app.py

import torch
from transformers import AutoTokenizer
import gradio as gr
from huggingface_hub import hf_hub_download
from model import LlamaForCausalLM  # Import your custom model class

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo2-tokenizer")
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token if tokenizer.eos_token else "[PAD]"

# Initialize model with reduced parameters (135M config)
class Config:
    pass

config = Config()
config.vocab_size = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo2-tokenizer").vocab_size
config.num_layers = 30
config.hidden_size = 576
config.num_attention_heads = 8
config.rms_norm_eps = 1.0e-05
config.max_position_embeddings = 2048
config.rope_theta = 500000.0
config.hidden_act = False
config.intermediate_size = 1536
config.rope_interleaved = False
#config.rope_scaling = null
config.rope_theta = 10000.0

model = LlamaForCausalLM(config)
device = "cpu"
model_id = "chbsaikiran/smollm2_135M_model"
checkpoint_path = hf_hub_download(repo_id=model_id, filename="model_state_dict.pt")

checkpoint = torch.load(checkpoint_path, map_location=device)
model.load_state_dict(checkpoint)
model.to(device)
model.eval()

def generate_text(prompt, max_length=100, temperature=0.7, top_k=50):
    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
    
    with torch.no_grad():
        for _ in range(max_length):
            outputs = model(input_ids)
            next_token_logits = outputs[:, -1, :] / temperature
            
            # Apply top-k sampling
            top_k_logits, top_k_indices = torch.topk(next_token_logits, top_k, dim=-1)
            probs = torch.softmax(top_k_logits, dim=-1)
            
            # Sample from distribution
            next_token_idx = torch.multinomial(probs, num_samples=1)
            next_token = top_k_indices[0, next_token_idx[0]]
            
            if next_token.item() == tokenizer.eos_token_id:
                break
                
            input_ids = torch.cat([input_ids, next_token.unsqueeze(0)], dim=1)
    
    return tokenizer.decode(input_ids[0], skip_special_tokens=True)

# Gradio interface
demo = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Input Prompt", lines=3),
        gr.Slider(20, 200, value=50, label="Max Length"),
        gr.Slider(0.1, 2.0, value=0.7, label="Temperature"),
        gr.Slider(10, 100, value=50, label="Top-k")
    ],
    outputs=gr.Textbox(label="Generated Text", lines=5),
    title="SmolLM2 Demo",
    description="A 135M parameter language model trained on Shakespeare's text"
)

if __name__ == "__main__":
    demo.launch()