Evaluate large models efficiently with evalscope, a customizable framework for startup founders, with 2.9k+ GitHub stars.
intermediateโฑ 30 minutes๐ต Free + LLM API costs
3,034 stars414 forksPythonQuality 9/10Updated 7/6/2026100% free ยท open source
What it is
Evaluates large language models efficiently to check their performance.
What you can make with it
Custom evaluation frameworks for comparing language models, which can help you choose the best one for your project.
How it helps
Efficiently evaluate the performance of your language models, reducing the time spent on testing and improvement.
Real use case example
"A founder building a product with a large language model wants to compare its performance with another model. They use evalscope to create a custom evaluation framework and run it on both models, getting results in under an hour."
If you're new
Pick up this skill if you're just starting out with large language models and want a simple way to compare their performance.
If you're senior
Reach for this skill when you need a customizable evaluation framework to optimize your large language models in production applications.
Common confusion cleared up
Don't confuse evalscope with an out-of-the-box solution, as it's a customizable framework that requires setup and configuration.
Best inside these AI tools
CursorClaude DesktopCodex CLIAny AI Client
Pairs with
Claude APINotion databaseStripe webhook
Why we list it on WorkflowStacks: This is a free and open-source evaluation framework for large language models, which can help you save time and money.
What it does
Evalscope is a framework for efficiently evaluating and benchmarking the performance of large models such as LLM, VLM, and AIGC
Install / run
pip install evalscope
When to use it
โขWhen you need to compare the performance of different large models on your dataset
โขWhen you want to optimize your model's performance on a specific task or metric
โขWhen you need to evaluate the effectiveness of your model in a production environment
Quick start
1Clone the evalscope repository using `git clone https://github.com/modelscope/evalscope.git`
2Navigate to the evalscope directory using `cd evalscope`
3Install the required dependencies using `pip install -r requirements.txt`
4Create a configuration file using `evalscope init` and modify it according to your needs
5Run an evaluation task using `evalscope run --config-path=<your_config_file>`
Ready-to-paste prompt
Run an evaluation task using `evalscope run --config-path=configs/llm_benchmark.yml --model-name=mymodel --dataset=mydataset`
Heads up: Make sure you have the necessary dependencies installed, including PyTorch and transformers, and that your model and dataset are compatible with the evalscope framework
Saves to your device
Topics
evaluation
llm
performance
rag
vlm
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.