Office Hours 1: Axolotl Q&A with Wing Lian

notes
llms
This Q&A session covered various topics, including template-free prompt construction, data type selection for HuggingFace datasets, DPO and RLHF, understanding chat templates and dataset types, ensuring consistent tokenization, multimodal fine-tuning, and future directions for Axolotl.
Author

Christian Mills

Published

June 11, 2024

This post is part of the following series:
  • Mastering LLMs Course Notes: My notes from the course Mastering LLMs: A Conference For Developers & Data Scientists by Hamel Husain and Dan Becker.

1. Template-Free Prompt Construction in Axolotl

  • Purpose: Offers flexibility in defining custom chat roles and formats.
  • Format: Uses simple input-output pairs with labels (true for model output, false for user input).
  • Advantages:
    • Easier to understand for some users.
    • Translates well to platforms with existing input-output data.
  • Disadvantages:
    • Less flexible for changing chat templates later.
    • Requires more manual string handling during inference.
  • Recommendation: Use only if existing chat templates are insufficient.
  • Documentation: https://openaccess-ai-collective.github.io/axolotl/docs/input_output.html
  • {
    "segments": [
        {
            "label": true,
            "text": "<s>Hello\n"
        },
        {
            "label": true,
            "text": "hi there!. "
        },
        {
            "label": false,
            "text": "goodbye "
        },
        {
            "label": true,
            "text": "farewell</s>"
        }
    ]
}

2. How to Decide Data Type for Datasets on HuggingFace?

  • Dataset Types:
    • Alpaca: Instruction-based, with separate fields for instruction, input, and output.
    • ShareGPT: Conversation-based, with variations in field names (e.g., human/user, GPT/assistant, value/content). Axolotl’s type: sharegpt handles most variations.
    • DPO: Includes chosen and rejected responses for preference learning. Variations exist in field names and prompt construction.
    • KTO: Similar to DPO but without explicit preference pairs.
    • User Defined: Allows custom formatting defined in the YAML file.
  • Recommendation: Choose the type that best matches the dataset structure and fine-tuning objective.
  • Examples:

3. DPO and RLHF

  • DPO (Direct Preference Optimization):
    • Trains a model to maximize the probability of choosing preferred responses over rejected ones.
    • Simpler to implement than RLHF but limited to single-turn preference learning.
  • RLHF (Reinforcement Learning from Human Feedback):
    • Uses reinforcement learning to optimize a model based on human feedback.
    • More complex but potentially leads to higher quality alignment and multi-turn capabilities.
  • Future Direction: Both DPO and RLHF are important, with a potential shift towards more robust RL-based methods.

4. Difference Between Chat Template and Datasets Type Parameters

  • Chat Template: Defines the specific format of the chat conversation (e.g., LLAMA3, Mistral, ChatML). It gets added to the tokenizer config.
  • Datasets Type: Specifies how Axolotl should parse and structure the input dataset (e.g., share_gpt, alpaca, dpo).
  • Interaction:
    • Setting a chat template can automatically set the output format for certain dataset types.
    • The chat_template parameter in the YAML file overrides any default settings.

5. No Ops for Validation

  • Currently, Axolotl lacks built-in validation checks for potential issues like:
    • Rounding errors when saving models in different precision formats (e.g., float32 vs. bfloat16).
    • Tokenization discrepancies between training and inference.
  • Recommendation:
    • Carefully manage model precision during saving.
    • Implement custom checks to compare tokenization between training and inference pipelines.

6. Trust No One for Tokenization

  • Key Takeaway: Always verify the actual tokens being fed into the model, as string handling and YAML parsing can introduce subtle errors.
  • Example: YAML can remove trailing spaces in non-quoted strings, potentially affecting tokenization.
  • Recommendation: Implement rigorous checks to ensure consistent tokenization between training and inference.

7. Ensuring Consistent Tokenization

  • Challenge: Tokenization differences can arise from:
    • Separate vs. concatenated tokenization of input and output strings.
    • Special token handling in different chat templates.
  • Recommendations:
    • Use the chat_template parameter in Axolotl to enforce consistent formatting.
    • Implement tests to compare tokenization between fine-tuning and inference setups.
    • Consider introducing minor tokenization variations during training as a form of data augmentation.

8. Tokenizer Configs from Training to Inference

  • Importance: Consistent tokenizer configurations are crucial for seamless transition from training to inference.
  • Axolotl’s Approach: Setting the chat_template parameter in the YAML file updates the tokenizer config, which is then used by inference engines.
  • Challenge: Not all inference engines may fully support or utilize the chat template information.
  • Recommendation: Verify that the chosen inference engine correctly interprets and applies the tokenizer config, including the chat template.

9. Multimodal Fine-tuning

  • Current Status: Axolotl lacks native support for multimodal datasets and models.
  • Challenges:
    • Handling image data and integrating it with text data.
    • Adapting to evolving approaches for multimodal tokenization and model architectures.
  • Future Direction:
    • Implementing dataset handling for images and other modalities.
    • Potentially supporting both Lava-like approaches and native multimodal models.
  • Call for Contributions: Help is needed in developing and implementing multimodal capabilities.

