Let's start by installing the necessary libraries. Here we install 🤗 Transformers, Modelbit, Accelerate and Bitsandbytes in order to load the model in Google Colab. This enables 4-bit inference with clever quantization techniques, shrinking the size of the model considerably, while maintaining performance of the original size.
Next, we load a model and corresponding processor from the hub. We specify device_map="auto" in order to automatically place the model on the available GPUs/CPUs.
The Pipeline API in Hugging Face Transformers is a convenient tool that simplifies the process of performing inference with pre-trained models. It abstracts away the underlying complexities of model loading, input preprocessing, and output postprocessing, allowing users to focus on their specific task.
Note: the pipeline API doesn't leverage a GPU by default, one needs to pass the device argument for that. See the collection of Hugging Face-compatible checkpoints.
The "get_llava_model"
function, decorated with @cache
, is our key player. This function uses "snapshot_download"
to fetch the specific backbone.
The use of @cache
is a clever optimization; it ensures that once the model and processor are loaded, they are stored in memory. This significantly speeds up future calls to this function, as it avoids reloading the model and processor from scratch each time, making it ideal for deployments.
modelbit
You can test your REST Endpoint by sending single or batch production images to it for inference.
Use the requests
package to POST a request to the API and use json
to format the response to print nicely:
⚠️ Replace the "ENTER_WORKSPACE_NAME"
placeholder with your workspace name.
You can also test your endpoint from the command line using:
⚠️ Replace the "ENTER_WORKSPACE_NAME"
placeholder with your workspace name.
LLaVa stands for Large Language and Vision Assistant, a pioneering model introduced at NeurIPS 2023. It represents a significant leap in combining vision encoding and language models to create a multimodal understanding system. Unlike traditional models, LLaVa integrates a vision encoder with a large language model called Vicuna, aiming to provide comprehensive visual and language understanding.
This approach allows LLaVa to perform exceptionally well in chat capabilities and scientific question answering, mimicking aspects of multimodal GPT-4.
The LLaVa model was developed by a collaborative effort between researchers from the University of Wisconsin-Madison, Microsoft Research, and Columbia University. It was released following the NeurIPS 2023 conference, demonstrating state-of-the-art performance on several benchmarks. LLaVa version 1.5, an enhancement over the original, underscores the model's rapid evolution and its commitment to utilizing all public data efficiently.
LLaVa is particularly effective in multimodal chat applications and scientific question answering. It demonstrates advanced capabilities in understanding and responding to visual and textual inputs, making it ideal for applications requiring comprehensive multimodal understanding. Its use cases extend to educational tools, customer service bots, and interactive systems requiring detailed visual and language comprehension.
The strengths of LLaVa include its exceptional multimodal understanding, ability to mimic aspects of GPT-4 in chat scenarios, and its state-of-the-art performance in science QA tasks. The model's innovative instruction tuning approach enables it to adapt effectively to new tasks and datasets, showcasing its flexibility and robustness.
Despite its advancements, LLaVa faces challenges common to multimodal models, such as the need for large and diverse training datasets and the complexity of integrating visual and textual data. Additionally, its performance heavily depends on the quality and scope of the instruction-following data used for training.
LLaVa employs a two-stage instruction tuning approach for learning, starting with pre-training for feature alignment and then moving to fine-tuning for specific applications. This method reflects a combination of supervised and unsupervised learning, focusing on enhancing the model's zero-shot capabilities in multimodal settings.