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clip/google/siglip-base-patch16-256/README.md

@@ -0,0 +1,110 @@
+---
+license: apache-2.0
+tags:
+- vision
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# SigLIP (base-sized model) 
+
+SigLIP model pre-trained on WebLi at resolution 256x256. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
+
+Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes.
+
+A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713).
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for
+other versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to perform zero-shot image classification:
+
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+
+model = AutoModel.from_pretrained("google/siglip-base-patch16-256")
+processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-256")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+texts = ["a photo of 2 cats", "a photo of 2 dogs"]
+inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+logits_per_image = outputs.logits_per_image
+probs = torch.sigmoid(logits_per_image) # these are the probabilities
+print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
+```
+
+Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user:
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+
+# load pipe
+image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-224")
+
+# load image
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# inference
+outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
+outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
+print(outputs)
+```
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
+
+## Training procedure
+
+### Training data
+
+SigLIP is pre-trained on the English image-text pairs of the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+
+### Preprocessing
+
+Images are resized/rescaled to the same resolution (256x256) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+Texts are tokenized and padded to the same length (64 tokens).
+
+### Compute
+
+The model was trained on 16 TPU-v4 chips for three days.
+
+## Evaluation results
+
+Evaluation of SigLIP compared to CLIP is shown below (taken from the paper).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
+alt="drawing" width="600"/>
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{zhai2023sigmoid,
+      title={Sigmoid Loss for Language Image Pre-Training}, 
+      author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer},
+      year={2023},
+      eprint={2303.15343},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

clip/google/siglip-base-patch16-256/config.json

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+{
+  "architectures": [
+    "SiglipModel"
+  ],
+  "initializer_factor": 1.0,
+  "model_type": "siglip",
+  "text_config": {
+    "model_type": "siglip_text_model"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0.dev0",
+  "vision_config": {
+    "image_size": 256,
+    "model_type": "siglip_vision_model"
+  }
+}

clip/google/siglip-base-patch16-256/preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 256,
+    "width": 256
+  }
+}

clip/google/siglip-base-patch16-256/special_tokens_map.json

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+    "single_word": false
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+    "single_word": false
+  }
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+    }
+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "model_input_names": [
+    "input_ids"
+  ],
+  "model_max_length": 64,
+  "pad_token": "</s>",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "SiglipTokenizer",
+  "unk_token": "<unk>"
+}

clip/google/siglip-large-patch16-256/.gitattributes

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clip/google/siglip-large-patch16-256/README.md

@@ -0,0 +1,110 @@
+---
+license: apache-2.0
+tags:
+- vision
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# SigLIP (large-sized model) 
+
+SigLIP model pre-trained on WebLi at resolution 256x256. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
+
+Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes.
+
+A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713).
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for
+other versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to perform zero-shot image classification:
+
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+
+model = AutoModel.from_pretrained("google/siglip-base-patch16-256")
+processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-256")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+texts = ["a photo of 2 cats", "a photo of 2 dogs"]
+inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+logits_per_image = outputs.logits_per_image
+probs = torch.sigmoid(logits_per_image) # these are the probabilities
+print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
+```
+
+Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user:
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+
+# load pipe
+image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-256")
+
+# load image
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# inference
+outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
+outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
+print(outputs)
+```
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
+
+## Training procedure
+
+### Training data
+
+SigLIP is pre-trained on the English image-text pairs of the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+
+### Preprocessing
+
+Images are resized/rescaled to the same resolution (256x256) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+Texts are tokenized and padded to the same length (64 tokens).
+
+### Compute
+
+The model was trained on 16 TPU-v4 chips for three days.
+
+## Evaluation results
+
+Evaluation of SigLIP compared to CLIP is shown below (taken from the paper).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
+alt="drawing" width="600"/>
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{zhai2023sigmoid,
+      title={Sigmoid Loss for Language Image Pre-Training}, 
+      author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer},
+      year={2023},
+      eprint={2303.15343},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

clip/google/siglip-large-patch16-256/config.json

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+{
+  "architectures": [
+    "SiglipModel"
+  ],
+  "initializer_factor": 1.0,
+  "model_type": "siglip",
+  "text_config": {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "model_type": "siglip_text_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0.dev0",
+  "vision_config": {
+    "hidden_size": 1024,
+    "image_size": 256,
+    "intermediate_size": 4096,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24
+  }
+}

clip/google/siglip-large-patch16-256/preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 256,
+    "width": 256
+  }
+}

