fb8db8a870
Feat: Added a launch.json to allow quicker launches of the bot docs: phoebe_model.pt will change every time we train.
135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
import torch
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import random
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from gpt_model import encode, decode, load_model
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Hyperparameters
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batch_size = 64
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block_size = 256
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max_iters = 3000
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learning_rate = 2e-4
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eval_iters = 250
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dropout = 0.2
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# Load the vocabulary and encoded data
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with open("vocab.txt", "r", encoding="utf-8") as f:
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text = f.read()
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chars = sorted(list(set(text)))
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# Ensure that space and other special characters are included
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required_chars = " \n\r\t"
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for char in required_chars:
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if char not in chars:
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chars.append(char)
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# Add a special token for unknown characters
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special_token = "<unk>"
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if special_token not in chars:
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chars.append(special_token)
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vocab_size = len(chars)
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string_to_int = {ch: i for i, ch in enumerate(chars)}
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int_to_string = {i: ch for i, ch in enumerate(chars)}
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# Load and preprocess training and validation data from .txt files
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with open("train_split.txt", "r", encoding="utf-8") as f:
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train_data = f.read()
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with open("eval_split.txt", "r", encoding="utf-8") as f:
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val_data = f.read()
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train_data = torch.tensor(encode(train_data, string_to_int), dtype=torch.long)
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val_data = torch.tensor(encode(val_data, string_to_int), dtype=torch.long)
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def get_random_chunk(data, chunk_size):
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start = random.randint(0, len(data) - chunk_size - 1)
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chunk = data[start : start + chunk_size]
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return chunk
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def get_batch(data, block_size, batch_size):
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chunk_size = block_size * (batch_size + 1)
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chunk = get_random_chunk(data, chunk_size)
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x = chunk[: block_size * batch_size].view(batch_size, block_size)
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y = chunk[1 : block_size * batch_size + 1].view(batch_size, block_size)
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x, y = x.to(device), y.to(device)
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return x, y
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model = load_model(vocab_size).to(device)
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@torch.no_grad()
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def estimate_loss():
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out = {}
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model.eval()
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for split in ["train", "val"]:
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data = train_data if split == "train" else val_data
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losses = torch.zeros(eval_iters)
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for k in range(eval_iters):
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X, Y = get_batch(data, block_size, batch_size)
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logits, loss = model(X, Y)
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losses[k] = loss.item()
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out[split] = losses.mean().item()
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model.train()
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return out
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def train_model():
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optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
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for iter in range(max_iters):
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if iter % eval_iters == 0:
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losses = estimate_loss()
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print(
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f"step {iter}: train loss {losses['train']:.3f}, "
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f"val loss {losses['val']:.3f}"
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)
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xb, yb = get_batch(train_data, block_size, batch_size)
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logits, loss = model(xb, yb)
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optimizer.zero_grad(set_to_none=True)
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loss.backward()
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optimizer.step()
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print(loss.item())
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torch.save(model.state_dict(), "phoebe_model.pt")
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print("Model Saved!")
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def check_input_chars(s, string_to_int):
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unknown_chars = [c for c in s if c not in string_to_int]
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if unknown_chars:
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print(f"Unknown characters in input: {unknown_chars}")
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return unknown_chars
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def process_message(message):
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print(f"Processing message: '{message}'") # Debug print
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if not message.strip():
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print("Message is empty or invalid.") # Debug print
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return "Message is empty or invalid."
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# Check for unknown characters
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unknown_chars = check_input_chars(message, string_to_int)
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if unknown_chars:
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print(f"Message contains unknown characters: {unknown_chars}")
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return f"Message contains unknown characters: {unknown_chars}"
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encoded_text = torch.tensor(
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[encode(message, string_to_int)], dtype=torch.long
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).to(device)
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print(f"Encoded text shape: {encoded_text.shape}") # Debug print
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if encoded_text.size(1) == 0:
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print("Message could not be processed.") # Debug print
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return "Message could not be processed."
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response = model.generate(encoded_text, max_new_tokens=50)
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decoded_response = decode(response[0].tolist(), int_to_string)
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print(f"Generated response: '{decoded_response}'") # Debug print
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return decoded_response
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if __name__ == "__main__":
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train_model()
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