Mellum2-Thinking

[!Note] Use this model when you want explicit chain-of-thought before the final answer — complex debugging, multi-step planning, agentic workflows, and math- or reasoning-heavy tasks. For direct, low-latency answers without reasoning traces, use Instruct instead.

Mellum2 Thinking Highlights

Mellum 2 Thinking is a post-trained reasoning-augmented 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 (loss computed only on the final assistant turn) followed by reinforcement learning with verifiable rewards (RLVR) on a harder data mix that includes a long-form math subset. The model emits its reasoning inside ... blocks before the final answer.

Mellum2 Model Family

This repository contains one checkpoint from the Mellum 2 family.

Model Overview

Mellum2 Thinking 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
• License: Apache 2.0

Serving with vLLM

# Without tool calling
vllm serve JetBrains/Mellum2-12B-A2.5B-Thinking \
  --max-model-len 131072 \
  --reasoning-parser qwen3

# With tool calling
vllm serve JetBrains/Mellum2-12B-A2.5B-Thinking \
  --max-model-len 131072 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Quickstart

Text-Only Input

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

messages = [
    {"role": "user", "content": "Is 1024 a power of 2? Explain your reasoning."},
]

chat_response = client.chat.completions.create(
    model="JetBrains/Mellum2-12B-A2.5B-Thinking",
    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 thinking/reasoning variants. All values are percentages; higher is better except HarmBench, where lower is better. All values self-reported by JetBrains.

Notes:

• AIME