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+ + A powerful, flexible tool designed to streamline post-training for various AI models with enterprise-grade features and optimizations. +
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+ Magistral with mistral-common tokenizer support has been added to Axolotl. + + See examples β + +
++ QAT support has been added to Axolotl. + + Explore the docs β + +
++ Llama 4 support has been added in Axolotl. + + See examples β + +
++ β’ Sequence Parallelism (SP) for scaling context length - + + Blog + | + + Docs + +
++ β’ (Beta) Multimodal models fine-tuning - + + Check docs β + +
++ β’ LoRA optimizations for better memory usage and speed - + + Docs β + +
++ β’ GRPO support added - + + Blog + | + + Example + +
++ Reward Modelling / Process Reward Modelling fine-tuning support added. + + See docs β + +
++ Train LLaMA, Mistral, Mixtral, Pythia, and more. Full compatibility with HuggingFace transformers causal language models. +
++ Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling. +
++ Reuse a single YAML file across dataset preprocessing, training, evaluation, quantization, and inference. +
++ Multipacking, Flash Attention, Xformers, Flex Attention, Liger Kernel, Sequence Parallelism, and Multi-GPU training. +
++ Load from local files, HuggingFace datasets, and cloud storage (S3, Azure, GCP, OCI). +
++ Pre-built Docker images and PyPI packages for seamless deployment on cloud platforms and local hardware. +
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+ # Install dependencies
pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
+
+# Install Axolotl with Flash Attention and DeepSpeed
pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
-# Download example axolotl configs, deepspeed configs
+# Download examples and configs
axolotl fetch examples
-axolotl fetch deepspeed_configs # OPTIONAL
-```
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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
-
-```bash
-# Fetch axolotl examples
+axolotl fetch deepspeed_configs # OPTIONAL+ Other installation methods available in our documentation +
+# Fetch examples
axolotl fetch examples
-# Or, specify a custom path
+# Or specify custom path
axolotl fetch examples --dest path/to/folder
-# Train a model using LoRA
-axolotl train examples/llama-3/lora-1b.yml
-```
+# Start training with LoRA
+axolotl train examples/llama-3/lora-1b.yml+ That's it! Check our Getting Started Guide for detailed walkthrough +
+Detailed setup instructions for different environments
+Full configuration options and examples
+Loading datasets from various sources
+Supported formats and usage instructions
+Scale your training across multiple GPUs
+Distributed training across multiple machines
+Efficient batch packing for training
+Auto-generated code documentation
+Frequently asked questions
++ We welcome contributions from the community! Whether it's bug fixes, \ No newline at end of file