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--- |
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library_name: diffusers |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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Script for creating dummy random model: |
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```python |
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import torch |
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from diffusers import HunyuanVideoTransformer3DModel, AutoencoderKLHunyuanVideo, FlowMatchEulerDiscreteScheduler, HunyuanVideoPipeline |
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from transformers import LlamaModel, LlamaTokenizerFast, CLIPTextModel, CLIPTokenizer, LlamaConfig, CLIPTextConfig |
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torch.manual_seed(0) |
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transformer = HunyuanVideoTransformer3DModel( |
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in_channels=4, |
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out_channels=4, |
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num_attention_heads=2, |
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attention_head_dim=10, |
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num_layers=1, |
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num_single_layers=1, |
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num_refiner_layers=1, |
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patch_size=1, |
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patch_size_t=1, |
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guidance_embeds=True, |
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text_embed_dim=16, |
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pooled_projection_dim=8, |
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rope_axes_dim=(2, 4, 4), |
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) |
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torch.manual_seed(0) |
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vae = AutoencoderKLHunyuanVideo( |
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in_channels=3, |
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out_channels=3, |
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latent_channels=4, |
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down_block_types=( |
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"HunyuanVideoDownBlock3D", |
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"HunyuanVideoDownBlock3D", |
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"HunyuanVideoDownBlock3D", |
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"HunyuanVideoDownBlock3D", |
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), |
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up_block_types=( |
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"HunyuanVideoUpBlock3D", |
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"HunyuanVideoUpBlock3D", |
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"HunyuanVideoUpBlock3D", |
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"HunyuanVideoUpBlock3D", |
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), |
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block_out_channels=(8, 8, 8, 8), |
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layers_per_block=1, |
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act_fn="silu", |
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norm_num_groups=4, |
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scaling_factor=0.476986, |
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spatial_compression_ratio=8, |
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temporal_compression_ratio=4, |
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mid_block_add_attention=True, |
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) |
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torch.manual_seed(0) |
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scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) |
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llama_text_encoder_config = LlamaConfig( |
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bos_token_id=0, |
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eos_token_id=2, |
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hidden_size=16, |
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intermediate_size=37, |
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layer_norm_eps=1e-05, |
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num_attention_heads=4, |
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num_hidden_layers=2, |
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pad_token_id=1, |
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vocab_size=1000, |
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hidden_act="gelu", |
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projection_dim=32, |
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) |
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clip_text_encoder_config = CLIPTextConfig( |
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bos_token_id=0, |
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eos_token_id=2, |
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hidden_size=8, |
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intermediate_size=37, |
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layer_norm_eps=1e-05, |
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num_attention_heads=4, |
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num_hidden_layers=2, |
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pad_token_id=1, |
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vocab_size=1000, |
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hidden_act="gelu", |
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projection_dim=32, |
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) |
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text_encoder = LlamaModel(llama_text_encoder_config) |
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tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM") |
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torch.manual_seed(0) |
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text_encoder_2 = CLIPTextModel(clip_text_encoder_config) |
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tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") |
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pipe = HunyuanVideoPipeline( |
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transformer=transformer, |
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text_encoder=text_encoder, |
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tokenizer=tokenizer, |
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text_encoder_2=text_encoder_2, |
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tokenizer_2=tokenizer_2, |
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vae=vae, |
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scheduler=scheduler, |
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) |
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pipe.push_to_hub("hf-internal-testing/tiny-random-hunyuanvideo") |
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``` |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- 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 Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
