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chore: update doc links (axolotl-ai-cloud#2509)
* chore: update doc links * fix: address pr feedback
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README.md

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axolotl fetch deepspeed_configs # OPTIONAL
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```
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Other installation approaches are described [here](https://axolotl-ai-cloud.github.io/axolotl/docs/installation.html).
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Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).
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### Your First Fine-tune
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axolotl train examples/llama-3/lora-1b.yml
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```
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That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/getting-started.html) for a more detailed walkthrough.
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That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/getting-started.html) for a more detailed walkthrough.
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## ✨ Key Features
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## 📚 Documentation
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- [Installation Options](https://axolotl-ai-cloud.github.io/axolotl/docs/installation.html) - Detailed setup instructions for different environments
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- [Configuration Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html) - Full configuration options and examples
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- [Dataset Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/) - Supported formats and how to use them
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- [Multi-GPU Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-gpu.html)
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- [Multi-Node Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html)
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- [Multipacking](https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html)
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- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/docs/api/) - Auto-generated code documentation
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- [FAQ](https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html) - Frequently asked questions
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- [Installation Options](https://docs.axolotl.ai/docs/installation.html) - Detailed setup instructions for different environments
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- [Configuration Guide](https://docs.axolotl.ai/docs/config.html) - Full configuration options and examples
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- [Dataset Guide](https://docs.axolotl.ai/docs/dataset-formats/) - Supported formats and how to use them
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- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
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- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
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- [Multipacking](https://docs.axolotl.ai/docs/multipack.html)
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- [API Reference](https://docs.axolotl.ai/docs/api/) - Auto-generated code documentation
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- [FAQ](https://docs.axolotl.ai/docs/faq.html) - Frequently asked questions
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## 🤝 Getting Help
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- Join our [Discord community](https://discord.gg/HhrNrHJPRb) for support
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- Check out our [Examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/) directory
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- Read our [Debugging Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/debugging.html)
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- Read our [Debugging Guide](https://docs.axolotl.ai/docs/debugging.html)
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- Need dedicated support? Please contact [✉️[email protected]](mailto:[email protected]) for options
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## 🌟 Contributing

docs/config.qmd

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# List[str]. Add plugins to extend the pipeline.
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# See `src/axolotl/integrations` for the available plugins or doc below for more details.
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# https://axolotl-ai-cloud.github.io/axolotl/docs/custom_integrations.html
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# https://docs.axolotl.ai/docs/custom_integrations.html
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plugins:
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# - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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# Apply custom LoRA autograd functions and activation function Triton kernels for
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# speed and memory savings
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# See: https://axolotl-ai-cloud.github.io/axolotl/docs/lora_optims.html
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# See: https://docs.axolotl.ai/docs/lora_optims.html
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lora_mlp_kernel: true
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lora_qkv_kernel: true
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lora_o_kernel: true
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# Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
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# E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
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# subsequences, or set to 4 to split into four equal-sized subsequences.
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# See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
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# See https://docs.axolotl.ai/docs/sequence_parallelism.html for more details.
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sequence_parallel_degree:
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# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
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# Must evenly divide the number of KV heads in your model.

docs/dataset-formats/index.qmd

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type: alpaca
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```
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Axolotl supports many kinds of instruction dataset. All of them can be found here (https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/inst_tune.html) with their respective type and sample row format.
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Reference: [Instruction Dataset Documentation](inst_tune.qmd).
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Axolotl supports many kinds of instruction dataset. All of them can be found in the [Instruction Dataset Documentation](inst_tune.qmd) with their respective type and sample row format.
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#### Custom Instruct Prompt Format
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pyproject.toml

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axolotl = "axolotl.cli.main:main"
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[project.urls]
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Homepage = "https://axolotl-ai-cloud.github.io/axolotl/"
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Homepage = "https://axolotl.ai/"
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Documentation = "https://docs.axolotl.ai/"
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Repository = "https://github.com/axolotl-ai-cloud/axolotl.git"
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[tool.setuptools_scm]

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