CancerLLM-Mistral7b

import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

base_model_id = "mistralai/Mistral-7B-v0.1" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 )

base_model = AutoModelForCausalLM.from_pretrained( base_model_id, # Mistral, same as before quantization_config=bnb_config, # Same quantization config as before device_map="auto", trust_remote_code=True, )

eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)

from peft import PeftModel

ft_model = PeftModel.from_pretrained(base_model, "ZEECO1/CancerLLM-Mistral7b/checkpoint-500")

eval_prompt = " what are the drugs against lung cancer: # " model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")

ft_model.eval() with torch.no_grad(): print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.15)[0], skip_special_tokens=True))