Ml Training
Machine learning model training with HuggingFace. Fine-tuning LLMs, dataset creation, GPU selection, training monitoring. Use for training custom models. Don't use for using pre-trained models via API.
Install
$ agentstack add skill-neuron-one-godmode-ml-training ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-neuron-one-godmode-ml-training) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
ML Model Training
Pipeline
- Define task and success metrics
- Collect/prepare training data
- Choose base model (by size and task fit)
- Select GPU and estimate cost
- Configure training (SFT, DPO, or GRPO)
- Monitor training run
- Evaluate on test set
- Deploy model
Model Selection
- Small tasks (classification): Qwen 0.6B-3B
- Medium tasks (generation): Mistral 7B, Llama 8B
- Complex tasks (reasoning): Qwen 27B, Llama 70B
Training Methods
- SFT: supervised fine-tuning on examples
- DPO: preference optimization (good vs bad)
- GRPO: group relative policy optimization
Deployment
- Ollama for local inference
- LitServe for MCP server
- llama.cpp for production
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: neuron-one
- Source: neuron-one/GODMODE
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.