Text Generation
MLX
Safetensors
English
qwen3
audio-description
accessibility
wcag
conversational
4-bit precision
Instructions to use ndgold/Qwen3-1.7B-WCAG-AD-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ndgold/Qwen3-1.7B-WCAG-AD-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("ndgold/Qwen3-1.7B-WCAG-AD-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
- Pi
How to use ndgold/Qwen3-1.7B-WCAG-AD-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 "ndgold/Qwen3-1.7B-WCAG-AD-4bit"
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": "ndgold/Qwen3-1.7B-WCAG-AD-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use ndgold/Qwen3-1.7B-WCAG-AD-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 "ndgold/Qwen3-1.7B-WCAG-AD-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ndgold/Qwen3-1.7B-WCAG-AD-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ndgold/Qwen3-1.7B-WCAG-AD-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use ndgold/Qwen3-1.7B-WCAG-AD-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 "ndgold/Qwen3-1.7B-WCAG-AD-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 ndgold/Qwen3-1.7B-WCAG-AD-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ndgold/Qwen3-1.7B-WCAG-AD-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ndgold/Qwen3-1.7B-WCAG-AD-4bit"
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 "ndgold/Qwen3-1.7B-WCAG-AD-4bit" \ --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"
Qwen3-1.7B-WCAG-AD (4-bit, MLX)
LoRA fine-tune of Qwen/Qwen3-1.7B that rewrites the raw Scene: output of the
Marlin-2B vision model into one clean, WCAG-compliant audio-description
line, for the wcaguar on-device tool.
Quantized to 4-bit for Apple-silicon inference via MLX.
It removes meta-reference ("we see", "the camera"), enforces present tense and objectivity (no emotion/intent), drops dialogue echoes, and fits the word budget.
Refiner I/O
- input = Marlin's parsed
Scene:text + context (dialogue / previous description / word budget) - output = one compliant audio-description line within the budget
Evaluation (held-out human-AD test set)
| metric | refiner (ft) | raw Marlin |
|---|---|---|
| compliance | 0.94 | 0.0813 |
| objectivity | 0.9888 | 0.4425 |
| present tense | 0.9938 | 0.95 |
| no meta-reference | 0.9912 | 0.2487 |
| budget adherence | 0.9625 | 0.675 |
| English LID | 1.0 | 1.0 |
| chrF vs gold | 99.59 | 93.06 |
| ROUGE-L vs gold | 0.9951 | 0.8362 |
Ship gate: PASS
Usage (MLX)
from mlx_lm import load, generate
model, tok = load("ndgold/Qwen3-1.7B-WCAG-AD-4bit")
msgs = [{"role":"system","content":"<wcaguar AD system prompt>"},
{"role":"user","content":"Marlin: <raw scene>\nDialogue: (none)\nPrevious: (none)\nBudget: 14 words\nRefined:"}]
p = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False, enable_thinking=False)
print(generate(model, tok, prompt=p, max_tokens=96))
Sources & licenses
- Base model: Qwen3 (Apache-2.0).
- Gold audio descriptions:
- VideoA11y-40K — WCAG-style audio descriptions (CHI 2025), CC BY-NC-4.0. Primary gold source.
- You Described, We Archived (YuWA) — human audio-description transcripts from YouDescribe (CC BY-NC-ND 4.0).
- All gold is filtered against the WCAG / wcaguar audio-description rules: present tense, no meta-reference ("we see"/camera), objectivity (no emotion/intent), no sound description, word budget, English LID.
- Marlin-style drafts: synthesized from the gold AD by reversing the wcaguar style rules (cold-start, denoising SFT). Real Marlin-2B outputs are the documented upgrade path (see DECISIONS.md).
- Intended for the wcaguar on-device WCAG audio-description tool, as a text refiner that rewrites the Marlin-2B vision model's raw
Scene:output into a compliant AD line. Non-commercial / academic use (CC BY-NC). Not a general chatbot. No source media is redistributed.
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Model size
2B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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4-bit