Axolotl

--- name: axolotl description: Expert guidance for fine-tuning LLMs with Axolotl:YAMLs configurations, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support version: 1.0.0 author: Orchestra Research license: MIT tags: [Fine-Tuning, Axolotl, LLM, LoRA, QLoRA, DPO, KTO, ORPO, GRPO, YAML, HuggingFace, DeepSpeed, Multimodal] dependencies: [axolotl, torch, transformers, datasets, peft, accelerate, deepspeed] --- # Axolotl Skill Comprehensive 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 verify that your training job has acceptable data transfer speeds, running NCCL Tests can help identify 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 the tutorials reference files to learn 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 documentation. ## 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