{ "schemaVersion": "1.0", "item": { "slug": "fine-tuning", "name": "Fine-Tuning", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/ivangdavila/fine-tuning", "canonicalUrl": "https://clawhub.ai/ivangdavila/fine-tuning", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/fine-tuning", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=fine-tuning", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md", "compliance.md", "costs.md", "data-prep.md", "evaluation.md", "providers.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/fine-tuning" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/fine-tuning", "agentPageUrl": "https://openagent3.xyz/skills/fine-tuning/agent", "manifestUrl": "https://openagent3.xyz/skills/fine-tuning/agent.json", "briefUrl": "https://openagent3.xyz/skills/fine-tuning/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "When to Use", "body": "User wants to fine-tune a language model, evaluate if fine-tuning is worth it, or debug training issues." }, { "title": "Quick Reference", "body": "TopicFileProvider comparison & pricingproviders.mdData preparation & validationdata-prep.mdTraining configurationtraining.mdEvaluation & debuggingevaluation.mdCost estimation & ROIcosts.mdCompliance & securitycompliance.md" }, { "title": "Core Capabilities", "body": "Decide fit — Analyze if fine-tuning beats prompting for the use case\nPrepare data — Convert raw data to JSONL, deduplicate, validate format\nSelect provider — Compare OpenAI, Anthropic (Bedrock), Google, open source based on constraints\nEstimate costs — Calculate training cost, inference savings, break-even point\nConfigure training — Set hyperparameters (learning rate, epochs, LoRA rank)\nRun evaluation — Compare fine-tuned vs base model on task-specific metrics\nDebug failures — Diagnose loss curves, overfitting, catastrophic forgetting\nHandle compliance — Scan for PII, configure on-premise training, generate audit logs" }, { "title": "Decision Checklist", "body": "Before recommending fine-tuning, ask:\n\nWhat's the failure mode with prompting? (format, style, knowledge, cost)\n How many training examples available? (minimum 50-100)\n Expected inference volume? (affects ROI calculation)\n Privacy constraints? (determines provider options)\n Budget for training + ongoing inference?" }, { "title": "Fine-Tune vs Prompt Decision", "body": "SignalRecommendationFormat/style inconsistencyFine-tune ✓Missing domain knowledgeRAG first, then fine-tune if neededHigh inference volume (>100K/mo)Fine-tune for cost savingsRequirements change frequentlyStick with prompting<50 quality examplesPrompting + few-shot" }, { "title": "Critical Rules", "body": "Data quality > quantity — 100 great examples beat 1000 noisy ones\nLoRA first — Never jump to full fine-tuning; LoRA is 10-100x cheaper\nHold out eval set — Always 80/10/10 split; never peek at test data\nSame precision — Train and serve at identical precision (4-bit, 16-bit)\nBaseline first — Run eval on base model before training to measure actual improvement\nExpect iteration — First attempt rarely optimal; plan for 2-3 cycles" }, { "title": "Common Pitfalls", "body": "MistakeFixTraining on inconsistent dataManual review of 100+ samples before trainingLearning rate too highStart with 2e-4 for SFT, 5e-6 for RLHFExpecting new knowledgeFine-tuning adjusts behavior, not knowledge — use RAGNo baseline comparisonAlways test base model on same eval setIgnoring forgettingMix 20% general data to preserve capabilities" } ], "body": "When to Use\n\nUser wants to fine-tune a language model, evaluate if fine-tuning is worth it, or debug training issues.\n\nQuick Reference\nTopic\tFile\nProvider comparison & pricing\tproviders.md\nData preparation & validation\tdata-prep.md\nTraining configuration\ttraining.md\nEvaluation & debugging\tevaluation.md\nCost estimation & ROI\tcosts.md\nCompliance & security\tcompliance.md\nCore Capabilities\nDecide fit — Analyze if fine-tuning beats prompting for the use case\nPrepare data — Convert raw data to JSONL, deduplicate, validate format\nSelect provider — Compare OpenAI, Anthropic (Bedrock), Google, open source based on constraints\nEstimate costs — Calculate training cost, inference savings, break-even point\nConfigure training — Set hyperparameters (learning rate, epochs, LoRA rank)\nRun evaluation — Compare fine-tuned vs base model on task-specific metrics\nDebug failures — Diagnose loss curves, overfitting, catastrophic forgetting\nHandle compliance — Scan for PII, configure on-premise training, generate audit logs\nDecision Checklist\n\nBefore recommending fine-tuning, ask:\n\n What's the failure mode with prompting? (format, style, knowledge, cost)\n How many training examples available? (minimum 50-100)\n Expected inference volume? (affects ROI calculation)\n Privacy constraints? (determines provider options)\n Budget for training + ongoing inference?\nFine-Tune vs Prompt Decision\nSignal\tRecommendation\nFormat/style inconsistency\tFine-tune ✓\nMissing domain knowledge\tRAG first, then fine-tune if needed\nHigh inference volume (>100K/mo)\tFine-tune for cost savings\nRequirements change frequently\tStick with prompting\n<50 quality examples\tPrompting + few-shot\nCritical Rules\nData quality > quantity — 100 great examples beat 1000 noisy ones\nLoRA first — Never jump to full fine-tuning; LoRA is 10-100x cheaper\nHold out eval set — Always 80/10/10 split; never peek at test data\nSame precision — Train and serve at identical precision (4-bit, 16-bit)\nBaseline first — Run eval on base model before training to measure actual improvement\nExpect iteration — First attempt rarely optimal; plan for 2-3 cycles\nCommon Pitfalls\nMistake\tFix\nTraining on inconsistent data\tManual review of 100+ samples before training\nLearning rate too high\tStart with 2e-4 for SFT, 5e-6 for RLHF\nExpecting new knowledge\tFine-tuning adjusts behavior, not knowledge — use RAG\nNo baseline comparison\tAlways test base model on same eval set\nIgnoring forgetting\tMix 20% general data to preserve capabilities" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/ivangdavila/fine-tuning", "publisherUrl": "https://clawhub.ai/ivangdavila/fine-tuning", "owner": "ivangdavila", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/fine-tuning", "downloadUrl": "https://openagent3.xyz/downloads/fine-tuning", "agentUrl": "https://openagent3.xyz/skills/fine-tuning/agent", "manifestUrl": "https://openagent3.xyz/skills/fine-tuning/agent.json", "briefUrl": "https://openagent3.xyz/skills/fine-tuning/agent.md" } }