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Cataloged from JetBrains/Mellum2-12B-A2.5B-Thinking-SFT
All models by JetBrains →[!Note] Research artifact: the SFT-only intermediate checkpoint of the Thinking training pipeline. Use it to study or build on the reasoning SFT stage in isolation, run post-training experiments (preference tuning, RLVR, etc.), or compare against the final RL-tuned variant. For production-quality reasoning use Thinking instead.
Mellum2 Thinking (SFT) is the supervised-fine-tuning-only intermediate checkpoint of the Mellum2 Thinking training pipeline, released by JetBrains as a research artifact.
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.
This checkpoint was produced from Mellum2-12B-A2.5B-Base with three epochs of SFT on a corpus of reasoning-trace-bearing data. The model emits its reasoning inside <think>...</think> blocks. It is the starting point of the RLVR stage that produces the final Mellum2-12B-A2.5B-Thinking.
This repository contains one checkpoint from the Mellum2 family.
Mellum2 Thinking (SFT) has the following features:
Text-Only Input
Evaluation results are available in the model card. All values are self-reported by JetBrains.
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-SFT \ --max-model-len 131072 \ --reasoning-parser qwen3
# With tool callingvllm serve JetBrains/Mellum2-12B-A2.5B-Thinking-SFT \ --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-SFT", messages=messages, max_tokens=81920, temperature=0.6, top_p=0.95, extra_body={ "top_k": 20, },)print("Chat response:", chat_response)