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import gradio as gr
from huggingface_hub import InferenceClient
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
model_id = "GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# def respond(
# message,
# history: list[tuple[str, str]],
# system_message,
# max_tokens,
# temperature,
# top_p,
# ):
# messages = [{"role": "system", "content": system_message}]
# 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})
# response = ""
# for message in client.chat_completion(
# messages,
# max_tokens=max_tokens,
# stream=True,
# temperature=temperature,
# top_p=top_p,
# ):
# token = message.choices[0].delta.content
# response += token
# yield response
# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
# respond,
# additional_inputs=[
# gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
# gr.Slider(minimum=0.1, maximum=4.0, 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 (nucleus sampling)",
# ),
# ],
# )
# if __name__ == "__main__":
# demo.launch()
# Function to generate text
def generate_text(prompt, max_length=100):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=max_length,
num_return_sequences=1,
no_repeat_ngram_size=2,
do_sample=True,
top_p=0.95,
temperature=0.7
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Gradio frontend
def gradio_interface(prompt, max_length):
if not prompt.strip():
return "Please enter a prompt."
try:
output = generate_text(prompt, max_length=max_length)
return output
except Exception as e:
return f"An error occurred: {str(e)}"
# Define Gradio components
with gr.Blocks() as demo:
gr.Markdown("# LLaMA3 8B CPT Sahabatai Instruct")
gr.Markdown("Generate text using the **LLaMA3 8B CPT Sahabatai Instruct** model.")
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(
label="Enter your prompt",
placeholder="Type something...",
lines=3,
)
max_length_slider = gr.Slider(
label="Max Length",
minimum=10,
maximum=200,
value=100,
step=10,
)
generate_button = gr.Button("Generate")
with gr.Column():
output_text = gr.Textbox(
label="Generated Text",
lines=10,
interactive=False,
)
# Link the button to the function
generate_button.click(
fn=gradio_interface,
inputs=[prompt_input, max_length_slider],
outputs=output_text,
)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch()
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