Instructions to use vasanth009/macwispr-qwen35-08b-polish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vasanth009/macwispr-qwen35-08b-polish 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("vasanth009/macwispr-qwen35-08b-polish")

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 vasanth009/macwispr-qwen35-08b-polish with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "vasanth009/macwispr-qwen35-08b-polish"

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": "vasanth009/macwispr-qwen35-08b-polish"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vasanth009/macwispr-qwen35-08b-polish 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 "vasanth009/macwispr-qwen35-08b-polish"

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 vasanth009/macwispr-qwen35-08b-polish

Run Hermes

hermes

OpenClaw new

How to use vasanth009/macwispr-qwen35-08b-polish with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "vasanth009/macwispr-qwen35-08b-polish"

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 "vasanth009/macwispr-qwen35-08b-polish" \
  --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 vasanth009/macwispr-qwen35-08b-polish with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "vasanth009/macwispr-qwen35-08b-polish"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "vasanth009/macwispr-qwen35-08b-polish"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "vasanth009/macwispr-qwen35-08b-polish",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MacWispr Qwen3.5-0.8B Polish (MLX 4-bit)

On-device post-dictation polish for MacWispr.

Base SFT: Qwen3.5-0.8B polish enum (lists, cleanup, course-correction)
Format: MLX 4-bit affine quant (~424 MB on disk)
Prompt: bare ### Input: / ### Output:
Does not answer questions you only meant to type

Not shipped inside the MacWispr app. Downloaded once when the user enables Local LLM polish in Settings (off by default). Saved under Application Support.

Smoke

### Input:
so uh get milk eggs and bread please

### Output:
- Milk
- Eggs
- Bread

Downloads last month: 46

Safetensors

Model size

0.1B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit