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-Smoke / README.md

TiGa-RCE

Cross-link Bonsai MLX experiment family and runtime gate

e472cd4 verified 3 days ago

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- mlx
	- safetensors
	- bonsai
	- oqe
	- calibration-smoke
	- experimental
	- not-for-production
	pipeline_tag: text-generation
	library_name: mlx
	base_model:
	- TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired
	---

	# 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](https://huggingface.co/TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired)
	- [oQ2e S32 calibration smoke](https://huggingface.co/TiGa-RCE/Bonsai-27B-oQ2e-S32-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](https://huggingface.co/TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired)
	- Original source:
	[prism-ml/Bonsai-27B-gguf](https://huggingface.co/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.