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clip/google/siglip-so400m-patch14-224/README.md

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+---
+license: apache-2.0
+tags:
+- vision
+widget:
+- src: >-
+    https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg
+  candidate_labels: bee in the sky, bee on the flower
+  example_title: Bee
+library_name: transformers
+pipeline_tag: zero-shot-image-classification
+---
+
+# SigLIP (shape-optimized model) 
+
+SigLIP model pre-trained on WebLi at resolution 224x224. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
+
+This model has the SoViT-400m architecture, which is the shape-optimized version as presented in [Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design](https://arxiv.org/abs/2305.13035) by Alabdulmohsin et al.
+
+Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes.
+
+A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713).
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for
+other versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to perform zero-shot image classification:
+
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+
+model = AutoModel.from_pretrained("google/siglip-so400m-patch14-224")
+processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-224")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+texts = ["a photo of 2 cats", "a photo of 2 dogs"]
+inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+logits_per_image = outputs.logits_per_image
+probs = torch.sigmoid(logits_per_image) # these are the probabilities
+print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
+```
+
+Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user:
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+
+# load pipe
+image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-so400m-patch14-224")
+
+# load image
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# inference
+outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
+outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
+print(outputs)
+```
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
+
+## Training procedure
+
+### Training data
+
+SigLIP is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+
+### Preprocessing
+
+Images are resized/rescaled to the same resolution (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+Texts are tokenized and padded to the same length (64 tokens).
+
+### Compute
+
+The model was trained on 16 TPU-v4 chips for three days.
+
+## Evaluation results
+
+Evaluation of SigLIP compared to CLIP is shown below (taken from the paper).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
+alt="drawing" width="600"/>
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{zhai2023sigmoid,
+      title={Sigmoid Loss for Language Image Pre-Training}, 
+      author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer},
+      year={2023},
+      eprint={2303.15343},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

clip/google/siglip-so400m-patch14-224/config.json

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+  "architectures": [
+    "SiglipModel"
+  ],
+  "initializer_factor": 1.0,
+  "model_type": "siglip",
+  "text_config": {
+    "hidden_size": 1152,
+    "intermediate_size": 4304,
+    "max_position_embeddings": 16,
+    "model_type": "siglip_text_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.0.dev0",
+  "vision_config": {
+    "hidden_size": 1152,
+    "intermediate_size": 4304,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "patch_size": 14
+  }
+}

clip/google/siglip-so400m-patch14-224/preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

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+  "processor_class": "SiglipProcessor",
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+  "tokenizer_class": "SiglipTokenizer",
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clip/google/siglip2-so400m-patch14-224/README.md

