Add paper and code links to model card

README.md

@@ -1,132 +1,21 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-4B
 language:
 - en
 - ko
 library_name: transformers
+license: apache-2.0
+pipeline_tag: audio-text-to-text
 tags:
 - audio
 - text-generation
-pipeline_tag: audio-text-to-text
-base_model:
-- Qwen/Qwen3-4B
-# model-index:
-# - name: FastSLM
-#   results:
-#   - task:
-#       name: Automatic Speech Recognition
-#       type: automatic-speech-recognition
-#     dataset:
-#       name: AMI (Meetings test)
-#       type: edinburghcstr/ami
-#       config: ihm
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 10.8
-#   - task:
-#       name: Automatic Speech Recognition
-#       type: automatic-speech-recognition
-#     dataset:
-#       name: Earnings-22
-#       type: revdotcom/earnings22
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 10.7
-#   - task:
-#       name: Automatic Speech Recognition
-#       type: automatic-speech-recognition
-#     dataset:
-#       name: GigaSpeech
-#       type: speechcolab/gigaspeech
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 10.7
-#   - task:
-#       name: Automatic Speech Recognition
-#       type: automatic-speech-recognition
-#     dataset:
-#       name: LibriSpeech (clean)
-#       type: librispeech_asr
-#       config: other
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 2.26
-#   - task:
-#       name: Automatic Speech Recognition
-#       type: automatic-speech-recognition
-#     dataset:
-#       name: LibriSpeech (other)
-#       type: librispeech_asr
-#       config: other
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 4.87
-#   - task:
-#       name: Automatic Speech Recognition
-#       type: automatic-speech-recognition
-#     dataset:
-#       name: Vox Populi
-#       type: facebook/voxpopuli
-#       config: en
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 6.55
-#   - task:
-#       type: Automatic Speech Recognition
-#       name: automatic-speech-recognition
-#     dataset:
-#       name: tedlium-v3
-#       type: LIUM/tedlium
-#       config: release1
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 3.97
-#   - task:
-#       type: Automatic Speech Recognition
-#       name: automatic-speech-recognition
-#     dataset:
-#       name: SPGI Speech
-#       type: kensho/spgispeech
-#       config: test
-#       split: test
-#       args:
-#         language: en
-#     metrics:
-#     - name: Test WER
-#       type: wer
-#       value: 2.33
 ---
 
 * FastSLM is an designed for efficient and accurate speech-to-text transcription.
 
+**Paper**: [FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation](https://huggingface.co/papers/2601.06199)  
+**Code**: [GitHub - Lee-junseok1025/FastSLM](https://github.com/Lee-junseok1025/FastSLM)
 
 <!-- * 🔊 **HFQ-Former**: Hierarchically compresses high-frame-rate audio features while preserving the audio's local and global contextual information. -->
 <!-- * 🔊 **Adaptor**:  -->
@@ -217,7 +106,8 @@ else:
 # A task token is not required, but it is recommended for achieving a more appropriate task.
 TASK_TOKEN = "<|ASR|>" 
 AUDIO_TOKEN = "<|audio_bos|><|AUDIO|><|audio_eos|>"
-user_prompt = f"{TASK_TOKEN}{AUDIO_TOKEN}\nTranscribe the audio clip into text."
+user_prompt = f"{TASK_TOKEN}{AUDIO_TOKEN}
+Transcribe the audio clip into text."
 
 prompt = [{"role": "user", "content": user_prompt}]
 input_ids = tokenizer.apply_chat_template(