Update README.md

README.md

@@ -39,19 +39,29 @@ login("Huggingface access token")
 model_id = "mistralai/Mistral-7B-Instruct-v0.1"
 peft_model_name="bpavlsh/Mistral-crypto-news"
 
- tokenizer = AutoTokenizer.from_pretrained(model_id)
+#Choose prompt query
+prompt_query_1="Generate a knowledge graph from cryptocurrency news:"
+prompt_query_2="Generate summaries of cryptocurrency news and detect sentiment signals:"
+prompt_query_3="Create a JSON representation of the summary of cryptocurrency news:"
+
+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 """
+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)
 
- prompt = f"""<s>[INST] <<SYS>>
-You are an expert in analyzing news for fake content, propaganda, and offensive language.
-<</SYS>> 
+text=""" News text for analysis, from 1Kb to 10Kb """
 
- Please analyze the following text: {text} [/INST]"""
+prompt = f"""<s>[INST] <<SYS>>
+You are an expert in analyzing cryptocurrency news.
+<</SYS>>
 
+{prompt_query_1}
+{text} [/INST]"""
 
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 output = model.generate(**inputs, max_new_tokens=1500)