Evaluate Command: Config Design and Functionality for Non-Training Model Evaluation

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评估

难度
5/5
预计耗时
一周以上
新手友好度
15/100
Issue 类型
功能
描述清晰度
需要澄清
活跃度
停滞
技术栈
huggingface, python
领域
machine-learning

调研方向

Start from the evaluate command that PR #264 added, and from the training config it reads to load the latest checkpoint. The issue asks for a config design for evaluating arbitrary models, including Hugging Face Hub downloads, so the first step is agreeing on that config shape in the thread rather than writing code. Done means an agreed config format and a tested evaluate path for models outside training runs.

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描述

enhancement need update

🎯 Goal (What & Why)

With the merging of PR #264, we will have a separate evaluate command that uses the training config to evaluate a trained model by loading the latest checkpoint.

However, we also need the capability to evaluate arbitrary models — potentially with automatic downloading from the Hugging Face Hub for all supported models.

Here, let's discuss the features we need for such a command and what the configuration file should look like.

🚀 Execution Plan

(This section may start as an incomplete draft but must be defined before implementation begins.)

Step 1: What is the smallest working version?

(Describe the simplest way to implement this feature with minimal effort.)

Step 2: What additional optimizations are possible (but optional)?

(List potential refinements that can be added in later PRs if needed.)

📌 Acceptance Criteria (Must-Haves for Completion)

  • The feature must be functional and tested.
  • The implementation must be documented in practical terms.
  • The PR must include a performance/impact summary.
  • No refactors unless directly necessary for feature completion.

🛠️ Project Management

  • Assign the project to the Fast-LLM project.
  • Set the Estimate field (in days) in the GitHub project.
  • Use the Size field to categorize the PR size (Small/Medium/Large).
  • Assign an owner when opening the issue.
主要语言
Python
星标
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派生
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30 天内没有已合并 PR

环境准备

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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