No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from JetBrains/Mellum2-12B-A2.5B-Thinking
All models by JetBrains →[!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.
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 <think>...</think> blocks before the final answer.
This repository contains one checkpoint from the Mellum 2 family.
Mellum2 Thinking has the following features:
Text-Only Input
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:
Qwen2.5-7B-Instruct on an internal benchmark.— indicates the model lacks native tool calling (OLMo-3-7B-Thinking).For more details, see the Mellum2 Technical Report.
Released under the Apache 2.0 license.
# Without tool callingvllm serve JetBrains/Mellum2-12B-A2.5B-Thinking \ --max-model-len 131072 \ --reasoning-parser qwen3
# With tool callingvllm serve JetBrains/Mellum2-12B-A2.5B-Thinking \ --max-model-len 131072 \ --reasoning-parser qwen3 \ --enable-auto-tool-choice \ --tool-call-parser hermesfrom openai import OpenAI# Configured by environment variablesclient = 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)