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

Libraries

How to use Etherll/Mellum-4b-sft-rust-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Etherll/Mellum-4b-sft-rust-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Etherll/Mellum-4b-sft-rust-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Etherll/Mellum-4b-sft-rust-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Etherll/Mellum-4b-sft-rust-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M

SGLang

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

Ollama
How to use Etherll/Mellum-4b-sft-rust-GGUF with Ollama:
```
ollama run hf.co/Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M
```

Unsloth Studio

How to use Etherll/Mellum-4b-sft-rust-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Etherll/Mellum-4b-sft-rust-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Etherll/Mellum-4b-sft-rust-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Etherll/Mellum-4b-sft-rust-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Etherll/Mellum-4b-sft-rust-GGUF with Docker Model Runner:
```
docker model run hf.co/Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M
```

Lemonade

How to use Etherll/Mellum-4b-sft-rust-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Etherll/Mellum-4b-sft-rust-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mellum-4b-sft-rust-GGUF-Q4_K_M

List all available models

lemonade list

Etherll/Mellum-4b-sft-rust

Etherll/Mellum-4b-sft-rust is a large language model (LLM) fine-tuned specifically for Rust code Fill-in-the-Middle (FIM) tasks. It is built upon JetBrains/Mellum-4b-base model.

This model has been fine-tuned on the Etherll/CodeFIM-Rust-Mellum dataset, which comprises approximately 57,000 Rust-specific FIM examples, to enhance its proficiency in completing Rust code snippets accurately and contextually.

A GGUF version for CPU inference is also available: Etherll/Mellum-4b-sft-rust-GGUF.

Model Description

This model leverages the LLaMA-style architecture of Mellum-4b-base (4 billion parameters) and its extensive pre-training on over 4 trillion tokens. The fine-tuning process focused on adapting the model to the nuances of Rust syntax and common coding patterns for FIM tasks.

Key Features:

Specialized for Rust: Optimized for Fill-in-the-Middle tasks in Rust.
Based on Mellum-4b-base: Benefits from JetBrains' robust base model.
Efficient: Suitable for both cloud and local deployment.
IDE Integration Ready: Designed for use in developer tooling, and works particularly well with Continue.dev for an enhanced coding assistant experience.

Fine-tuning Data

Dataset: Etherll/CodeFIM-Rust-Mellum
Size: ~57,000 rows
Focus: Rust code Fill-in-the-Middle

FIM Format

This model is trained to recognize a specific format for Fill-in-the-Middle tasks. When providing input for FIM, please use the following structure:

<filename>{{{filename}}}
<fim_suffix>{{{suffix_code}}}<fim_prefix>{{{prefix_code}}}<fim_middle>

How to Use

With Continue.dev

For the best integrated development experience, it's highly recommended to use this model with Continue.dev.

Refer to the Continue.dev documentation for instructions on how to add custom LLMs.

GGUF Version

A GGUF version is available at Etherll/Mellum-4b-sft-rust-GGUF. This format is suitable for local inference on CPU (and GPU with appropriate llama.cpp/Ollama builds) using tools like:

Support & Community

If you need any help, have questions, or just want to chat, feel free to message me on Discord: etherl

Downloads last month: 142

GGUF

Model size

4B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Etherll/Mellum-4b-sft-rust-GGUF

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