Video Tutorials

Watch step-by-step tutorials on our YouTube channel:

trusteval_video_003.mov

Table of Contents

• Video Tutorials
• 📍 Overview
• 👾 Features
• 🚀 Getting Started
  • 1. Set Up a Conda Environment
  • 3. Install Dependencies
  • 🤖 Usage
    • Configure API Keys
    • Quick Start
    • Step 0: Set Your Project Base Directory
      • Step 1: Download Metadata
      • Step 2: Generate Datasets Dynamically
      • Step 3: Apply Contextual Variations
      • Step 4: Generate Model Responses
      • Step 5: Evaluate and Generate Reports
• Trustworthiness Report
  • Test Model Results
  • Model Performance Summary
  • Error Case Study
  • Leaderboard
• Contributing
• Citation
• License

📍 Overview

Trustgen is a dynamic and comprehensive framework for evaluating the trustworthiness of Generative Foundation Models (GenFMs) across dimensions such as safety, fairness, robustness, privacy, and more.

👾 Features

• 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.
• Performance Optimizations: xformers memory efficiency, attention slicing, and optimized pipelines.
• Customizable Metrics: Configure workflows with flexible metrics and evaluation methods.
• Metadata-Driven Pipelines: Design and execute test cases efficiently using metadata.
• Comprehensive Dimensions: Evaluate models across safety, fairness, robustness, privacy, and truthfulness.
• Detailed Reports: Generate interactive, easy-to-interpret evaluation reports.

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📖 The documentation can be viewed directly with a browser at TrustGen/docs/html/index.html.

🚀 Getting Started

1. Set Up a Conda Environment

Create and activate a new environment with Python 3.10:

conda create -n trustgen_env python=3.10
conda activate trustgen_env

3. Install Dependencies

Basic Installation (API Models Only):

Install the package with basic dependencies for API-based models:

pip install .

Full Installation (Including Local Models):

If you want to run local Text-to-Image models (such as Stable Diffusion, FLUX, HunyuanDiT, etc.), install with additional dependencies:

pip install -e ".[local]"

This will install additional packages required for local model inference:

• diffusers==0.31.0 - Hugging Face Diffusers library for T2I models
• torch>=2.1.0 - PyTorch for deep learning
• transformers>=4.41.2 - Transformers library
• datasets>=2.15.0 - Dataset utilities
• accelerate==0.30.1 - Hardware acceleration utilities

🤖 Supported Models

TrustEval supports a comprehensive range of foundation models across different modalities and providers:

Recommended API Providers: For API-based models, we recommend OpenRouter, DeepInfra, and Replicate for reliable and cost-effective inference.

📝 Large Language Models (LLMs)

OpenAI Models

Model	Name	Type	Provider
GPT-4o	gpt-4o	Chat + Vision	OpenAI API
GPT-4o Mini	gpt-4o-mini	Chat + Vision	OpenAI API
GPT-3.5 Turbo	gpt-3.5-turbo	Chat	OpenAI API
o1	o1	Reasoning	OpenAI API
o1 Mini	o1-mini	Reasoning	OpenAI API
o1 Preview	o1-preview	Reasoning	OpenAI API

Anthropic Claude Models

Model	Name	Type	Provider
Claude 3.5 Sonnet	claude-3.5-sonnet	Chat + Vision	Anthropic API
Claude 3 Haiku	claude-3-haiku	Chat + Vision	Anthropic API
Claude 3 Opus	claude-3-opus	Chat	Anthropic API

Meta LLaMA Models

Model	Name	Type	Provider
LLaMA 2 13B	llama-2-13B	Chat	DeepInfra API / Local
LLaMA 3 8B	llama-3-8B	Chat	DeepInfra API
LLaMA 3 70B	llama-3-70B	Chat	DeepInfra API
LLaMA 3.1 8B	llama-3.1-8B	Chat	DeepInfra API
LLaMA 3.1 70B	llama-3.1-70B	Chat	DeepInfra API
LLaMA 3.2 11B Vision	llama-3.2-11B-V	Chat + Vision	DeepInfra API
LLaMA 3.2 90B Vision	llama-3.2-90B-V	Chat + Vision	DeepInfra API

