Instructions to use JetBrains/Mellum2-12B-A2.5B-Base-Pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetBrains/Mellum2-12B-A2.5B-Base-Pretrain with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="JetBrains/Mellum2-12B-A2.5B-Base-Pretrain")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains/Mellum2-12B-A2.5B-Base-Pretrain")
model = AutoModelForCausalLM.from_pretrained("JetBrains/Mellum2-12B-A2.5B-Base-Pretrain", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JetBrains/Mellum2-12B-A2.5B-Base-Pretrain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetBrains/Mellum2-12B-A2.5B-Base-Pretrain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum2-12B-A2.5B-Base-Pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JetBrains/Mellum2-12B-A2.5B-Base-Pretrain

SGLang

How to use JetBrains/Mellum2-12B-A2.5B-Base-Pretrain with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "JetBrains/Mellum2-12B-A2.5B-Base-Pretrain" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum2-12B-A2.5B-Base-Pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "JetBrains/Mellum2-12B-A2.5B-Base-Pretrain" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum2-12B-A2.5B-Base-Pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JetBrains/Mellum2-12B-A2.5B-Base-Pretrain with Docker Model Runner:
```
docker model run hf.co/JetBrains/Mellum2-12B-A2.5B-Base-Pretrain
```

Mellum2 Base Pretrain

Use this checkpoint as a starting point for research on long-context extension or for 8K-context continued pretraining and fine-tuning. For downstream applications use Base, Instruct, or Thinking instead.

Mellum2 Base Highlights

Mellum2 Base is a 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 8,192 tokens.

This is a checkpoint before long-context extension.

Mellum2 Model Family

This repository contains one checkpoint from the Mellum2 family.

Checkpoint	Description
Base Pretrain	Base checkpoint before long-context extension
Base	Final base model
Instruct SFT	Supervised instruction-tuned checkpoint
Thinking SFT	Supervised thinking checkpoint
Instruct	RL-tuned instruction model
Thinking	RL-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: 8,192
Sliding Window: 1,024
Vocabulary Size: 98,304
Precision: bfloat16
License: Apache 2.0

Serving with vLLM

This checkpoint has an 8K context length (long-context extension is applied in Base).

vllm serve JetBrains/Mellum2-12B-A2.5B-Base-Pretrain --max-model-len 8192

Quickstart

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

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

completion = client.completions.create(
    model="JetBrains/Mellum2-12B-A2.5B-Base-Pretrain",
    prompt="def fibonacci(n):\n    ",
    max_tokens=4096,
    temperature=0.6,
    top_p=0.95,
    extra_body={
        "top_k": 20,
    },
)
print("Completion:", completion)