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

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

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"

Laguna-XS-2.1-sbQ-4bit / README.md

uttergreatness

sbQ initial publish: weights + card + provenance + license

71fe2ff verified 23 days ago

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metadata

license: openmdw-1.1
base_model: poolside/Laguna-XS-2.1
base_model_relation: quantized
library_name: mlx
pipeline_tag: text-generation
tags:
  - mlx
  - laguna
  - quantized
  - 4-bit
  - imatrix
  - sbq
  - apple-silicon

Laguna-XS-2.1-sbQ-4bit

A 4-bit imatrix MLX quantization of poolside/Laguna-XS-2.1, calibrated on a generic code + multilingual corpus.

This repo exists as the control. It is the exact same build — same bits, same tool, same commit — as Laguna-XS-2.1-sbQ-cal-4bit, except the imatrix calibration corpus: generic text here, real agent traces there. The comparison between the two is the finding — calibration shifts behaviour and speed at equal perplexity — and a comparison you cannot reproduce is a story, so both sides are published. Read the sbQ-cal card for the full grid and the probe battery; this card carries this build's own numbers.

Published by SoftBacon Software. We publish the whole ladder, losing rungs included. On behavioural probes this build ties or loses to its agent-calibrated sibling on every probe (by 1–2 passes of 96, zero inversions) — that is precisely why it is here.

Measurements (same regime as the sbQ-cal card, 2026-08-23/24)

	this build	sbQ-cal-4bit
size	18 GB	18 GB
decode tok/s	136.5	131.5
speculative tok/s	320.6	315.8
held-out NLL (merged split, cap 8192)	1.4806	1.4802
needle n=72	71/72	71/72
verify_before_assert (n=96)	0.833	0.844
say_not_measured (n=96)	0.354	0.375

It is the slightly faster of the pair — calibration shapes bit allocation, and bit allocation shapes the compute layout — so if you want raw decode speed over agentic-behaviour margins, this is the rung to take. The single dropped needle cell (ctx32000_pos0.5) is dropped by the 8-bit reference too: workload artifact, not quant damage.

Full expert-activation report from calibration: oq_imatrix_report.json (0 dark experts of 256 for this corpus).

What's in the repo

MLX safetensors + tokenizer + chat template, PROVENANCE.md, oq_build.json, oq_imatrix_report.json, and LICENSE.md (OpenMDW-1.1, inherited from the base model).

Reproduce

Build tool and measurement harness: github.com/SoftBacon-Software. Built from poolside's official BF16 with no third party in the chain. The sbQ family name marks SoftBacon builds — unrelated to poolside releases and to community oQ-series quants.