Update README.md

README.md

@@ -44,11 +44,16 @@ base_model_relation: finetune
 > You can find these codes in the repository.
 
 ```python
->>> from transformers import AutoModelForCausalLM, AutoProcessor
+>>> from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
 >>> model = AutoModelForCausalLM.from_pretrained("zhoukz/MiDashengLM-HF-dev", trust_remote_code=True)
 >>> model.eval()
 >>> processor = AutoProcessor.from_pretrained("zhoukz/MiDashengLM-HF-dev", trust_remote_code=True)
 
+>>> tokenizer = AutoTokenizer.from_pretrained("mispeech/MiDashengLM-HF-dev")
+>>> processor = AutoProcessor.from_pretrained("mispeech/MiDashengLM-HF-dev", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("mispeech/MiDashengLM-HF-dev", trust_remote_code=True)
+>>> model.eval()
+
 >>> messages = [
 ...     {
 ...         "role": "system",
@@ -78,12 +83,49 @@ base_model_relation: finetune
 ...         return_dict=True,
 ...     )
 ...     generation = model.generate(**model_inputs)
-...     output = processor.batch_decode(generation, skip_special_tokens=True)
+...     output = tokenizer.batch_decode(generation, skip_special_tokens=True)
 
 >>> print(output)
 ["An engine is idling."]
 ```
 
+[`processor.apply_chat_template`] accepts audio inputs specified in various ways, including file paths, URLs, and `np.ndarray`:
+
+[`processor.apply_chat_template`]: https://huggingface.co/docs/transformers/v4.53.1/en/main_classes/processors#transformers.ProcessorMixin.apply_chat_template
+
+```python
+>>> messages_by_path = [
+...     {
+...         "role": "user",
+...         "content": [
+...             {"type": "text", "text": "Caption the audio."},
+...             {"type": "audio", "path": "/path/to/audio.wav"},
+...         ],
+...     },
+... ]
+
+>>> messages_by_url = [
+...     {
+...         "role": "user",
+...         "content": [
+...             {"type": "text", "text": "Caption the audio."},
+...             {"type": "audio", "url": "https://example.com/audio.wav"},
+...         ],
+...     },
+... ]
+
+>>> import numpy as np
+>>> messages_by_data = [
+...     {
+...         "role": "user",
+...         "content": [
+...             {"type": "text", "text": "Caption the audio."},
+...             {"type": "audio", "audio": np.random.randn(16000)},
+...         ],
+...     },
+... ]
+```
+
 ## Citation
 
 ```bibtex