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keenanpepper
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keenanpepper
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Trained on bilingual Japanese-English story data with masked loss on Japanese prefixes to enforce one-way generation.
python
from transformers import LlamaForCausalLM, AutoTokenizer model = LlamaForCausalLM.from_pretrained("one-way-polyglot-125m-untied")tokenizer = AutoTokenizer.from_pretrained("one-way-polyglot-125m-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 transliterationprompt = "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 lowercaseinputs = 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))This is part of a series exploring one-way polyglot capabilities:
Apache 2.0
from transformers import LlamaForCausalLM, AutoTokenizer model = LlamaForCausalLM.from_pretrained("one-way-polyglot-125m-untied")tokenizer = AutoTokenizer.from_pretrained("one-way-polyglot-125m-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 transliterationprompt = "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 lowercaseinputs = 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))