2d81521019
* add llama2 and chatglm optimize_model examples * update default usage * update command and some descriptions * move folder and remove general_int4 descriptions * change folder name
74 lines
3.8 KiB
Markdown
74 lines
3.8 KiB
Markdown
# Llama2
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In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Llama2 models. For illustration purposes, we utilize the [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) and [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) as reference Llama2 models.
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## Requirements
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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.
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## Example: Predict Tokens using `generate()` API
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In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
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### 1. Install
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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#).
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After installing conda, create a Python environment for BigDL-LLM:
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```bash
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conda create -n llm python=3.9 # recommend to use Python 3.9
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conda activate llm
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pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
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```
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### 2. Run
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After setting up the Python environment, you could run the example by following steps.
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#### 2.1 Client
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On client Windows machines, it is recommended to run directly with full utilization of all cores:
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```powershell
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python ./generate.py --prompt 'What is AI?'
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```
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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.
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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-Nano env variables
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source bigdl-nano-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 ./generate.py --prompt 'What is AI?'
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```
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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.
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#### 2.3 Arguments Info
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In the example, several arguments can be passed to satisfy your requirements:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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#### 2.3 Sample Output
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#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
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```log
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Inference time: xxxx s
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-------------------- Output --------------------
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### HUMAN:
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What is AI?
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### RESPONSE:
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AI is a branch of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as understanding natural language,
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```
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#### [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
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```log
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Inference time: xxxx s
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-------------------- Output --------------------
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### HUMAN:
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What is AI?
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### RESPONSE:
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AI, or artificial intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, problem-solving,
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```
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