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---
library_name: transformers
license: mit
base_model:
- meta-llama/Llama-3.2-11B-Vision-Instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** ASUS, NTHU, NTU
- **Model type:** Based on Llama-3.2-11B-Vision-Instruct, with added support for voice input.
- **Language(s) (NLP):** Supports multiple languages, but optimized for Traditional Chinese.
- **License:** MIT
- **Finetuned from model [optional]:** meta-llama/Llama-3.2-11B-Vision-Instruct
## Uses
The purpose of this multimodal model is to enrich knowledge about tourist attractions in Taiwan and engage travelers through interactive voice responses. You can provide a picture of a Taiwan's landscape to initiate a conversation.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import torch
from transformers import pipeline
import librosa
from PIL import Image
model_path = "taipei-1-mllama-project-2024/multi-modal-llama-tp1"
pipe = pipeline(model=model_path, trust_remote_code=True, device_map='auto')
audio, sr = librosa.load("/path/to/θ«εεηδΈηζ―ι»ζ―εͺ裑.wav", sr=16000)
image = Image.open("/path/to/ε°εεε».jpg")
turns = [
dict(
role='system',
content = "You are a travel expert who can accurately analyze the attractions in the pictures. All conversations should be conducted in Traditional Chinese.",
),
dict(
role='user',
content='<|image|><|begin_of_audio|><|audio|><|end_of_audio|>'
)
]
y_pred = pipe({'audio': [audio], 'images': [image], 'turns': turns, 'sampling_rate': sr}, max_new_tokens=300)
print(y_pred) # ιεΌ΅η
§ηδΈηζ―ι»ζ―ε°η£ηγε°εεε»γγ...
```
## Training Details
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[Taiwan-1 Super Computer](https://en.wikipedia.org/wiki/Taipei-1_(supercomputer))
#### Hardware
H100 x 8 GPUs per node x 16 nodes
|