[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CAAYWFJMD154AV394THXFN":3,"skill-same:01M6CAAYWFJMD154AV394THXFN":93},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6CAAYWFJMD154AV394THXFN","axolotl",null,"Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).","adnichols\u002Fai-configs","adnichols","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F493746?v=4","01M6C33VRY9WF6XWYPT1AFA7JJ","Aaron Nichols","01M6C33VWTNKGRM92E9G643V4W",5,"MIT","https:\u002F\u002Fgithub.com\u002Fadnichols\u002Fai-configs\u002Fblob\u002Fmain\u002F_hermes\u002Fdefault\u002Fprofiles\u002Fnerd\u002Fskills\u002Fmlops\u002Ftraining\u002Faxolotl\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"Axolotl","research","multimodal","yaml","DeepSpeed","Fine-Tuning","LoRA","QLoRA","DPO","KTO","ORPO","GRPO","HuggingFace","Transformers","tuning","datasets","2026-11-10 10:12:30",false,"---\nname: axolotl\ndescription: \"Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\"\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [axolotl, torch, transformers, datasets, peft, accelerate, deepspeed]\nmetadata:\n  hermes:\n    tags: [Fine-Tuning, Axolotl, LLM, LoRA, QLoRA, DPO, KTO, ORPO, GRPO, YAML, HuggingFace, DeepSpeed, Multimodal]\n\n---\n\n# Axolotl Skill\n\n## What's inside\n\nExpert guidance for fine-tuning LLMs with Axolotl — YAML configs, 100+ models, LoRA\u002FQLoRA, DPO\u002FKTO\u002FORPO\u002FGRPO, multimodal support.\n\nComprehensive assistance with axolotl development, generated from official documentation.\n\n## When to Use This Skill\n\nThis skill should be triggered when:\n- Working with axolotl\n- Asking about axolotl features or APIs\n- Implementing axolotl solutions\n- Debugging axolotl code\n- Learning axolotl best practices\n\n## Quick Reference\n\n### Common Patterns\n\n**Pattern 1:** To validate that acceptable data transfer speeds exist for your training job, running NCCL Tests can help pinpoint bottlenecks, for example:\n\n```\n.\u002Fbuild\u002Fall_reduce_perf -b 8 -e 128M -f 2 -g 3\n```\n\n**Pattern 2:** Configure your model to use FSDP in the Axolotl yaml. For example:\n\n```\nfsdp_version: 2\nfsdp_config:\n  offload_params: true\n  state_dict_type: FULL_STATE_DICT\n  auto_wrap_policy: TRANSFORMER_BASED_WRAP\n  transformer_layer_cls_to_wrap: LlamaDecoderLayer\n  reshard_after_forward: true\n```\n\n**Pattern 3:** The context_parallel_size should be a divisor of the total number of GPUs. For example:\n\n```\ncontext_parallel_size\n```\n\n**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\n\n```\ncontext_parallel_size=4\n```\n\n**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)\n\n```\nsave_compressed: true\n```\n\n**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:\u002F\u002Fgithub.com\u002Faxolotl-ai-cloud\u002Fdiff-transformer\n\n```\nintegrations\n```\n\n**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]]\n\n```\nutils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2)\n```\n\n### Example Code Patterns\n\n**Example 1** (python):\n```python\ncli.cloud.modal_.ModalCloud(config, app=None)\n```\n\n**Example 2** (python):\n```python\ncli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None)\n```\n\n**Example 3** (python):\n```python\ncore.trainers.base.AxolotlTrainer(\n    *_args,\n    bench_data_collator=None,\n    eval_data_collator=None,\n    dataset_tags=None,\n    **kwargs,\n)\n```\n\n**Example 4** (python):\n```python\ncore.trainers.base.AxolotlTrainer.log(logs, start_time=None)\n```\n\n**Example 5** (python):\n```python\nprompt_strategies.input_output.RawInputOutputPrompter()\n```\n\n## Reference Files\n\nThis skill includes comprehensive documentation in `references\u002F`:\n\n- **api.md** - Api documentation\n- **dataset-formats.md** - Dataset-Formats documentation\n- **other.md** - Other documentation\n\nUse `view` to read specific reference files when detailed information is needed.\n\n## Working with This Skill\n\n### For Beginners\nStart with the getting_started or tutorials reference files for foundational concepts.\n\n### For Specific Features\nUse the appropriate category reference file (api, guides, etc.) for detailed information.