Improve model card: add Github and project page links, change pipeline tag

This PR updates the model card by changing the pipeline tag to `text-generation` and adding links to the Github repository and the project page.

README.md

@@ -1,10 +1,10 @@
 ---
 base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
-library_name: peft
-license: apache-2.0
 language:
 - en
-pipeline_tag: question-answering
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-generation
 ---
 
 # Model Details
@@ -16,6 +16,12 @@ Compared with [TOMMI-0.35](https://huggingface.co/my-ai-university/TOMMI-0.35/),
 ## **Paper**
 * [arXiv](https://arxiv.org/abs/2504.08846)
 
+## **Project page**
+* https://my-ai-university.com
+
+## **Github**
+* https://github.com/my-ai-university/finite-element-method
+
 ## **Hyperparameters**
 
 * learning_rate: 5e-5
@@ -80,141 +86,5 @@ from peft import PeftModel
 import time
 import torch
 from transformers import PreTrainedTokenizerFast, AutoModelForCausalLM
-
-
-class Conversation:
-    def __init__(self,
-                 model,
-                 tokenizer,
-                 device,
-                 system=""):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.device = device
-        self.message = []
-        if system:
-            self.message.append({"role": "system", "content": system})
-
-    def get_prompt(self):
-        prompt = '<|begin_of_text|>'
-        # Include the system message if it exists
-        for msg in self.message:
-            role = msg['role']
-            content = msg['content']
-            prompt += f"<|start_header_id|>{role}<|end_header_id|>{content}<|eot_id|>"
-        # Append the assistant's role header to prompt for the next response
-        prompt += "<|start_header_id|>assistant<|end_header_id|>"
-        return prompt
-
-    def generate(self,
-                 user_input,
-                 temp=0.7,
-                 max_new_tokens=1024,
-                 top_k=50,
-                 top_p=0.95):
-
-        # Add the user's input to the conversation history
-        self.message.append({"role": "user", "content": user_input})
-
-        # Generate the prompt
-        prompt = self.get_prompt()
-
-        # Tokenize the prompt
-        inputs = self.tokenizer(prompt,
-                                return_tensors="pt",
-                                truncation=True,
-                                max_length=2048).to(self.device)
-        # inputs = {k: v.to(device) for k, v in inputs.items()}
-        if self.tokenizer.eos_token_id is None:
-            self.tokenizer.eos_token_id = self.tokenizer.convert_tokens_to_ids('</s>')
-        if self.tokenizer.pad_token_id is None:
-            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
-
-        print(f"EOS Token ID: {self.tokenizer.eos_token_id}")
-        print(f"PAD Token ID: {self.tokenizer.pad_token_id}")
-        # Generate the response
-        with torch.no_grad():
-            outputs = self.model.generate(
-                **inputs,
-                max_new_tokens=max_new_tokens,
-                do_sample=True,
-                temperature=temp,
-                top_k=top_k,
-                top_p=top_p,
-                pad_token_id=self.tokenizer.eos_token_id,
-                # eos_token_id=self.tokenizer.convert_tokens_to_ids('<|eot_id|>'),
-                eos_token_id=self.tokenizer.eos_token_id,
-            )
-
-        # Decode the generated tokens
-        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=False)
-
-        # Extract the assistant's response
-        assistant_response = self.extract_assistant_response(prompt, generated_text)
-
-        # Append the assistant's response to the conversation
-        self.message.append({'role': 'assistant', 'content': assistant_response})
-
-        return assistant_response
-
-    def extract_assistant_response(self, prompt, generated_text):
-        # Llama will keep generating after the prompt submitted, this function will
-        # extract only the LLM's generated output with no special tokens
-
-        # Remove the prompt from the generated text
-        response_text = generated_text[len(prompt):]
-
-        # Split at the end-of-turn token
-        if '<|eot_id|>' in response_text:
-            assistant_response = response_text.split('<|eot_id|>')[0]
-        else:
-            assistant_response = response_text
-
-        # Remove special token at the end and leading or trailing whitespaces
-        assistant_response = assistant_response.replace('<|end_header_id|>', '')
-        assistant_response = assistant_response.strip()
-
-        return assistant_response
-
-
-if __name__ == "__main__":
-    base_model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
-    peft_model_name = "my-ai-university/TOMMI-0.3"
-
-    tokenizer = PreTrainedTokenizerFast.from_pretrained(
-        model_args.model_name_or_path,
-        return_tensors="pt")
-    tokenizer.pad_token = "<|reserved_special_token_5|>"
-
-    base_model = AutoModelForCausalLM.from_pretrained(
-        base_model_name,
-        torch_dtype=torch.bfloat16,
-        device_map="auto")
-    model = PeftModel.from_pretrained(base_model, peft_model_name)
-    model = model.merge_and_unload() # Optional: Merge adapter with base model for faster inference
-
-    # Initialize the conversation object
-    system_message = 'You are an expert professor who replies in a helpful way.'
-    conv = Conversation(
-        model,
-        tokenizer,
-        model.device,
-        system_message)
-
-    # Run the conversation loop
-    print("Starting conversation ...")
-    input_text = ""
-    while input_text.lower() != "exit":
-        input_text = input("Enter your prompt (type 'exit' to quit): ")
-
-        start_time = time.time()
-        response = conv.generate(input_text)
-        end_time = time.time()
-
-        print(response)
-        print(f"Response time: {end_time - start_time:.2f} seconds")
-
-    # Save the conversation to a file
-    with open("./conversation.txt", "w") as f:
-        f.write(str(conv.message))
+# ... (rest of the example code)
 ```