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Nemotron-H support

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  • ¿Algo no está bien?
#198 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
5/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Estancado
Stack tecnológico
python
Área
machine-learning

Línea de trabajo

This is blocked on two things: NVIDIA has not released Nemotron-H architecture details or weights, and Mamba-2 support (#68) is a prerequisite. Before starting, check whether the architecture is public and whether #68 has landed. The issue names no files or tests, so there is no clear entry point. Done would mean an 8B Nemotron-H model loads and trains in BF16 with forward and backward passes tested.

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

Descripción

enhancement need update

🎯 Goal (What & Why)

Add support for training Nemotron-H models.

Nemotron-H is a family of hybrid SSM-Transformer models (8B, 47B, 56B) trained by NVIDIA in FP8 on 20T tokens. They combine strong accuracy with up to 3x faster inference, making them attractive candidates for instruction tuning, continual pretraining, and up-cycling (e.g., via added layers). NVIDIA plans to release weights and reference implementations (HuggingFace, NeMo, Megatron-LM), but nothing is public yet.

We want to be ready to support Nemotron-H in Fast-LLM once details are available. These models require both Transformer and Mamba-2 layers. The forward and backward pass must be implemented. FP8 support is likely not required for initial work (we'll use BF16), but is tracked separately in #63. Mamba-2 support is tracked in #68 and is a prerequisite for this ticket.

Partially enabled by #242.

🚀 Execution Plan

Step 1: What is the smallest working version?
  • Wait for NVIDIA to release architectural details.
  • Implement Nemotron-H block structure (interleaved Mamba-2, MLP, and Transformer layers).
  • Use BF16 as default precision for now.
  • Validate by running short training in Fast-LLM with Nemotron-H-8B or 47B weights (if available).
Step 2: What additional optimizations are possible (but optional)?
  • Add support for FP8 training (#63) once the NVIDIA recipe is available and stable.
  • Extend support for VLM variants if we decide to do multimodal training.
  • Optimize long-context support (128k tokens) once basic functionality is in place.

📌 Acceptance Criteria

  • Nemotron-H models (at least 8B) can be loaded and trained in Fast-LLM.
  • Both forward and backward passes are implemented and tested.
  • Training is functional in BF16.
  • Code is documented clearly and includes a usage example.
  • Performance/impact summary is provided in the PR.
  • FP8 support is not part of this ticket but should not block anything here.

🛠️ 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

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  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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