Update README.md

README.md

@@ -6,18 +6,9 @@ language:
 - en
 metrics:
 - bleu
-library_name: adapter-transformers
 tags:
-- chemistry
-- biology
-- finance
-- legal
-- music
-- art
-- code
-- climate
-- medical
 - text-generation-inference
+pipeline_tag: text-generation
 ---
 
 # Deployed Model
@@ -27,9 +18,13 @@ AjayMukundS/Llama-2-7b-chat-finetune
 This is a Llama 2 Fine Tuned Model with 7 Billion Parameters on the Dataset from **mlabonne/guanaco-llama2**. The training data is basically a Chat between a Human and an Assistant where the Human poses some queries and the Assistant responds to those queries in a suitable fashion.
 In the case of Llama 2, the following Chat Template is used for the chat models: 
 
-**[INST] SYSTEM PROMPT**
+**(s)[INST] ((sys))**
 
-**User Prompt [/INST] Model Answer**
+**SYSTEM PROMPT**
+
+**((/sys))**
+
+**User Prompt [/INST] Model Answer (/s)**
 
 System Prompt (optional) --> to guide the model
 
@@ -46,6 +41,8 @@ The Instruction Dataset is reformated to follow the above Llama 2 template.
 
 **Complete Reformated Datset** --> https://huggingface.co/datasets/mlabonne/guanaco-llama2
 
+To know how this dataset was created, you can check this notebook --> https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing
+
 To drastically reduce the VRAM usage, we must fine-tune the model in 4-bit precision, which is why we’ll use QLoRA here and the GPU on which the model was fined tuned on was **L4 (Google Colab Pro)**
 
 ## Process