Text Generation
MLX
Safetensors
English
qwen3_5
bonsai
oqe
calibration-smoke
experimental
not-for-production
conversational
4-bit precision
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
- MLX
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
- Pi
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"} ] }'
File size: 2,615 Bytes
c76306a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | {
"model_id": "oq4e-smoke-s32",
"benchmark": "arc_challenge",
"sampling": "deterministic_thinking_off",
"total": 10,
"correct": 6,
"accuracy": 0.6,
"time_s": 39.548,
"peak_memory_gb": 15.415,
"question_results": [
{
"id": "Mercury_7043820",
"expected": "B",
"predicted": "A",
"correct": false,
"raw_response": "A."
},
{
"id": "MCAS_2003_5_33",
"expected": "C",
"predicted": "C",
"correct": true,
"raw_response": "C"
},
{
"id": "Mercury_7219678",
"expected": "A",
"predicted": "B",
"correct": false,
"raw_response": "The question describes a scenario where a beach community's water supply (surface and groundwater) is decreasing. They decide to build a large-scale desalination plant to remove salt from bay water. The key reason for this decision is the **decrease in water resources**.\n\nLet's evaluate the options:\n\nA. **needed increased resources.** - This aligns with the problem statement: the water supply is decreasing, so they need more water resources. Desalination is a method to create fresh water resources from saltwater.\nB. **had access to advanced technology.** - While desalination uses technology, the *reason* for"
},
{
"id": "AKDE&ED_2008_8_48",
"expected": "A",
"predicted": "A",
"correct": true,
"raw_response": "A"
},
{
"id": "Mercury_7210455",
"expected": "C",
"predicted": "C",
"correct": true,
"raw_response": "C"
},
{
"id": "Mercury_407517",
"expected": "C",
"predicted": "C",
"correct": true,
"raw_response": "C"
},
{
"id": "TIMSS_2007_8_pg109",
"expected": "C",
"predicted": "C",
"correct": true,
"raw_response": "C"
},
{
"id": "Mercury_SC_400134",
"expected": "D",
"predicted": "B",
"correct": false,
"raw_response": "B"
},
{
"id": "Mercury_402501",
"expected": "B",
"predicted": "B",
"correct": true,
"raw_response": "B"
},
{
"id": "Mercury_400084",
"expected": "D",
"predicted": "C",
"correct": false,
"raw_response": "To balance the chemical equation for the combustion of methane:\n\n$$ \\text{CH}_4 + \\Box \\text{O}_2 \\rightarrow 2\\text{CO}_2 + 4\\text{H}_2\\text{O} $$\n\nLet's count the atoms on each side:\n\n**Right Side (Products):**\n* Carbon (C): 2 atoms (from $2\\text{CO}_2$)\n* Hydrogen (H): 8 atoms (from $4\\text{H}_2\\text{O}$)\n* Oxygen (O): $(2"
}
]
}
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