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New architecture request: Recursive Flint (RecursiveSI/flint — 39.8B MoE coding model, ~2.5B active)

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#786 0 comentarios 2 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
machine-learning

Línea de trabajo

Start by reading the linked RecursiveSI/flint model card and compare its architecture details with the supported model families and the GGUF architecture list in the error. The issue requests BF16 safetensors support for a new MoE architecture; the GGUF mapping is a follow-up once the family lands. Done means FreeToken can load and serve the BF16 checkpoint, with tests covering the Flint architecture.

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

Descripción

Before you start
  • I have checked the supported models and this checkpoint is not listed there. A listed checkpoint that fails is a bug; use a Bug report instead.
  • I have read the Roadmap and this model is not already planned there.
  • I have read the FAQ.
  • I have searched existing issues and found no duplicate.
  • This is not a GGUF checkpoint. GGUF support is on the Roadmap; please do not open an issue for it.

(The ask below is the architecture, via the BF16 safetensors checkpoint. The GGUF file is only mentioned as context.)

Hugging Face link

https://huggingface.co/RecursiveSI/flint (BF16 checkpoint, model config, tokenizer, chat template, SGLang serving kit)

Is the model architecture already supported

No, this is a new model architecture

Is the quantization already supported

Not quantized (BF16 checkpoint; an NVFP4 quantization of the same architecture also exists, see "Anything else")

Error or log

Tried the released NVFP4 GGUF of the same architecture on FreeToken Desktop v0.2.0+g425dc7bf1 (Windows, CUDA 13 build). Startup refuses the GGUF metadata before any weights load, which confirms the architecture itself has no family in 0.2.0:

ValueError: GGUF architecture 'flint' is not supported (known: ['gemma4', 'glm4moe', 'glm5-next', 'glm5next', 'llama', 'minimax-m2', 'minimax-m3', 'muse-glimmer', 'qwen2', 'qwen3', 'qwen35', 'qwen35moe', 'qwen3moe', 'qwen4exp'])

I have not tried the BF16 checkpoint (single 10 GB GPU here), so this report carries no safetensors load log.

Anything else

Flint is a coding-agent MoE from Recursive (release post): 74.2% SWE-bench Verified, 89.7% SWE-bench Pro v2. Its ~2.5 B active parameters per token look like a good fit for FreeToken's expert-offload strategies on small-VRAM hosts.

Architecture facts from the model card:

  • 39.8 B total parameters, ~2.5 B active per token (including the output head)
  • 49 layers: 2 dense + 47 MoE; 256 routed experts, 6 selected per token, plus 1 always-active shared expert
  • Attention: 32 query heads, 4 KV heads, head dim 128; per-head QK RMSNorm and learned per-head attention sinks
  • 35 sliding-window layers (window 513 tokens, RoPE) + 14 full-attention layers with no positional encoding
  • Vocabulary 150,144; context 262,144

Other engines that already run it: llama.cpp (a release with Flint support serves the NVFP4 GGUF, 22.9 GB, declared architecture: flint), and the SGLang serving kit shipped in the HF repo. Once the family lands, the GGUF reader would only need to map arch=flint to it.

Lenguaje dominante
Python
Estrellas
14.2k
Forks
1.4k
Merge medio
21 h 39 min
PR fusionados (30 d)
100

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

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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