Update README.md

README.md

@@ -1,21 +1,30 @@
+# Model Card: Model ID
+
+## License
+
+MIT License
+
+## Languages Supported
+
+- English (en)
+
 ---
-license: mit
-language:
-- en
----
 
-# Model Card for Model ID
+## Overview
+
+This model is part of the VCC project and has been fine-tuned on the TESTtm7873/ChatCat dataset using the `mistralai/Mistral-7B-Instruct-v0.2` as the base model. The fine-tuning process utilized QLoRA for improved performance.
+
+---
 
-Finetuned mistralai/Mistral-7B-Instruct-v0.2 on TESTtm7873/ChatCat dataset
+## Getting Started
 
-### Model Description
-Developed of our VCC project 
-Finetuned with QLoRA 
+To use this model, you'll need to set up your environment first:
 
-To use it:
-```import torch
+```python
+import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 
+# Base model configuration
 base_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
 bnb_config = BitsAndBytesConfig(
     load_in_4bit=True,
@@ -24,25 +33,29 @@ bnb_config = BitsAndBytesConfig(
     bnb_4bit_compute_dtype=torch.bfloat16
 )
 
+# Loading the base model with quantization config
 base_model = AutoModelForCausalLM.from_pretrained(
-    base_model_id,  # Mistral, same as before
-    quantization_config=bnb_config,  # Same quantization config as before
+    base_model_id,
+    quantization_config=bnb_config,
     device_map="auto",
     trust_remote_code=True,
 )
 
+# Setting up tokenizer
 eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)
 
 from peft import PeftModel
 
+# Loading the fine-tuned model
 ft_model = PeftModel.from_pretrained(base_model, "mistral-journal-finetune/checkpoint-150")
 
+# Sample evaluation
 eval_prompt = "You have the softest fur."
 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))```
+    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.15)[0], skip_special_tokens=True))