Instructions to use wordspace/proofreader-0.6B-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use wordspace/proofreader-0.6B-MLX-6bit 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("wordspace/proofreader-0.6B-MLX-6bit") 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 wordspace/proofreader-0.6B-MLX-6bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "wordspace/proofreader-0.6B-MLX-6bit"
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": "wordspace/proofreader-0.6B-MLX-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use wordspace/proofreader-0.6B-MLX-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "wordspace/proofreader-0.6B-MLX-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "wordspace/proofreader-0.6B-MLX-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wordspace/proofreader-0.6B-MLX-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use wordspace/proofreader-0.6B-MLX-6bit 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 "wordspace/proofreader-0.6B-MLX-6bit"
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 wordspace/proofreader-0.6B-MLX-6bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use wordspace/proofreader-0.6B-MLX-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "wordspace/proofreader-0.6B-MLX-6bit"
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 "wordspace/proofreader-0.6B-MLX-6bit" \ --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"
File size: 1,560 Bytes
1d34b34 | 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 | """Style suggestions for a few sentences in different languages. Run it from
the model folder:
python example.py
Needs `mlx-lm` (Apple silicon). The prompt lives in `protocol.py` and must
not be changed: the model was trained on exactly that text.
"""
import os
import time
from mlx_lm import generate, load
import protocol as P
HERE = os.path.dirname(os.path.abspath(__file__))
TEXTS = [
("en", "Due to the fact that the office is closed, we will send the documents on Monday."),
("en", "The plumber comes on Tuesday between 9 and 12."),
("it", "Al fine di risparmiare, abbiamo deciso di spegnere la caldaia di notte."),
("de", "Zum jetzigen Zeitpunkt ist die Werkstatt geschlossen."),
("fr", "Nous avons procédé à la vérification des compteurs ce matin."),
("es", "El curso de cocina tuvo una duración de seis semanas."),
]
def main():
model, tokenizer = load(HERE)
for lang, text in TEXTS:
tokens = tokenizer.apply_chat_template(P.messages(text, P.LANGUAGE_NAMES[lang]),
tokenize=True, add_generation_prompt=True)
t0 = time.time()
answer = generate(model, tokenizer, prompt=tokens, max_tokens=256, verbose=False)
found = P.style_suggestions(answer, text)
print("%s %s" % (lang, text))
for o, r in found:
print(" «%s» → «%s»" % (o, r))
if not found:
print(" (nothing to change)")
print(" %.2f s" % (time.time() - t0))
if __name__ == "__main__":
main()
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