# Fuyu In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Fuyu models. For illustration purposes, we utilize the [adept/fuyu-8b](https://huggingface.co/adept/fuyu-8b) as a reference Fuyu model. ## Requirements To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information. ## Example: Predict Tokens using `generate()` API In the example [generate.py](./generate.py), we show a basic use case for an Fuyu model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations. ### 1. Install We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#). After installing conda, create a Python environment for BigDL-LLM: ```bash conda create -n llm python=3.9 # recommend to use Python 3.9 conda activate llm pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option pip install transformers==4.35 pillow # additional package required for Fuyu to conduct generation ``` ### 2. Run After setting up the Python environment, you could run the example by following steps. > **Note**: When loading the model in 4-bit, BigDL-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference. > > Please select the appropriate size of the Fuyu model based on the capabilities of your machine. #### 2.1 Client On client Windows machines, it is recommended to run directly with full utilization of all cores: ```powershell python ./generate.py --image-path demo.jpg ``` More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section. #### 2.2 Server For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket. E.g. on Linux, ```bash # set BigDL-Nano env variables source bigdl-nano-init # e.g. for a server with 48 cores per socket export OMP_NUM_THREADS=48 numactl -C 0-47 -m 0 python ./generate.py --image-path demo.jpg ``` More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section. #### 2.3 Arguments Info In the example, several arguments can be passed to satisfy your requirements: - `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Fuyu model (e.g. `adept/fuyu-8b`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'adept/fuyu-8b'`. - `--prompt PROMPT`: argument defining the prompt to be inferred (with the image for chat). It is default to be `'Generate a coco-style caption.'`. - `--image-path IMAGE_PATH`: argument defining the input image that the chat will focus on. It is required and should be a local path (not url). - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `512`. #### 2.4 Sample Output #### [adept/fuyu-8b](https://huggingface.co/adept/fuyu-8b) ```log Inference time: xxxx s -------------------- Prompt -------------------- Generate a coco-style caption. -------------------- Output -------------------- An orange bus parked on the side of a road. ``` The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=178242)): [demo.jpg](https://cocodataset.org/#explore?id=178242)