Instructions to use OsaurusAI/Bonsai-27b-1bit-JANG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/Bonsai-27b-1bit-JANG with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("OsaurusAI/Bonsai-27b-1bit-JANG")
config = load_config("OsaurusAI/Bonsai-27b-1bit-JANG")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use OsaurusAI/Bonsai-27b-1bit-JANG with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Bonsai-27b-1bit-JANG"

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": "OsaurusAI/Bonsai-27b-1bit-JANG"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OsaurusAI/Bonsai-27b-1bit-JANG 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 "OsaurusAI/Bonsai-27b-1bit-JANG"

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 OsaurusAI/Bonsai-27b-1bit-JANG

Run Hermes

hermes

OpenClaw new

How to use OsaurusAI/Bonsai-27b-1bit-JANG with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Bonsai-27b-1bit-JANG"

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 "OsaurusAI/Bonsai-27b-1bit-JANG" \
  --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"

Bonsai-27b-1bit-JANG

Binary 1-bit JANG-affine conversion of prism-ml/Bonsai-27B-unpacked. This is proper affine JANG storage, not JANGTQ, MXTQ, or a codebook sidecar format.

OsaurusAI · osaurus.ai · JANG source

Bundle

Property	Value
Architecture	Dense Qwen3.5 conditional-generation VLM, 27B
JANG profile	`JANG_AFFINE_1BIT`
Text matrices	1-bit codes, group size 128
Vision linears	4-bit affine, group size 64
Norms and state tensors	float16 passthrough
Weight shards	4.346 GiB
Modalities	text, image, video
Audio	not supported

Embeddings, the untied language-model head, full-attention projections, GatedDeltaNet projections, and MLP matrices use the same discrete text profile. Bonsai is dense: it has no routed experts or router tensors. The packed one-bit codes are widened losslessly to native two-bit MLX slots in memory; scales and biases are unchanged.

The bundle contains the original tokenizer vocabulary, tokenizer config, Qwen chat template with thinking and tool support, image processor config, and video processor config. EOS metadata is normalized to <|im_end|> (248046). Source license and notice files are included.

Runtime

Use a vMLX Python build containing schema-2 discrete JANG-affine storage and mixed-precision vision support. Stock mlx_lm does not implement one-bit affine storage and does not honor this bundle's per-module mixed precision.

vmlx serve OsaurusAI/Bonsai-27b-1bit-JANG --host 127.0.0.1 --port 8000

OpenAI-compatible chat requests support text, image_url, and video_url content parts. The included chat template supports enable_thinking and tool definitions.

Verification

Verified on 2026-07-14 with vMLX Python on an Apple M5 Max with 128 GB unified memory.

Gate	Result
Single-turn text	PASS — coherent Paris answer
Multi-turn	PASS — exact `ORCHID-4729` recall through three turns
Hybrid cache	PASS — paged KV plus SSM companion cache hit
Image	PASS — identified red background and centered blue square
Video	PASS — identified red frames followed by blue frames

The conversion report is included as jang_affine_report.json; authoritative per-tensor storage metadata is in jang_config.json.

Quantization notes

498 discrete text matrices use the selected binary/ternary affine profile.
83 eligible vision linears use native 4-bit affine storage.
603 norms, state tensors, convolutions, biases, and incompatible vision tensors remain float16.
No tq_packed, tq_norms, mxtq_bits, or jangtq_runtime.safetensors artifacts are present.

License and attribution

Apache-2.0. See LICENSE, LICENSE.txt, and NOTICE.txt. This repository is a quantized conversion of the linked PrismML Bonsai source checkpoint.

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

U32

F16

MLX

Hardware compatibility

1-bit

Model tree for OsaurusAI/Bonsai-27b-1bit-JANG

Base model

prism-ml/Bonsai-27B-unpacked

Finetuned

(2)

this model