Text Generation
MLX
Safetensors
laguna
quantized
4-bit precision
imatrix
sbq
apple-silicon
conversational
custom_code
Instructions to use SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit 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("SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit") 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
- LM Studio
- Pi
How to use SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit"
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": "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit 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 "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit"
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 SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit"
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 "SoftBacon-Software/Laguna-XS-2.1-sbQ-4bit" \ --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"
PROVENANCE — Laguna-XS-2.1-mlx-oq4e-ours
This is OUR quantization of an OFFICIAL poolside release. It is not a poolside artifact and must not be described as one.
| field | value |
|---|---|
| upstream model | poolside/Laguna-XS-2.1 (OpenMDW-1.1) |
| upstream revision | local-copy (see its PROVENANCE.md) |
| local source | /Users/grb/.omlx/models/Laguna-XS-2.1-bf16 |
| produced by | jarvis/tools/oq_factory.py --arm oq4e |
| quantization | oQ4 (oQ4e imatrix-weighted) |
| imatrix corpus | built-in oqe corpus |
| omlx | 782ef068c088 on m5max/oq-calibration |
| mlx / mlx_lm | 0.32.0 / 0.31.3 |
| output size | 19.5 GB |
| built | 2026-08-24T01:16:32+00:00 |
| wall time | 6.3 min |
Machine-readable copy: oq_build.json. Do not promote this build on size
or on this document — the held-out NLL ladder
(training/glm-distill/eval/run_quant_ladder.sh) is the arbiter.