unsloth-mcp-server
CLAUDE.md2 starsChanged 9 months agoApache-2.0View on GitHub
Updated: 2026-01-18 Status: 33 tools working, 180 tests passing, training has environment issues MCP server giving Claude access to Unsloth fine-tuning. 33 tools for model training, RunPod GPU management, knowledge capture, and cost tracking.
- Reads credentials
In this repository8 other files it gives its agents.
- adaptive-workflowsSkill
- dataset-engineeringSkill
- model-deploymentSkill
- superbpeSkill
- training-optimizationSkill
- unsloth-finetuningSkill
- unsloth-mcp-serverSkill
- unsloth-tokenizerSkill
The file
We split this CLAUDE.md into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
NO OpenAI: - Use Anthropic Claude or Google Gemini only
- Distinctive: line 9
API keys in .env only: - Never hardcode
≈ “Common” is an estimate: at least 1 in 5 of the 26,980 CLAUDE.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Unsloth MCP Server
Updated: 2026-01-18 Status: 33 tools working, 180 tests passing, training has environment issues
Critical Rules
- NO OpenAI - Use Anthropic Claude or Google Gemini only
- API keys in .env only - Never hardcode
- RunPod API key stored in .env
What This Project Is
MCP server giving Claude access to Unsloth fine-tuning. 33 tools for model training, RunPod GPU management, knowledge capture, and cost tracking.
Current Status
| Component | Status |
| MCP tools (33) | Working |
| Test suite (180) | Passing |
| RunPod pod management | Working |
| Knowledge capture | Working |
| GRPO training | Dtype issues |
| SFT training | Environment-dependent |
Key Files
src/index.ts # Main MCP server (33 tools)
src/utils/runpod.ts # RunPod API client
src/utils/checkpoint.ts # Training checkpoints
src/utils/cost-tracker.ts # GPU cost tracking
src/knowledge/ # OCR + training data pipeline
src/__tests__/ # 180 testsTool Categories
- Core (12): finetune_model, load_model, export_model, etc.
- Knowledge (10): OCR, enhancement, training pair generation
- RunPod (11): Pod lifecycle, training jobs, cost estimation
Development
npm run build # Build TypeScript
npm run test # 180 tests
npm run start # Start serverEnvironment
RUNPOD_API_KEY=rpa_XXX
HUGGINGFACE_TOKEN=hf_XXX # OptionalDoes it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

