Instructions to use UnimeType/Transliteration-2.0-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UnimeType/Transliteration-2.0-4B 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("UnimeType/Transliteration-2.0-4B")

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

How to use UnimeType/Transliteration-2.0-4B with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "UnimeType/Transliteration-2.0-4B"

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": "UnimeType/Transliteration-2.0-4B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use UnimeType/Transliteration-2.0-4B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "UnimeType/Transliteration-2.0-4B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "UnimeType/Transliteration-2.0-4B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "UnimeType/Transliteration-2.0-4B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use UnimeType/Transliteration-2.0-4B 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 "UnimeType/Transliteration-2.0-4B"

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 UnimeType/Transliteration-2.0-4B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use UnimeType/Transliteration-2.0-4B with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "UnimeType/Transliteration-2.0-4B"

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 "UnimeType/Transliteration-2.0-4B" \
  --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"

Transliteration-2.0-4B / README.md

losdwind

Release v2.1 (v4 multi-clause long-sentence upgrade)

9bd1fec verified 1 day ago

preview code

Raw History Blame Contribute Delete

8.93 kB

metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3.5-4B
library_name: mlx
pipeline_tag: text-generation
language:
  - en
  - zh
  - ja
  - hi
  - ar
tags:
  - mlx
  - transliteration
  - pinyin
  - romaji
  - hinglish
  - arabizi
  - ime
  - qwen3.5
  - v2.1

UnimeType Transliteration 2.0 4B — v2.1 (Long-Sentence Upgrade · MLX 6-bit)

Current Release: v2.1 (2.0-v4 Multi-Clause Long-Sentence Upgrade) · Protocol: unimetype-convert-short-v3 (100% drop-in compatible with all Transliteration-2.0 clients)

UnimeType Transliteration 2.0 4B (v2.1) is a specialized multilingual input-method transliteration model built on Qwen/Qwen3.5-4B. It converts Chinese Pinyin, Japanese Romaji, Romanized Hindi / Hinglish, and Arabic Arabizi into their native scripts across both short phrases and multi-clause long sentences (60–240+ chars) while preserving English technical terms, @handles, URLs, code spans, JSON literals, numbers, line breaks, and emoji in place.

🚀 Launch Interactive Playground Space → (Test 25 verified multi-clause & edge presets or run live WebAssembly inference in your browser)
⌨️ Download the UnimeType Keyboard App → (Type in-place across macOS, Windows, Linux, iOS, Android & ChromeOS — no copy-paste or manual JSON wrapping needed)
📖 Technical Report & LM Studio Setup Guide →

Quickstart: `unimetype-convert-short-v3` JSON Protocol (LM Studio / `llama.cpp` / `mlx_lm`)

Important for LM Studio / Ollama / GGUF Users: This model is a deterministic IME transliteration engine, not a general chat bot. Pass a single-line compact JSON object as the user message (with empty System Prompt and temperature: 0.0), or use the UnimeType Keyboard App which formats requests and replaces text in-place automatically.

Language	`user` Message JSON (`content`)	Direct `assistant` Output
Chinese Pinyin (`zh`)	`{"operation":"convert","input":"qing xian zai staging huanjing pao yi bian suoyou unit test, queren API latency meiyou shenggao zhi hou, women zai anpai production deploy","enabled_languages":["chinese","english"],"primary_language":"chinese"}`	`请先在 staging 环境跑一遍所有 unit test, 确认 API latency 没有升高之后, 我们再安排 production deploy`
Japanese Romaji (`ja`)	`{"operation":"convert","input":"staging kankyou de atarashii API endpoint no dousa wo kakunin shite kara, mondai ga nakereba ban-gohan no ato ni production deploy shimasu","enabled_languages":["english","japanese"],"primary_language":"japanese","japanese_output_style":"natural"}`	`staging 環境で新しい API endpoint の動作を確認してから, 問題がなければ晩ご飯の後に production deploy します`
Hindi Hinglish (`hi`)	`{"operation":"convert","input":"maine GitHub par naya PR open kar diya hai, aap ek baar code review kar lijiye, phir hum shaam ko staging server par deploy karenge","enabled_languages":["english","hindi"],"primary_language":"hindi"}`	`मैंने GitHub पर नया PR open कर दिया है, आप एक बार code review कर लीजिए, फिर हम शाम को staging server पर deploy करेंगे`
Arabic Arabizi (`ar`)	`{"operation":"convert","input":"ana 7a2a3od fel office l7ad el sa3a 8 3ashan a5allas el deployment, law 3andak ay update eb3atli 3ala Slack aw WhatsApp","enabled_languages":["arabic","english"],"primary_language":"arabic"}`	`أنا هقعد في ال office لحد الساعة 8 عشان أخلص ال deployment, لو عندك أي update ابعتلي على Slack أو WhatsApp`

curl http://127.0.0.1:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "UnimeType/Transliteration-2.0-4B",
    "temperature": 0,
    "max_tokens": 256,
    "messages": [
      {
        "role": "user",
        "content": "{\"operation\":\"convert\",\"input\":\"mingtian 3pm he @alex review auth-service de PR #412, ranhou deploy dao staging\",\"enabled_languages\":[\"chinese\",\"english\"],\"primary_language\":\"chinese\"}"
      }
    ]
  }'

What's New in `v2.1` (Long-Sentence Upgrade)

