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Skill

axolotl

From Orchestra-Research

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

Facts

Repository
Orchestra-Research/AI-Research-SKILLs
Status
Actively maintained
Last commit
Source file
03-fine-tuning/axolotl/SKILL.md

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The instructions Claude Code reads when this skill runs.

# 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_compress
View full source on GitHub →

Reused elsewhere

An identical copy of this skill ships in 2 repositories.

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