Create README.md

README.md

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+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))