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

Libraries

How to use JetBrains/Mellum2-12B-A2.5B-Instruct-SFT with Transformers:

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

pipe = pipeline("text-generation", model="JetBrains/Mellum2-12B-A2.5B-Instruct-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains/Mellum2-12B-A2.5B-Instruct-SFT")
model = AutoModelForCausalLM.from_pretrained("JetBrains/Mellum2-12B-A2.5B-Instruct-SFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JetBrains/Mellum2-12B-A2.5B-Instruct-SFT 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-Instruct-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum2-12B-A2.5B-Instruct-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JetBrains/Mellum2-12B-A2.5B-Instruct-SFT

SGLang

How to use JetBrains/Mellum2-12B-A2.5B-Instruct-SFT 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-Instruct-SFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum2-12B-A2.5B-Instruct-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Instruct-SFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum2-12B-A2.5B-Instruct-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JetBrains/Mellum2-12B-A2.5B-Instruct-SFT with Docker Model Runner:
```
docker model run hf.co/JetBrains/Mellum2-12B-A2.5B-Instruct-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mellum2 Instruct (SFT)

Research artifact: the SFT-only intermediate checkpoint of the Instruct training pipeline. Use it to study or build on the SFT stage in isolation, run post-training experiments (preference tuning, RLVR, etc.), or compare against the final RL-tuned variant. For production-quality instruction following use Instruct instead.

Mellum2 Instruct (SFT) Highlights

Mellum2 Instruct (SFT) is the supervised-fine-tuning-only intermediate checkpoint of the Mellum2 Instruct 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 mixed corpus of coding, agentic, tool use, instruction following, general chat, identity, and safety data. It is the starting point of the RLVR stage that produces the final Mellum2-12B-A2.5B-Instruct.

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 Instruct (SFT) 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

# Without tool calling
vllm serve JetBrains/Mellum2-12B-A2.5B-Instruct-SFT --max-model-len 131072

# With tool calling
vllm serve JetBrains/Mellum2-12B-A2.5B-Instruct-SFT \
  --max-model-len 131072 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Quickstart

Text-Only Input

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

messages = [
    {"role": "user", "content": "Write a Python function to reverse a string."},
]

chat_response = client.chat.completions.create(
    model="JetBrains/Mellum2-12B-A2.5B-Instruct-SFT",
    messages=messages,
    max_tokens=81920,
    temperature=0.6,
    top_p=0.95,
    extra_body={
        "top_k": 20,
    },
)
print("Chat response:", chat_response)