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LLaMA-3-2-Text-Generator

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Update app.py

b6ed085 verified about 2 years ago

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import os
import gradio as gr
from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from transformers import pipeline

# Fetch API token from environment variable
api_token = os.getenv("Llama_Token")

# Authenticate with Hugging Face
login(api_token)

# Load LLaMA 3.2 model and tokenizer with the API token
model_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name, token=api_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=api_token)

# Define the function to generate text
def generate_text(prompt, max_length=100, temperature=0.7):
    inputs = tokenizer(prompt, return_tensors="pt")
    output = model.generate(
        inputs['input_ids'], 
        max_length=max_length, 
        temperature=temperature, 
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Enter your prompt", placeholder="Start typing...", lines=5),
        gr.Slider(minimum=50, maximum=200, value=100, step=1, label="Max Length"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
    ],
    outputs="text",
    title="LLaMA 3.2 Text Generator",
    description="Generate text using the LLaMA 3.2 model. Adjust the settings and input a prompt to generate responses.",
)

# Launch the Gradio app
iface.launch(share=True)

	import os
	import gradio as gr
	from huggingface_hub import login
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch
	from transformers import pipeline

	# Fetch API token from environment variable
	api_token = os.getenv("Llama_Token")

	# Authenticate with Hugging Face
	login(api_token)

	# Load LLaMA 3.2 model and tokenizer with the API token
	model_name = "meta-llama/Llama-3.2-1B"
	tokenizer = AutoTokenizer.from_pretrained(model_name, token=api_token)
	model = AutoModelForCausalLM.from_pretrained(model_name, token=api_token)

	# Define the function to generate text
	def generate_text(prompt, max_length=100, temperature=0.7):
	inputs = tokenizer(prompt, return_tensors="pt")
	output = model.generate(
	inputs['input_ids'],
	max_length=max_length,
	temperature=temperature,
	pad_token_id=tokenizer.eos_token_id
	)
	return tokenizer.decode(output[0], skip_special_tokens=True)

	# Create the Gradio interface
	iface = gr.Interface(
	fn=generate_text,
	inputs=[
	gr.Textbox(label="Enter your prompt", placeholder="Start typing...", lines=5),
	gr.Slider(minimum=50, maximum=200, value=100, step=1, label="Max Length"),
	gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
	],
	outputs="text",
	title="LLaMA 3.2 Text Generator",
	description="Generate text using the LLaMA 3.2 model. Adjust the settings and input a prompt to generate responses.",
	)

	# Launch the Gradio app
	iface.launch(share=True)