
axolotl
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill axolotl- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads axolotl automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
- Re-run the scraper with the same configuration
- The skill will be rebuilt with the latest information
FAQ
What does the axolotl skill do?
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
How do I install axolotl?
Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill axolotl in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/axolotl/ (personal) or .claude/skills/axolotl/ (project).
Which AI agents can use axolotl?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is axolotl safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains axolotl?
It is published by orchestra-research in orchestra-research/ai-research-skills. It has 965 recorded installs.
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