one-way-polyglot-12m-untied

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

Model Details

• Architecture: LLaMA-based transformer
• Parameters: 12,714,240 (12.7M)
• Vocabulary: 16,384 tokens (bilingual SentencePiece)
• Context Length: 512 tokens
• Embedding Strategy: Untied

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

from transformers import LlamaForCausalLM, AutoTokenizer

model = LlamaForCausalLM.from_pretrained("one-way-polyglot-12m-untied")
tokenizer = AutoTokenizer.from_pretrained("one-way-polyglot-12m-untied")

# 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