69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
import random
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import torch
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import torch.nn.functional as F
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from model.memory import save_dream
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from model.brain_state import model, tokenizer, DEVICE
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from model.journal import record_to_journal
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from model.trainer import train_on_message
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from context.context import get_recent_context
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recent_dreams = []
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def generate_response():
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model.eval()
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context_texts = get_recent_context(5)
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if context_texts:
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start = random.choice(context_texts)
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seed_tokens = tokenizer.tokenize(start)
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if seed_tokens:
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seed = torch.tensor([seed_tokens[-1]], device=DEVICE).unsqueeze(0)
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seed = seed[:, -128:]
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else:
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seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
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else:
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seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
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output = model(seed)
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pred = torch.argmax(output, dim=-1).squeeze().item()
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# Clamp prediction into known vocab range
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if pred >= tokenizer.next_id:
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pred = random.randint(0, tokenizer.next_id - 1)
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return tokenizer.detokenize([pred])
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def score_sentence(sentence: str) -> float:
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words = sentence.strip().split()
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length = len(words)
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diversity = len(set(words)) / (length + 1)
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if length < 4:
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return 0.0
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return diversity * min(length, 20)
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def daydream():
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model.eval()
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seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
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dream = []
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for _ in range(12):
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out = model(seed)
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logits = out[:, -1, :]
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probs = F.softmax(logits, dim=-1)
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token = torch.multinomial(probs, num_samples=1)
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dream.append(token.item())
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seed = torch.cat([seed, token], dim=1)
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sentence = tokenizer.detokenize(dream)
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score = score_sentence(sentence)
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if score > 0.45:
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save_dream(sentence, score)
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record_to_journal(sentence)
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train_on_message(sentence)
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if len(recent_dreams) > 10:
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recent_dreams.pop(0)
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