ipex-llm/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF

Loading GGUF models

In this directory, you will find examples on how to load GGUF model into ipex-llm.

Verified Models(Q4_0)

• Llama-2-7B-Chat-GGUF
• Mistral-7B-Instruct-v0.1-GGUF
• Mixtral-8x7B-v0.1-GGUF
• Baichuan2-7B-Chat-GGUF
• Bloomz-7b1-GGUF
• falcon-7b-quantized-gguf
• mpt-7b-chat-gguf
• Yuan2-2B-Februa-hf-GGUF

Requirements

To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to here for more information.

Important: Please make sure you have installed transformers==4.36.0 to run the example.

Example: Load gguf model using from_gguf() API

In the example generate.py, we show a basic use case to load a GGUF LLaMA2 model into ipex-llm using from_gguf() API, with IPEX-LLM optimizations.

1. Install

We suggest using conda to manage the Python environment. For more information about conda installation, please refer to here.

After installing conda, create a Python environment for IPEX-LLM:

On Linux

conda create -n llm python=3.11 # recommend to use Python 3.11
conda activate llm

# install the latest ipex-llm nightly build with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install transformers==4.36.0  # upgrade transformers

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all] 
pip install transformers==4.36.0

2. Run

After setting up the Python environment, you could run the example by following steps.

2.1 Client

On client Windows machines, it is recommended to run directly with full utilization of all cores:

python ./generate.py --model <path_to_gguf_model> --prompt 'What is AI?'

More information about arguments can be found in Arguments Info section. The expected output can be found in Sample Output section.

2.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set IPEX-LLM env variables
source ipex-llm-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 --model <path_to_gguf_model> --prompt 'What is AI?'

More information about arguments can be found in Arguments Info section. The expected output can be found in Sample Output section.

2.3 Arguments Info

In the example, several arguments can be passed to satisfy your requirements:

• --model: path to GGUF model, it should be a file with name like llama-2-7b-chat.Q4_0.gguf
• --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.
• --low_bit: use what low_bit to run, default is sym_int4.

2.4 Sample Output

llama-2-7b-chat.Q4_0.gguf

Inference time: xxxx s
-------------------- Output --------------------
### HUMAN:
What is AI?

### RESPONSE:

AI is a term used to describe a type of computer software that is designed to perform tasks that typically require human intelligence, such as visual perception, speech