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Cataloged from JetBrains/Mellum2-12B-A2.5B-Base
All models by JetBrains →[!Note] Use this checkpoint as the starting point for your own fine-tuning, alignment, or domain adaptation on top of the long-context base. For instruction-following or reasoning tasks out of the box, use Instruct or Thinking instead.
Mellum2 Base is a long-context pretrained causal language 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.
This is the long-context base, produced from Mellum2-12B-A2.5B-Base-Pretrain by a layer-selective YaRN extension stage that re-maps RoPE frequencies on the global-attention layers only. It is the shared starting point for the released Instruct and Thinking variants.
This repository contains one checkpoint from the Mellum2 family.
Mellum2 Base has the following features:
Text-Only Input (base model — use the completions endpoint, not chat)
Mellum2 Base pretraining results compared with similarly-sized open base models. All values are self-reported by JetBrains.
For more details, see the Mellum2 Technical Report.
Released under the Apache 2.0 license.
vllm serve JetBrains/Mellum2-12B-A2.5B-Base --max-model-len 131072from openai import OpenAI# Configured by environment variablesclient = OpenAI()
completion = client.completions.create( model="JetBrains/Mellum2-12B-A2.5B-Base", prompt="def fibonacci(n):\n ", max_tokens=81920, temperature=0.6, top_p=0.95, extra_body={ "top_k": 20, },)print("Completion:", completion)