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JetBrains/Mellum2-12B-A2.5B-Thinking-GGUF-MXFP4_MOE

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face
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Model Card

Mellum2 Thinking — GGUF (MXFP4_MOE)

This repository contains a GGUF MXFP4_MOE quantization of `JetBrains/Mellum2-12B-A2.5B-Thinking`, ready to run with `llama.cpp`, Ollama, LM Studio, and other GGUF-compatible runtimes.

This quantization (MXFP4_MOE): MXFP4 microscaling 4-bit applied to the MoE expert tensors. Smallest footprint, with a modest quality cost (KLD ~0.088, 87% top-token agreement).

FileSize
Mellum2-12B-A2.5B-Thinking-MXFP4_MOE.gguf7.0 GB

Mellum 2 Thinking is a Mixture-of-Experts reasoning model (64 experts, 8 activated per token, 131,072-token context) that emits its chain of thought inside <think>...</think> blocks before the final answer. For the full model description, evaluation results, and architecture details, see the original model card: [JetBrains/Mellum2-12B-A2.5B-Thinking](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Thinking).

Available quantizations

QuantizationDescriptionSizeKLD vs BF16 ↓Top-token match ↑
`BF16`16-bit, no quantization (reference)24.3 GB——
`Q8_0`8-bit, effectively lossless12.9 GB0.00497.4%
`Q6_K`6-bit k-quant, very high quality10.9 GB0.01495.1%
`Q4_K_M`4-bit k-quant, balanced (recommended)8.1 GB0.05289.8%
`MXFP4_MOE` (this repo)MXFP4 4-bit on MoE experts, smallest7.0 GB0.08887.3%

KL divergence and top-token agreement are measured against the BF16 logits on Wikitext-2 (n_ctx=512); lower KLD / higher agreement means closer to the unquantized model.

Download

sh
hf download JetBrains/Mellum2-12B-A2.5B-Thinking-GGUF-MXFP4_MOE Mellum2-12B-A2.5B-Thinking-MXFP4_MOE.gguf --local-dir .

Run with llama.cpp

sh
# Pull and serve in one step (downloads the GGUF automatically)
llama-server -hf JetBrains/Mellum2-12B-A2.5B-Thinking-GGUF-MXFP4_MOE \
  --ctx-size 131072 \
  --temp 0.6 --top-p 0.95 --top-k 20

# Or run a one-off prompt with a local file
llama-cli -m Mellum2-12B-A2.5B-Thinking-MXFP4_MOE.gguf \
  --ctx-size 131072 \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  -p "Is 1024 a power of 2? Explain your reasoning."

The server exposes an OpenAI-compatible API on http://localhost:8080/v1:

python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="llama.cpp")

chat_response = client.chat.completions.create(
    model="JetBrains/Mellum2-12B-A2.5B-Thinking-GGUF-MXFP4_MOE",
    messages=[
        {"role": "user", "content": "Is 1024 a power of 2? Explain your reasoning."},
    ],
    max_tokens=81920,
    temperature=0.6,
    top_p=0.95,
    extra_body={"top_k": 20},
)
print(chat_response.choices[0].message.content)

Run with Ollama

sh
ollama run hf.co/JetBrains/Mellum2-12B-A2.5B-Thinking-GGUF-MXFP4_MOE

License

Released under the Apache 2.0 license.


For the full model card, evaluation results, and architecture details, refer to the original model: [JetBrains/Mellum2-12B-A2.5B-Thinking](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Thinking).