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14119f6 adc3b2c 14119f6 adc3b2c 14119f6 5fc1bb6 adc3b2c 14119f6 9b1cf5f 14119f6 5fc1bb6 14119f6 9d811a8 | 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 | import gradio as gr
import torch
import torch.nn.functional as F
import tiktoken
from huggingface_hub import hf_hub_download
from transformers import GPT, GPTConfig # Import your model class
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load the model from Hugging Face Hub
def load_model_from_huggingface():
# Replace with your Hugging Face model ID (username/model-name)
model_id = "EzhirkoArulmozhi/DecoderTransformerModel"
checkpoint_path = hf_hub_download(repo_id=model_id, filename="gpt_checkpoint.pth")
checkpoint = torch.load(checkpoint_path, map_location=device)
config = checkpoint['config']
model = GPT(config)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval() # Set to evaluation mode
# Disable gradient computation
for param in model.parameters():
param.requires_grad = False
return model
model = load_model_from_huggingface()
# Force model to stay in eval mode
model.train(False)
def generate_text(prompt, max_length=25, num_samples=1):
enc = tiktoken.get_encoding('gpt2')
tokens = enc.encode(prompt)
tokens = torch.tensor(tokens, dtype=torch.long)
tokens = tokens.unsqueeze(0).repeat(num_samples, 1)
tokens = tokens.to(device)
with torch.no_grad():
for _ in range(max_length):
if tokens.size(1) >= 1024: # GPT context length
break
logits = model(tokens)[0]
logits = logits[:, -1, :]
probs = F.softmax(logits, dim=-1)
# Top-k sampling
topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
ix = torch.multinomial(topk_probs, 1)
next_token = torch.gather(topk_indices, -1, ix)
tokens = torch.cat((tokens, next_token), dim=1)
# Remove special token check entirely
# Just generate for the specified length or until context limit
generated_texts = []
for i in range(num_samples):
text = enc.decode(tokens[i].tolist())
generated_texts.append(text)
return '\n\n---\n\n'.join(generated_texts)
# Create Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Prompt", value="Before we proceed any further, hear"),
gr.Radio(choices=[25, 50, 75, 100], value=100, label="Max Length", type="value"),
gr.Radio(choices=[1, 2, 3], value=1, label="Number of Samples", type="value"),
],
outputs=gr.Textbox(label="Generated Text"),
title="Shakespeare style Dialog Generator",
description="Enter a prompt to generate a diaglog.",
examples=[
["No more talking on't; let it be done", 50, 1],
["We are accounted poor citizens", 100, 2],
["What he cannot help in his nature", 75, 3],
]
)
if __name__ == "__main__":
iface.launch() |