Update README.md

README.md

@@ -34,26 +34,26 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
 from huggingface_hub import login
 
- #Login to Huggingface to load Mistral LLM
+#Login to Huggingface to load Mistral LLM
 login("Huggingface access token")
 
- model_id = "mistralai/Mistral-7B-Instruct-v0.1"
+model_id = "mistralai/Mistral-7B-Instruct-v0.1"
 peft_model_name="bpavlsh/Mistral-Fake-News-Detection"
 
- tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 base_model = AutoModelForCausalLM.from_pretrained( model_id, load_in_4bit=True,
                                   device_map="auto", torch_dtype="auto")
 model = PeftModel.from_pretrained(base_model, peft_model_name)
 
- text=""" News text for analysis, from 1Kb to 10Kb """
+text=""" News text for analysis, from 1Kb to 10Kb """
 
- prompt = f"""<s>[INST] <<SYS>>
+prompt = f"""<s>[INST] <<SYS>>
 You are an expert in analyzing news for fake content, propaganda, and offensive language.
 <</SYS>> 
 
- Please analyze the following text: {text} [/INST]"""
+Please analyze the following text: {text} [/INST]"""
 
- inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 output = model.generate(**inputs, max_new_tokens=1500)
 output_result=tokenizer.decode(output[0], skip_special_tokens=True)
 result=output_result.split('[/INST]')[1]