How to use from
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"
Quick Links

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.

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