此外,大部分人对于 LLM 的需求都是强应用相关的,那么基于自己的需求,构造一个专属的 test set(真-private set)要可靠的多。 LLM Evaluation 是个有趣/有用的研究方向 如何公平、有效的评估一个模型不仅仅是个数据工程,也是个值得深究的学术问题。光从上面的讨论,我们就已经提到了各种评估方法的种种问题,那么如何...
🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23 open-source playground monitoring analytics evaluation self-hosted ycombinator openai gpt ...
llm_evaluation Run pip install -r requirements.txt 下载【语义评估】所需模型: huggingface-cli download --resume-download thenlper/gte-large-zh --local-dir /home/wangguisen/models/gte-large-zh 下载【扮演能力】所需模型: huggingface-cli download --resume-download morecry/BaichuanCharRM --local-...
根据Huggingface leaderboard 的说明,该排行榜使用了 lm-evaluation-harness 来进行指标计算。 lm-evaluation-harness 是一个专门为 LLM 进行 few shot 任务测评的工具,包括了 200 多种指标的测评。lm-evaluation-harness 输出的 LLM 评分文件,也可以直接用 Huggingface Leaderboard 官方提供的 load_results.py 来转换成...
公开项目>ChatLLM-EVALUATION ChatLLM-EVALUATION Fork 0 喜欢 2 分享 探索以用户体验为基础的大模型测评机制 Thomas-yanxin 10枚 BML Codelab develop Python3 中级自然语言处理 2023-05-11 13:56:08 应用体验 版本内容 Fork记录 评论(0) 运行一下 未登录状态无法使用该应用,请您登录后再试关于...
语言模型评估工具是Hugging Face的Open LLM Leaderboard的后台,已在数百篇论文中使用,并被包括NVIDIA、Cohere、BigScience、BigCode、Nous Research和Mosaic ML在内的几十个组织内部使用。 2、公告 lm-evaluation-harness的新版本v0.4.0已发布! 新更新和功能包括: ...
(LM) performance stands as a pivotal aspect in gauging their efficacy across various downstream applications. Different applications demand distinct performance indicators aligned with their goals. In this article, we'll take a detailed look at variousLLM evaluation metrics, exploring how they apply to...
Humanloop is an enterprise-grade AI evaluation platform with best-in-class prompt management and LLM observability.
“Arize offers an AI observability and LLM evaluation platform that helps AI developers and data scientists monitor, troubleshoot, and evaluate LLM models. This offering is critical to observe and evaluate applications for performance improvements in the build-learn-improve development loop..” Mike Hu...
evaluation of the most advanced LLMs on C-Eval, including both English- and Chinese-oriented models. Results indicate that only GPT-4 could achieve an average accuracy of over 60%, suggesting that there is still significant room for improvement for current LLMs. We anticipate C-Eval will ...