This repository contains a GGUF Q4_K_M quantization of JetBrains/Mellum2-12B-A2.5B-Instruct, ready to run with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.

This quantization (Q4_K_M): 4-bit k-quant (medium). Strong quality/size trade-off (KLD ~0.106, 87% top-token agreement) — a good default.

File	Size
`Mellum2-12B-A2.5B-Instruct-Q4_K_M.gguf`	8.1 GB

Mellum 2 Instruct is a Mixture-of-Experts assistant model (64 experts, 8 activated per token, 131,072-token context) that answers directly, without an externalized chain of thought. For the full model description, evaluation results, and architecture details, see the original model card: JetBrains/Mellum2-12B-A2.5B-Instruct.

Available quantizations

Quantization	Description	Size	KLD vs BF16 ↓	Top-token match ↑
`BF16`	16-bit, no quantization (reference)	24.3 GB	—	—
`Q8_0`	8-bit, effectively lossless	12.9 GB	0.016	95.2%
`Q6_K`	6-bit k-quant, very high quality	10.9 GB	0.038	92.9%
`Q4_K_M` (this repo)	4-bit k-quant, balanced (recommended)	8.1 GB	0.106	87.2%
`MXFP4_MOE`	MXFP4 4-bit on MoE experts, smallest	7.0 GB	0.166	84.2%

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. (Perplexity is omitted here — it is unreliable for instruction-tuned models on Wikitext-2, which is out of distribution.)

Download

hf download JetBrains/Mellum2-12B-A2.5B-Instruct-GGUF-Q4_K_M Mellum2-12B-A2.5B-Instruct-Q4_K_M.gguf --local-dir .

Run with llama.cpp

# Pull and serve in one step (downloads the GGUF automatically)
llama-server -hf JetBrains/Mellum2-12B-A2.5B-Instruct-GGUF-Q4_K_M \
  --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-Instruct-Q4_K_M.gguf \
  --ctx-size 131072 \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  -p "Write a Python function to reverse a string."

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-Instruct-GGUF-Q4_K_M",
    messages=[
        {"role": "user", "content": "Write a Python function to reverse a string."},
    ],
    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

ollama run hf.co/JetBrains/Mellum2-12B-A2.5B-Instruct-GGUF-Q4_K_M

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-Instruct.