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EVALSCOPE
Streamlined evaluation and benchmarking framework for LLMs, VLMs, and AIGC models
Apache-2.0
ABOUT
Evaluating large language models and vision-language models requires complex, fragmented pipelines with inconsistent benchmarks, reporting formats, and setup procedures. EvalScope solves this by providing a unified evaluation framework with standardized APIs for academic benchmarks, custom datasets, RAG evaluation, and performance testing — eliminating the need to wire up separate evaluation scripts for each benchmark.
INSTALL
pip install evalscopeINTEGRATION GUIDE
1. Benchmark LLMs on standard academic datasets like MMLU, GSM8K, HumanEval, and BBH with a single command
2. Evaluate vision-language models on multimodal benchmarks for image understanding and generation quality
3. Assess RAG pipeline quality with built-in retrieval and generation evaluation metrics
4. Profile model inference performance including latency, throughput, and memory usage across backends
5. Compare model quantization and compression quality against original model baselines
TAGS
llm-evaluationbenchmarkingrag-evaluationmodel-evaluationvlmperformance