Instructions to use prism-ml/Ternary-Bonsai-8B-mlx-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prism-ml/Ternary-Bonsai-8B-mlx-2bit 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("prism-ml/Ternary-Bonsai-8B-mlx-2bit")

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 prism-ml/Ternary-Bonsai-8B-mlx-2bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "prism-ml/Ternary-Bonsai-8B-mlx-2bit"

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": "prism-ml/Ternary-Bonsai-8B-mlx-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prism-ml/Ternary-Bonsai-8B-mlx-2bit 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 "prism-ml/Ternary-Bonsai-8B-mlx-2bit"

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 prism-ml/Ternary-Bonsai-8B-mlx-2bit

Run Hermes

hermes

MLX LM

How to use prism-ml/Ternary-Bonsai-8B-mlx-2bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "prism-ml/Ternary-Bonsai-8B-mlx-2bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "prism-ml/Ternary-Bonsai-8B-mlx-2bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "prism-ml/Ternary-Bonsai-8B-mlx-2bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

30+ Billion Models?

by bobbytaylor - opened Apr 18

Discussion

bobbytaylor

Apr 18

Any chance you folks will be releasing larger versions of these models? It would be very interesting to see how it scales!

TomLucidor

May 4

Seconding this, we need finetunes that would be comparable to Qwen3.5+ and Gemma4

snapo

9 days ago

2 models would be realy interesting... Qwen3.6 27B and Gemma4 31B .... both dense models

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