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For 14 of 74 compressed skills, the original description contained trigger keywords, technique counts, attribution, or use-case phrases not covered by the existing body content. Prepends a 'When to use' / 'What's inside' block near the top so the agent still has the full context when the skill is loaded. Skills salvaged: - codex, ascii-video, creative-ideation, excalidraw, manim-video, p5js - gif-search, heartmula, youtube-content - lm-evaluation-harness, obliteratus, vllm, axolotl - powerpoint Remaining 60 skills were verified to already cover the dropped content in their existing body sections (When to Use, overview, intro prose) or had short descriptions fully captured by the new compressed form.
165 lines
4.8 KiB
Markdown
165 lines
4.8 KiB
Markdown
---
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name: axolotl
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description: "Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO)."
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version: 1.0.0
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author: Orchestra Research
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license: MIT
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dependencies: [axolotl, torch, transformers, datasets, peft, accelerate, deepspeed]
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metadata:
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hermes:
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tags: [Fine-Tuning, Axolotl, LLM, LoRA, QLoRA, DPO, KTO, ORPO, GRPO, YAML, HuggingFace, DeepSpeed, Multimodal]
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---
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# Axolotl Skill
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## What's inside
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Expert guidance for fine-tuning LLMs with Axolotl — YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support.
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Comprehensive assistance with axolotl development, generated from official documentation.
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## When to Use This Skill
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This skill should be triggered when:
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- Working with axolotl
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- Asking about axolotl features or APIs
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- Implementing axolotl solutions
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- Debugging axolotl code
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- Learning axolotl best practices
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## Quick Reference
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### Common Patterns
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**Pattern 1:** To validate that acceptable data transfer speeds exist for your training job, running NCCL Tests can help pinpoint bottlenecks, for example:
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```
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./build/all_reduce_perf -b 8 -e 128M -f 2 -g 3
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```
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**Pattern 2:** Configure your model to use FSDP in the Axolotl yaml. For example:
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```
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fsdp_version: 2
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fsdp_config:
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offload_params: true
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state_dict_type: FULL_STATE_DICT
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auto_wrap_policy: TRANSFORMER_BASED_WRAP
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transformer_layer_cls_to_wrap: LlamaDecoderLayer
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reshard_after_forward: true
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```
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**Pattern 3:** The context_parallel_size should be a divisor of the total number of GPUs. For example:
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```
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context_parallel_size
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```
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**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
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```
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context_parallel_size=4
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```
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**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)
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```
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save_compressed: true
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```
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**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
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```
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integrations
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```
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**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]]
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```
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utils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2)
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```
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### Example Code Patterns
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**Example 1** (python):
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```python
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cli.cloud.modal_.ModalCloud(config, app=None)
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```
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**Example 2** (python):
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```python
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cli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None)
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```
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**Example 3** (python):
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```python
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core.trainers.base.AxolotlTrainer(
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*_args,
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bench_data_collator=None,
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eval_data_collator=None,
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dataset_tags=None,
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**kwargs,
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)
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```
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**Example 4** (python):
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```python
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core.trainers.base.AxolotlTrainer.log(logs, start_time=None)
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```
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**Example 5** (python):
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```python
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prompt_strategies.input_output.RawInputOutputPrompter()
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```
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## Reference Files
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This skill includes comprehensive documentation in `references/`:
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- **api.md** - Api documentation
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- **dataset-formats.md** - Dataset-Formats documentation
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- **other.md** - Other documentation
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Use `view` to read specific reference files when detailed information is needed.
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## Working with This Skill
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### For Beginners
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Start with the getting_started or tutorials reference files for foundational concepts.
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### For Specific Features
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Use the appropriate category reference file (api, guides, etc.) for detailed information.
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### For Code Examples
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The quick reference section above contains common patterns extracted from the official docs.
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## Resources
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### references/
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Organized documentation extracted from official sources. These files contain:
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- Detailed explanations
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- Code examples with language annotations
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- Links to original documentation
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- Table of contents for quick navigation
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### scripts/
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Add helper scripts here for common automation tasks.
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### assets/
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Add templates, boilerplate, or example projects here.
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## Notes
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- This skill was automatically generated from official documentation
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- Reference files preserve the structure and examples from source docs
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- Code examples include language detection for better syntax highlighting
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- Quick reference patterns are extracted from common usage examples in the docs
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## Updating
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To refresh this skill with updated documentation:
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1. Re-run the scraper with the same configuration
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2. The skill will be rebuilt with the latest information
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