HuggingFace Experiment Tracking
Track experiments, metrics, and model performance across training runs for reproducible AI research.
Key Features
- Experiment logging
- Metric tracking
- Model versioning
- Performance comparison
- Reproducibility
Use Cases
ML experiment management, performance tracking, model comparison
Comments
No comments yet. Be the first to comment!
Related Tools
HuggingFace Datasets
github.com/huggingface/skills
Manage, load, and process datasets from HuggingFace Hub for machine learning training and evaluation.
HuggingFace Evaluation
github.com/huggingface/skills
Model evaluation tools with standard metrics, benchmarks, and comprehensive performance analysis for AI models.
HuggingFace CLI
github.com/huggingface/skills
Command-line tools for HuggingFace Hub interactions, model management, and dataset operations.
Related Insights

Grok Bot and Hermes Bot: one person finally gets a think tank and a secretariat
Grok Bot now ships with Cursor Pro+. Hermes Bot runs on a VPS. They are not smarter chat boxes. The think tank advises, the secretariat executes, and you still make the call.
Hook OpenCode Go into Codex on Windows. Do Not Open a Second Toolkit.
A ChatGPT-signed Codex desktop app still shows mostly GPT in the picker. On Windows, enable only OpenCode Go and the Grok, GLM, Kimi, DeepSeek, and MiniMax models you already pay for appear in the same selector. Keys stay local. Native GPT stays put.
What Locks Codex Is the Picker, Not the Models
You already pay for OpenCode Go, Grok, and Z.ai, but the Codex picker still shows mostly GPT. The community project codex-router does not teach another install ritual. It puts subscriptions you already bought back into the selector, keeps keys on the machine, and leaves native GPT alone.