/axolotl

description: Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+
  models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
version: 1.0.0
author: Orchestra Research
license: MIT
dependencies:
- axolotl
- torch
- transformers
- datasets
- peft
- accelerate
- deepspeed
metadata:
  catalog:
    tags:
    - Fine-Tuning
    - Axolotl
    - LLM
    - LoRA
    - QLoRA
    - DPO
    - KTO
    - ORPO
    - GRPO
    - YAML
    - HuggingFace
    - DeepSpeed
    - Multimodal

Kheish Compatibility

This skill is repo-local and stays inactive until explicitly activated.

When the original instructions refer to legacy tool names, use these Kheish mappings:

• `terminal` => `bash`
• `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
• `search_files` => `grep_search` and `glob_search`
• `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly

When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.

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 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):

cli.cloud.modal_.ModalCloud(config, app=None)

**Example 2** (python):

cli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None)

**Example 3** (python):

core.trainers.base.AxolotlTrainer(
    *_args,
    bench_data_collator=None,
    eval_data_collator=None,
    dataset_tags=None,
    **kwargs,
)

**Example 4** (python):

core.trainers.base.AxolotlTrainer.log(logs, start_time=None)

**Example 5** (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