[whatmodel.sh](../../README.md) / Mellum2 12B-A2.5B Instruct
# Mellum2 12B-A2.5B Instruct
JetBrains · released 2026-05 · 12.15B params (2.5B active) · apache-2.0
**score 50.5** ±1.6 · #362 of 732 · from 8 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Mellum2 12B-A2.5B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #188 of 313* 10% 2
coding ▁▁▁▃▅●█▇▅▄▃▁ #332 of 469* 25% 2
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #248 of 402* 4.0% 1
agent tasks · #188 of 313 · 10% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) (… 44.2% ▁▁▁▂▆▆▅●█▂▄▃ beats 60/114 medium · avg 36% 6.8% at its level
BFCL v3 (maker) 66.3% ▁▁▁▁▁▃▅█▆●▄▁ beats 129/171 medium · avg 55% 3.6% blunt · at its level
coding · #332 of 469 · 25% of its score
Beats [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md), [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Llama 3.2 11B Vision Instruct](llama-3-2-11b-vision.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v6 (window unspecif… 37.2% ▂▃●▄▅██▆▄▇▄▆ beats 6/67 hard · avg 33% 20% at its level
MultiPL-E (maker) 67.1% ▃··▄·▅▅●▄█▃▄ beats 17/31 medium · avg 54% 4.8% at its level
chat & writing · #248 of 402 · 4.0% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 75.8% ▁▂▄▂▂●▆██▅▂▁ beats 45/145 easy · avg 75% 4.0% typical
other tests (3)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 40.9% ▁▁▁▁▂●▅▅███▄ beats 53/322 hard · avg 46% 36% sharp · at its level
MMLU-Redux (maker) 78.1% ▁▁▃▁●▃▄▅▇▅█▄ beats 8/63 easy · avg 78% 19% at its level
GSM-Plus (maker) 80.5% ▁··▁▁▁·●▁▄█▁ beats 5/16 easy · avg 82% 3.9% blunt
source results share of the score
JetBrains/Mellum2-12B-A2.5B-Instruct mo… 8 99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 50.5
Licences: JetBrains/Mellum2-12B-A2.5B-Instruct model card ([Apache-2.0](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Instruct/blob/273d9c1c6882ac8e1562e5b7e043857d35a787b6/README.md))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Mellum2 12B-A2.5B Instruct (Q4_K_M, needs ~8.2 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Mellum2-12B-A2.5B-Instruct-GGUF/tree/978d3b633817824470ef48a53141e3dccf1863f0))
```sh
llama cli -hf bartowski/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~12.9 GB) `llama cli -hf bartowski/Mellum2-12B-A2.5B-Instruct-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Mellum2 12B-A2.5B Instruct (Q4_K_M, needs ~8.2 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Mellum2-12B-A2.5B-Instruct-GGUF/tree/978d3b633817824470ef48a53141e3dccf1863f0))
```sh
lms get https://huggingface.co/bartowski/Mellum2-12B-A2.5B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~12.9 GB) `lms get https://huggingface.co/bartowski/Mellum2-12B-A2.5B-Instruct-GGUF@Q8_0 -y` · 4bit (~6.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mellum2-12B-A2.5B-Instruct-4bit -y` · 8bit (~12.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mellum2-12B-A2.5B-Instruct-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Mellum2 12B-A2.5B Instruct (Q4_K_M, needs ~8.2 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Mellum2-12B-A2.5B-Instruct-GGUF/tree/978d3b633817824470ef48a53141e3dccf1863f0))
```sh
ollama run hf.co/bartowski/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~12.9 GB) `ollama run hf.co/bartowski/Mellum2-12B-A2.5B-Instruct-GGUF:Q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Mellum2 12B-A2.5B Instruct (4bit, needs ~6.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Mellum2-12B-A2.5B-Instruct-4bit/tree/714f4fdf47a46832e41cc80f446eb56541666ee9))
```sh
mlx_lm.chat --model mlx-community/Mellum2-12B-A2.5B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Mellum2-12B-A2.5B-Instruct-4bit
```
Other versions: 8bit (~12.9 GB, Mac only) `mlx_lm.chat --model mlx-community/Mellum2-12B-A2.5B-Instruct-8bit`
### Other ways to run it
1. Run it as published (BF16, needs ~24.3 GB, NVIDIA only, from [huggingface.co/JetBrains](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Instruct/tree/273d9c1c6882ac8e1562e5b7e043857d35a787b6))
```sh
vllm serve JetBrains/Mellum2-12B-A2.5B-Instruct
```
Model repository and documentation: [huggingface.co/JetBrains](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Instruct)
[interactive version](https://whatmodel.sh/model/mellum2-12b-a2-5b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)