How to use from
Pi
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ssdataanalysis/AI21-Jamba2-3B-mlx-fp16"
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": "ssdataanalysis/AI21-Jamba2-3B-mlx-fp16"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

AI21-Jamba2-3B MLX

This repository contains a public MLX safetensors export of ai21labs/AI21-Jamba2-3B for Apple Silicon workflows with mlx-lm.

Model Details

  • Base model: ai21labs/AI21-Jamba2-3B
  • Format: MLX safetensors
  • Quantization: none
  • Intended use: local text generation and chat on MLX-compatible Apple devices

Quick Start

Install the runtime:

pip install -U mlx-lm

Run a one-shot generation:

mlx_lm.generate --model ssdataanalysis/AI21-Jamba2-3B-mlx-fp16 --prompt "Write a short haiku about the sea."

Start an interactive chat:

mlx_lm.chat --model ssdataanalysis/AI21-Jamba2-3B-mlx-fp16

Run the HTTP server:

mlx_lm.server --model ssdataanalysis/AI21-Jamba2-3B-mlx-fp16 --host 127.0.0.1 --port 8080

You can replace the model ID above with a local path if you have already downloaded the repository.

Notes

  • This is an MLX export intended for mlx-lm.
  • The upstream model license remains Apache-2.0.
  • For the original source checkpoint and upstream documentation, see ai21labs/AI21-Jamba2-3B.
Downloads last month
21
Safetensors
Model size
3B params
Tensor type
BF16
·
MLX
Hardware compatibility
Log In to add your hardware

Quantized

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ssdataanalysis/AI21-Jamba2-3B-mlx-fp16

Finetuned
(1)
this model