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a78f386 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
MODEL_NAME = "NousResearch/Hermes-2-Pro-Mistral" # ✅ Uncensored & efficient
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
model.eval()
def generate_code(prompt: str, max_tokens: int = 256):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
inputs.input_ids,
max_new_tokens=max_tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.1,
pad_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(output[0], skip_special_tokens=True)
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