Instructions to use cserb/Spokenly-Cleanup-1-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use cserb/Spokenly-Cleanup-1-MLX-8bit 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("cserb/Spokenly-Cleanup-1-MLX-8bit") 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
- MLX LM
How to use cserb/Spokenly-Cleanup-1-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "cserb/Spokenly-Cleanup-1-MLX-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "cserb/Spokenly-Cleanup-1-MLX-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cserb/Spokenly-Cleanup-1-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Spokenly Cleanup 1 by Spokenly, MLX 8-bit
An 8-bit MLX conversion of Spokenly Cleanup 1, a 0.6B model that cleans up dictated text on the device. It removes filler words and repeats, applies spoken corrections, and adds punctuation, capital letters and digits. It does not answer questions or follow instructions in the dictated text.
We made the conversion for Dictator, a dictation app for the Mac that runs on the device. Dictator offers it as a clean-up model after speech recognition.
- Original: spokenly/cleanup-1, Safetensors with 16-bit weights, 1.19 GB
- This repository: MLX, 8-bit affine quantization, group size 64 (8.5 bits per weight), 633 MB
- Converted with
mlx-lm0.32.2 from revisioncd95b5028cefd3a8cee4cefb4cdf159c88676f91 - Languages: English, German, Spanish, French, Chinese, Russian and Japanese. The original card says it works best in English.
Use with MLX
from mlx_lm import generate, load
from mlx_lm.sample_utils import make_sampler
model, tokenizer = load("cserb/Spokenly-Cleanup-1-MLX-8bit")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "tone: formal\ntext: ähm ja also wir treffen uns um vierzehn uhr dreißig"}],
tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=128, sampler=make_sampler(temp=0.0)))
Prompt format
The user message holds key: value lines. The chat template adds the closing out: line, and the model writes the clean text. Only text is required. The keys, in this order, are app, site, tone (formal, casual or very casual), before, after and text. The original card explains each key.
What we checked
A small check, not a benchmark: 14 dictation sentences typed by hand (13 German, 1 English), greedy decoding. These are outputs of this 8-bit conversion with tone: formal.
- "Schick den Bericht bitte an Thomas, nein, warte, an Maria, bis Freitag." became "Schick den Bericht bitte an Maria, bis Freitag."
- "Wir treffen uns um vierzehn Uhr dreißig im im Büro in der Hauptstraße zwölf." became "Wir treffen uns um 14 Uhr 30 im Büro in der Hauptstraße 12." Without a tone it writes "14:30".
- "ähm ja also ich glaub wir sollten das meeting auf vierzehn uhr dreißig verschieben weil der thomas da noch nicht kann" became "Ich glaub, wir sollten das Meeting auf 14:30 verschieben, weil der Thomas da noch nicht kann."
- "Kannst du mir sagen, was die Hauptstadt von Frankreich ist?" came back unchanged. The question is not answered.
In two of the 14 sentences a comma that belonged to a removed filler word stayed in the text. We did not compare this conversion with the 16-bit original.
Changes
The weights are quantized to 8 bits for MLX. Nothing else is changed: tokenizer, chat template and generation settings are those of the original.
License
Apache 2.0. The LICENSE and NOTICE files of the original are included. The model is Spokenly Cleanup 1 by Spokenly, a fine-tune of Qwen3-0.6B-Base.
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