Instructions to use ndgold/Qwen3-1.7B-EasyLanguage-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ndgold/Qwen3-1.7B-EasyLanguage-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-1.7B-EasyLanguage-4bit ndgold/Qwen3-1.7B-EasyLanguage-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Qwen3-1.7B-EasyLanguage (4-bit, MLX)
LoRA fine-tune of Qwen/Qwen3-1.7B that rewrites live speech transcripts into easy-to-read registers across 20 language locales — German (Leichte Sprache), French (FALC), English (Easy/Plain English), Spanish (Lectura Fácil), Easy-to-Read Arabic (Inclusion Europe / Information for All), Letlæst (Inclusion Europe ETR), and more — for the Live Linguist on-device captioner. Each locale follows its own national / European Easy-to-Read or plain-language standard. Quantized to 4-bit for Apple-silicon inference via MLX.
It splits run-ons into short sentences, drops disfluencies, keeps names/numbers, and stays in the input language. Trained on a lean prompt (no few-shots) so the register is internalized — shorter prompts, lower live-caption latency.
Evaluation (held-out test set; SARI = simplification quality)
| lang | SARI ft | SARI stock | chrF ft | LID ft | compliance ft |
|---|---|---|---|---|---|
| de | 50.61 | 41.92 | 46.31 | 1.0 | 0.995 |
| fr | 61.55 | 54.15 | 55.31 | 1.0 | 0.995 |
| es | 62.37 | 56.9 | 58.0 | 1.0 | 0.97 |
| en | 62.84 | 52.0 | 57.32 | 1.0 | 0.99 |
| ar | 52.85 | 51.05 | 51.56 | 1.0 | 0.97 |
| da | 55.97 | 49.17 | 54.21 | 0.995 | 0.975 |
| et | 49.92 | 45.59 | 52.72 | 0.995 | 0.99 |
| fi | 51.72 | 47.25 | 58.09 | 1.0 | 0.995 |
| hi | 54.85 | 49.22 | 48.12 | 0.975 | 0.845 |
| it | 55.64 | 49.53 | 52.24 | 1.0 | 0.8196 |
| ja | 8.35 | 8.36 | 46.21 | 1.0 | 0.965 |
| ko | 51.43 | 46.48 | 44.41 | 1.0 | 0.975 |
| nl | 54.15 | 51.81 | 52.22 | 1.0 | 0.935 |
| pt-BR | 57.89 | 56.87 | 54.45 | 1.0 | 0.87 |
| pt-PT | 60.65 | 54.3 | 61.36 | 1.0 | 0.98 |
| ru | 55.61 | 51.04 | 53.23 | 0.995 | 0.93 |
| sk | 51.32 | 46.89 | 48.72 | 0.995 | 0.975 |
| sv | 56.1 | 49.53 | 58.24 | 0.99 | 0.97 |
| tr | 51.31 | 46.35 | 51.85 | 1.0 | 0.995 |
| vi | 63.22 | 57.28 | 62.09 | 1.0 | 0.805 |
| zh-CN | 8.77 | 8.8 | 54.19 | 0.96 | 0.905 |
Usage (MLX)
from mlx_lm import load, generate
model, tok = load("ndgold/Qwen3-1.7B-EasyLanguage-4bit")
msgs = [{"role":"system","content":"<framework system prompt>"},
{"role":"user","content":"Original: <utterance>\nRewritten:"}]
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).
- German seed data: tum-nlp/German4All-Corpus (German Wikipedia, CC BY-SA).
- Synthetic pairs (all other languages + de augmentation): spoken→easy-language pairs generated by Claude (Anthropic) — Leichte Sprache (de), FALC (fr), Easy/Plain English (en), Lectura Fácil (es), and 25 further locales following each language's Easy-to-Read / plain-language standard (Inclusion Europe "Information for All", Selkokieli, Lättläst, やさしい日本語, ISO 24495-1, …). Every pair is filtered by a deterministic validator suite (per-language sentence-length caps, language-ID, fidelity anchoring, number preservation, anti-parroting).
- Intended for the Live Linguist on-device live-caption simplifier. Not a general chatbot.
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4-bit