Instructions to use JetBrains/Mellum-4b-sft-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetBrains/Mellum-4b-sft-python with Transformers:

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

pipe = pipeline("text-generation", model="JetBrains/Mellum-4b-sft-python")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains/Mellum-4b-sft-python")
model = AutoModelForCausalLM.from_pretrained("JetBrains/Mellum-4b-sft-python")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JetBrains/Mellum-4b-sft-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetBrains/Mellum-4b-sft-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum-4b-sft-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JetBrains/Mellum-4b-sft-python

SGLang

How to use JetBrains/Mellum-4b-sft-python 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/Mellum-4b-sft-python" \
    --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/Mellum-4b-sft-python",
		"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/Mellum-4b-sft-python" \
        --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/Mellum-4b-sft-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JetBrains/Mellum-4b-sft-python with Docker Model Runner:
```
docker model run hf.co/JetBrains/Mellum-4b-sft-python
```

topshik commited on Apr 30, 2025

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@@ -15,7 +15,7 @@ base_model:
 # Model Description
 Mellum-4b-sft-python is a fine-tuned version of JetBrains' first open-source large language model (LLM) optimized for code-related tasks.
-Trained on over 4 trillion tokens with a context window of 8192 tokens across multiple programming languages, Mellum-4b-sft-python is tailored specifically for code completion in Python.
 The model follows a LLaMA-style architecture with 4 billion parameters, making it efficient for both cloud inference (e.g., via vLLM) and local deployment (e.g., using llama.cpp or Ollama).
 Mellum was trained using Automatic Mixed Precision (AMP) with bf16 precision.

 # Model Description
 Mellum-4b-sft-python is a fine-tuned version of JetBrains' first open-source large language model (LLM) optimized for code-related tasks.
+Pre-trained on over 4 trillion tokens with a context window of 8192 tokens across multiple programming languages, and then fine-tuned, Mellum-4b-sft-python is tailored specifically for code completion in Python.
 The model follows a LLaMA-style architecture with 4 billion parameters, making it efficient for both cloud inference (e.g., via vLLM) and local deployment (e.g., using llama.cpp or Ollama).
 Mellum was trained using Automatic Mixed Precision (AMP) with bf16 precision.