Instructions to use pipenetwork/VISTA-4B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pipenetwork/VISTA-4B-MLX-4bit 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("pipenetwork/VISTA-4B-MLX-4bit")

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 pipenetwork/VISTA-4B-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pipenetwork/VISTA-4B-MLX-4bit"

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": "pipenetwork/VISTA-4B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pipenetwork/VISTA-4B-MLX-4bit 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 "pipenetwork/VISTA-4B-MLX-4bit"

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 pipenetwork/VISTA-4B-MLX-4bit

Run Hermes

hermes

MLX LM

How to use pipenetwork/VISTA-4B-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "pipenetwork/VISTA-4B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pipenetwork/VISTA-4B-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "pipenetwork/VISTA-4B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

VISTA-4B-MLX-4bit

MLX (Apple Silicon) conversion of inclusionAI/VISTA-4B, quantized to 4-bit. First MLX build of this model. Text-only build of the backbone.

Quantizations

Part of the VISTA MLX collection.

Variant
8-bit
6-bit
5-bit
4-bit (this repo)

Use with mlx-lm

pip install mlx-lm
python -m mlx_lm generate --model pipenetwork/VISTA-4B-MLX-4bit --prompt "Hello" -m 200

Validation

Smoke-tested locally: loads and generates coherent text.

License

apache-2.0 (inherited from base). Quantization config: {"group_size": 64, "bits": 4, "mode": "affine"}.

Downloads last month: 19

Safetensors

Model size

0.7B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for pipenetwork/VISTA-4B-MLX-4bit

Base model

inclusionAI/VISTA-4B

Quantized

(10)

this model

Collection including pipenetwork/VISTA-4B-MLX-4bit

VISTA MLX

Collection

First MLX (Apple Silicon) quantizations of inclusionAI VISTA-9B and VISTA-4B (qwen3_5). • 8 items • Updated about 16 hours ago