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582d1c6 798827c 582d1c6 798827c 582d1c6 798827c 582d1c6 798827c 582d1c6 798827c 582d1c6 798827c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import os
# Check if CUDA is available
if torch.cuda.is_available():
print(f"Using GPU: {torch.cuda.get_device_name(0)}")
device = "cuda"
else:
print("GPU not available, using CPU")
device = "cpu"
# Model constants
MODEL_ID = "Ansah-AI/E1-4BIT-GGUF"
MODEL_REVISION = "main" # Change this if you need a specific revision
# Function to download and load the model and tokenizer
def load_model():
print(f"Loading model: {MODEL_ID}")
# Load model with 4-bit quantization
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
revision=MODEL_REVISION,
device_map="auto",
load_in_4bit=True, # Enable 4-bit quantization
trust_remote_code=True
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
print("Model and tokenizer loaded successfully!")
return model, tokenizer
# Function to generate text from the model
def generate_text(prompt, max_length=256, temperature=0.7, top_p=0.9, top_k=40):
# Ensure the model and tokenizer are loaded
global model, tokenizer
# Print generation parameters for debugging
print(f"Generating with parameters: max_length={max_length}, temp={temperature}, top_p={top_p}, top_k={top_k}")
# Create the text generation pipeline
text_generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto"
)
# Generate the text
generation_config = {
"max_length": max_length,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k,
"num_return_sequences": 1,
"do_sample": temperature > 0.1, # Use sampling if temperature is significant
"pad_token_id": tokenizer.eos_token_id
}
try:
result = text_generator(
prompt,
**generation_config
)
# Return the generated text
return result[0]["generated_text"]
except Exception as e:
return f"Error generating text: {str(e)}"
# Main function to create and run the Gradio interface
def main():
# Load the model and tokenizer
global model, tokenizer
model, tokenizer = load_model()
# Create the Gradio interface
with gr.Blocks(title="E1-4BIT-GGUF Model Interface") as demo:
gr.Markdown("# E1-4BIT-GGUF Model Interface")
gr.Markdown("Enter your prompt below to generate text using the Ansah-AI/E1-4BIT-GGUF model.")
with gr.Row():
with gr.Column(scale=4):
prompt_input = gr.Textbox(
label="Prompt",
placeholder="Enter your prompt here...",
lines=5
)
with gr.Column(scale=1):
max_length = gr.Slider(
minimum=64,
maximum=2048,
value=256,
step=32,
label="Max Length"
)
temperature = gr.Slider(
minimum=0.1,
maximum=1.5,
value=0.7,
step=0.1,
label="Temperature"
)
top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.05,
label="Top P"
)
top_k = gr.Slider(
minimum=1,
maximum=100,
value=40,
step=1,
label="Top K"
)
generate_button = gr.Button("Generate")
output_text = gr.Textbox(label="Generated Text", lines=10)
# Set up the button click event
generate_button.click(
fn=generate_text,
inputs=[prompt_input, max_length, temperature, top_p, top_k],
outputs=output_text
)
# Add examples
gr.Examples(
examples=[
["Write a short story about a space explorer discovering a new planet."],
["Explain quantum computing to a high school student."],
["Create a recipe for a chocolate cake."]
],
inputs=prompt_input
)
# Launch the interface
demo.launch(share=True) # share=True creates a public link
if __name__ == "__main__":
main() |