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

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#285 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
15/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Estancado
Stack tecnológico
huggingface, python
Área
machine-learning

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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.
Lenguaje dominante
Python
Estrellas
337
Forks
52
Sin PR fusionados en 30 d

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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