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ai4bharat
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Model Details
Model Provider
ai4bharat
Model Tree
sarvamai/sarvam-translate
Input Modalities
Output Modalities
Supported Functionality
The recommended inference recipe combines three things:
num_beams=4) instead of greedy. Improves decoding quality without retraining.glossary.json, included in this repo) containing 6,400+ Marathi↔Bhili term pairs reviewed by native Bhili speakers.post_edit.py, included in this repo) that substitutes terms the model left in Marathi with their Bhili equivalents from the glossary.This addresses the most common failure mode observed in production: agricultural crop names, scheme names, and technical terms passing through untranslated.
⚠️ For the best results, we strongly recommend using the Translate Gemma system prompt when interacting with this model. Using the recommended system prompt helps ensure more consistent and accurate translation outputs.
bash
pip install torch transformers peft accelerate huggingface_hubpython
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelfrom huggingface_hub import hf_hub_download # Load base model + adapterbase_model_name = "sarvamai/sarvam-translate"adapter_name = "ai4bharat/bhili-translate-mar-bhb" tokenizer = AutoTokenizer.from_pretrained(base_model_name)model = AutoModelForCausalLM.from_pretrained( base_model_name, torch_dtype=torch.bfloat16, device_map="auto",)model = PeftModel.from_pretrained(model, adapter_name)model.eval() # Load glossary + post-edit module from this repoglossary_path = hf_hub_download(adapter_name, "glossary.json")post_edit_path = hf_hub_download(adapter_name, "post_edit.py") import importlib.utilspec = importlib.util.spec_from_file_location("post_edit", post_edit_path)pe = importlib.util.module_from_spec(spec)spec.loader.exec_module(pe) glossary = pe.load_glossary(glossary_path) # Translatedef translate(text: str) -> str: messages = [ { "role": "system", "content": ( "You are a professional Marathi (mr) to Bhili (bhb) translator. " "Your goal is to accurately convey the meaning and nuances of the " "original Marathi text while adhering to Bhili grammar, vocabulary, " "and cultural sensitivities. Produce only the Bhili translation, " "without any additional explanations or commentary. Please translate " "the following Marathi text into Bhili:" ), }, {"role": "user", "content": text}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer([prompt], return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=256, do_sample=False, num_beams=4, early_stopping=True, ) raw = tokenizer.decode( output[0][len(inputs.input_ids[0]):], skip_special_tokens=True, ).strip() # Apply glossary post-edit edited, _edits = pe.post_edit( text, raw, glossary["mar2bhb_sorted_keys"], glossary["mar2bhb"] ) return edited # Exampleprint(translate("शेतकऱ्यांना सकाळी फवारणी करणे आवश्यक आहे."))print(translate("कापसावरील गुलाबी बोंडअळीसाठी कॉपर ऑक्सिक्लोराईड फवारा."))This model was predominantly trained on agricultural-domain data. As a result, performance on domains outside agriculture may vary and may not be representative of the performance observed on agricultural-domain.
pip install torch transformers peft accelerate huggingface_hubimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelfrom huggingface_hub import hf_hub_download # Load base model + adapterbase_model_name = "sarvamai/sarvam-translate"adapter_name = "ai4bharat/bhili-translate-mar-bhb" tokenizer = AutoTokenizer.from_pretrained(base_model_name)model = AutoModelForCausalLM.from_pretrained( base_model_name, torch_dtype=torch.bfloat16, device_map="auto",)model = PeftModel.from_pretrained(model, adapter_name)model.eval() # Load glossary + post-edit module from this repoglossary_path = hf_hub_download(adapter_name, "glossary.json")post_edit_path = hf_hub_download(adapter_name, "post_edit.py") import importlib.utilspec = importlib.util.spec_from_file_location("post_edit", post_edit_path)pe = importlib.util.module_from_spec(spec)spec.loader.exec_module(pe) glossary = pe.load_glossary(glossary_path) # Translatedef translate(text: str) -> str: messages = [ { "role": "system", "content": ( "You are a professional Marathi (mr) to Bhili (bhb) translator. " "Your goal is to accurately convey the meaning and nuances of the " "original Marathi text while adhering to Bhili grammar, vocabulary, " "and cultural sensitivities. Produce only the Bhili translation, " "without any additional explanations or commentary. Please translate " "the following Marathi text into Bhili:" ), }, {"role": "user", "content": text}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer([prompt], return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=256, do_sample=False, num_beams=4, early_stopping=True, ) raw = tokenizer.decode( output[0][len(inputs.input_ids[0]):], skip_special_tokens=True, ).strip() # Apply glossary post-edit edited, _edits = pe.post_edit( text, raw, glossary["mar2bhb_sorted_keys"], glossary["mar2bhb"] ) return edited # Exampleprint(translate("शेतकऱ्यांना सकाळी फवारणी करणे आवश्यक आहे."))print(translate("कापसावरील गुलाबी बोंडअळीसाठी कॉपर ऑक्सिक्लोराईड फवारा."))