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+---
+license: apache-2.0
+tags:
+- vision
+widget:
+  - src: >-
+      https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg
+    candidate_labels: bee in the sky, bee on the flower
+    example_title: Bee
+library_name: transformers
+pipeline_tag: zero-shot-image-classification
+---
+
+# SigLIP 2 So400m
+
+[SigLIP 2](https://huggingface.co/papers/2502.14786) extends the pretraining objective of
+[SigLIP](https://huggingface.co/papers/2303.15343) with prior, independently developed techniques
+into a unified recipe, for improved semantic understanding, localization, and dense features.
+
+## Intended uses
+
+You can use the raw model for tasks like zero-shot image classification and
+image-text retrieval, or as a vision encoder for VLMs (and other vision tasks).
+
+Here is how to use this model to perform zero-shot image classification:
+
+```python
+from transformers import pipeline
+
+# load pipeline
+ckpt = "google/siglip2-so400m-patch14-224"
+image_classifier = pipeline(model=ckpt, task="zero-shot-image-classification")
+
+# load image and candidate labels
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+candidate_labels = ["2 cats", "a plane", "a remote"]
+
+# run inference
+outputs = image_classifier(image, candidate_labels)
+print(outputs)
+```
+
+You can encode an image using the Vision Tower like so:
+
+```python
+import torch
+from transformers import AutoModel, AutoProcessor
+from transformers.image_utils import load_image
+
+# load the model and processor
+ckpt = "google/siglip2-so400m-patch14-224"
+model = AutoModel.from_pretrained(ckpt, device_map="auto").eval()
+processor = AutoProcessor.from_pretrained(ckpt)
+
+# load the image
+image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg")
+inputs = processor(images=[image], return_tensors="pt").to(model.device)
+
+# run infernece
+with torch.no_grad():
+    image_embeddings = model.get_image_features(**inputs)    
+
+print(image_embeddings.shape)
+```
+
+For more code examples, we refer to the [siglip documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
+
+## Training procedure
+
+SigLIP 2 adds some clever training objectives on top of SigLIP:
+
+1. Decoder loss
+2. Global-local and masked prediction loss
+3. Aspect ratio and resolution adaptibility 
+
+### Training data
+
+SigLIP 2 is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+
+### Compute
+
+The model was trained on up to 2048 TPU-v5e chips.
+
+## Evaluation results
+
+Evaluation of SigLIP 2 is shown below (taken from the paper).
+
+![Evaluation Table](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/sg2-blog/eval_table.png)
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{tschannen2025siglip2multilingualvisionlanguage,
+      title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features}, 
+      author={Michael Tschannen and Alexey Gritsenko and Xiao Wang and Muhammad Ferjad Naeem and Ibrahim Alabdulmohsin and Nikhil Parthasarathy and Talfan Evans and Lucas Beyer and Ye Xia and Basil Mustafa and Olivier Hénaff and Jeremiah Harmsen and Andreas Steiner and Xiaohua Zhai},
+      year={2025},
+      eprint={2502.14786},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.14786}, 
+}
+```

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+  "model_type": "siglip",
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+    "intermediate_size": 4304,
+    "model_type": "siglip_text_model",
+    "num_attention_heads": 16,
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+    "vocab_size": 256000
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+    "patch_size": 14
+  }
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+  },
+  "additional_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "bos_token": "<bos>",
+  "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
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+    "input_ids"
+  ],
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+  "pad_token": "<pad>",
+  "padding_side": "right",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

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clip/laion/CLIP-ViT-B-32-laion2B-s34B-b79K/README.md

@@ -0,0 +1,140 @@
+---
+license: mit
+widget:
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+pipeline_tag: zero-shot-image-classification
+---
+# Model Card for CLIP ViT-B/32 - LAION-2B
+
+#  Table of Contents
+
+1. [Model Details](#model-details)
+2. [Uses](#uses)
+3. [Training Details](#training-details)
+4. [Evaluation](#evaluation)
+5. [Acknowledgements](#acknowledgements)
+6. [Citation](#citation)
+7. [How To Get Started With the Model](#how-to-get-started-with-the-model)
+
+
+# Model Details
+
+## Model Description
+
+A CLIP ViT-B/32 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).
+
+Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. 
+
+# Uses
+
+As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. 
+
+The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. 
+
+## Direct Use
+
+Zero-shot image classification, image and text retrieval, among others.
+
+## Downstream Use
+
+Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
+
+## Out-of-Scope Use
+
+As per the OpenAI models,
+
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+
+Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
+
+Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
+
+Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.
+
+# Training Details
+
+## Training Data
+
+This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).
+
+**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
+
+## Training Procedure
+
+Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/B-32-2B--VmlldzoyNDkwNDMy).
+
+# Evaluation
+
+Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).
+
+## Testing Data, Factors & Metrics
+
+### Testing Data
+
+The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
+
+**TODO** - more detail
+
+## Results
+
+The model achieves a 66.6 zero-shot top-1 accuracy on ImageNet-1k.
+
+An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb
+
+**TODO** - create table for just this model's metrics.
+
+# Acknowledgements
+
+Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model.
+
+# Citation
+
+**BibTeX:**
+
+In addition to forthcoming LAION-5B (https://laion.ai/blog/laion-5b/) paper, please cite:
+
+OpenAI CLIP paper
+```
+@inproceedings{Radford2021LearningTV,
+  title={Learning Transferable Visual Models From Natural Language Supervision},
+  author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
+  booktitle={ICML},
+  year={2021}
+}
+```
+
+OpenCLIP software
+```
+@software{ilharco_gabriel_2021_5143773,
+  author       = {Ilharco, Gabriel and
+                  Wortsman, Mitchell and
+                  Wightman, Ross and
+                  Gordon, Cade and
+                  Carlini, Nicholas and
+                  Taori, Rohan and
+                  Dave, Achal and
+                  Shankar, Vaishaal and
+                  Namkoong, Hongseok and
+                  Miller, John and
+                  Hajishirzi, Hannaneh and
+                  Farhadi, Ali and
+                  Schmidt, Ludwig},
+  title        = {OpenCLIP},
+  month        = jul,
+  year         = 2021,
+  note         = {If you use this software, please cite it as below.},
+  publisher    = {Zenodo},
+  version      = {0.1},
+  doi          = {10.5281/zenodo.5143773},
+  url          = {https://doi.org/10.5281/zenodo.5143773}
+}
+```
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets

