license: apache-2.0
language:
- en
library_name: transformers
Compressed Meta Llama-3-8B-Instruct with Palu
Overview
This repository contains a compressed version of the Meta Llama-3-8B-Instruct model, utilizing the Palu framework for KV-Cache compression. Palu reduces the hidden dimensions of the KV-Cache through low-rank decomposition, significantly reducing the model's memory footprint while maintaining or enhancing performance.
Key Features
- Model: Meta Llama-3-8B-Instruct
- Compression Framework: Palu
- Compression Rate: Up to 91.25% memory reduction
- Accuracy: Maintained or improved perplexity compared to the base model
Installation
Clone the Repository
Ensure you have Git and Conda installed on your system.
git clone --recurse-submodules https://github.com/shadowpa0327/Palu.git
cd Palu
Set Up the Environment
Create and activate a Conda environment.
conda create -n Palu python=3.10
conda activate Palu
pip install -r requirements.txt
Install Third-Party Libraries
pip install -e 3rdparty/lm-evaluation-harness
pip install -e 3rdparty/fast-hadamard-transform
Usage
Compress the Model
To compress Meta Llama-3-8B-Instruct using Palu's low-rank decomposition, use the following command:
python compress.py \
--model_id="meta-llama/Llama-3-8b-instruct" \
--calib_dataset wikitext2 \
--param_ratio_target 0.7 \
--search_method fisher_uniform \
--head_group_size 4 \
--dump_huggingface_model \
--use_cache
The compressed model will be saved in the Meta-Llama-3-8b-instruct_ratio-0.7_gs-4-fisher_uniform directory in Hugging Face format.
Evaluate the Compressed Model
Perplexity
To evaluate the perplexity on the wikitext2 dataset with sequence length 2048, run:
python run_ppl_eval.py \
--model_name_or_path /Path/To/Palu/Model \
--datasets wikitext2 \
--seqlen 2048
To evaluate with 3-bit low-rank aware quantization, use:
python run_ppl_eval.py \
--model_name_or_path /Path/To/Palu/Model \
--datasets wikitext2 \
--seqlen 4096 \
--lt_bits 3 \
--lt_hadamard
Zero-shot Evaluation
For zero-shot evaluations, use the following command:
CUDA_VISIBLE_DEVICES=0 python run_lm_eval.py \
--model_name_or_path "/Path/To/Palu/Model" \
--tasks "openbookqa,hellaswag,piqa,arc_easy,arc_challenge,winogrande"
Long-Bench Evaluation
Evaluate the compressed model on long-bench tasks:
CUDA_VISIBLE_DEVICES=0 python run_long_bench.py \
--model_name_or_path /Path/To/Palu/Model
Latency Evaluation
Attention Module
Evaluate the latency of the Palu-compressed attention module:
CUDA_VISIBLE_DEVICES=0 python run_latency_attention.py \
--rank_k 1024 --rank_v 3072 --group_size 4 \
--prompt_len 65536 --palu
Reconstruction Kernel
Evaluate the latency of the reconstruction kernel:
CUDA_VISIBLE_DEVICES=0 python run_latency_kernel.py \
--total_rank 1024 --group_size 4
Conclusion
This compressed version of Meta Llama-3-8B-Instruct, powered by Palu, is optimized for memory efficiency without compromising performance. Whether you're working with large datasets or deploying models in memory-constrained environments, this setup is designed to provide robust results.