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import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
with open("config.json") as f:
cfg = json.load(f)
tokenizer = AutoTokenizer.from_pretrained(cfg["base_model"])
model = AutoModelForCausalLM.from_pretrained(
cfg["base_model"],
torch_dtype=torch.float32,
device_map="cpu"
)
with open("prompt.txt") as f:
BASE_SYSTEM_PROMPT = f.read().strip()
reasoning_enabled = False
def build_system_prompt():
if reasoning_enabled:
return BASE_SYSTEM_PROMPT + "\n\nWhen solving problems, reason step by step and explain your thinking clearly."
else:
return BASE_SYSTEM_PROMPT + "\n\nGive concise, direct answers unless explanation is required."
def chat(user_input):
messages = [
{"role": "system", "content": build_system_prompt()},
{"role": "user", "content": user_input}
]
input_ids = tokenizer.apply_chat_template(
messages,
return_tensors="pt"
)
with torch.no_grad():
output = model.generate(
input_ids,
max_new_tokens=cfg["max_new_tokens"],
temperature=cfg["temperature"],
top_p=cfg["top_p"],
do_sample=True
)
return tokenizer.decode(output[0], skip_special_tokens=True)
if __name__ == "__main__":
global reasoning_enabled
print("Brad AI 1.12.2x")
print("Commands: /reason on | /reason off | exit")
while True:
user = input("You: ")
if user.lower() in ("exit", "quit"):
break
if user.lower() == "/reason on":
reasoning_enabled = True
print("Reasoning mode ENABLED")
continue
if user.lower() == "/reason off":
reasoning_enabled = False
print("Reasoning mode DISABLED")
continue
print(chat(user))
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