From 45721f34737e43ee19e7924bec887a4b80a79ad8 Mon Sep 17 00:00:00 2001 From: ZehuaCao <47251317+Romanticoseu@users.noreply.github.com> Date: Mon, 11 Dec 2023 14:26:05 +0800 Subject: [PATCH] verfiy llava (#9649) --- .../Advanced-Quantizations/AWQ/README.md | 16 ++++++++++++++++ .../Advanced-Quantizations/AWQ/README.md | 14 +++++++++++++- 2 files changed, 29 insertions(+), 1 deletion(-) diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md index f961a9a9..c0f0c288 100644 --- a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md +++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md @@ -1,21 +1,29 @@ # AWQ + This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel CPU. ## Verified Models + - [Llama-2-7B-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) - [Mistral-7B-Instruct-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ) - [Mistral-7B-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ) - [vicuna-7B-v1.5-AWQ](https://huggingface.co/TheBloke/vicuna-7B-v1.5-AWQ) - [vicuna-13B-v1.5-AWQ](https://huggingface.co/TheBloke/vicuna-13B-v1.5-AWQ) +- [llava-v1.5-13B-AWQ](https://huggingface.co/TheBloke/llava-v1.5-13B-AWQ) - [Yi-6B-AWQ](https://huggingface.co/TheBloke/Yi-6B-AWQ) ## Requirements + To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../../../README.md#system-support) for more information. ## Example: Predict Tokens using `generate()` API + In the example [generate.py](./generate.py), we show a basic use case for a AWQ model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations. + ### 1. Install + We suggest using conda to manage environment: + ```bash conda create -n llm python=3.9 conda activate llm @@ -28,11 +36,13 @@ pip install einops ``` ### 2. Run + ``` python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT ``` Arguments info: + - `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the AWQ model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`, `TheBloke/Mistral-7B-Instruct-v0.1-AWQ`, `TheBloke/Mistral-7B-v0.1-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`. - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`. - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. @@ -42,15 +52,19 @@ Arguments info: > Please select the appropriate size of the model based on the capabilities of your machine. #### 2.1 Client + On client Windows machine, it is recommended to run directly with full utilization of all cores: + ```powershell python ./generate.py ``` #### 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-LLM env variables source bigdl-llm-init @@ -61,7 +75,9 @@ numactl -C 0-47 -m 0 python ./generate.py ``` #### 2.3 Sample Output + #### [TheBloke/Llama-2-7B-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) + ```log Inference time: xxxx s -------------------- Prompt -------------------- diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md index 3c7dfaca..18bf4ade 100644 --- a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md +++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md @@ -1,21 +1,29 @@ # AWQ + This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel GPU. ## Verified Models + - [Llama-2-7B-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) - [Mistral-7B-Instruct-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ) - [Mistral-7B-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ) - [vicuna-7B-v1.5-AWQ](https://huggingface.co/TheBloke/vicuna-7B-v1.5-AWQ) - [vicuna-13B-v1.5-AWQ](https://huggingface.co/TheBloke/vicuna-13B-v1.5-AWQ) +- [llava-v1.5-13B-AWQ](https://huggingface.co/TheBloke/llava-v1.5-13B-AWQ) - [Yi-6B-AWQ](https://huggingface.co/TheBloke/Yi-6B-AWQ) ## Requirements + To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. ## Example: Predict Tokens using `generate()` API + In the example [generate.py](./generate.py), we show a basic use case for a AWQ model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations. + ### 1. Install + We suggest using conda to manage environment: + ```bash conda create -n llm python=3.9 conda activate llm @@ -28,6 +36,7 @@ pip install einops ``` ### 2. Configures OneAPI environment variables + ```bash source /opt/intel/oneapi/setvars.sh ``` @@ -46,6 +55,7 @@ python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROM ``` Arguments info: + - `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the AWQ model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`, `TheBloke/Mistral-7B-Instruct-v0.1-AWQ`, `TheBloke/Mistral-7B-v0.1-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`. - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`. - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. @@ -55,7 +65,9 @@ Arguments info: > Please select the appropriate size of the Llama2 model based on the capabilities of your machine. #### 2.3 Sample Output -#### ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) + +#### ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) + ```log Inference time: xxxx s -------------------- Prompt --------------------