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[bot] Instrument mlx-lm (136,095 weekly downloads)

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还没有人认领这个 Issue。

评估

难度
4/5
预计耗时
3-5 天
新手友好度
52/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
活跃
技术栈
python

调研方向

Start by reading the existing HuggingFace Transformers integration under py/src/braintrust/integrations/ and inspect the integration matrix in py/pyproject.toml and sessions in py/noxfile.py. Review mlx_lm.generate.generate(), mlx_lm.generate.stream_generate(), and the mlx_lm.chat/server entry points. Done means native instrumentation exists, its package and test coverage are wired into the repository, and README.md documents the integration.

由索引模型根据 Issue 内容生成。

描述

new-integration python

What's missing

mlx-lm (Apple's MLX-based LLM generation/serving library for Apple Silicon) has no native instrumentation in the Braintrust Python SDK. There is no py/src/braintrust/integrations/mlx_lm/ directory, no entry in py/pyproject.toml [tool.braintrust.matrix], and no nox session in py/noxfile.py. The library provides execution-oriented generation APIs (mlx_lm.generate.generate(), mlx_lm.generate.stream_generate(), and the mlx_lm.chat/mlx_lm.server OpenAI-compatible serving entry points) that run local text-generation inference — directly analogous to the HuggingFace Transformers integration Braintrust already auto-instruments.

Weekly downloads

136,095 (as of 2026-10-05; https://pypistats.org/api/packages/mlx-lm/recent)

Braintrust docs status

not_found — searched site:braintrust.dev docs mlx-lm OR "MLX LM"; no dedicated Braintrust integration page or mention of MLX/mlx-lm exists in the Braintrust docs (https://www.braintrust.dev/docs).

Upstream sources

Local repo files inspected

  • README.md (root) — "Integrations included in braintrust" table, no MLX entry
  • py/pyproject.toml — grepped for mlx, no matches in [tool.braintrust.matrix] or dependency groups
  • py/noxfile.py — grepped for mlx, no matching session
  • py/src/braintrust/integrations/ — no mlx_lm subdirectory
  • repo-wide grep for mlx — no source-code references found outside this audit

Filed by an automated library-gap audit. Please verify before prioritizing.

主要语言
Python
星标
21
派生
23
平均合并
19 小时 23 分钟
30 天内合并 PR
97

环境准备

  • 提供 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

  1. 先读完整个 Issue,再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库,在一个分支上完成修改。
  4. 提交 Pull Request,并在描述里引用这个 Issue 编号。

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