
# 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. -->



- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### 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

{'setting': {'seed': 20000812,
  'os_environ': {'WANDB_API_KEY': None,
   'WANDB_RUN_ID': None,
   'CUDA_VISIBLE_DEVICES': '0,1,2,3,4,5,6,7',
   'MASTER_ADDR': 'localhost',
   'MASTER_PORT': '12315',
   'WORLD_SIZE': '1',
   'NODE_RANK': '0'},
  'wandb_config': {'project': 'DeepLoc_cls2', 'name': 'SaProt_650M_AF2'}},
 'model': {'model_py_path': 'esm/esm_classification_model',
  'kwargs': {'config_path': 'westlake-repl/SaProt_35M_AF2',
   'load_pretrained': True,
   'num_labels': 2,
   'use_lora': True,
   'lora_config_path': PosixPath('/content/saprot/weights/DeepLoc')},
  'lr_scheduler_kwargs': {'last_epoch': -1, 'init_lr': 2e-05, 'on_use': False},
  'optimizer_kwargs': {'betas': [0.9, 0.98], 'weight_decay': 0.01},
  'save_path': PosixPath('/content/saprot/weights/DeepLoc')},
 'dataset': {'dataset_py_path': 'esm/esm_classification_dataset',
  'dataloader_kwargs': {'batch_size': 1, 'num_workers': 12},
  'train_lmdb': '/content/saprot/LMDB/DeepLoc/cls2/foldseek/train',
  'valid_lmdb': '/content/saprot/LMDB/DeepLoc/cls2/foldseek/valid',
  'test_lmdb': '/content/saprot/LMDB/DeepLoc/cls2/foldseek/test',
  'kwargs': {'tokenizer': 'westlake-repl/SaProt_35M_AF2',
   'plddt_threshold': 70}},
 'Trainer': {'max_epochs': 1,
  'log_every_n_steps': 1,
  'strategy': {'find_unused_parameters': True},
  'logger': False,
  'enable_checkpointing': False,
  'val_check_interval': 0.5,
  'accelerator': 'gpu',
  'devices': 1,
  'num_nodes': 1,
  'accumulate_grad_batches': 1,
  'precision': 16,
  'num_sanity_val_steps': 0,
  'limit_train_batches': 1.0,
  'limit_val_batches': 1.0,
  'limit_test_batches': 1.0}}


## 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]


### Framework versions

- PEFT 0.7.1
{"library_name": "peft", "base_model": "westlake-repl/SaProt_35M_AF2"}
peft safetensors arxiv:1910.09700 base_model:westlake-repl/SaProt_35M_AF2 region:us