This is quantized version of
CoRover/BharatGPT-3B-Indic created using llama.cpp
This model is fine-tuned and designed to generate multilingual outputs across multiple Indic languages. The model has been trained on a diverse and curated dataset comprising Hindi, Punjabi, Marathi, Malayalam, Oriya, Kannada, Gujarati, Bengali, Urdu, Tamil, and Telugu. It is optimized to handle natural language tasks such as translation, summarization, and conversational generation across these languages. This model is trained on authentic Indian conversational data in 12 languages. However, it is not designed for direct use as a standalone chatbot, as it lacks the latest data updates. It is best suited for S-RAG (Secure Retrieval-Augmented Generation) or fine-tuning with your own data. For enhanced performance, integration with
Conversational Gen AI platform is recommended (though not mandatory). This platform enables the creation of multi-modal and multi-lingual AI Agents, Co-Pilots, and Virtual Assistants (such as ChatBots, VoiceBots, and VideoBots) using a sovereign AI and composite AI approach. It leverages classic NLP, grounded generative AI, and Generally Available LLMs to deliver powerful, versatile solutions.
Use the code below to get started with the model.
1import torch
2from transformers import pipeline
3
4model_id = "CoRover/BharatGPT-3B-Indic"
5pipe = pipeline(
6 "text-generation",
7 model=model_id,
8 torch_dtype=torch.bfloat16,
9 device_map="auto",
10)
11messages = [
12 {"role": "system", "content": "You are a helpful assistant who responds in English"},
13 {"role": "user", "content": "who created you?"},
14]
15outputs = pipe(
16 messages,
17 max_new_tokens=256,
18)
19print(outputs[0]["generated_text"][-1])
20
1import torch
2from transformers import pipeline
3
4model_id = "CoRover/BharatGPT-3B-Indic"
5pipe = pipeline(
6 "text-generation",
7 model=model_id,
8 torch_dtype=torch.bfloat16,
9 device_map="auto",
10)
11messages = [
12 {"role": "system", "content": "You are a helpful assistant who responds in Hindi"},
13 {"role": "user", "content": "भारत की राजधानी क्या है"},
14]
15outputs = pipe(
16 messages,
17 max_new_tokens=256,
18)
19print(outputs[0]["generated_text"][-1])
20
1import torch
2from transformers import pipeline
3
4model_id = "CoRover/BharatGPT-3B-Indic"
5pipe = pipeline(
6 "text-generation",
7 model=model_id,
8 torch_dtype=torch.bfloat16,
9 device_map="auto",
10)
11messages = [
12 {"role": "system", "content": "You are a helpful assistant who responds in Gujarati"},
13 {"role": "user", "content": "શું છે ભારતની રાજધાની"},
14]
15outputs = pipe(
16 messages,
17 max_new_tokens=256,
18)
19print(outputs[0]["generated_text"][-1])
20
1import torch
2from transformers import pipeline
3
4model_id = "CoRover/BharatGPT-3B-Indic"
5pipe = pipeline(
6 "text-generation",
7 model=model_id,
8 torch_dtype=torch.bfloat16,
9 device_map="auto",
10)
11messages = [
12 {"role": "system", "content": "You are a helpful assistant who responds in Marathi"},
13 {"role": "user", "content": "भारताची राजधानी कोणती?"},
14]
15outputs = pipe(
16 messages,
17 max_new_tokens=256,
18)
19print(outputs[0]["generated_text"][-1])
20
1import torch
2from transformers import pipeline
3
4model_id = "CoRover/BharatGPT-3B-Indic"
5pipe = pipeline(
6 "text-generation",
7 model=model_id,
8 torch_dtype=torch.bfloat16,
9 device_map="auto",
10)
11messages = [
12 {"role": "system", "content": "You are a helpful assistant who responds in Malayalam"},
13 {"role": "user", "content": "ഭരത് കി രാജധാനി ഉണ്ട്"},
14]
15outputs = pipe(
16 messages,
17 max_new_tokens=256,
18)
19print(outputs[0]["generated_text"][-1])
20
To ensure top-tier performance and scalability, the model was fine-tuned using state-of-the-art hardware and software configurations:
