format required by the model. See Llama 3 Model Cards & Prompt formats for more into.
• Despite Meta stating here to use <|end_of_text|> as the end of sentence special token which "on generating this token, Llama 3 will cease to generate more tokens". At the time of writing (May 2024) there was some confusion in the community as to whether to use <|end_of_text|> or <|eot_id|>. Whether correct or not, the script explicitly sets <|eot_id|> as the eos token, as without this set the fine-tuned model would not cease generation. This should be revisited to ensure we are following best practices.

Thank you for considering contributing!

1. Fork the Repository: Click the 'Fork' button at the top right of this page to create a copy of this repository under your GitHub account.
2. Clone Your Fork: Use git clone to clone your fork to your local machine.
3. Install Dependencies: Run npm install to install the necessary dependencies.
4. Create a Branch: Create a new branch for your feature or bug fix.
5. Make Your Changes: Make your changes in your local repository.
6. Submit a Pull Request: Push your branch to GitHub and submit a pull request to the main repository.

If you have any questions please open an issue.