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lm-evaluation-harness

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AI 模型与开发框架 evaluation-frameworklanguage-modeltransformer
14,458
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3,790
Forks
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1,341
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MIT
许可证
Python
语言
2020-08-28
创建时间
2026-11-04
最近更新

项目能做什么

用于语言模型少样本(few-shot)评估的测试框架。
来源:基于仓库元数据 · 内容以仓库实际 README 为准

使用说明 查看原始 README 原仓库文档

中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
# Language Model Evaluation Harness

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10256836.svg)](https://doi.org/10.5281/zenodo.10256836)

---

## Latest News 📣
- [2026/09] **Plugins**: model backends, filters, metrics and aggregations can now be registered from your own package instead of forking — declare an `lm_eval.*` entry point for zero-config discovery, or point `--plugins` at a local module. See the [Plugin Guide](./docs/plugins.md).
- [2025/12] **CLI refactored** with subcommands (`run`, `ls`, `validate`) and YAML config file support via `--config`. See the [CLI Reference](./docs/interface.md) and [Configuration Guide](./docs/config_files.md).
- [2025/12] **Lighter install**: Base package no longer includes `transformers`/`torch`. Install model backends separately: `pip install lm_eval[hf]`, `lm_eval[vllm]`, etc.
- [2025/07] Added `think_end_token` arg to `hf` (token/str), `vllm` and `sglang` (str) for stripping CoT reasoning traces from models that support it.
- [2025/03] Added support for steering HF models!
- [2025/02] Added [SGLang](https://docs.sglang.ai/) support!
- [2024/09] We are prototyping allowing users of LM Evaluation Harness to create and evaluate on text+image multimodal input, text output tasks, and have just added the `hf-multimodal` and `vllm-vlm` model types and `mmmu` task as a prototype feature. We welcome users to try out this in-progress feature and stress-test it for themselves, and suggest they check out [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval), a wonderful project originally forking off of the lm-evaluation-harness, for a broader range of multimodal tasks, models, and features.
- [2024/07] [API model](docs/API_guide.md) support has been updated and refactored, introducing support for batched and async requests, and making it significantly easier to customize and use for your own purposes. **To run Llama 405B, we recommend using VLLM's OpenAI-compliant API to host the model, and use the `local-completions` model type to evaluate the model.**
- [2024/07] New Open LLM Leaderboard tasks have been added ! You can find them under the [leaderboard](lm_eval/tasks/leaderboard/README.md) task group.

---

## Announcement

**A new v0.4.0 release of lm-evaluation-harness is available** !

New updates and features include:

- **New Open LLM Leaderboard tasks have been added ! You can find them under the [leaderboard](lm_eval/tasks/leaderboard/README.md) task group.**
- Internal refactoring
- Config-based task creation and configuration
- Easier import and sharing of externally-defined task config YAMLs
- Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource
- More advanced configuration options, including output post-processing, answer extraction, and multiple LM generations per document, configurable fewshot settings, and more
- Speedups and new modeling libraries supported, including: faster data-parallel HF model usage, vLLM support, MPS support with 
阅读完整原文

商业机会分析 规则生成(AI 未配置)

综合评分 66.6 分。该项目在“个人可实施性”维度得 60 分,“付费可能性”维度得 60.0 分,差异化空间 88.0 分(评分依据见评分卡)。

将 lm-evaluation-harness 改造为垂直场景 SaaS

产品构想保留核心能力,针对某一类用户(如某行业团队)做托管版,收取月费。
目标用户有该需求的中小团队 / 独立站长
难度/周期中 · 2~4 周(MVP)
收费模式订阅制(如 ¥29~99/月)
竞争风险需验证付费意愿;注意许可证合规
依据 数据支持的判断:许可证与关键词显示存在商用与付费场景

面向细分需求的二次开发产品

产品构想基于 lm-evaluation-harness 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。
目标用户该细分场景用户
难度/周期中 · 2~6 周
收费模式买断制 / 订阅制 / 广告
竞争风险细分需求规模需验证
依据 AI 推测:基于项目主题标签的判断

封装为在线小工具或 API 服务

产品构想把项目最常用的一个功能做成网页小工具,靠流量变现或按量计费。
目标用户搜索引擎带来的长尾用户
难度/周期低 · 1~3 周
收费模式免费引流 + 增值功能 / API 按量计费
竞争风险同质化竞争,需做好 SEO
依据 AI 推测:基于技术栈与部署特征的判断
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心

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项目优势与风险

技术成熟度创建于 2020-08-28,近 30 天仍在推送,发布过 19 个 Release
社区活跃Star 14,458 / Fork 3,790 / Issue 1,341(数据支持,可自行核实)
许可证MIT
主要风险上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价

安装与使用

快速开始(以仓库 README 为准)
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
cd lm-evaluation-harness
# 请阅读仓库 README 获取确切安装步骤(依赖、配置、启动命令)
阅读原仓库文档
术语说明:Docker=应用容器(把程序和依赖打包运行);API Key=调用外部服务的密钥;Git Clone=把仓库代码下载到本地。常见错误:API Key 未配置 / 端口占用 / 数据库连接失败,多数项目在 README 的 Troubleshooting 章节有说明。