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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": [
+    "BiomedCLIPModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_biomedclip.BiomedCLIPConfig",
+    "AutoModel": "modeling_biomedclip.BiomedCLIPModel"
+  },
+  "dtype": "float32",
+  "embed_dim": 512,
+  "model_type": "biomed_clip",
+  "text_cfg": {
+    "act_kwargs": null,
+    "block_type": null,
+    "context_length": 256,
+    "embed_cls": false,
+    "eos_id": 2,
+    "final_ln_after_pool": false,
+    "heads": 8,
+    "hf_model_name": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract",
+    "hf_model_pretrained": false,
+    "hf_pooler_type": "cls_last_hidden_state_pooler",
+    "hf_proj_type": "mlp",
+    "hf_tokenizer_name": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract",
+    "layers": 12,
+    "ls_init_value": null,
+    "mlp_ratio": 4.0,
+    "no_causal_mask": false,
+    "norm_kwargs": null,
+    "output_tokens": false,
+    "pad_id": 0,
+    "pool_type": "argmax",
+    "proj_bias": false,
+    "proj_type": "linear",
+    "qk_norm": false,
+    "scale_attn": false,
+    "scale_attn_inner": false,
+    "scale_fc": false,
+    "scale_heads": false,
+    "scaled_cosine_attn": false,
+    "tokenizer_kwargs": null,
+    "tokenizer_mode": null,
+    "vocab_size": 49408,
+    "width": 512
+  },
+  "transformers_version": "5.0.0",
+  "vision_cfg": {
+    "act_kwargs": null,
+    "attentional_pool": false,
+    "attn_pooler_heads": 8,
+    "attn_pooler_queries": 256,
+    "block_type": null,
+    "final_ln_after_pool": false,
+    "head_width": 64,
+    "image_size": 224,
+    "layers": 12,
+    "ls_init_value": null,
+    "mlp_ratio": 4.0,
+    "no_ln_pre": false,
+    "norm_kwargs": null,
+    "output_tokens": false,
+    "patch_dropout": 0.0,
+    "patch_size": 16,
+    "pool_type": "tok",
+    "pos_embed_type": "learnable",
+    "qk_norm": false,
+    "scale_attn": false,
+    "scale_attn_inner": false,
+    "scale_fc": false,
+    "scale_heads": false,
+    "scaled_cosine_attn": false,
+    "timm_drop": 0.0,
+    "timm_drop_path": null,
+    "timm_model_name": "vit_base_patch16_224",
+    "timm_model_pretrained": false,
+    "timm_pool": "",
+    "timm_proj": "linear",
+    "timm_proj_bias": false,
+    "width": 768
+  }
+}

configuration_biomedclip.py

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+from typing import Any, Dict
+from transformers import PretrainedConfig
+
+class BiomedCLIPConfig(PretrainedConfig):
+    model_type = "biomed_clip"
+
+    def __init__(self, 
+                 embed_dim: int = 0,
+                 vision_cfg: Dict[str, Any] = None,
+                 text_cfg: Dict[str, Any] = None,
+                 **kwargs):
+        super().__init__(**kwargs)
+
+        self.embed_dim = embed_dim
+        self.vision_cfg = vision_cfg or dict()
+        self.text_cfg = text_cfg or dict()
+
+

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb6ca3b968752bebb66566507634992e3ca5462e92dd1d20c72557839427a7d
+size 783654820

modeling_biomedclip.py

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+from typing import Optional, Dict, Union, List
+import torch
+from transformers import PreTrainedModel
+from open_clip import CLIPTextCfg, CLIPVisionCfg, CustomTextCLIP
+from .configuration_biomedclip import BiomedCLIPConfig
+
+class BiomedCLIPModel(PreTrainedModel):
+    config_class = BiomedCLIPConfig
+    base_model_prefix = "model"          # optional but recommended
+    main_input_name = "pixel_values"     # optional; pick what makes sense for your usage
+
+    @property
+    def all_tied_weights_keys(self):
+        # Transformers v5 loader expects a dict-like with `.keys()`.
+        # This model has no tied weights in the HF sense.
+        return {}
+
+    def __init__(self, config: BiomedCLIPConfig):
+        super().__init__(config)
+        self.model = CustomTextCLIP(
+            embed_dim=config.embed_dim,
+            vision_cfg=CLIPVisionCfg(**config.vision_cfg),
+            text_cfg=CLIPTextCfg(**config.text_cfg),
+        )
+
+    def forward(
+        self,
+        vision_inputs: Optional[torch.Tensor] = None,
+        text_inputs: Optional[torch.Tensor] = None,
+        **kwargs
+    ):
+        return self.model(vision_inputs, text_inputs, **kwargs)
+
+    def forward_intermediates(
+        self,
+        vision_inputs: Optional[torch.Tensor] = None,
+        text_inputs: Optional[torch.Tensor] = None,
+        **kwargs
+    ) -> Dict[str, Union[torch.Tensor, List[torch.Tensor]]]:
+        return self.model.forward_intermediates(vision_inputs, text_inputs, **kwargs)
+