Instructions to use FermionResearch/Neutrino-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FermionResearch/Neutrino-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="FermionResearch/Neutrino-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FermionResearch/Neutrino-0.6B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FermionResearch/Neutrino-0.6B 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 FermionResearch/Neutrino-0.6B
# Run inference directly in the terminal:
llama cli -hf FermionResearch/Neutrino-0.6B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FermionResearch/Neutrino-0.6B
# Run inference directly in the terminal:
llama cli -hf FermionResearch/Neutrino-0.6B

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf FermionResearch/Neutrino-0.6B
# Run inference directly in the terminal:
./build/bin/llama cli -hf FermionResearch/Neutrino-0.6B

Use Docker

docker model run hf.co/FermionResearch/Neutrino-0.6B

Llama new
LM Studio
Jan

vLLM

How to use FermionResearch/Neutrino-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FermionResearch/Neutrino-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FermionResearch/Neutrino-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FermionResearch/Neutrino-0.6B

SGLang

How to use FermionResearch/Neutrino-0.6B 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 "FermionResearch/Neutrino-0.6B" \
    --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": "FermionResearch/Neutrino-0.6B",
		"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 "FermionResearch/Neutrino-0.6B" \
        --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": "FermionResearch/Neutrino-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use FermionResearch/Neutrino-0.6B with Ollama:
```
ollama run hf.co/FermionResearch/Neutrino-0.6B
```
Unsloth Desktop

How to use FermionResearch/Neutrino-0.6B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FermionResearch/Neutrino-0.6B

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FermionResearch/Neutrino-0.6B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use FermionResearch/Neutrino-0.6B with Docker Model Runner:
```
docker model run hf.co/FermionResearch/Neutrino-0.6B
```

Lemonade

How to use FermionResearch/Neutrino-0.6B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FermionResearch/Neutrino-0.6B

Run and chat with the model

lemonade run user.Neutrino-0.6B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use FermionResearch/Neutrino-0.6B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FermionResearch/Neutrino-0.6B

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default FermionResearch/Neutrino-0.6B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use FermionResearch/Neutrino-0.6B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FermionResearch/Neutrino-0.6B

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "FermionResearch/Neutrino-0.6B" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Neutrino-0.6B / mlx /README.md

FermionResearch

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Neutrino-0.6B — MLX pack (Apple silicon)

Neutrino-0.6B is the speculative-decoding draft for Neutrino-8B. It is a base model: not an assistant, no chat, instruction or tool use. For a small conversational model use Neutrino-0.6B-Chat.

Run the Neutrino-0.6B container natively from Python on Apple silicon with MLX and compiled Metal kernels.

Requirements: a Mac with Apple silicon, macOS 14 or later, Python 3.10 to 3.14 (standard CPython build). requirements.txt installs mlx 0.32.3, the version the compiled kernels are built for.

Install & run

pip install -r requirements.txt        # mlx, numpy, transformers
python -m fermion_mlx --model ../neutrino-0.6b_v4.bin --mode gen \
    --tokenizer . --prompt "2, 3, 5, 7, 11,"

Python API:

import fermion_mlx
from transformers import AutoTokenizer

model = fermion_mlx.load("../neutrino-0.6b_v4.bin")  # memory-maps the container
tok = AutoTokenizer.from_pretrained(".")             # tokenizer files ship here
text, stats = fermion_mlx.generate(model, tok, "2, 3, 5, 7, 11,",
                                   max_tokens=64)
print(text, stats)                                   # {'tok_s': ..., ...}

This is a base model: prompt it as a completion engine.

What runs on the GPU

The model runs through compiled Metal kernels, which ship in fermion_mlx/ as Metal libraries with a small compiled extension that loads them; no Metal compiler is needed on your machine.

Speed

path	machine	512-token greedy decode
this MLX pack	Apple M5 MacBook, 16 GB	201 tok/s, peak MLX memory 0.53 GiB
`fermion-run` CPU binary (`../bin/`)	same M5, 9 threads	225-236 tok/s

Drafting for Neutrino-8B

The same package runs --mode spec, where this model drafts and the Neutrino-8B pack verifies; the output is token-identical to plain 8B greedy decoding. See the Neutrino-8B MLX pack README for the command.

Files

fermion_mlx/: the runtime package, including the compiled kernels.
tokenizer.json, tokenizer_config.json, vocab.json, merges.txt, generation_config.json: Qwen3-0.6B tokenizer.
requirements.txt: mlx==0.32.3, numpy, transformers.
MANIFEST.json: size and sha256 of every file in this folder.

Weights are not duplicated in this folder; the container is ../neutrino-0.6b_v4.bin (sha256 ba0e9258d866d5eb309352dd7c1b810c402eac289fb98e871c63b38a3bcd8168).

License

Apache-2.0 for the Python code and the weights (derivative of Qwen/Qwen3-0.6B; see the parent LICENSE). The compiled kernels (fermion_mlx/*.metallib, fermion_mlx/_native*.so) are free to use with these weights; no redistribution outside this repo; no reverse engineering.