Instructions to use zeroMN/auto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeroMN/auto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="zeroMN/auto")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zeroMN/auto", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| - zh | |
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - multimodal | |
| - vqa | |
| - text | |
| - audio | |
| datasets: | |
| - synthetic-dataset | |
| - zeroMN/nlp_corpus_zh | |
| - zeroMN/hanlp_date-zh | |
| 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: question-answering | |
| model_index: | |
| - name: AutoModel | |
| results: | |
| - task: | |
| type: vqa | |
| name: Visual Question Answering | |
| dataset: | |
| type: synthetdataset | |
| name: Synthetic Multimodal Dataset | |
| config: default | |
| split: test | |
| revision: main | |
| metrics: | |
| - type: accuracy | |
| value: 85 | |
| 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 | |
| ### 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 | |
| [More Information Needed] | |
| #### 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 | |
| [More Information Needed] | |
| #### Hardware | |
| [More Information Needed] | |
| #### Software | |
| [More Information Needed] | |
| ## 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:** | |
| [More Information Needed] | |
| **APA:** | |
| [More Information Needed] | |
| ## Glossary [optional] | |
| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> | |
| [More Information Needed] | |
| ## More Information [optional] | |
| [More Information Needed] | |
| ## Model Card Authors [optional] | |
| [More Information Needed] | |
| ## Model Card Contact | |
| [More Information Needed] |