CoolFace
Modelpublic

JetBrains/Mellum2-12B-A2.5B-Base

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face
26likes24kdownloads
Model Card

<img alt="Mellum" src="mellum-logo-dark.svg" width="320">

Mellum2 Base

[!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 Highlights

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.

Mellum2 Model Family

This repository contains one checkpoint from the Mellum2 family.

CheckpointDescription
Base PretrainBase checkpoint before long-context extension
BaseFinal base model
Instruct SFTSupervised instruction-tuned checkpoint
Thinking SFTSupervised thinking checkpoint
InstructRL-tuned instruction model
ThinkingRL-tuned thinking model

Model Overview

Mellum2 Base 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

sh
vllm serve JetBrains/Mellum2-12B-A2.5B-Base --max-model-len 131072

Quickstart

Text-Only Input (base model — use the completions endpoint, not chat)

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

Evaluation

Mellum2 Base pretraining results compared with similarly-sized open base models. All values are self-reported by JetBrains.

BenchmarkMellum2 (12B-A2.5B)OLMo-3 (7B)Qwen2.5 (7B)Qwen3 (4B)Qwen3.5 (4B)
Code Generation
HumanEval41.545.155.557.350.0
HumanEval+37.239.647.051.243.9
MBPP62.450.663.667.052.2
MBPP+61.452.964.064.555.0
MultiPL-E (7 langs)21.010.019.226.012.1
CRUXEval-I45.438.844.044.649.1
CRUXEval-O43.936.642.943.543.2
Knowledge & Reasoning
MMLU70.962.171.871.174.2
MMLU-Pro59.334.548.651.552.4
BBH74.963.669.071.380.2
ARC-Challenge53.553.651.351.254.9
HellaSwag73.774.278.973.775.3
WinoGrande65.569.573.371.270.8
TruthfulQA MC244.547.056.453.552.1
Math & Science
GSM8K81.773.581.982.080.1
MATH10.018.724.627.725.3
GPQA Diamond31.328.832.836.941.4
GPQA Main35.027.934.236.840.2

For more details, see the Mellum2 Technical Report.

License

Released under the Apache 2.0 license.