[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KJYVHVB566HDS650QSP4GW":3,"skill-same:01M6KJYVHVB566HDS650QSP4GW":65},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":48,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M6KJYVHVB566HDS650QSP4GW","pytorch-nlp-setup",null,"Set up Python environments for NLP\u002FML projects using PyTorch, transformers, and TRL. Use this skill when installing dependencies for preference optimization, RLHF, or transformer-based training pipelines.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",93,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-claude-opus-4-6\u002Fnlp-paper-reproduction\u002Fpytorch-nlp-setup\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"pytorch nlp setup","setup","pytorch","nlp","projects","for","Preference Optimization","Transformer","TRL","dependencies","pipelines","up","use","SET","Transformers","RLHF","Optimization","training","ml","python","2026-11-10 10:12:30",false,"---\nname: pytorch-nlp-setup\ndescription: Set up Python environments for NLP\u002FML projects using PyTorch, transformers, and TRL. Use this skill when installing dependencies for preference optimization, RLHF, or transformer-based training pipelines.\n---\n\n# PyTorch NLP Environment Setup\n\n## Standard Stack for Preference Optimization Projects\n\n```bash\npip install torch transformers datasets accelerate trl peft wandb numpy\n```\n\n## Version Compatibility Notes\n\n- TRL (Transformer Reinforcement Learning) provides base trainers like CPOTrainer, DPOTrainer\n- Older TRL versions (\u003C 0.8) have different import paths for utilities\n- Check `from trl.trainer.utils import DPODataCollatorWithPadding` availability\n- For `trl_sanitze_kwargs_for_tagging` (note: typo is intentional in some versions)\n\n## Common Issues\n\n- If `from trl.import_utils import is_peft_available` fails, check TRL version\n- Some projects pin specific transformers\u002FTRL versions — check requirements.txt or setup.py\n- CUDA availability: use `torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")`\n\n## Running Unit Tests\n\n```bash\ncd \u002Fpath\u002Fto\u002Fproject && python -m pytest unit_test\u002F -v\n# or\ncd \u002Fpath\u002Fto\u002Fproject && python -m unittest unit_test.unit_test_1\n```\n","# PyTorch NLP Environment Setup\n\n## Standard Stack for Preference Optimization Projects\n\n```bash\npip install torch transformers datasets accelerate trl peft wandb numpy\n```\n\n## Version Compatibility Notes\n\n- TRL (Transformer Reinforcement Learning) provides base trainers like CPOTrainer, DPOTrainer\n- Older TRL versions (\u003C 0.8) have different import paths for utilities\n- Check `from trl.trainer.utils import DPODataCollatorWithPadding` availability\n- For `trl_sanitze_kwargs_for_tagging` (note: typo is intentional in some versions)\n\n## Common Issues\n\n- If `from trl.import_utils import is_peft_available` fails, check TRL version\n- Some projects pin specific transformers\u002FTRL versions — check requirements.txt or setup.py\n- CUDA availability: use `torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")`\n\n## Running Unit Tests\n\n```bash\ncd \u002Fpath\u002Fto\u002Fproject && python -m pytest unit_test\u002F -v\n# or\ncd \u002Fpath\u002Fto\u002Fproject && python -m unittest unit_test.unit_test_1\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb4-skill-creator-claude-opus-4-6\u002Fnlp-paper-reproduction\u002Fpytorch-nlp-setup",8,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench","https:\u002F\u002Fcxcscmu.github.io\u002FSkillLearnBench","https:\u002F\u002Fgithub.com\u002Fcxcscmu","2026-10-02 22:55:36",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill pytorch-nlp-setup",{"tool":57,"command":58},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill pytorch-nlp-setup",{"tool":60,"command":61},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill pytorch-nlp-setup","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-claude-opus-4-6\u002Fnlp-paper-reproduction\u002Fpytorch-nlp-setup\u002FSKILL.md",true,[],[66,84,108,125,145,156],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":77,"publishedAt":38},"01M6KM5KH175P5ABP5JMX2SBDQ","object_counter","Count occurrences of an object in the image using computer vision algorithm.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fvideo-object-counting\u002Fobject_counter\u002FSKILL.md",[72],{"categoryNo":73,"categoryName":74,"taxonomyType":75,"taxonomyTypeDesc":76},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[78,79,80,81,82,83],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":93,"publishedAt":38},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow","Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":75,"taxonomyTypeDesc":76},"01JBSEEDCAT000000000000023","Skill与智能体开发",[94,95,96,97,98,99,100,101,102,23,103,104,105,106,107],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":38},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool","invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[115,116,117,118,119,120,29,97,121,122,98,123,23,124,95],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","look","flights","you","database",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":38},"01M6KM465BB9PQ0B548HQSVAWZ","glm-output","Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final \u002Froot\u002Fmetrics.json.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-output\u002FSKILL.md",[],[132,133,134,23,135,136,137,138,139,140,141,142,123,143,144],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":152,"publishedAt":38},"01M6KM40KY3QWDW5W97S39R2BC","glm-calibration","Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-calibration\u002FSKILL.md",[151],{"categoryNo":91,"categoryName":92,"taxonomyType":75,"taxonomyTypeDesc":76},[153,144,134,133,139,138,135,19,154,23,155],"glm calibration","companion","metric",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":162,"publishedAt":38},"01M6KM3VHHVSGMGHJQB3PPT6YG","glm-basics","A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-basics\u002FSKILL.md",[],[163,134,138,164,153,144,23,133,165,166,167,30,168,169,135],"glm basics","inspect","relevant","files","good","how","Evaluation"]