\n\n### For Code Examples\nThe quick reference section above contains common patterns extracted from the official docs.\n\n## Resources\n\n### references\u002F\nOrganized documentation extracted from official sources. These files contain:\n- Detailed explanations\n- Code examples with language annotations\n- Links to original documentation\n- Table of contents for quick navigation\n\n### scripts\u002F\nAdd helper scripts here for common automation tasks.\n\n### assets\u002F\nAdd templates, boilerplate, or example projects here.\n\n## Notes\n\n- This skill was automatically generated from official documentation\n- Reference files preserve the structure and examples from source docs\n- Code examples include language detection for better syntax highlighting\n- Quick reference patterns are extracted from common usage examples in the docs\n\n## Updating\n\nTo refresh this skill with updated documentation:\n1. Re-run the scraper with the same configuration\n2. The skill will be rebuilt with the latest information\n\n\n","# Axolotl Skill\n\n## What's inside\n\nExpert guidance for fine-tuning LLMs with Axolotl — YAML configs, 100+ models, LoRA\u002FQLoRA, DPO\u002FKTO\u002FORPO\u002FGRPO, multimodal support.\n\nComprehensive assistance with axolotl development, generated from official documentation.\n\n## When to Use This Skill\n\nThis skill should be triggered when:\n- Working with axolotl\n- Asking about axolotl features or APIs\n- Implementing axolotl solutions\n- Debugging axolotl code\n- Learning axolotl best practices\n\n## Quick Reference\n\n### Common Patterns\n\n**Pattern 1:** To validate that acceptable data transfer speeds exist for your training job, running NCCL Tests can help pinpoint bottlenecks, for example:\n\n```\n.\u002Fbuild\u002Fall_reduce_perf -b 8 -e 128M -f 2 -g 3\n```\n\n**Pattern 2:** Configure your model to use FSDP in the Axolotl yaml. For example:\n\n```\nfsdp_version: 2\nfsdp_config:\n  offload_params: true\n  state_dict_type: FULL_STATE_DICT\n  auto_wrap_policy: TRANSFORMER_BASED_WRAP\n  transformer_layer_cls_to_wrap: LlamaDecoderLayer\n  reshard_after_forward: true\n```\n\n**Pattern 3:** The context_parallel_size should be a divisor of the total number of GPUs. For example:\n\n```\ncontext_parallel_size\n```\n\n**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\n\n```\ncontext_parallel_size=4\n```\n\n**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)\n\n```\nsave_compressed: true\n```\n\n**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:\u002F\u002Fgithub.com\u002Faxolotl-ai-cloud\u002Fdiff-transformer\n\n```\nintegrations\n```\n\n**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]]\n\n```\nutils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2)\n```\n\n### Example Code Patterns\n\n**Example 1** (python):\n```python\ncli.cloud.modal_.ModalCloud(config, app=None)\n```\n\n**Example 2** (python):\n```python\ncli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None)\n```\n\n**Example 3** (python):\n```python\ncore.trainers.base.AxolotlTrainer(\n    *_args,\n    bench_data_collator=None,\n    eval_data_collator=None,\n    dataset_tags=None,\n    **kwargs,\n)\n```\n\n**Example 4** (python):\n```python\ncore.trainers.base.AxolotlTrainer.log(logs, start_time=None)\n```\n\n**Example 5** (python):\n```python\nprompt_strategies.input_output.RawInputOutputPrompter()\n```\n\n## Reference Files\n\nThis skill includes comprehensive documentation in `references\u002F`:\n\n- **api.md** - Api documentation\n- **dataset-formats.md** - Dataset-Formats documentation\n- **other.md** - Other documentation\n\nUse `view` to read specific reference files when detailed information is needed.\n\n## Working with This Skill\n\n### For Beginners\nStart with the getting_started or tutorials reference files for foundational concepts.\n\n### For Specific Features\nUse the appropriate category reference file (api, guides, etc.) for detailed information.\n\n### For Code Examples\nThe quick reference section above contains common patterns extracted from the official docs.\n\n## Resources\n\n### references\u002F\nOrganized documentation extracted from official sources. These files contain:\n- Detailed explanations\n- Code examples with language annotations\n- Links to original documentation\n- Table of contents for quick navigation\n\n### scripts\u002F\nAdd helper scripts here for common automation tasks.