README.md

---
language:
- en
- zh
license: apache-2.0
library_name: zero
tags:
- multimodal
- vqa
- text
- audio
datasets:
- synthetic-dataset
metrics:
- accuracy
- bleu
- wer
model-index:
- name: AutoModel
  results:
  - task:
      type: vqa
      name: Visual Question Answering
    dataset:
      type: synthetic-dataset
      name: Synthetic Multimodal Dataset
      split: test
    metrics:
    - type: accuracy
      value: 85
pipeline_tag: any-to-any

model_index:
  - name: AutoModel
    results: 
      - task:
          type: vqa  # 支持视觉问答任务
          name: Visual Question Answering
        dataset:
          type: synthetdataset
          name: Synthetic Multimodal Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: accuracy
            value: 85.0
            name: VQA Accuracy
      - task:
          type: automatspeerecognition
          name: Automatic Speech Recognition
        dataset:
          type: synthetdataset
          name: Synthetic Multimodal Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: wer
            value: 15.3
            name: Test WER
      - task:
          type: captioning
          name: Image Captioning
        dataset:
          type: synthetdataset
          name: Synthetic Multimodal Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: bleu
            value: 27.5
            name: BL4



---
### **3. 提供可下载文件**
确保以下文件已上传到仓库，便于用户下载和运行：
- **模型权重文件**（如 `AutoModel.pth`）。
- **配置文件**（如 `config.json`）。
- **依赖文件**（如 `requirements.txt`）。
- **运行脚本**（如 `run_model.py`）。
--
用户可以直接下载这些文件并运行模型。

---

### **4. 自动运行模型的限制**
Hugging Face Hub 本身不能自动运行上传的模型，但通过 `Spaces` 提供的接口可以解决这一问题。`Spaces` 能够运行托管的推理服务，让用户无需本地配置即可测试模型。

---

### **推荐方法**
- **快速测试**：使用 Hugging Face `Spaces` 创建在线演示。
- **高级使用**：在模型卡中提供完整的运行说明，允许用户本地运行模型。

通过这些方式，您可以让模型仓库既支持在线运行，也便于用户离线部署。

## Uses
```python
import os
import torch
from model import AutoModel, Config

def load_model(model_path, config_path):
    """
    加载模型权重和配置
    """
    # 加载配置
    if not os.path.exists(config_path):
        raise FileNotFoundError(f"配置文件未找到: {config_path}")
    print(f"加载配置文件: {config_path}")
    config = Config()
    
    # 初始化模型
    model = AutoModel(config)
    
    # 加载权重
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"模型文件未找到: {model_path}")
    print(f"加载模型权重: {model_path}")
    state_dict = torch.load(model_path, map_location=torch.device("cpu"))
    model.load_state_dict(state_dict)
    model.eval()
    print("模型加载成功并设置为评估模式。")
    
    return model, config


def run_inference(model, config):
    """
    使用模型运行推理
    """
    # 模拟示例输入
    image = torch.randn(1, 3, 224, 224)  # 图像输入
    text = torch.randn(1, config.max_position_embeddings, config.hidden_size)  # 文本输入
    audio = torch.randn(1, config.audio_sample_rate)  # 音频输入

    # 模型推理
    outputs = model(image, text, audio)
    vqa_output, caption_output, retrieval_output, asr_output, realtime_asr_output = outputs

    # 打印结果
    print("\n推理结果:")
    print(f"VQA output shape: {vqa_output.shape}")
    print(f"Caption output shape: {caption_output.shape}")
    print(f"Retrieval output shape: {retrieval_output.shape}")
    print(f"ASR output shape: {asr_output.shape}")
    print(f"Realtime ASR output shape: {realtime_asr_output.shape}")

if __name__ == "__main__":
    # 文件路径
    model_path = "AutoModel.pth"
    config_path = "config.json"

    # 加载模型
    try:
        model, config = load_model(model_path, config_path)

        # 运行推理
        run_inference(model, config)
    except Exception as e:
        print(f"运行失败: {e}")
```

### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### 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 -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## 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

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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