Improve model card: Add pipeline tag, library name, and GitHub link

This PR enhances the model card by:
- Adding the `pipeline_tag: text-generation` to ensure better discoverability on the Hub (https://huggingface.co/models?pipeline_tag=text-generation).
- Specifying the `library_name: transformers` to indicate compatibility with the Hugging Face Transformers library.
- Including a direct link to the GitHub repository (https://github.com/azzzacs/ASAP) for easy access to the code.

Please review and merge this PR.

README.md

@@ -1,11 +1,13 @@
 ---
-license: mit
-datasets:
-- open-r1/codeforces-cots
 base_model:
 - deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+datasets:
+- open-r1/codeforces-cots
+license: mit
 tags:
 - code
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # Paper Page
@@ -18,6 +20,9 @@ tags:
 
 This model was fine-tuned on pruned CoTs examples derived via our **ASAP** method(**A**nchor-guided, **S**urpris**a**l-polished **P**runing), focusing on highly compressed yet semantically informative reasoning traces.
 
+## Code
+For the official implementation, please refer to the [ASAP GitHub repository](https://github.com/azzzacs/ASAP).
+
 # 🧠 Reasoning Mode
 
 We recommend **explicitly activating reasoning mode by inserting ```<think>``` in the prompt**.
@@ -30,8 +35,10 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("azzzacs/LogicCoder-8B", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("azzzacs/LogicCoder-8B", device_map="auto", trust_remote_code=True).eval()
 
-message = [{"role": "user", "content": "Please write a Python quick sort algorithm.\n"}]
-prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + "<｜Assistant｜><think>\n"
+message = [{"role": "user", "content": "Please write a Python quick sort algorithm.
+"}]
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) + "<｜Assistant｜><think>
+"
 
 model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)