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--- |
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library_name: keras-hub |
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extra_gated_heading: Access PaliGemma on Hugging Face |
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extra_gated_prompt: >- |
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To access PaliGemma on Hugging Face, you’re required to review and agree to |
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Google’s usage license. To do this, please ensure you’re logged-in to Hugging |
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Face and click below. Requests are processed immediately. |
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extra_gated_button_content: Acknowledge license |
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license: gemma |
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pipeline_tag: image-text-to-text |
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--- |
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PaliGemma is a set of multi-modal large language models published by Google based on the Gemma model. Both a pre-trained and instruction tuned models are available. See the model card below for benchmarks, data sources, and intended use cases. |
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## Links |
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* [PaliGemma API Documentation](https://keras.io/api/keras_nlp/models/pali_gemma/) |
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* [KerasNLP Beginner Guide](https://keras.io/guides/keras_nlp/getting_started/) |
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* [KerasNLP Model Publishing Guide](https://keras.io/guides/keras_nlp/upload/) |
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## Installation |
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Keras and KerasNLP can be installed with: |
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``` |
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pip install -U -q keras-nlp |
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pip install -U -q keras>=3 |
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``` |
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Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page. |
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## Presets |
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The following model checkpoints are provided by the Keras team. Full code examples for each are available below. |
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| Preset name | Parameters | Description | |
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|-----------------------|------------|-------------------------------------------------------------| |
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| [paligemma-3b-224-mix-keras](https://huggingface.co/google/paligemma-3b-224-mix-keras) | 2.92B | image size 224, mix fine tuned, text sequence length is 256 | |
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| [**paligemma-3b-448-mix-keras**](https://huggingface.co/google/paligemma-3b-448-mix-keras) | 2.92B | image size 448, mix fine tuned, text sequence length is 512 | |
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| [paligemma-3b-224-keras](https://huggingface.co/google/paligemma-3b-224-keras) | 2.92B | image size 224, pre trained, text sequence length is 128 | |
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| [paligemma-3b-448-keras](https://huggingface.co/google/paligemma-3b-448-keras) | 2.92B | image size 448, pre trained, text sequence length is 512 | |
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| [paligemma-3b-896-keras](https://huggingface.co/google/paligemma-3b-896-keras) | 2.93B | image size 896, pre trained, text sequence length is 512 | |
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## Prompts |
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The PaliGemma `"mix"` models can handle a number of prompting structures out of the box. It is important to stick exactly to these prompts, including the newline. Lang can be a language code such as `"en"` or `"fr"`. Support for languages outside of English will vary depending on the prompt type. |
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* `"cap {lang}\n"`: very raw short caption (from WebLI-alt). |
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* `"caption {lang}\n"`: coco-like short captions. |
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* `"describe {lang}\n"`: somewhat longer more descriptive captions. |
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* `"ocr\n"`: optical character recognition. |
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* `"answer en {question}\n"`: question answering about the image contents. |
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* `"question {lang} {answer}\n"`: question generation for a given answer. |
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* `"detect {thing} ; {thing}\n"`: count objects in a scene. |
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Not `"mix"` presets should be fine-tuned for a specific task. |
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``` |
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!pip install -U -q keras-nlp |
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``` |
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Pick a backend of your choice |
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``` |
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import os |
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os.environ["KERAS_BACKEND"] = "jax" |
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``` |
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Now we can load the PaliGemma "causal language model" from the Kaggle Models hub. A causal language model is just a LLM that is ready for generation, by training with a causal mask, and running generation a token at a time in a recurrent loop. |
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``` |
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keras.config.set_floatx("bfloat16") |
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pali_gemma_lm = keras_nlp.models.PaliGemmaCausalLM.from_preset( |
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"hf://google/paligemma-3b-448-mix-keras" |
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) |
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``` |
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Function that reads an image from a given URL |
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``` |
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def read_image(url): |
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contents = io.BytesIO(requests.get(url).content) |
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image = PIL.Image.open(contents) |
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image = np.array(image) |
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# Remove alpha channel if neccessary. |
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if image.shape[2] == 4: |
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image = image[:, :, :3] |
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return image |
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``` |
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``` |
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image_url = 'https://storage.googleapis.com/keras-cv/models/paligemma/cow_beach_1.png' |
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image = read_image(image_url) |
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``` |
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Use `generate()` call with a single image and prompt. The text prompt |
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has to end with `\n`. |
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``` |
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prompt = 'answer en where is the cow standing?\n' |
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output = pali_gemma_lm.generate( |
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inputs={ |
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"images": image, |
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"prompts": prompt, |
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} |
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) |
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print(output) |
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``` |
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Use `generate()` call with a batched images and prompts. |
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``` |
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prompts = [ |
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'answer en where is the cow standing?\n', |
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'answer en what color is the cow?\n', |
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'describe en\n', |
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'detect cow\n', |
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'segment cow\n', |
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] |
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images = [image, image, image, image, image] |
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outputs = pali_gemma_lm.generate( |
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inputs={ |
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"images": images, |
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"prompts": prompts, |
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} |
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) |
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for output in outputs: |
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print(output) |
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``` |
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There's a few other style of prompts this model can handle out of the box... |
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`cap {lang}\n`: very raw short caption (from WebLI-alt). |
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`caption {lang}\n`: nice, coco-like short captions. |
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`describe {lang}\n`: somewhat longer more descriptive captions. |
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`ocr\n`: optical character recognition. |
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`answer en {question}\n`: question answering about the image contents. |
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`question {lang} {answer}\n`: question generation for a given answer. |
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`detect {thing} ; {thing}\n`: count objects in a scene. |
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Call `fit()` on a single batch |
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``` |
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import numpy as np |
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image = np.random.uniform(-1, 1, size=(224, 224, 3)) |
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x = { |
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"images": [image, image], |
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"prompts": ["answer en Where is the cow standing?\n", "caption en\n"], |
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} |
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y = { |
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"responses": ["beach", "A brown cow standing on a beach next to the ocean."], |
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} |
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pali_gemma_lm = keras_nlp.models.PaliGemmaCausalLM.from_preset("hf://google/paligemma-3b-448-mix-keras") |
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pali_gemma_lm.fit(x=x, y=y, batch_size=2) |
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``` |
