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rudrashah
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rudrashah
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Dataset: findnitai/english-to-hinglish Format: Each training example is formatted as a user/assistant chat pair:
python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "rudrashah/RLM-hinglish-translator-2" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) messages = [ { "role": "user", "content": "aapka name kya hai?" } ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to("cuda") outputs = model.generate( **inputs, max_new_tokens=128, use_cache=True, temperature=1.0, top_p=0.95, top_k=64, ) print(tokenizer.batch_decode(outputs))
Note: v2 uses a chat template format (apply_chat_template) instead of v1's raw prompt template. Make sure to use the messages format shown above for correct outputs.
| Hinglish Input | English Output |
|---|---|
| aapka name kya hai? | What is your name? |
| kal mujhe office jaana hai | I have to go to the office tomorrow. |
| Tumne kal ki match dekhi thi? kon man of the match bana tha? | Did you watch yesterday's match? Who was man of the match? |
Looking for the original model? → RLM-hinglish-translator (v1)