clip/laion/CLIP-ViT-B-32-laion2B-s34B-b79K/config.json

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+  },
+  "vision_config_dict": {
+    "hidden_act": "gelu"
+  }
+}

clip/laion/CLIP-ViT-B-32-laion2B-s34B-b79K/open_clip_config.json

@@ -0,0 +1,30 @@
+{
+    "model_cfg": {
+        "embed_dim": 512,
+        "vision_cfg": {
+            "image_size": 224,
+            "layers": 12,
+            "width": 768,
+            "patch_size": 32
+        },
+        "text_cfg": {
+            "context_length": 77,
+            "vocab_size": 49408,
+            "width": 512,
+            "heads": 8,
+            "layers": 12
+        }
+    },
+    "preprocess_cfg": {
+        "mean": [
+            0.48145466,
+            0.4578275,
+            0.40821073
+        ],
+        "std": [
+            0.26862954,
+            0.26130258,
+            0.27577711
+        ]
+    }
+}

clip/laion/CLIP-ViT-B-32-laion2B-s34B-b79K/preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

clip/laion/CLIP-ViT-B-32-laion2B-s34B-b79K/special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": "<|endoftext|>"}

clip/laion/CLIP-ViT-B-32-laion2B-s34B-b79K/tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+    "unk_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "bos_token": {
+        "content": "<|startoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "eos_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "pad_token": "<|endoftext|>",
+    "add_prefix_space": false,
+    "errors": "replace",
+    "do_lower_case": true,
+    "name_or_path": "openai/clip-vit-base-patch32",
+    "model_max_length": 77,
+    "special_tokens_map_file": "./special_tokens_map.json",
+    "tokenizer_class": "CLIPTokenizer"
+}

clip/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/config.json

@@ -0,0 +1,179 @@
+{
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 1024,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
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+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
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+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 77,
+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "projection_dim": 1024,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.3",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "vocab_size": 49408
+  },
+  "text_config_dict": {
+    "hidden_act": "gelu",
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "projection_dim": 1024
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
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+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_size": 1280,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 5120,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_channels": 3,
+    "num_hidden_layers": 32,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "projection_dim": 1024,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.3",
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_act": "gelu",
+    "hidden_size": 1280,
+    "intermediate_size": 5120,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 32,
+    "patch_size": 14,
+    "projection_dim": 1024
+  }
+}

clip/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

clip/laion/CLIP-ViT-L-14-laion2B-s32B-b82K/.gitattributes

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clip/laion/CLIP-ViT-L-14-laion2B-s32B-b82K/README.md

