pytorch_fine_tuning_code/chat_t4_test.py

import torch
import torch.nn as nn

# Configuration
MODEL_SAVE_PATH = "char_lm_model.pth"
SEQ_LENGTH = 32
EMBEDDING_DIM = 64
HIDDEN_DIM = 64

# Load vocabulary
with open('dataset.txt', 'r', encoding='utf-8') as f:
    text = f.read()

chars = sorted(list(set(text)))
vocab_size = len(chars)
char_to_idx = {ch: i for i, ch in enumerate(chars)}
idx_to_char = {i: ch for i, ch in enumerate(chars)}

# Model architecture
class CharLM(nn.Module):
    def __init__(self):
        super(CharLM, self).__init__()
        self.embedding = nn.Embedding(vocab_size, EMBEDDING_DIM)
        self.rnn = nn.GRU(EMBEDDING_DIM, HIDDEN_DIM, batch_first=True)
        self.fc = nn.Linear(HIDDEN_DIM, vocab_size)
        
    def forward(self, x, hidden=None):
        x = self.embedding(x)
        out, hidden = self.rnn(x, hidden)
        out = self.fc(out)
        return out, hidden

# Load the trained model
model = CharLM()
model.load_state_dict(torch.load(MODEL_SAVE_PATH))
model.eval()

def generate_text(model, start_str, length=100, temperature=0.7, top_k=0):
    """
    Generate text with temperature scaling and top-k sampling
    """
    model.eval()
    chars = [ch for ch in start_str]
    input_seq = torch.tensor([char_to_idx[ch] for ch in chars]).unsqueeze(0)
    hidden = None
    
    with torch.no_grad():
        for _ in range(length):
            outputs, hidden = model(input_seq, hidden)
            logits = outputs[0, -1] / temperature
            
            if top_k > 0:
                top_vals, top_idx = torch.topk(logits, top_k)
                logits[logits < top_vals[-1]] = -float('Inf')
            
            probs = torch.softmax(logits, dim=-1)
            next_char = torch.multinomial(probs, num_samples=1).item()
            chars.append(idx_to_char[next_char])
            input_seq = torch.tensor([[next_char]])
    
    return ''.join(chars)

# Chat loop
def chat():
    print("Chat with the model! Type 'exit' to stop.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'exit':
            break
        response = generate_text(model, user_input, length=100, temperature=0.7, top_k=5)
        print("Bot:", response)

chat()