axolotl

# Axolotl Skill ## What's inside Expert guidance for fine-tuning LLMs with Axolotl — YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support. Assistance with axolotl development, generated from official documentation. ## When to Use This Skill This skill should be triggered when: - Working with axolotl - Asking about axolotl features or APIs - Implementing axolotl solutions - Debugging axolotl code - Learning axolotl best practices ## Quick Reference ### Common Patterns **Pattern 1:** To validate that acceptable data transfer speeds exist for your training job, running NCCL Tests can help pinpoint bottlenecks, for example: ``` ./build/all_reduce_perf -b 8 -e 128M -f 2 -g 3 ``` **Pattern 2:** Configure your model to use FSDP in the Axolotl yaml. For example: ``` fsdp_version: 2 fsdp_config: offload_params: true state_dict_type: FULL_STATE_DICT auto_wrap_policy: TRANSFORMER_BASED_WRAP transformer_layer_cls_to_wrap: LlamaDecoderLayer reshard_after_forward: true ``` **Pattern 3:** The context_parallel_size should be a divisor of the total number of GPUs. For example: ``` context_parallel_size ``` **Pattern 4:** For example: - With 8 GPUs and no sequence parallelism: 8 different batches processed per step - With 8 GPUs and context_parallel_size=4: Only 2 different batches processed per step (each split across 4 GPUs) - If your per-GPU micro_batch_size is 2, the global batch size decreases from 16 to 4 ``` context_parallel_size=4 ``` **Pattern 5:** Setting save_compressed: true in your configuration enables saving models in a compressed format, which: - Reduces disk space usage by approximately 40% - Maintains compatibility with vLLM for accelerated inference - Maintains compatibility with llmcompressor for further optimization (example: quantization) ``` save_compressed: true ``` **Pattern 6:** Note It is not necessary to place your integration in the integrations folder. It can be in any location, so long as it’s installed in a package in your python env. See this repo for an example: https://github.com/axolotl-ai-cloud/diff-transformer ``` integrations ``` **Pattern 7:** Handle both single-example and batched data. - single example: sample[‘input_ids’] is a list[int] - batched data: sample[‘input_ids’] is a list[list[int]] ``` utils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2) ``` ### Example Code Patterns **Example 1** (python): ```python cli.cloud.modal_.ModalCloud(config, app=None) ``` **Example 2** (python): ```python cli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None) ``` **Example 3** (python): ```python core.trainers.base.AxolotlTrainer( *_args, bench_data_collator=None, eval_data_collator=None, dataset_tags=None, **kwargs, ) ``` **Example 4** (python): ```python core.trainers.base.AxolotlTrainer.log(logs, start_time=None) ``` **Example 5** (python): ```python prompt_strategies.input_output.RawInputOutputPrompter() ``` ## Reference Files This skill includes comprehensive documentation in `references/`: - **api.md** - Api documentation - **dataset-formats.md** - Dataset-Formats documentation - **other.md** - Other documentation Use `view` to read specific reference files when detailed information is needed. ## Working with This Skill ### For Beginners Start with the getting_started or tutorials reference files for foundational concepts. ### For Specific Features Use the appropriate category reference file (api, guides, etc.) for detailed information. ### For Code Examples The quick reference section above contains common patterns extracted from the official docs. ## Resources ### references/ Organized documentation extracted from official sources. These files contain: - Detailed explanations - Code examples with language annotations - Links to original documentation - Table of contents for quick navigation ### scripts/ Add helper scripts here for common automation tasks. ### assets/ Add templates, boilerplate, or example projects here. ## Notes - This skill was automatically generated from official documentation - Reference files preserve the structure and examples from source docs - Code examples include language detection for better syntax highlighting - Quick reference patterns are extracted from common usage examples in the docs ## Updating To refresh this skill with updated documentation: 1. Re-run the scraper with the same configuration 2. The skill will be rebuilt with the latest information