Chinese Models

Model	Name	Type	Provider
GLM-4	glm-4	Chat	Zhipu API
GLM-4 Plus	glm-4-plus	Chat	Zhipu API
GLM-4V	glm-4v	Chat + Vision	Zhipu API
GLM-4V Plus	glm-4v-plus	Chat + Vision	Zhipu API
DeepSeek Chat	deepseek-chat	Chat	DeepSeek API
Qwen 2.5 72B	qwen-2.5-72B	Chat	DeepInfra API
QwQ 32B	qwq-32B	Reasoning	DeepInfra API
Qwen VL Max	qwen-vl-max-0809	Chat + Vision	Qwen API
Qwen 2 VL 72B	qwen-2-vl-72B	Chat + Vision	OpenRouter API
Yi Lightning	yi-lightning	Chat	Yi API

Google Models

Model	Name	Type	Provider
Gemini 1.5 Flash	gemini-1.5-flash	Chat + Vision	Google API
Gemini 1.5 Pro	gemini-1.5-pro	Chat + Vision	Google API
Gemma 2 27B	gemma-2-27B	Chat	DeepInfra API

Mistral Models

Model	Name	Type	Provider
Mistral 7B	mistral-7B	Chat	DeepInfra API
Mixtral 8x7B	mistral-8x7B	Chat	DeepInfra API
Mixtral 8x22B	mistral-8x22B	Chat	DeepInfra API

Other Models

Model	Name	Type	Provider
Command R	command-r	Chat	Cohere API
Command R Plus	command-r-plus	Chat	Cohere API
InternLM 72B	internLM-72B	Chat + Vision	InternLM API

🎨 Text-to-Image Models (T2I)

Local T2I Models (Requires pip install -e ".[local]")

Model	Name
Stable Diffusion 3.5 Large	sd-3.5-large
Stable Diffusion 3.5 Turbo	sd-3.5-large-turbo
Stable Diffusion XL	stable-diffusion-xl-base-1.0
Stable Diffusion 3 Medium	stable-diffusion-3-medium
FLUX.1-dev	FLUX.1-dev
HunyuanDiT	HunyuanDiT
Kolors	kolors
Playground v2.5	playground-v2.5

API T2I Models

Model	Name	Provider
CogView3-Plus	cogview-3-plus	Zhipu API
DALL-E 3	dalle3	OpenAI API
FLUX 1.1 Pro	flux-1.1-pro	Replicate API
FLUX Schnell	flux_schnell	Replicate API

🔍 Embedding Models

Model	Name	Provider
Text Embedding Ada 002	text-embedding-ada-002	OpenAI API

🚀 Performance Features

• Multi-GPU Support: Automatic load balancing across 1-8 GPUs for local models
• Memory Optimization: xformers attention, slicing, CPU offloading
• Concurrent Inference: 7-8x speed improvement with multiple GPUs
• Auto-Detection: Automatic hardware detection and optimization

🤖 Usage

Configure API Keys

Run the configuration script to set up your API keys:

python trusteval/src/configuration.py

Quick Start

The following example demonstrates an Advanced AI Risk Evaluation workflow.

