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

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25/100
Issue 类型
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技术栈
python
领域
machine-learning

调研方向

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.

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描述

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.

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环境准备

这个项目没有提供开发容器、Dockerfile 或贡献指南，环境需要你自己搭建：先看它的 README，通用步骤见我们的新手贡献指南。

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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