Instructions to use TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke 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("TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke")

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 TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke 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 "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke"

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 TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke

Run Hermes

hermes

OpenClaw new

How to use TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke"

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 "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke" \
  --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"

MLX LM

How to use TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "TiGa-RCE/Bonsai-27B-oQ4e-S32-Smoke",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Bonsai 27B oQ4e S32 Calibration Smoke

Experimental calibration artifact. Not a quality release. Do not use this model to judge Bonsai quality or as a production checkpoint.

Experiment family and current status

This is one public checkpoint in an unfinished MLX/oMLX compatibility and quantization experiment:

BF16 config-repaired archival baseline
oQ2e S32 calibration smoke
oQ4e S32 calibration smoke — this repository

The experiment remains incomplete until maintained MLX/MLX-LM/oMLX support can load and generate through the relevant Bonsai paths without the current local compatibility patches, explicit calibration proxies, or imatrix-boundary workarounds. The larger predeclared evaluations follow that runtime gate.

The Hub's displayed parameter count reflects packed quantized storage tensors. It does not describe a newly trained smaller model; the logical source architecture is Bonsai 27B.

This repository preserves a bounded oQe-enhanced 4-bit MLX artifact that loaded and generated normally on a 32 GB Apple Silicon host. Its purpose is reproducible pipeline evidence and archival storage, not a claim that it outperforms another Bonsai checkpoint.

What this proves

An oQe-enhanced 4-bit MLX artifact was produced from the public, config-repaired BF16 conversion baseline.
Structural and load checks passed.
The 32-sample imatrix collection completed with 496 entries.
The allocation uses an effective 4.70 bits per weight: embeddings at 8-bit and 22 sensitivity-selected modules at 5-bit.
The artifact produced coherent reasoning in the bounded behavioral smoke.
On fixed 10-question screens it scored HellaSwag 6/10 and ARC-Challenge 6/10.

What this does not prove

The sensitivity stage was intentionally minimal: 2 samples of 64 tokens. The 10-question screens are too small for a quality ranking. This artifact has not passed the predeclared 100-question evaluations or a quality-sized sensitivity pass, and it did not beat the smaller oQ2e smoke artifact on the tiny screen.

Do not present it as a production model, a quality release, or evidence that oQ4e is superior to ordinary Q4, oQ2e, Q1, or another quantization method.

Reproduction parameters

Setting	Value
oQ level	4
effective allocation	4.70 bpw
default quantization	affine 4-bit, group size 64
embedding quantization	affine 8-bit, group size 64
sensitivity-selected modules	22 at affine 5-bit
imatrix samples	32
imatrix sequence length	512
sensitivity samples	2
sensitivity sequence length	64
calibration dataset	`oqe_code_multilingual`
imatrix entries	496

oq_imatrix_report.json, PROVENANCE.json, and the included IMATRIX_CACHE_S32.npz preserve the emitted artifact and calibration evidence. The NPZ contains aggregate activation statistics, not raw calibration prompts, and is not needed to run the model.

Bounded evaluation

The fixed screens used deterministic, thinking-disabled decoding and the same letter parser as the earlier oQ2e smoke:

Benchmark	Score	Peak generation memory
HellaSwag, fixed 10	6/10	15.70 GB
ARC-Challenge, fixed 10	6/10	15.42 GB

The raw records are retained under docs/. These are pipeline screens, not statistically useful benchmark claims.

Provenance

Derived baseline: TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired
Original source: prism-ml/Bonsai-27B-gguf
Source revision used for conversion: 0cf7e3d21581b169b4df1de8bf01316000e2fbb7
Original source file: Bonsai-27B-F16.gguf
Original source file SHA-256: d4a381a6d07131c34af888607bdbda49fc885c97673a0d22aa3e0f0284bba566
Output hashes: MODEL_SHA256SUMS.txt

The project retains the upstream Apache-2.0 LICENSE.txt and NOTICE.txt.

Runtime

The artifact uses heterogeneous MLX quantization metadata. It was validated with the experimental oMLX/oQe path used to create it. Compatibility with stock MLX-LM or other runtimes is not claimed.

Attribution

Created by Technologies Brewster Jennings du Canada for the Bonsai MLX quantization experiment. Created using Bonsai by Prism ML, derived from Qwen3.6-27B.