Text Generation
MLX
Safetensors
English
qwen3_5
lora
qlora
dictation
speech-to-text
asr-post-processing
text-cleanup
apple-silicon
conversational
8-bit precision
Instructions to use ReFyneLabs/simplewords-dictation-cleanup-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ReFyneLabs/simplewords-dictation-cleanup-v2 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("ReFyneLabs/simplewords-dictation-cleanup-v2") 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 ReFyneLabs/simplewords-dictation-cleanup-v2 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ReFyneLabs/simplewords-dictation-cleanup-v2"
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": "ReFyneLabs/simplewords-dictation-cleanup-v2" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use ReFyneLabs/simplewords-dictation-cleanup-v2 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ReFyneLabs/simplewords-dictation-cleanup-v2"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ReFyneLabs/simplewords-dictation-cleanup-v2" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ReFyneLabs/simplewords-dictation-cleanup-v2", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use ReFyneLabs/simplewords-dictation-cleanup-v2 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 "ReFyneLabs/simplewords-dictation-cleanup-v2"
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 ReFyneLabs/simplewords-dictation-cleanup-v2
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ReFyneLabs/simplewords-dictation-cleanup-v2 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ReFyneLabs/simplewords-dictation-cleanup-v2"
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 "ReFyneLabs/simplewords-dictation-cleanup-v2" \ --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"
Commit History
-
v2: fix self-correction inversion (8-bit fused + LoRA adapter)
Verified signatureQLoRA fine-tune of mlx-community/Qwen3.5-2B-MLX-4bit for post-ASR dictation cleanup. Fixes v1's disqualifying self-correction inversion (kept the replaced side of a spoken correction). Self-correction accuracy: ~0% (v1) -> 100% on both a 44-case verbatim regression set and 1,546 held-out rows whose correction templates are absent from training. Overall behavioral 97.7% / 99.6%; production-guard pass 100% / 99.9%. Ships the 8-bit FUSED model at the repo root and the LoRA adapter under adapter/. The 8-bit fuse is deliberate: re-quantizing the merged weights to 4-bit rounds the low-rank delta away and collapses held-out self-correction to 57.8%, silently.b00972f