UnimeType/Transliteration-4B

UnimeType Transliteration 4B

Type with familiar Latin characters. Get the writing system your message needs.

UnimeType Transliteration 4B is a focused language conversion model for everyday, mixed-language writing. It converts Pinyin, Romaji, Romanized Hindi, Hinglish, and Arabizi, with English words, product names, links, code, numbers, punctuation, and emoji intended to stay in place. Review outputs before using them; preservation is not guaranteed.

It was created for the Convert experience from UnimeType.

Download formats

Format	Download
MLX 6-bit	This repository
Standard floating-point Safetensors	Safetensors repository
GGUF Q8_0	GGUF repository

The new exports share the retained fine-tuned source described below. Their model cards document a known Arabic trailing-newline error and runtime-specific validation. This repository's MLX weights remain unchanged.

Four writing systems, one familiar input

Input style	Example input	Model output
Chinese Pinyin	jintian yao finish README	今天要 finish README
Japanese Romaji	ashita Tokyo de meeting ga arimasu	明日 Tokyo で meeting があります
Romanized Hindi / Hinglish	aaj meeting hai	आज meeting है
Arabic Arabizi	fee meeting bokra	في meeting بكرة

These examples are direct outputs from the published 6-bit model in LM Studio.

Made for real messages

UnimeType Transliteration 4B is designed for text that does not fit into a traditional transliteration box:

• Mixed-language sentences that combine romanized text with English terms
• Short chat messages and conversational phrases
• Product names, technical vocabulary, URLs, email addresses, and code spans
• Punctuation, line breaks, Markdown, numbers, and emoji that should remain intact
• Text that is already in the target writing system and should not be changed

The model returns the converted text directly, ready to review and use.

Supported conversion modes

Target	Latin input	Output
Simplified Chinese	Pinyin and natural unmarked Pinyin	Chinese characters
Japanese	Romaji	Kanji, Hiragana, and Katakana
Hindi	Romanized Hindi and Hinglish	Devanagari with preserved English
Arabic	Arabizi and conversational romanized Arabic	Arabic script with preserved English

Run locally

This repository contains an MLX model for Apple silicon Macs. The model can be downloaded from Hugging Face and loaded with MLX-LM or imported into an MLX-compatible local runtime such as LM Studio.

Recommended generation settings:

Setting	Value
Thinking / reasoning	Off
Temperature	0
Maximum output	128 tokens for short messages
Context used for validation	2048 tokens

Select one target language from chinese, japanese, hindi, or arabic, then provide the original text. The model is built for conversion, not translation, rewriting, polishing, or explanation.

Model details

Property	Value
Base model	Qwen3.5-4B
Parameters	4B class
Format	MLX
Quantization	6-bit affine, group size 64
Model file size	3.42 GB
Primary task	Context-aware transliteration and script conversion
License	Apache 2.0

Historical standalone evaluation

The published model was tested with exact final-text matching. A result passes only when the complete output matches an accepted answer, including preserved English, punctuation, code, and spacing.

Runtime	Everyday conversion suite	Reserved multilingual suite
Direct MLX	98 / 127	57 / 104
LM Studio	94 / 127	57 / 104

The reserved multilingual score measures exact agreement with its reference responses. Exact-match scoring is strict: a valid alternative spelling or wording can still count as a mismatch. Review ambiguous names, dialect terms, and very short fragments before sending them.

Product-pipeline validation

On September 6, 2026, the unchanged published MLX weights were tested with UnimeType's production Convert prompts and evaluator, using LM Studio on an Apple M5 Pro MacBook Pro with 48 GB memory and a 16,384-token context setting.

Check	Result
Fixed conversion suite, exact accepted output	97 / 127
Conversational-context regression suite, valid-reference exact agreement	12 / 71
Context-suite structure and protected-literal count checks	74 / 76
Protected-literal count and order, cases containing protected literals	31 / 32
Latin-word preservation in eligible fixed-suite cases	43 / 48
URL preservation in eligible context cases	16 / 16
Code-span preservation in eligible context cases	16 / 16
Numeric-literal preservation in eligible context cases	16 / 16
Punctuation-sequence preservation in eligible context cases	15 / 16

These are product-pipeline results, including input protection, output validation, and 20 local-route cases in the fixed suite. They are not raw-model accuracy. Five structurally invalid reference answers are excluded from context agreement, but all 76 cases remain in the structure check. Context references are model-generated and have not been reviewed by native speakers. These suites have been used during development and are not an independent blind benchmark.

Preservation counts measure exact literal counts and order within eligible cases, not general language accuracy. The Latin-word check covers case-sensitive words present in the input and every accepted reference, excluding URL, email, and code spans. Context preservation checks cover explicitly marked literals; categories overlap.

Known limitations include ambiguous short inputs, Arabic dialect and spelling variation, and context-related formatting errors. Short English fragments can be converted instead of preserved. A Japanese regression case also failed to preserve punctuation and an emoji. NVIDIA CUDA, AMD ROCm, iOS, and Android inference have not been validated for this release. The historical standalone scores above use a different evaluation setup and should not be treated as interchangeable with this product-pipeline run.

Release provenance and files

This repository contains the retained MLX 6-bit release. The model was fine-tuned with LoRA on a quantized 4-bit Qwen3.5-4B base, fused into a floating-point checkpoint, and then quantized to 6-bit MLX. Floating-point storage does not recover the original base model's pre-quantization precision.

The retained floating-point checkpoint was audited against this release: its 426 tensors reproduce all 924 published tensors under 6-bit affine quantization. It is not a dequantization of the published 6-bit weights. See PROVENANCE.json for source hashes and SHA256SUMS for public-file checksums.

训练数据集未公开。 The training dataset is not publicly released. This repository does not include training data, data-generation scripts, training code, or training logs.

The base model uses Apache 2.0. A copy is included in LICENSE. Safetensors and GGUF downloads are in the separate repositories linked above.

About UnimeType

UnimeType helps multilingual writers type naturally across languages and writing systems. Transliteration 4B is the dedicated local model behind that direction: focused on conversion, compact enough for local use, and designed around the way people actually mix languages in daily writing.