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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
MODEL_REPO = "Rahul-8799/software_architect_command_r"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_REPO,
trust_remote_code=True,
quantization_config=bnb_config,
device_map={"": 0}, # force full GPU load
torch_dtype=torch.float16
)
model.eval()
def run(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=512)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
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