---
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.
```bash
git clone --recurse-submodules https://github.com/shadowpa0327/Palu.git
cd Palu
```

### Set Up the Environment
Create and activate a Conda environment.
```bash
conda create -n Palu python=3.10
conda activate Palu
pip install -r requirements.txt
```

### Install Third-Party Libraries
```bash
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:

```bash
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:

```bash
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:
```bash
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:
```bash
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:
```bash
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:
```bash
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:
```bash
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.