Image-Text-to-Text
MLX
Safetensors
qwen3_5
jang
affine-quantization
qwen3.8
bonsai
bonsai-2
ternary
packed-trits
hadamard
multimodal
vision
video
reasoning
thinking
conversational
8-bit precision
Instructions to use OsaurusAI/Bonsai-2-27B-1.75bit-JANG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/Bonsai-2-27B-1.75bit-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-2-27B-1.75bit-JANG") config = load_config("OsaurusAI/Bonsai-2-27B-1.75bit-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
- Pi
How to use OsaurusAI/Bonsai-2-27B-1.75bit-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-2-27B-1.75bit-JANG"
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": "OsaurusAI/Bonsai-2-27B-1.75bit-JANG" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use OsaurusAI/Bonsai-2-27B-1.75bit-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-2-27B-1.75bit-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-2-27B-1.75bit-JANG
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use OsaurusAI/Bonsai-2-27B-1.75bit-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-2-27B-1.75bit-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-2-27B-1.75bit-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"
Download jang_affine_report.json from OsaurusAI/Bonsai-2-27B-1.75bit-JANG: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/OsaurusAI/Bonsai-2-27B-1.75bit-JANG/resolve/main/jang_affine_report.json
- Command line
-
hf download hf://OsaurusAI/Bonsai-2-27B-1.75bit-JANG/jang_affine_report.json
-
curl -L -o jang_affine_report.json https://huggingface.co/OsaurusAI/Bonsai-2-27B-1.75bit-JANG/resolve/main/jang_affine_report.json
1.7 kB
| { | |
| "status": "converted_not_runtime_verified", | |
| "profile": "JANG_AFFINE_TERNARY_PACKED", | |
| "storage": "ternary_packed", | |
| "source": "/Volumes/EricsLLMDrive/jangq-ai/sources/bonsai2-27b-20260917/Ternary-Bonsai-2-27B-mlx-2bit", | |
| "output": "/Users/eric/models/JANGQ-AI/Bonsai-2-27B-1.75bit-JANG", | |
| "counts": { | |
| "ternary_modules": 402, | |
| "vision_affine": 83, | |
| "passthrough": 699 | |
| }, | |
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| "all_codes_in_0_1_2": true, | |
| "biases_equal_minus_scales": true, | |
| "signs_match_hadamard_json": true, | |
| "scale_min": 3.635883331298828e-06, | |
| "scale_max": 0.444580078125 | |
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| "vision_rel_l1": { | |
| "tensor_count": 83, | |
| "mean": 0.02736296, | |
| "max": 0.04872843 | |
| }, | |
| "copied_auxiliary_files": [ | |
| "hadamard.json", | |
| "chat_template.jinja", | |
| "preprocessor_config.json", | |
| "LICENSE", | |
| "NOTICE.txt", | |
| "generation_config.json", | |
| "video_preprocessor_config.json" | |
| ], | |
| "stamp": { | |
| "bundle": "Bonsai-2-27B-1.75bit-JANG", | |
| "mtp": 0, | |
| "video": true, | |
| "mtp_depth": 1, | |
| "mtp_depth_basis": "no MTP artifact", | |
| "proposal_head": "stamped ineligible (native_head_already_low_bit): /Users/eric/models/JANGQ-AI/Bonsai-2-27B-1.75bit-JANG/vmlx_mtp_proposal_head.json" | |
| }, | |
| "verification": { | |
| "status": "ok", | |
| "indexed_tensor_count": 2154, | |
| "quantized_module_count": 485, | |
| "hadamard_forward_modules": 401, | |
| "hadamard_inverse_modules": 1, | |
| "indexed_shard_bytes": 6523610032, | |
| "indexed_shard_gib": 6.076 | |
| } | |
| } | |