Multi-Clause Long-Sentence Mastery (v2.0 → v2.1): Trained on a balanced curriculum where 67.2% of training inputs are multi-clause sentences (60–240 chars, 2–5 clauses, max_seq_len=1024) covering spaced, phrase-grouped, and unspaced romanization, bilingual technical code-switching, protected placeholders, multiline layouts, and Another (reconvert) cycling:
- 0.8B (v2.0 → v2.1): Multi-clause strict pass up 13× (0.6% → 8.1%), long-sentence character accuracy 69.2% → 76.5% (ja +11.9%, ar +9.2%), and standard 414-case benchmark up from 66.4% (275/414) to 72.7% (301/414), surpassing DeepSeek V4.1 Flash (70.5%).
- 4B (v2.0 → v2.1): Multi-clause strict pass up from 20.0% (32/160) to 28.8% (46/160, 29.4% normalized), long-sentence character accuracy 90.4% → 93.21% (all 4 languages >90.9%), contract violations cut by -76.9% (13 → 3), and standard 414-case benchmark up from 87.4% (362/414) to 88.4% (366/414) (ja 61.5% → 76.9%).
Unconditional Non-Thinking Generation Template: Ships with an unconditional non-thinking chat_template.jinja (<|im_start|>assistant\n<think>\n\n</think>\n\n) across all formats (MLX 6-bit, BF16 Safetensors, and GGUF), preventing Thinking Process: token waste or truncation in LM Studio, mlx-lm, and llama.cpp.

Evaluation Benchmarks (`v2.0` vs `v2.1`)

1. Multi-Clause Long-Sentence Benchmark (160 cases · 2–5 clauses, 60–240 chars)

Language / Metric	v2.0 (`0.8B` before)	v2.1 (`0.8B` now)	v2.0 (`4B` before)	v2.1 (`4B` now)
Overall Strict Pass (160 cases)	1 / 160 (0.6%)	12 / 160 (7.5%)	32 / 160 (20.0%)	46 / 160 (28.7%)
Overall Normalized Pass (160 cases)	1 / 160 (0.6%)	12 / 160 (7.5%)	33 / 160 (20.6%)	47 / 160 (29.4%)
Mean Character Accuracy (160 cases)	69.2%	76.49%	90.4%	93.21%
Chinese Pinyin (`zh`, 40 cases)	0 / 40 (0.0% · 61.7% char)	1 / 40 (2.5%) · 66.7% char	6 / 40 (15.0% · 92.6% char)	9 / 40 (22.5%) · 92.6% char
Japanese Romaji (`ja`, 40 cases)	0 / 40 (0.0% · 51.7% char)	0 / 40 (0.0%) · 63.7% char	6 / 40 (15.0% · 86.0% char)	9 / 40 (22.5%) · 91.0% char
Hindi Hinglish (`hi`, 40 cases)	1 / 40 (2.5% · 91.9% char)	11 / 40 (27.5%) · 94.8% char	20 / 40 (50.0% · 96.7% char)	25 / 40 (62.5%) · 98.1% char
Arabic Arabizi (`ar`, 40 cases)	0 / 40 (0.0% · 71.6% char)	0 / 40 (0.0%) · 80.8% char	0 / 40 (0.0% · 86.4% char)	3 / 40 (7.5%) · 91.2% char

2. Standard 414-Case Short-Sentence & Edge-Case Benchmark

Slice / Metric	v1.0 (`Transliteration-4B`)	DeepSeek V4.1 Flash	v2.1 (`Transliteration-2.0-0.8B`)	v2.1 (`Transliteration-2.0-4B`)
Overall Strict Pass (414 cases)	230 / 414 (55.6%)	292 / 414 (70.5%)	301 / 414 (72.7%)	366 / 414 (88.4%)
Overall Expanded Pass (414 cases)	242 / 414 (58.5%)	297 / 414 (71.7%)	301 / 414 (72.7%)	366 / 414 (88.4%)
Chinese Pinyin (`zh`)	151 / 207 (72.9%)	171 / 207 (82.6%)	168 / 207 (81.2%)	195 / 207 (94.2%)
Japanese Romaji (`ja`)	15 / 52 (28.8%)	37 / 52 (71.2%)	22 / 52 (42.3%)	40 / 52 (76.9%)
Hindi / Hinglish (`hi`)	41 / 71 (57.7%)	49 / 71 (69.0%)	51 / 71 (71.8%)	61 / 71 (85.9%)
Arabic Arabizi (`ar`)	23 / 84 (27.4%)	35 / 84 (41.7%)	60 / 84 (71.4%)	70 / 84 (83.3%)

Version History & Reproducible Revisions

This repository tracks the latest 2.x release on main while preserving immutable Git tags for every milestone:

v2.1 (default / main): Multi-clause long-sentence upgrade (2.0-v4, max_seq_len=1024).
```
huggingface-cli download UnimeType/Transliteration-2.0-4B --revision v2.1
```

v2.0: Initial short-protocol release (2.0-v3, max_seq_len=640).

huggingface-cli download UnimeType/Transliteration-2.0-4B --revision v2.0

UnimeType Transliteration 2.0 4B — v2.1 (Long-Sentence Upgrade · MLX 6-bit)

Quickstart: unimetype-convert-short-v3 JSON Protocol (LM Studio / llama.cpp / mlx_lm)

What's New in v2.1 (Long-Sentence Upgrade)

Evaluation Benchmarks (v2.0 vs v2.1)

1. Multi-Clause Long-Sentence Benchmark (160 cases · 2–5 clauses, 60–240 chars)

2. Standard 414-Case Short-Sentence & Edge-Case Benchmark

Version History & Reproducible Revisions

Quickstart: `unimetype-convert-short-v3` JSON Protocol (LM Studio / `llama.cpp` / `mlx_lm`)

What's New in `v2.1` (Long-Sentence Upgrade)

Evaluation Benchmarks (`v2.0` vs `v2.1`)