10. Is RLHF Still a Common Fine-tuning Technique?

  • Answer: Yes, RLHF and other preference-based tuning methods (like DPO) are becoming increasingly common.
  • Reasoning:
    • Supervised fine-tuning has limitations in achieving high-quality alignment.
    • RLHF and DPO enable learning from human preferences, leading to better model behavior.
  • Future Trend: Expect to see wider adoption of both RL-based and non-RL preference optimization techniques.

11. DPO Limitations and RL Advantages

  • DPO Limitation: Primarily designed for single-turn preference learning.
  • RL Advantages:
    • Supports multi-turn conversations and intermediate rewards.
    • Can lead to better alignment and more nuanced model behavior.
  • Trade-offs:
    • RLHF is more complex and data-intensive than DPO.
    • DPO is simpler to implement and doesn’t require a separate reward model.

12. Sample Files for PaliGemma and Phi-3

  • Phi-3:
    • Should work with existing Phi-2 configurations by swapping the baseline model.
    • May require setting trust_remote_code: true in the YAML file.
  • PaliGemma:
    • No specific examples available yet.
    • LLM fine-tuning might be possible, but full support requires multimodal dataset handling.

13. Conversational Datasets vs. QA Pairs

  • Assumption: Conversational datasets are always more effective for fine-tuning.
  • Clarification: The choice depends on the specific use case and desired model behavior.
  • Recommendations:
    • QA Pairs: Suitable for single-turn interactions or when mimicking a retrieval-based system.
    • Conversational Datasets: Beneficial for training models to engage in multi-turn dialogue.
  • Instruction Tuning: Recommended for gaining intuition about conversational datasets and fine-tuning.

14. Training Datasets for Completion Models

  • Dataset Characteristics: Typically similar to pre-training datasets, often with a single “text” field.
  • Examples:
    • Story generation datasets.
    • Any dataset focused on text completion or continuation.

15. Prompt Template for LLAMA3 and LLAMA Index

  • Goal: Fine-tune LLAMA3 for use with LLAMA Index, which uses an OpenAI-like message abstraction.
  • Recommendation:
    • Choose a chat template that aligns with the message-based format (e.g., ChatML).
    • Avoid instruction-based templates as they might not be suitable for multi-turn interactions.

16. Future Directions of Axolotl

  • Areas for Contribution:
    • Join the Axolotl Discord server and contribute to discussions.
    • Explore the GitHub repository for open issues and feature requests.
    • Developing new features and improving existing ones.
  • Ongoing Development:
    • Building a turnkey platform for simplified fine-tuning and deployment (similar to Modal).
    • Integrating DPO, PPO, and enhanced dataset pipelines.
    • Creating a user-friendly CLI and cloud integration.

17. VRAM Estimation

  • Need: A tool for accurate VRAM estimation based on Axolotl configurations.
  • Challenges:
    • Complexities introduced by techniques like FSDP and DeepSpeed.
    • Variations in VRAM usage based on batch sizes and model parallelism.
  • Potential Approach: Leverage existing LLM math estimations and account for the impact of distributed training techniques.

18. Vibe Checks During Training

  • Goal: Evaluate model performance and “vibes” during training.
  • Options:
    • Periodic Checkpointing: Pause training, run inference on the checkpoint, and resume.
    • Dedicated Evaluation: Use a separate process to run inference on an eval dataset and log the results.
    • Callbacks: Implement callbacks to trigger inference on demand during training.
  • Challenges:
    • VRAM limitations might make it difficult to run inference alongside training.
    • Ensuring consistent tokenization and prompt handling between training and evaluation.

19. Familiarizing with Prompt Templates

  • Recommendation: Use the axolotl.cli.preprocess command with the debug flag to visualize how Axolotl processes and tokenizes prompts.
  • Output: Displays the tokenized prompt with color-coding to distinguish between input, output, and masked tokens.

20. Axolotl vs. Unsloth

  • Unsloth:
    • Specialized for Lora fine-tuning.
    • Offers memory optimizations but might be limited in GPU scalability.
  • Axolotl:
    • Provides a more comprehensive framework for fine-tuning, including prompt management and dataset handling.
    • Focuses on performance optimizations like sample packing.
  • Recommendation: Choose the tool that best aligns with your specific needs and priorities.

21. Quick and Dirty Fine-tuning

  • Recommendation:
    • Start with a small “tiny llama” example for faster iteration.
    • Use Gradio inference for quick model evaluation.

22. Function Calling Fine-Tunes

  • Dataset Example: Glade datasets from the Noose team.
  • Configuration: Might require specific role handling and a compatible version of the ShareGPT dataset type.

23. Visualizing Tokenization in Batches

  • Challenge: Axolotl’s sample packing happens at runtime, making it difficult to visualize tokenization in batches during pre-processing.
  • Potential Approach: Modify the Transformers or LLAMA model code to print or log input IDs during the forward pass.
About Me:

I’m Christian Mills, a deep learning consultant specializing in practical AI implementations. I help clients leverage cutting-edge AI technologies to solve real-world problems.

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