TrustGen / TrustEval-toolkit
[ICLR'26, NAACL'25 Demo] Toolkit & Benchmark for evaluating the trustworthiness of generative foundation models.
Why this job: says: benchmark; topics: evaluation
What it does
- Dynamic Dataset Generation. Automatically generate datasets tailored for evaluation tasks.
- Multi-Model Compatibility. Evaluate LLMs, VLMs, T2I models, and more.
- Local & API Model Support. Run models locally or via API endpoints for maximum flexibility.
- Multi-GPU Acceleration. Concurrent inference across multiple GPUs for 7-8x speed improvement.
- Advanced T2I Models. Support for Stable Diffusion 3.5, FLUX.1-dev, HunyuanDiT, Kolors, and more.
Get started
pip install .- Maintenance
- Active
- Last commit 21 days ago
- Stars
- 136
- On GitHub
- Licence
- Custom
- Read before business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 97% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 75% closed
- 2 still open
- Pull requests
- 0 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
Similar repos
| Repo | Stars | Last commit | Licence | Downloads for |
|---|---|---|---|---|
| HowieHwong / TrustLLM | 635 | 37 days ago | MIT | No downloads |
| modelscope / evalscope | 3,674 | today | Apache-2.0 | No downloads |
| huggingface / evaluate | 2,493 | 35 days ago | Apache-2.0 | No downloads |
More: languages, topics and project record
Languages
- Python 97%
- Jupyter Notebook 1%
- HTML 1%
- Shell <1%
Topics
- deep-learning
- evaluation
- generative-ai
- llm
- machine-learning
- text-to-image
- toolkit
- trustworthy-ai
- vlm
Project record
- JobEvals and observability
- StatusListed
- Commits197
- Forks11
- Watching1
- Stars in 28 days0
- CreatedDec 16, 2024
- First seen by usNov 7, 2026
- CheckedNov 8, 2026