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from typing import Optional
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
class OpenPeerLLMInterface:
def __init__(
self,
model_path: str,
checkpoint: str,
device: str
):
self.device = device
self.model_path = f"{model_path}/{checkpoint}"
self.model = None
self.tokenizer = None
self._load_model()
def _load_model(self):
"""Load the model and tokenizer"""
try:
self.model = AutoModelForCausalLM.from_pretrained(
self.model_path
).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
except Exception as e:
print(f"Error loading model: {e}")
raise
def generate(
self,
prompt: str,
quantum_state: Optional[np.ndarray] = None,
max_length: int = 100
) -> str:
if self.model is None or self.tokenizer is None:
raise RuntimeError("Model not properly initialized")
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
if quantum_state is not None:
self._apply_quantum_state(quantum_state)
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_length=max_length,
num_return_sequences=1,
pad_token_id=self.tokenizer.eos_token_id
)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
def _apply_quantum_state(self, quantum_state: np.ndarray):
if self.model is None:
return
state_magnitude = np.abs(quantum_state) ** 2
attention_modifier = torch.tensor(
state_magnitude,
device=self.device
).float()
with torch.no_grad():
for layer in self.model.transformer.h[:1]:
if hasattr(layer, 'attn'):
attention = layer.attn
if hasattr(attention, 'c_attn'):
weights = attention.c_attn.weight
scale = attention_modifier[:weights.size(0)].reshape(-1, 1)
attention.c_attn.weight.data *= scale |