[!Note] Use this model when you want direct, low-latency answers without an explicit chain of thought — interactive chat, code assistance, tool use, and instruction following. If you need explicit reasoning before the answer (complex debugging, planning, multi-step agentic flows), use Thinking instead.

Mellum2 Instruct Highlights

Mellum2 Instruct is a post-trained assistant model trained by JetBrains.

The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens.

It is produced from Mellum2-12B-A2.5B-Base by supervised fine-tuning followed by reinforcement learning with verifiable rewards (RLVR) on math, executable coding, tool use, instruction following, reasoning, and knowledge tasks. Mellum2 Instruct answers directly, without an externalized chain of thought.

Mellum2 Model Family

This repository contains one checkpoint from the Mellum2 family.

Checkpoint	Description
Base Pretrain	Base checkpoint before long-context extension
Base	Final base model
Instruct SFT	Supervised instruction-tuned checkpoint
Thinking SFT	Supervised thinking checkpoint
Instruct	RL-tuned instruction model
Thinking	RL-tuned thinking model

Model Overview

Mellum2 Instruct has the following features:

Number of Layers: 28
Hidden Size: 2304
Intermediate Size: 7168
MoE Intermediate Size: 896
Number of Experts: 64
Number of Activated Experts: 8
Number of Attention Heads (GQA): 32 for Q and 4 for KV
Context Length: 131,072
Sliding Window: 1,024
Vocabulary Size: 98,304
Precision: bfloat16

Serving with vLLM

# Without tool calling
vllm serve JetBrains/Mellum2-12B-A2.5B-Instruct --max-model-len 131072

# With tool calling
vllm serve JetBrains/Mellum2-12B-A2.5B-Instruct \
  --max-model-len 131072 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Quickstart

Text-Only Input

python

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

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

chat_response = client.chat.completions.create(
    model="JetBrains/Mellum2-12B-A2.5B-Instruct",
    messages=messages,
    max_tokens=81920,
    temperature=0.6,
    top_p=0.95,
    extra_body={
        "top_k": 20,
    },
)
print("Chat response:", chat_response)

Evaluation

Post-training evaluation for the instruct (no-thinking) variants. All values are percentages; higher is better except HarmBench, where lower is better. All values self-reported by JetBrains.

Benchmark	Mellum2 Instruct SFT	Mellum2 Instruct	Qwen3.5 (4B)	Qwen3.5 (9B)	OLMo-3 (7B)	Ministral 3 (14B)	Seed-Coder (8B)
Coding
LiveCodeBench v6	30.9	37.2	51.0	63.7	28.2	42.4	28.1
EvalPlus	76.2	78.4	69.4	71.8	67.3	74.1	73.8
MultiPL-E	64.6	67.1	51.0	67.1	36.1	71.5	77.0
Tool Use
BFCL v4	31.8	44.2	52.0	60.6	19.8	38.8	—
BFCL v3	43.1	66.3	64.1	70.5	41.9	52.7	—
Math
AIME	29.9	41.7	38.3	58.3	40.0	33.3	0.0
GSM-Plus	73.0	80.5	85.2	87.9	85.8	86.6	50.4
Knowledge
MMLU-Redux	77.4	78.1	87.5	91.1	71.8	85.9	38.1
GPQA Diamond	38.9	40.9	76.8	79.8	40.9	58.6	20.2
Conversational
IFEval	69.3	75.8	82.1	83.9	83.2	67.3	56.2
JetBrains pairwise	66.7	68.1	60.6	77.8	44.4	72.4	43.0
MixEval	62.9	62.2	65.9	71.1	59.4	71.2	37.2
BS-Bench	24.0	18.0	56.9	61.0	22.0	9.0	5.0
Safety
HarmBench (↓)	8.4	23.1	20.3	20.9	14.7	56.5	40.0
XSTest	78.3	81.2	93.2	91.2	91.2	96.8	86.3

Notes:

EvalPlus is the mean of HumanEval+ and MBPP+.
AIME is the mean of AIME 2025 and AIME 2026 (30 questions each).
BFCL v4 is the macro-average of five subtasks: v1, v2, v3, web search, memory.
JetBrains pairwise is win rate against Qwen2.5-7B-Instruct on an internal benchmark.
— indicates the model lacks native tool calling.

For more details, see the Mellum2 Technical Report.

License

Released under the Apache 2.0 license.

🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

TurboLLM (GPT-4.1-mini)
HugLLM (Hugginface Open-source models)
TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

Function calling against live network services
How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊

Mungert/Mellum2-12B-A2.5B-Instruct-GGUF

Mellum2-12B-A2.5B-Instruct GGUF Models

Model Generation Details

Mellum2 Instruct