Instructions to use Mincofficial/Sonoma-1.2B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Mincofficial/Sonoma-1.2B-Preview with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Mincofficial/Sonoma-1.2B-Preview") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Mincofficial/Sonoma-1.2B-Preview 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 Mincofficial/Sonoma-1.2B-Preview:F16 # Run inference directly in the terminal: llama cli -hf Mincofficial/Sonoma-1.2B-Preview:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Mincofficial/Sonoma-1.2B-Preview:F16 # Run inference directly in the terminal: llama cli -hf Mincofficial/Sonoma-1.2B-Preview:F16
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 Mincofficial/Sonoma-1.2B-Preview:F16 # Run inference directly in the terminal: ./llama-cli -hf Mincofficial/Sonoma-1.2B-Preview:F16
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 Mincofficial/Sonoma-1.2B-Preview:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Mincofficial/Sonoma-1.2B-Preview:F16
Use Docker
docker model run hf.co/Mincofficial/Sonoma-1.2B-Preview:F16
- LM Studio
- Jan
- vLLM
How to use Mincofficial/Sonoma-1.2B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mincofficial/Sonoma-1.2B-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mincofficial/Sonoma-1.2B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Mincofficial/Sonoma-1.2B-Preview:F16
- Ollama
How to use Mincofficial/Sonoma-1.2B-Preview with Ollama:
ollama run hf.co/Mincofficial/Sonoma-1.2B-Preview:F16
- Unsloth Desktop
- Pi
How to use Mincofficial/Sonoma-1.2B-Preview with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Mincofficial/Sonoma-1.2B-Preview"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Mincofficial/Sonoma-1.2B-Preview" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use Mincofficial/Sonoma-1.2B-Preview with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Mincofficial/Sonoma-1.2B-Preview"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Mincofficial/Sonoma-1.2B-Preview" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mincofficial/Sonoma-1.2B-Preview", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use Mincofficial/Sonoma-1.2B-Preview with Docker Model Runner:
docker model run hf.co/Mincofficial/Sonoma-1.2B-Preview:F16
- Lemonade
How to use Mincofficial/Sonoma-1.2B-Preview with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Mincofficial/Sonoma-1.2B-Preview:F16
Run and chat with the model
lemonade run user.Sonoma-1.2B-Preview-F16
List all available models
lemonade list
- Hermes Agent
How to use Mincofficial/Sonoma-1.2B-Preview with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Mincofficial/Sonoma-1.2B-Preview"
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 Mincofficial/Sonoma-1.2B-Preview
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Mincofficial/Sonoma-1.2B-Preview with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Mincofficial/Sonoma-1.2B-Preview"
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 "Mincofficial/Sonoma-1.2B-Preview" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Sonoma-1.2B Preview
Sonoma-1.2B Preview is an early compact reasoning and instruction model derived from
LiquidAI/LFM2.5-1.2B-Thinking.
It was post-trained on an Apple M5 Mac with assistant-only rank-32 LoRA across
all 16 model blocks. This repository includes fused MLX weights, the selected
LoRA adapter, an F16 GGUF, and a 697 MiB Q4_K_M GGUF.
The release improves on its untouched base in the local held-out evaluation used during training. That result is useful, but narrow. It does not establish that Sonoma beats larger models or frontier systems.
Results
| Model | Assistant-masked test loss | Perplexity | 8-task smoke test |
|---|---|---|---|
| LFM2.5-1.2B-Thinking | 0.938 | 2.556 | 3/8 |
| Sonoma-1.2B | 0.729 | 2.074 | 4/8 |
The held-out split has 77 examples. Sonoma reduced loss by 22.3% and perplexity by 18.9% relative to the base. The eight-task test is a deterministic regression check, not a general benchmark.
Read the system card for the training mixture, selection decision, evaluation details, known limitations, and safety notes.
Use with MLX
pip install mlx-lm
mlx_lm.generate \
--model Mincofficial/Sonoma-1.2B-Preview \
--prompt "Solve 5x - 7 = 53."
Use the Q4_K_M GGUF
hf download Mincofficial/Sonoma-1.2B-Preview \
gguf/Sonoma-1.2B-Q4_K_M.gguf \
--local-dir .
llama-cli \
-m gguf/Sonoma-1.2B-Q4_K_M.gguf \
-cnv -ngl 99 -c 4096
The root weights use MLX's fused LFM2 convolution layout and are intended for MLX. Use the GGUF files for llama.cpp. They are not advertised as a drop-in Transformers checkpoint.
Files
model.safetensors: fused MLX modelgguf/Sonoma-1.2B-Q4_K_M.gguf: quantized llama.cpp releasegguf/Sonoma-1.2B-F16.gguf: full-precision GGUFadapter/: selected rank-32 LoRA at release scale 8eval/: metrics and deterministic smoke-test reportsSHA256SUMS: hashes for the four weight artifacts
License
Sonoma-1.2B is a derivative of LFM2.5-1.2B-Thinking and remains subject to the LFM Open License v1.0. Review the license before use or redistribution.
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LiquidAI/LFM2.5-1.2B-Base