aoiandroid/TranslateBlue-v2-MLX-4bit

TranslateBlue v2 (MLX 4-bit)

Translation model focused on 29 languages with emphasis on African languages, in MLX 4-bit format for Apple Silicon (M1+ Mac, and mlx-swift on supported devices).

Model description

• Base model: Qwen3-4B-Instruct
• Format: MLX, 4-bit quantized
• Size: ~2.1 GB
• Training: LoRA fine-tuning on parallel translation data (10,000 steps, 16 LoRA layers)
• Training data: 563,986 sentence pairs from 29 languages

Intended use

• Text translation between the supported languages, especially to/from African languages
• Offline translation on Mac (and in apps using mlx-swift where supported)
• Low-latency translation on Apple Silicon with Metal acceleration

Supported languages (29)

Code	Language	Code	Language	Code	Language
sw	Swahili	ha	Hausa	yo	Yoruba
ig	Igbo	am	Amharic	zu	Zulu
xh	Xhosa	af	Afrikaans	so	Somali
rw	Kinyarwanda	sn	Shona	tw	Twi
ee	Ewe	wo	Wolof	ny	Chichewa
ti	Tigrinya	nso	Northern Sotho	tn	Tswana
om	Oromo	ve	Venda	nd	Ndebele
ar	Arabic	fr	French	pt	Portuguese
es	Spanish	de	German	zh	Chinese
ja	Japanese	ko	Korean	en	English

Limitations

• Apple Silicon only for this MLX build (Mac with M1 or later; mlx-swift on supported iOS/iPadOS when available).
• Best for short to medium sentences; very long texts may lose quality.
• Low-resource pairs may be less accurate than high-resource ones.
• No built-in language detection; source and target languages should be specified in the prompt.

How to use

Prompt format

Use a clear translation instruction, for example:

Translate from English to Swahili:

Hello, how are you?

With Python (mlx-lm)

pip install mlx mlx-lm

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load("aoiandroid/TranslateBlue-v2-MLX-4bit")
sampler = make_sampler(temp=0.3, top_p=0.9)

messages = [{"role": "user", "content": "Translate from English to Swahili:\n\nHello, how are you?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=64, sampler=sampler, verbose=False)
print(response)

With Swift (mlx-swift / TranslateBlue)

The model is registered as TranslateBlue v2 (MLX). After downloading via the app (or placing the model in the expected path), it runs with MLXModelService using the same prompt format above.

Training details

Setting	Value
Base model	Qwen3-4B-Instruct
Method	LoRA
LoRA layers	16
Steps	10,000
Training samples	563,986
Validation loss	~2.5

Related models

• GGUF version (llama.cpp, cross-platform): aoiandroid/TranslateBlue-v2-GGUF

License

MIT.

Citation

If you use this model in research or a product, please cite the base model (Qwen3) and the TranslateBlue project as appropriate.