README.md

---
license: apache-2.0
base_model: llama
library_name: transformers
pipeline_tag: text-generation
tags:
- one-way-polyglot
- japanese
- english
- bilingual
- small-model
---

# one-way-polyglot-8m-tied

A one-way polyglot language model trained to understand Japanese but generate only English.

## Model Details

- **Architecture**: LLaMA-based transformer
- **Parameters**: 8,519,936 (8.5M)
- **Vocabulary**: 16,384 tokens (bilingual SentencePiece)
- **Context Length**: 512 tokens
- **Embedding Strategy**: Tied

## Capabilities

- **Semantic Transfer**: Understands Japanese input and generates contextually appropriate English
- **One-Way Constraint**: Strong bias toward English-only generation
- **Name Transliteration**: Can transliterate Japanese names to English (context-dependent)

## Training Data

Trained on bilingual Japanese-English story data with masked loss on Japanese prefixes to enforce one-way generation.

## Usage

```python
from transformers import LlamaForCausalLM, AutoTokenizer

model = LlamaForCausalLM.from_pretrained("one-way-polyglot-8m-tied")
tokenizer = AutoTokenizer.from_pretrained("one-way-polyglot-8m-tied")

# Japanese input → English output (primary use case)
prompt = "昔々、赤い傘を持った少女がいました。"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Mixed-language name transliteration
prompt = "太郎は公園で花子と遊んでいました。After playing, Taro told Hanako that"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# English text (works perfectly with case folding)
prompt = "Hello World"  # Automatically normalized to lowercase
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Tokenizer Features
- **✅ Case Folding**: "Hello", "hello", and "HELLO" produce identical tokenization
- **✅ Japanese Support**: Full Japanese text support with proper normalization  
- **✅ No UNK Tokens**: Proper handling of uppercase/lowercase English text
- **✅ SentencePiece Compatibility**: Built using proper Unigram model with normalization

## Model Variants

This is part of a series exploring one-way polyglot capabilities:
- 1.25M parameters (tied embeddings)
- 8.5M parameters (tied embeddings) 
- 12.7M parameters (untied embeddings)
- 15.7M parameters (tied embeddings)

## License

Apache 2.0