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# import torch
# import gradio as gr
# from transformers import AutoModelForCausalLM, AutoTokenizer
# # Load the model and tokenizer
# MODEL_NAME = "sarvamai/sarvam-1"
# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
# model.eval()
# def respond(message, history, max_tokens, temperature, top_p):
# # Convert chat history to format
# messages = [{"role": "system", "content": "You are a friendly AI assistant."}]
# for val in history:
# if val[0]:
# messages.append({"role": "user", "content": val[0]})
# if val[1]:
# messages.append({"role": "assistant", "content": val[1]})
# messages.append({"role": "user", "content": message})
# # Tokenize and generate response
# inputs = tokenizer.apply_chat_template(messages, tokenize=False)
# input_tokens = tokenizer(inputs, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
# output_tokens = model.generate(
# **input_tokens,
# max_new_tokens=max_tokens,
# temperature=temperature,
# top_p=top_p,
# pad_token_id=tokenizer.pad_token_id,
# eos_token_id=tokenizer.eos_token_id,
# )
# response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
# return response
# # Define Gradio Chat Interface
# demo = gr.ChatInterface(
# fn=respond,
# additional_inputs=[
# gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens"),
# gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
# gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
# ],
# title="Sarvam-1 Chat Interface",
# description="Chat with the Sarvam-1 language model"
# )
# if __name__ == "__main__":
# demo.launch()
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer
MODEL_NAME = "sarvamai/sarvam-1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
model.eval()
def respond(message, history, max_tokens, temperature, top_p):
# Convert chat history to format
messages = [{"role": "system", "content": "You are a friendly AI assistant."}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
# Tokenize and generate response
inputs = tokenizer.apply_chat_template(messages, tokenize=False)
input_tokens = tokenizer(inputs, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
output_tokens = model.generate(
**input_tokens,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
return response
# Define Gradio Chat Interface
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
],
title="Sarvam-1 Chat Interface",
description="Chat with the Sarvam-1 language model"
)
if __name__ == "__main__":
demo.launch() |