Low-Bit Streaming LLM using IPEX-LLM

In this example, we apply low-bit optimizations to Streaming-LLM using IPEX-LLM, which can deploy low-bit(including FP4/INT4/FP8/INT8) LLMs for infinite-length inputs. Only one code change is needed to load the model using ipex-llm as follows:

from ipex_llm.transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_4bit=True, trust_remote_code=True, optimize_model=False)

Prepare Environment

We suggest using conda to manage environment:

conda create -n llm python=3.11
conda activate llm
pip install -U transformers==4.34.0
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

Run Example

python ./run_streaming_llama.py  --repo-id-or-model-path REPO_ID_OR_MODEL_PATH  --enable-streaming

arguments info:

• --repo-id-or-model-path: str value, argument defining the huggingface repo id for the large language model to be downloaded, or the path to the huggingface checkpoint folder, the value is 'meta-llama/Llama-2-7b-chat-hf' by default.
• --data-root: str value, the directory to save downloaded questions data.
• --enable-streaming: to enable efficient streaming while computing.
• --start-size: int value, the start size of recent KV cache.
• --recent-size: optional str value. The path to load low-bit model.

Sample Output for Inference

'decapoda-research/llama-7b-hf' Model

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The report contains data analysis of the company's performance during the quarter ending 31st March 2019...