rudrashah/RLM-hinglish-translator-2

Hinglish to English Translator 2

Hinglish v2 is an upgraded Hinglish-to-English translation model, fine-tuned on a larger and more capable 4B instruction-tuned base model using PEFT (LoRA). It significantly improves on v1 in both fluency and contextual understanding of informal Hinglish, the code-mixed blend of Hindi and English widely used in everyday conversations across India.

What's New in v2

• Stronger base model — Upgraded from a 2B to a 4B instruction-tuned language model for better language understanding and generation quality.
• Chat-style fine-tuning — v2 is trained using a conversational (chat template) format, making it more natural and instruction-following.
• Larger and cleaner dataset — Trained on 15,000 samples from the findnitai/english-to-hinglish dataset, a rich parallel Hinglish–English corpus.
• Improved LoRA config — Rank 64 with lora_alpha 64 for better adaptation.
• TRL SFTTrainer — Uses the SFTTrainer from TRL for robust supervised fine-tuning.

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Dataset: findnitai/english-to-hinglish Format: Each training example is formatted as a user/assistant chat pair: - User: Hinglish sentence (hi_ng field) - Assistant: English translation (en field)

Usage

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.

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Example

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?

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Limitations

• Best suited for short, informal Hinglish sentences (similar to everyday chat messages).
• May struggle with very long sentences, heavy slang, or regional dialect variations.
• The model outputs only English text — it is not a bidirectional translator.

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Previous Version

Looking for the original model? → RLM-hinglish-translator (v1)