d85a430a8c
* Replace `bigdl-nano-init` with `bigdl-llm-init`. * Install `bigdl-llm` instead of `bigdl-nano`. * Remove nano in README.
73 lines
3.3 KiB
Markdown
73 lines
3.3 KiB
Markdown
# BigDL-LLM Transformers INT4 Optimization for HuggingFace Transformers Agent
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In this example, we apply low-bit optimizations to [HuggingFace Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents) using BigDL-LLM, which allows LLMs to use tools such as image generation, image captioning, text summarization, etc.
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For illustration purposes, we utilize the [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) as the reference model. We use [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) to create an agent, and then ask the agent to generate the caption for an image from coco dataset, i.e. [demo.jpg](https://cocodataset.org/#explore?id=264959)
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## 0. Requirements
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To run this example with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model#recommended-requirements) for more information.
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### 1. Install
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.9
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conda activate llm
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pip install bigdl-llm[all] # install bigdl-llm with 'all' option
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pip install pillow # additional package required for opening images
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```
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### 2. Run
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```
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python ./run_agent.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --image-path IMAGE_PATH
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Vicuna model (e.g. `lmsys/vicuna-7b-v1.5`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'lmsys/vicuna-7b-v1.5'`.
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- `--image-path IMAGE_PATH`: argument defining the image to be infered.
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> **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.
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>
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> Please select the appropriate size of the Vicuna model based on the capabilities of your machine.
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#### 2.1 Client
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On client Windows machine, it is recommended to run directly with full utilization of all cores:
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```powershell
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python ./run_agent.py --image-path IMAGE_PATH
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```
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#### 2.2 Server
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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.
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E.g. on Linux,
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```bash
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# set BigDL-LLM env variables
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source bigdl-llm-init
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# e.g. for a server with 48 cores per socket
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export OMP_NUM_THREADS=48
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numactl -C 0-47 -m 0 python ./run_agent.py --image-path IMAGE_PATH
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```
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#### 2.3 Sample Output
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#### [demo.jpg](https://cocodataset.org/#explore?id=264959)
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<p align="center">
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<img src="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg" alt="demo.jpg" width="400"/>
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</p>
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#### [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5)
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```log
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Image path: demo.jpg
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== Prompt ==
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Generate a caption for the 'image'
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==Explanation from the agent==
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I will use the following tool: `image_captioner` to generate a caption for the image.
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==Code generated by the agent==
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caption = image_captioner(image)
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==Result==
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a little girl holding a stuffed teddy bear
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```
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