Step 0: Set Your Project Base Directory

import os
base_dir = os.getcwd() + '/advanced_ai_risk'

Step 1: Download Metadata

from trusteval import download_metadata

download_metadata(
    section='advanced_ai_risk',
    output_path=base_dir
)

Step 2: Generate Datasets Dynamically

from trusteval.dimension.ai_risk import dynamic_dataset_generator

dynamic_dataset_generator(
    base_dir=base_dir,
)

Step 3: Apply Contextual Variations

from trusteval import contextual_variator_cli

contextual_variator_cli(
    dataset_folder=base_dir
)

Step 4: Generate Model Responses

from trusteval import generate_responses

request_type = ['llm']  # Options: 'llm', 'vlm', 't2i'
async_list = ['your_async_model']
sync_list = ['your_sync_model']

await generate_responses(
    data_folder=base_dir,
    request_type=request_type,
    async_list=async_list,
    sync_list=sync_list,
)

Step 5: Evaluate and Generate Reports

1. Judge the Responses

   from trusteval import judge_responses
   
   target_models = ['your_target_model1', 'your_target_model2']
   judge_type = 'llm'  # Options: 'llm', 'vlm', 't2i'
   judge_key = 'your_judge_key'
   async_judge_model = ['your_async_model']
   
   await judge_responses(
       data_folder=base_dir,
       async_judge_model=async_judge_model,
       target_models=target_models,
       judge_type=judge_type,
   )

2. Generate Evaluation Metrics

   from trusteval import lm_metric
   
   lm_metric(
       base_dir=base_dir,
       aspect='ai_risk',
       model_list=target_models,
   )

3. Generate Final Report

   from trusteval import report_generator
   
   report_generator(
       base_dir=base_dir,
       aspect='ai_risk',
       model_list=target_models,
   )

Your report.html will be saved in the base_dir folder. For additional examples, check the examples folder.

Trustworthiness Report

A detailed trustworthiness evaluation report is generated for each dimension. The reports are presented as interactive web pages, which can be opened in a browser to explore the results. The report includes the following sections:

The data shown in the images below is simulated and does not reflect actual results.

Test Model Results

Displays the evaluation scores for each model, with a breakdown of average scores across evaluation dimensions.

Model Performance Summary

Summarizes the model's performance in the evaluated dimension using LLM-generated summaries, highlighting comparisons with other models.

Error Case Study

Presents error cases for the evaluated dimension, including input/output examples and detailed judgments.

Leaderboard

Shows the evaluation results for all models, along with visualized comparisons to previous versions (e.g., our v1.0 results).

Contributing

We welcome contributions from the community! To contribute:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit your changes (git commit -m 'Add feature').
4. Push to your branch (git push origin feature-name).
5. Open a pull request.

Citation

@article{huang2025trustgen,
    title={On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective},
    author={Yue Huang and Chujie Gao and Siyuan Wu and Haoran Wang and Xiangqi Wang and Yujun Zhou and Yanbo Wang and Jiayi Ye and Jiawen Shi and Qihui Zhang and Yuan Li and Han Bao and Zhaoyi Liu and Tianrui Guan and Dongping Chen and Ruoxi Chen and Kehan Guo and Andy Zou and Bryan Hooi Kuen-Yew and Caiming Xiong and Elias Stengel-Eskin and Hongyang Zhang and Hongzhi Yin and Huan Zhang and Huaxiu Yao and Jaehong Yoon and Jieyu Zhang and Kai Shu and Kaijie Zhu and Ranjay Krishna and Swabha Swayamdipta and Taiwei Shi and Weijia Shi and Xiang Li and Yiwei Li and Yuexing Hao and Zhihao Jia and Zhize Li and Xiuying Chen and Zhengzhong Tu and Xiyang Hu and Tianyi Zhou and Jieyu Zhao and Lichao Sun and Furong Huang and Or Cohen Sasson and Prasanna Sattigeri and Anka Reuel and Max Lamparth and Yue Zhao and Nouha Dziri and Yu Su and Huan Sun and Heng Ji and Chaowei Xiao and Mohit Bansal and Nitesh V. Chawla and Jian Pei and Jianfeng Gao and Michael Backes and Philip S. Yu and Neil Zhenqiang Gong and Pin-Yu Chen and Bo Li and Xiangliang Zhang},
    journal={arXiv preprint arXiv:2502.14296},
    year={2025}
}

License

This project is licensed under the CC BY-NC 4.0.