axolotl

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

curl -LsSf https://astral.sh/uv/install.sh | sh

Audit 0.4.1 · source sha 6c04b84d00a4 · 31 Oct 2026Report an issue or request a re-scan

• Publisherorchestra-research GitHub organizationOwner of the repository this skill is read from
• Source folderorchestra-research/ai-research-skills/03-fine-tuning/axolotlThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict6c04b84d00a4A new audit runs when the source changes
• Repository statusNot archivedLast push 20 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

---
name: 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
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 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

Excerpt, first 4,784 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

uv is a fast, reliable Python package installer and resolver built in Rust. It offers significant performance improvements
uv venv --no-project --relocatable
uv pip install packaging setuptools wheel
uv pip install torch==2.6.0
uv pip install awscli pydantic

git clone https://github.com/orchestra-research/ai-research-skills
git -C ai-research-skills checkout 6c04b84d00a4

Works in

1. 2026-10-31
   ✓SAFE B 89 / 100

   first audit · source 6c04b84d00a4

Questions about axolotl

• github.com/orchestra-research/ai-research-skills/tree/main/03-fi — the audited source at commit 6c04b84d00a4 and every finding aboveObserved · 31 Oct 2026

Spotted something wrong? Report a correction · Methodology