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Model: keenanpepper/one-way-polyglot-22m-untied
Source: Original Platform

README.md

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+---
+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-22m-untied
+
+A one-way polyglot language model trained to understand Japanese but generate only English.
+
+## Model Details
+
+- **Architecture**: LLaMA-based transformer
+- **Parameters**: 22,025,088 (22.0M)
+- **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
+
+```python
+from transformers import LlamaForCausalLM, AutoTokenizer
+
+model = LlamaForCausalLM.from_pretrained("one-way-polyglot-22m-untied")
+tokenizer = AutoTokenizer.from_pretrained("one-way-polyglot-22m-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