# Introduction

This is Continuum's documentation for training large language models.

This training platform has been put together by a dedicated group of people, whose generosity has allowed a community to form and become able to fine tune a variety of large language models.

{% embed url="<https://github.com/OpenAccess-AI-Collective/axolotl>" %}
Link to Axolotl GitHub Repository
{% endembed %}

### <mark style="color:blue;">Background</mark>

The GitHub repository "Axolotl" provides a versatile tool designed for the fine-tuning of various AI models, specifically targeting ease of use and flexibility in handling different model configurations and architectures.&#x20;

### <mark style="color:blue;">Core Purpose</mark>

Axolotl is aimed at streamlining the fine-tuning process of AI models, offering compatibility with a wide range of Huggingface models and fine-tuning techniques.

### <mark style="color:blue;">Supported Features</mark>

* **Model Support**: It supports training with various Huggingface models
* **Fine-tuning Techniques**: The tool supports several fine-tuning methods&#x20;
* **Configuration Flexibility**: Users can customise configurations using a YAML file or override settings via the CLI.
* **Dataset Compatibility**: Axolotl can load different dataset formats, support custom formats, or handle user-provided tokenized datasets.
* **Integration with Advanced Tools**: The tool integrates with xformer, flash attention, rope scaling, and multipacking for enhanced model performance and efficiency.
* **Multi-GPU Support**: It facilitates training on single or multiple GPUs using FSDP or Deepspeed.
* **Docker Support**: Axolotl can be easily run with Docker, either locally or on the cloud.
* **Experiment Tracking**: It allows logging results and optionally checkpoints to WandB or MLflow.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://axolotl.continuumlabs.pro/introduction.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.