lighteval vs LLMBox
Python toolkits for LLM training and evaluation compared
lighteval and LLMBox are Python projects for evaluating large language models. lighteval centers on running evaluations across backends and inspecting results, while LLMBox combines evaluation with model training and data workflows.
lighteval: Evaluate Language Models Across Backends
Lighteval is a Python toolkit for running LLM evaluations across local models and remote inference backends. It combines a broad task catalog with custom metrics and detailed sample-level results for teams comparing or debugging model performance.
LLMBox: Train and Evaluate Large Language Models
LLMBox is a Python library for training and evaluating large language models through a unified pipeline. It suits researchers and developers who want configurable fine-tuning workflows and a broad set of model and benchmark evaluation options.
| lighteval | LLMBox | |
|---|---|---|
| Language | Python | Python |
| License | MIT | MIT |
| Stars | 2.5k | 848 |
| Forks | 566 | 104 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- lighteval supports evaluation through backends including Accelerate, Nanotron, vLLM, SGLang, and inference endpoints; LLMBox supports Hugging Face or API-based models and offers inference options such as vLLM.
- lighteval provides more than 1,000 tasks across domains and languages; LLMBox evaluates across 59+ datasets and benchmarks, including MMLU, GSM8K, and HellaSwag.
- lighteval emphasizes sample-level results and custom tasks and metrics; LLMBox includes generation, likelihood-based ranking, and option-probability evaluation methods.
- LLMBox adds training workflows such as supervised fine-tuning, pre-training, PPO, and DPO, plus LoRA and QLoRA; lighteval is described as an evaluation toolkit.
- Both projects use Python and the MIT license. The provided project data lists lighteval at 2.5k stars and 566 forks, and LLMBox at 848 stars and 104 forks.
Choose lighteval if you…
- need to run evaluations across supported inference backends or models already loaded in memory.
- want a catalog of more than 1,000 tasks spanning multiple domains and languages.
- want sample-by-sample results or the option to add custom evaluation tasks and metrics.
Choose LLMBox if you…
- need one library for fine-tuning workflows as well as evaluation.
- want options for dataset mixing, instruction-data construction, or tokenizer vocabulary merging.
- need to compare evaluation methods such as generation, likelihood-based ranking, and option probabilities.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.