Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Fine-Tuning
Fine-tune LLMs with data preparation, provider selection, cost estimation, evaluation, and compliance checks.
skill openclawclawhub Free
Fine-tune LLMs with data preparation, provider selection, cost estimation, evaluation, and compliance checks.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, compliance.md, costs.md, data-prep.md, evaluation.md, providers.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
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.
Upgrade existing
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
When to Use
User wants to fine-tune a language model, evaluate if fine-tuning is worth it, or debug training issues.
Quick Reference
TopicFileProvider comparison & pricingproviders.mdData preparation & validationdata-prep.mdTraining configurationtraining.mdEvaluation & debuggingevaluation.mdCost estimation & ROIcosts.mdCompliance & securitycompliance.md
Core Capabilities
Decide fit β Analyze if fine-tuning beats prompting for the use case Prepare data β Convert raw data to JSONL, deduplicate, validate format Select provider β Compare OpenAI, Anthropic (Bedrock), Google, open source based on constraints Estimate costs β Calculate training cost, inference savings, break-even point Configure training β Set hyperparameters (learning rate, epochs, LoRA rank) Run evaluation β Compare fine-tuned vs base model on task-specific metrics Debug failures β Diagnose loss curves, overfitting, catastrophic forgetting Handle compliance β Scan for PII, configure on-premise training, generate audit logs
Decision Checklist
Before recommending fine-tuning, ask: What's the failure mode with prompting? (format, style, knowledge, cost) How many training examples available? (minimum 50-100) Expected inference volume? (affects ROI calculation) Privacy constraints? (determines provider options) Budget for training + ongoing inference?
Fine-Tune vs Prompt Decision
SignalRecommendationFormat/style inconsistencyFine-tune βMissing 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
Critical Rules
Data quality > quantity β 100 great examples beat 1000 noisy ones LoRA first β Never jump to full fine-tuning; LoRA is 10-100x cheaper Hold out eval set β Always 80/10/10 split; never peek at test data Same precision β Train and serve at identical precision (4-bit, 16-bit) Baseline first β Run eval on base model before training to measure actual improvement Expect iteration β First attempt rarely optimal; plan for 2-3 cycles
Common Pitfalls
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 β use RAGNo baseline comparisonAlways test base model on same eval setIgnoring forgettingMix 20% general data to preserve capabilities
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- compliance.md
- costs.md
- data-prep.md
- evaluation.md
- providers.md