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 / PROVENANCE.json

TiGa-RCE

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	{
	"artifact_id": "Bonsai-27B-oQ4e-S32-Smoke",
	"status": "experimental_calibration_smoke_not_quality_release",
	"producer": "Technologies Brewster Jennings du Canada",
	"source": {
	"derived_baseline": "TiGa-RCE/Bonsai-27B-MLX-BF16-Config-Repaired",
	"upstream_repository": "prism-ml/Bonsai-27B-gguf",
	"upstream_revision": "0cf7e3d21581b169b4df1de8bf01316000e2fbb7",
	"upstream_file": "Bonsai-27B-F16.gguf",
	"upstream_file_sha256": "d4a381a6d07131c34af888607bdbda49fc885c97673a0d22aa3e0f0284bba566",
	"license": "Apache-2.0"
	},
	"quantization": {
	"method": "oQe enhanced streaming quantization",
	"oq_level": 4,
	"default_bits": 4,
	"effective_bits_per_weight": 4.70,
	"group_size": 64,
	"embedding_bits": 8,
	"sensitivity_selected_modules": 22,
	"sensitivity_selected_bits": 5,
	"imatrix_samples": 32,
	"imatrix_sequence_length": 512,
	"sensitivity_samples": 2,
	"sensitivity_sequence_length": 64,
	"calibration_dataset": "oqe_code_multilingual",
	"imatrix_entries": 496
	},
	"validation": {
	"runtime_loaded": true,
	"behavioral_smoke_passed": true,
	"hellaswag_fixed_10": 6,
	"arc_challenge_fixed_10": 6,
	"quality_release_eligible": false,
	"reason": "The bounded sensitivity stage and 10-question screens are pipeline evidence only."
	}
	}