@@ -0,0 +1,232 @@
+---
+license: mit
+widget:
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+library_name: open_clip
+pipeline_tag: zero-shot-image-classification
+---
+# Model Card for CLIP ViT-L/14 - LAION-2B
+
+#  Table of Contents
+
+1. [Model Details](#model-details)
+2. [Uses](#uses)
+3. [Training Details](#training-details)
+4. [Evaluation](#evaluation)
+5. [Acknowledgements](#acknowledgements)
+6. [Citation](#citation)
+7. [How To Get Started With the Model](#how-to-get-started-with-the-model)
+
+
+# Model Details
+
+## Model Description
+
+A CLIP ViT L/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).
+
+Model training ('babysitting') done by Ross Wightman on the [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) supercomputer. See acknowledgements below.
+
+# Uses
+
+As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. 
+
+The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. 
+
+## Direct Use
+
+Zero-shot image classification, image and text retrieval, among others.
+
+## Downstream Use
+
+Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
+
+## Out-of-Scope Use
+
+As per the OpenAI models,
+
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+
+Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
+
+Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
+
+Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.
+
+# Training Details
+
+## Training Data
+
+This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).
+
+**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
+
+## Training Procedure
+
+The model was trained on 384 A100 GPUs using 200M sample 'virtual' epochs where dataset shards were sampled with replacement. The model was trained with 160 virtual epochs for a total of 32B samples seen.
+
+The first 68 epochs were trained with float16 AMP, global batch size 79K (208 per GPU). Initially running to epoch 75, where the loss spiked and training failed with NaN.
+
+Romain Beaumont was training H/14 and g/14 models at the same time on Stability cluster and hit similar instabilities. Collectively we tried restarts with, 
+* different dataset shuffle seed
+* different LR
+* gradient clipping
+* modifications to the architecture
+  * Norm modifications (stable norm for final, post embed norm for text transformer) as per https://github.com/mlfoundations/open_clip/pull/153 thanks to Phil Wang
+  * Extra attention block norms ala Normformer (https://arxiv.org/abs/2110.09456)
+  * Scaled cosine attention ala Swin-V2 (https://arxiv.org/abs/2111.09883)
+
+None of the above ended up working. Most blew up within the same epoch as original, with the exception of architecture mods.
+  * Normformer mods signifcantly altered the network such that resuming did not quickly converge to previous performance, this was abandoned but might be worth trying from start.
+  * Scaled cosine attn initially looked promising and lasted until epoch 90 before loss suddenly increased and appeared to remain 'stuck'.
+
+In the end, restarting at epoch 69 with `float32` precision solved all instabilities and training continued from there with global batch size 86k (224 per GPU). On A100 GPUs, `float32` had a minimal impact on the throughput once `tf32` matmuls were enabled in PyTorch. Approximately 10% slower than `float16 AMP`. Romain similary changed the precision but ended up using `bfloat16 AMP` to resolve issues.
+
+### Slum Script
+
+```
+#SBATCH --nodes=96
+#SBATCH --gres=gpu:4
+#SBATCH --ntasks-per-node=4
+#SBATCH --cpus-per-task=6
+#SBATCH --wait-all-nodes=1
+#SBATCH --job-name=open_clip_laion2b
+
+# load low-level libraries
+ml purge
+source /conda/bin/activate pytorch-112
+
+export NCCL_ASYNC_ERROR_HANDLING=1
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+export MASTER_PORT=12802
+
+### get the first node name as master address - customized for vgg slurm
+### e.g. master(gnodee[2-5],gnoded1) == gnodee2
+echo "NODELIST="${SLURM_NODELIST}
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=$master_addr"i"
+echo "MASTER_ADDR="$MASTER_ADDR
+
+cd /home/me/open_clip
+export PYTHONPATH="$PYTHONPATH:$PWD/src"
+
+srun --cpu_bind=none,v --accel-bind=gn python -u src/training/main.py \
+    --save-frequency 1 \
+    --zeroshot-frequency 1 \
+    --train-data="/data/laion2B-en/{00000..23295}.tar" \
+    --train-num-samples=200000000 \
+    --warmup 10000 \
+    --lr "1e-3" \
+    --batch-size=224 \
+    --epochs=160 \
+    --workers=6 \
+    --model ViT-L-14 \
+    --name "L14-laion2B" \
+    --report-to "tensorboard" \
+    --seed 0 \
+    --precision 'fp32' \
+    --ddp-static-graph \
+    --local-loss \
+    --dataset-resampled \
+    --gather-with-grad \
+    --grad-checkpointing
+```
+
+# Evaluation
+
+Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).
+
+## Testing Data, Factors & Metrics
+
+### Testing Data
+
+The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
+
+**TODO** - more detail
+
+## Results
+
+The model achieves a 75.3 zero-shot top-1 accuracy on ImageNet-1k.
+
+An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb
+
+**TODO** - create table for just this model's metrics.
+
+# Acknowledgements
+
+Acknowledging the Gauss Centre for Supercomputing e.V. (http://gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC).
+
+# Citation
+
+**BibTeX:**
+
+LAION-5B
+```bibtex
+@inproceedings{schuhmann2022laionb,
+  title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
+  author={Christoph Schuhmann and
+          Romain Beaumont and
+          Richard Vencu and
+          Cade W Gordon and
+          Ross Wightman and
+          Mehdi Cherti and
+          Theo Coombes and
+          Aarush Katta and
+          Clayton Mullis and
+          Mitchell Wortsman and
+          Patrick Schramowski and
+          Srivatsa R Kundurthy and
+          Katherine Crowson and
+          Ludwig Schmidt and
+          Robert Kaczmarczyk and
+          Jenia Jitsev},
+  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+  year={2022},
+  url={https://openreview.net/forum?id=M3Y74vmsMcY}
+}
+```
+
+OpenAI CLIP paper
+```
+@inproceedings{Radford2021LearningTV,
+  title={Learning Transferable Visual Models From Natural Language Supervision},
+  author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
+  booktitle={ICML},
+  year={2021}
+}
+```
+
+OpenCLIP software
+```
+@software{ilharco_gabriel_2021_5143773,
+  author       = {Ilharco, Gabriel and
+                  Wortsman, Mitchell and
+                  Wightman, Ross and
+                  Gordon, Cade and
+                  Carlini, Nicholas and
+                  Taori, Rohan and
+                  Dave, Achal and
+                  Shankar, Vaishaal and
+                  Namkoong, Hongseok and
+                  Miller, John and
+                  Hajishirzi, Hannaneh and
+                  Farhadi, Ali and
+                  Schmidt, Ludwig},
+  title        = {OpenCLIP},
+  month        = jul,
+  year         = 2021,
+  note         = {If you use this software, please cite it as below.},
+  publisher    = {Zenodo},
+  version      = {0.1},
+  doi          = {10.5281/zenodo.5143773},
+  url          = {https://doi.org/10.5281/zenodo.5143773}
+}
+```
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets

clip/laion/CLIP-ViT-L-14-laion2B-s32B-b82K/config.json

@@ -0,0 +1,174 @@
+{
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
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+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
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+  }
+}

clip/laion/CLIP-ViT-L-14-laion2B-s32B-b82K/open_clip_config.json

@@ -0,0 +1,30 @@
+{
+    "model_cfg": {
+        "embed_dim": 768,
+        "vision_cfg": {
+            "image_size": 224,
+            "layers": 24,
+            "width": 1024,
+            "patch_size": 14
+        },
+        "text_cfg": {
+            "context_length": 77,
+            "vocab_size": 49408,
+            "width": 768,
+            "heads": 12,
+            "layers": 12
+        }
+    },
+    "preprocess_cfg": {
+        "mean": [
+            0.5,
+            0.5,
+            0.5
+        ],
+        "std": [
+            0.5,
+            0.5,
+            0.5
+        ]
+    }
+}

clip/laion/CLIP-ViT-L-14-laion2B-s32B-b82K/preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 3,
+  "size": 224
+}

clip/laion/CLIP-ViT-L-14-laion2B-s32B-b82K/special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": "<|endoftext|>"}

clip/laion/CLIP-ViT-L-14-laion2B-s32B-b82K/tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+    "unk_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "bos_token": {
+        "content": "<|startoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "eos_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "pad_token": "<|endoftext|>",
+    "add_prefix_space": false,
+    "errors": "replace",
+    "do_lower_case": true,
+    "name_or_path": "openai/clip-vit-base-patch32",
+    "model_max_length": 77,
+    "special_tokens_map_file": "./special_tokens_map.json",
+    "tokenizer_class": "CLIPTokenizer"
+}