\n\n### assets\u002F\nAdd templates, boilerplate, or example projects here.\n\n## Notes\n\n- This skill was automatically generated from official documentation\n- Reference files preserve the structure and examples from source docs\n- Code examples include language detection for better syntax highlighting\n- Quick reference patterns are extracted from common usage examples in the docs\n\n## Updating\n\nTo refresh this skill with updated documentation:\n1. Re-run the scraper with the same configuration\n2. 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Keep plans as local files unless the user explicitly asks to register them in Doct.","https:\u002F\u002Fgithub.com\u002Fadnichols\u002Fai-configs\u002Fblob\u002Fmain\u002F_hermes\u002Fdefault\u002Fskills\u002Fsoftware-development\u002Fwriting-plans\u002FSKILL.md",[121,126,129],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":127,"categoryName":128,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":130,"categoryName":131,"taxonomyType":132,"taxonomyTypeDesc":133},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[135,136,137,138,139,140,141,142,143,144,145,146,106,147,108,109,148],"writing","writing plans","documentation","test","workflow","design","user","development","planning","files","implementation","html","ops","plan",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":164,"publishedAt":35},"01M6CB7JME246KZQHP73Z2STDF","test-driven-development","TDD: enforce RED-GREEN-REFACTOR, tests before code.","https:\u002F\u002Fgithub.com\u002Fadnichols\u002Fai-configs\u002Fblob\u002Fmain\u002F_hermes\u002Fdefault\u002Fskills\u002Fsoftware-development\u002Ftest-driven-development\u002FSKILL.md",[155,158,161],{"categoryNo":156,"categoryName":157,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":159,"categoryName":160,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":162,"categoryName":163,"taxonomyType":132,"taxonomyTypeDesc":133},"01JBSEEDCAT000000000000037","律师与法务",[138,142,165,166,167,108,109,168,148,169,170,171,114,172,113,173,112],"test driven development","red-green-refactor","superpowers","quality","refactor","code","debugging","tdd","testing",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":181,"publishedAt":35},"01M6CB7FQ407KQTD4Z9VAWKY54","systematic-debugging","4-phase root cause debugging: understand bugs before fixing.","https:\u002F\u002Fgithub.com\u002Fadnichols\u002Fai-configs\u002Fblob\u002Fmain\u002F_hermes\u002Fdefault\u002Fskills\u002Fsoftware-development\u002Fsystematic-debugging\u002FSKILL.md",[180],{"categoryNo":159,"categoryName":160,"taxonomyType":124,"taxonomyTypeDesc":125},[171,182,142,167,108,109,183,148,184,112,138,113,114],"systematic debugging","troubleshooting","investigation",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":194,"publishedAt":35},"01M6CB7D57MK88HE5YPZHKH68Q","spike","Throwaway experiments to validate an idea before build.","https:\u002F\u002Fgithub.com\u002Fadnichols\u002Fai-configs\u002Fblob\u002Fmain\u002F_hermes\u002Fdefault\u002Fskills\u002Fsoftware-development\u002Fspike\u002FSKILL.md",[191],{"categoryNo":192,"categoryName":193,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000021","资料检索与研究",[187,112,195,196,20,197,113,114,198,199,200,108,109,148,143],"validate","experiments","prototype","feasibility","sketch","experiment",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":211,"publishedAt":35},"01M6CB7APAQ37YE5YE1MAAFT42","simplify-code","Parallel 3-agent cleanup of recent code changes.","https:\u002F\u002Fgithub.com\u002Fadnichols\u002Fai-configs\u002Fblob\u002Fmain\u002F_hermes\u002Fdefault\u002Fskills\u002Fsoftware-development\u002Fsimplify-code\u002FSKILL.md",[207,208],{"categoryNo":127,"categoryName":128,"taxonomyType":124,"taxonomyTypeDesc":125},{"categoryNo":209,"categoryName":210,"taxonomyType":132,"taxonomyTypeDesc":133},"01JBSEEDCAT000000000000030","开发工程师",[212,213,170,214,108,109,215,148,169,216,217,218,142,112,138,113,219,114],"simplify code","simplify","subagent","parallel","code-review","delegation","cleanup","review"]