Update README.md

README.md

@@ -15,22 +15,31 @@ tags:
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 This is the fine tuned model which got further trained on the top of base model Mistral-7B-v0.1 on the Skillate customer support dataset. 
 The fine-tuned model understands the nuances about how the Skillate product works, its navigation, features, monologue and respond accordingly. 
 
 
 
+# Instruction Fine-Tuning Example
 
+In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens.
 
+E.g.
+
+```python
+prompt = "Answer the below query as a customer support assistant about Skillate Product: "
+question = "What are the different ways to log in to the product?"
+answer = "You can log in to Skillate in any of the three methods here: [https://help.skillate.com/en/support/solutions/articles/82000881022](https://help.skillate.com/en/support/solutions/articles/82000881022) The conventional method of entering a username and password Using SSO (Single Sign-On) login via Google Using SSO (Single Sign-On) login via Microsoft"
+text = f"<s>[INST] {prompt} {question} [/INST] {answer} </s>"
+```
 
-## Instruction format
 
-In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens.
 
 ## How to Get Started with the Model
 
+```python
+
 from transformers import AutoTokenizer,AutoModelForCausalLM, BitsAndBytesConfig
 
 
@@ -48,7 +57,14 @@ base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct
 peft_model = PeftModel.from_pretrained(base_model, "bipulai/mistral-7b-v1-skillate-helpdesk",device_map="auto")
 peft_model.merge_and_unload()
 
+
+'''Evaluating on the helpdesk related query'''
+system_prompt = "Answer the below query as a customer support assistant about Skillate Product: "
+question = "How to configure the job approval chain?"
+prompt = f"<s>[INST] {system_prompt} {question} [/INST]"
+
 tokenize = tokenizer(text = [prompt],return_tensors = "pt")
 x = peft_model.generate(input_ids = tokenize["input_ids"].to(device),attention_mask = tokenize["attention_mask"].to(device),max_length = 500)
 response = tokenizer.batch_decode(x,skip_special_tokens=True)
 print(f"Model Output: {reponse}\n\n")
+```