Text Generation
MLX
Safetensors
English
qwen3_5
qwen3.6
bonsai
apple-silicon
experimental
conversational
Instructions to use TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired 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-MLX-BF16-Config-Repaired") 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
- Pi
How to use TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired 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-MLX-BF16-Config-Repaired"
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-MLX-BF16-Config-Repaired" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired 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-MLX-BF16-Config-Repaired"
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-MLX-BF16-Config-Repaired
Run Hermes
hermes
- OpenClaw new
How to use TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired 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-MLX-BF16-Config-Repaired"
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-MLX-BF16-Config-Repaired" \ --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-MLX-BF16-Config-Repaired 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-MLX-BF16-Config-Repaired"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired" # 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-MLX-BF16-Config-Repaired", "messages": [ {"role": "user", "content": "Hello"} ] }'
| license: apache-2.0 | |
| language: | |
| - en | |
| tags: | |
| - mlx | |
| - safetensors | |
| - qwen3.6 | |
| - bonsai | |
| - apple-silicon | |
| - experimental | |
| pipeline_tag: text-generation | |
| library_name: mlx | |
| base_model: | |
| - prism-ml/Bonsai-27B-gguf | |
| # Bonsai 27B MLX BF16 Config-Repaired | |
| This is a mechanically converted MLX BF16 archival baseline for the Bonsai 27B | |
| quantization experiment on Apple Silicon. It exists to make later Q4, Q8, oQ4, | |
| and oQ8 builds reproducible without treating an opaque local directory as the | |
| source of truth. | |
| It is **not a recommended inference checkpoint**. The 50 GB BF16 footprint | |
| cannot be run on the 32 GB Apple Silicon host used to create it, so this upload | |
| establishes structure and provenance, not runtime quality. | |
| ## Provenance | |
| - Upstream source: [`prism-ml/Bonsai-27B-gguf`](https://huggingface.co/prism-ml/Bonsai-27B-gguf) | |
| - Source revision: `0cf7e3d21581b169b4df1de8bf01316000e2fbb7` | |
| - Source file: `Bonsai-27B-F16.gguf` | |
| - Source SHA-256: `d4a381a6d07131c34af888607bdbda49fc885c97673a0d22aa3e0f0284bba566` | |
| - Base architecture: Qwen/Qwen3.6-27B through Bonsai 27B | |
| - Conversion output: MLX-compatible safetensors, 11 shards, 851 indexed tensors | |
| - License: Apache-2.0. Upstream `LICENSE.txt` and `NOTICE.txt` are retained. | |
| The conversion preserved tensor values. One configuration-only repair was made: | |
| the source declares one MTP layer but includes no `mtp.*` tensor. The derived | |
| `config.json` sets `text_config.mtp_num_hidden_layers` to `0`; the untouched | |
| configuration is retained as `config.pre-mtp-repair.json`. See | |
| [`MTP_CONFIGURATION_REPAIR.md`](./MTP_CONFIGURATION_REPAIR.md). | |
| ## Integrity | |
| `output-sha256.txt` records SHA-256 hashes for every model shard and core | |
| metadata file. `PROVENANCE.json` records the source, structural checks, and | |
| local artifact hashes. The current derived `config.json` hash is | |
| `4dd1fd34daa8916c5eecc8e6a9ca9ce3564226518704768efe9b76d4deb34b2f`. | |
| ## Intended Use | |
| Use this only as a reproducible conversion baseline for a controlled local | |
| quantization study. Follow-on releases should publish their conversion recipe, | |
| calibration settings, evaluation results, and exact artifact hashes. A generic | |
| 2-bit affine experiment from this study failed deterministic output screens and | |
| is intentionally not distributed as a usable model. | |
| ## Attribution | |
| Created by Technologies Brewster Jennings du Canada for the Bonsai MLX | |
| quantization experiment. Bonsai is created by Prism ML and derived from | |
| Qwen3.6-27B. Please consult the upstream Bonsai card for original model | |
| limitations, benchmarks, and citations. | |