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---
library_name: transformers
tags:
- hindi
- bilingual
license: llama2
language:
- hi
- en
---
# LLama3-Gaja-Hindi-8B-v0.1
## Overview
LLama3-Gaja-Hindi-8B-v0.1 is an extension of the Ambari series, a bilingual English/Hindi model developed and released by [Cognitivelab.in](https://www.cognitivelab.in/). This model is specialized for natural language understanding tasks, particularly in the context of instructional pairs. It is built upon the [Llama3 8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B) model, utilizing a fine-tuning process with a curated dataset of translated instructional pairs.
<img src="https://cdn-uploads.huggingface.co/production/uploads/6442d975ad54813badc1ddf7/G0u9L6RQJFinST0chQmfL.jpeg" width="500px">
## Generate
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig, TextStreamer , TextIteratorStreamer
model = AutoModelForCausalLM.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", trust_remote_code=True)
# Existing messages list
messages = [
{"role": "system", "content": " You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
{"role": "user", "content": "Who are you"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
# tokenize=False,
return_tensors="pt"
).to("cuda")
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=tokenizer.convert_tokens_to_ids("<|eot_id|>"),
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## Multi-turn Chat
To use the Ambari-7B-Instruct-v0.1 model, you can follow the example code below:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig, TextStreamer , TextIteratorStreamer
model = AutoModelForCausalLM.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", trust_remote_code=True)
# Existing messages list
messages = [
{"role": "system", "content": " You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
]
# Function to add user input and generate response
def process_user_input(user_input):
global messages
# Add user's input to messages list
messages.append({"role": "user", "content": user_input})
# Prepare the prompt for generation
prompt_formatted_message = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=False
)
# Configure generation parameters
generation_config = GenerationConfig(
repetition_penalty=1.2,
max_new_tokens=8000,
temperature=0.2,
top_p=0.95,
top_k=40,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.convert_tokens_to_ids("<|eot_id|>"),
pad_token_id=tokenizer.pad_token_id,
do_sample=True,
use_cache=True,
return_dict_in_generate=True,
output_attentions=False,
output_hidden_states=False,
output_scores=False,
)
streamer = TextStreamer(tokenizer)
batch = tokenizer(str(prompt_formatted_message.strip()), return_tensors="pt")
print("\033[32mResponse: \033[0m") # Print an empty response
# Generate response
generated = model.generate(
inputs=batch["input_ids"].to("cuda"),
generation_config=generation_config,
streamer=streamer,
)
# Extract and format assistant's response
# print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
assistant_response = tokenizer.decode(generated["sequences"].cpu().tolist()[0])
# Find the last occurrence of "assistant" and empty string ("")
assistant_start_index = assistant_response.rfind("<|start_header_id|>assistant<|end_header_id|>")
empty_string_index = assistant_response.rfind("<|eot_id|>")
# Extract the text between the last "assistant" and ""
if assistant_start_index != -1 and empty_string_index != -1:
final_response = assistant_response[assistant_start_index + len("<|start_header_id|>assistant<|end_header_id|>") : empty_string_index]
else:
# final_response = assistant_response # If indices not found, use the whole response
assert "Filed to generate multi turn prompt formate"
# Append the extracted response to the messages list
messages.append({"role": "assistant", "content": final_response})
# messages.append({"role": "assistant", "content": assistant_response})
# Print assistant's response
# print(f"Assistant: {assistant_response}")
# Main interaction loop
while True:
print("=================================================================================")
user_input = input("Input: ") # Prompt user for input
# Check if user_input is empty
if not user_input.strip(): # .strip() removes any leading or trailing whitespace
break # Break out of the loop if input is empty
# Print response placeholder
process_user_input(user_input) # Process user's input and generate response
```
## Prompt formate
system prompt = `You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model(LLM), proficient in English and Hindi. You can respond in both languages based on the users request.`
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Benchmarks
coming soon
## Bilingual Instruct Fine-tuning
The model underwent a pivotal stage of supervised fine-tuning with low-rank adaptation, focusing on bilingual instruct fine-tuning. This approach involved training the model to respond adeptly in either English or Hindi based on the language specified in the user prompt or instruction.
## References
- [Ambari-7B-Instruct Model](https://huggingface.co/Cognitive-Lab/Ambari-7B-Instruct-v0.1) |