Add `library_name: transformers` to metadata

This PR adds the `library_name: transformers` metadata tag to the model card.

Evidence from the `Quickstart` section clearly shows the model's compatibility with the `transformers` library, as it imports `AutoTokenizer` and `AutoModelForCausalLM` from `transformers`. The `config.json` also defines `auto_map` entries for `transformers`-compatible classes like `Qwen3ForCausalLM`.

Adding this tag enables the automated "how to use" widget on the Hugging Face Hub, providing users with a quick and convenient way to interact with the model.

README.md

@@ -1,11 +1,12 @@
 ---
 license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - dllm
 - diffusion
 - llm
 - text_generation
-pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # ReFusion
@@ -91,7 +92,7 @@ def generate_refusion(model, tokenizer, prompt, gen_length=128, temperature=0.,
         cur_gen_pos_ids = gen_pos_ids[:, serial_num_block*block_length:(serial_num_block+1)*block_length] # (batch_size, block_length)
 
         cur_gen_blocks_x = cur_gen_x.reshape(batch_size, -1, cur_slot_size)
-        cur_gen_blocks_pos_ids = cur_gen_pos_ids.reshape(batch_size, -1, cur_slot_size)
+        cur_gen_blocks_pos_ids = cur_gen_blocks_pos_ids.reshape(batch_size, -1, cur_slot_size)
 
         # slot level generation
         while cur_gen_blocks_x.numel() > 0:
@@ -416,7 +417,15 @@ def main():
     model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to(device).eval()
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 
-    prompt = "You are an expert Python programmer. Your task is to write a single Python function to solve the problem described below, and here is your task: Write a function to sum all amicable numbers from 1 to a specified number.\n\nDirectly after the '[BEGIN]' marker, you must write only the Python code for the function. Do not provide any explanations, comments, or introductory text. The function must include the 'def' line, its arguments, the function body, and a 'return' statement. Your code should pass these tests:\n\nassert amicable_numbers_sum(999)==504\nassert amicable_numbers_sum(9999)==31626\nassert amicable_numbers_sum(99)==0\n[BEGIN]\n"
+    prompt = "You are an expert Python programmer. Your task is to write a single Python function to solve the problem described below, and here is your task: Write a function to sum all amicable numbers from 1 to a specified number.
+
+Directly after the '[BEGIN]' marker, you must write only the Python code for the function. Do not provide any explanations, comments, or introductory text. The function must include the 'def' line, its arguments, the function body, and a 'return' statement. Your code should pass these tests:
+
+assert amicable_numbers_sum(999)==504
+assert amicable_numbers_sum(9999)==31626
+assert amicable_numbers_sum(99)==0
+[BEGIN]
+"
 
     m = [{"role": "user", "content": prompt}, ]
     prompt = tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False, enable_thinking=True)