---
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 🤗 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/請問圖片中的景點是哪裡.wav", sr=16000)
image = Image.open("/path/to/台南孔廟.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) # 這張照片中的景點是台灣的「台南孔廟」。...
```

## 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