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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# 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:** [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
+
+
+
+## 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]

config.json

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+{
+  "architectures": [
+    "GenBioModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_genbio.GenBioConfig",
+    "AutoModel": "modeling_genbio.GenBioModel"
+  },
+  "hparams": {
+    "_class_path": "genbio_finetune.tasks.SequenceClassification",
+    "_instantiator": "lightning.pytorch.cli.instantiate_module",
+    "adapter": "genbio_finetune.models.LinearCLSAdapter",
+    "backbone": {
+      "class_path": "genbio_finetune.models.dummy",
+      "init_args": {
+        "config_overwrites": null,
+        "from_scratch": false,
+        "lora_alpha": 32,
+        "lora_dropout": 0.1,
+        "lora_r": 16,
+        "max_length": null,
+        "model_init_args": null,
+        "save_peft_only": true,
+        "use_peft": false
+      }
+    },
+    "batch_size": 128,
+    "lr_scheduler": null,
+    "n_classes": 2,
+    "optimizer": {
+      "class_path": "torch.optim.AdamW",
+      "init_args": {
+        "amsgrad": false,
+        "betas": [
+          0.9,
+          0.999
+        ],
+        "capturable": false,
+        "differentiable": false,
+        "eps": 1e-08,
+        "foreach": null,
+        "fused": null,
+        "lr": 0.001,
+        "maximize": false,
+        "weight_decay": 0.01
+      }
+    },
+    "reset_optimizer_states": false,
+    "strict_loading": true,
+    "use_legacy_adapter": false
+  },
+  "model_type": "genbio",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b88839655ec839cb2d5fdd95551007d9b7879f66d8b77aade74a3bfa1b0f6c3
+size 18209560

modeling_genbio.py

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+from lightning.pytorch import LightningModule
+from lightning.pytorch.core.saving import _load_state
+from transformers import PreTrainedModel, PretrainedConfig
+
+
+class GenBioConfig(PretrainedConfig):
+    model_type = "genbio"
+
+    def __init__(self, hparams=None, **kwargs):
+        self.hparams = hparams
+        super().__init__(**kwargs)
+
+
+class GenBioModel(PreTrainedModel):
+    config_class = GenBioConfig
+
+    def __init__(self, config: GenBioConfig, genbio_model=None, **kwargs):
+        super().__init__(config, **kwargs)
+        # if genbio_model is provided, we don't need to initialize it
+        if genbio_model is not None:
+            self.genbio_model = genbio_model
+            return
+        # otherwise, initialize the model from hyperparameters
+        cls_path = config.hparams["_class_path"]
+        module_path, name = cls_path.rsplit(".", 1)
+        genbio_cls = getattr(__import__(module_path, fromlist=[name]), name)
+        checkpoint = {
+            LightningModule.CHECKPOINT_HYPER_PARAMS_KEY: config.hparams,
+            "state_dict": {},
+        }
+        # TODO: _load_state is a private function and it throws a warning for an
+        # empty state_dict. We need a fucntion to intialize our model; this
+        # is the only choice we have for now.
+        self.genbio_model = _load_state(genbio_cls, checkpoint, strict_loading=False)
+
+    @classmethod
+    def from_genbio_model(cls, model: LightningModule):
+        return cls(GenBioConfig(hparams=model.hparams), genbio_model=model)
+
+    def forward(self, *args, **kwargs):
+        return self.genbio_model(*args, **kwargs)
+
+
+GenBioModel.register_for_auto_class("AutoModel")
+GenBioConfig.register_for_auto_class("AutoConfig")