llama.cpp/awq-py/README.md

AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp

[Paper][Original Repo][Easy-to-use Repo]

Supported models:

• LLaMA
• LLaMA 2
• MPT
• Mistral AI v0.1
• Bloom
• Mixtral MoE

TODO:

• Update version work with both MPT and MPT-AWQ model
• Add OPT model
• Add Bloom model
• Add Mixtral MoE
• Support w3, w2

Contents

• Install
• Convert
• Quantize
• Test
• Benchmark
• Results

Install

Install requirements

pip install -r requirements.txt

Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT

git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache

Convert

Example for llama model

# For llama7b and llama2 models
python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf
# For mistral and mpt models
python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf

Quantize

# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types.
./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0

Test

# For all models.
./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time"

Benchmark

The perplexity measurements in table above are done against the wikitext2 test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.

# For llama and llama2, and mistral models.
./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw

Results

Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k

Llama 7B (Build with OpenBLAS)

Model	Measure	F16	Q4_0	Q4_1	Q2_K
Llama 7B	perplexity	5.9066	6.1214	6.0643	6.5808
Llama 7B	file size	12.9G	3.5G	3.9G	2.7G
Llama 7B	bits/weight	16.0	4.5	5.0	2.6
AWQ-LLama 7B	perplexity	5.9175	6.0252	5.9987	6.3692
AWQ-LLama 7B	file size	12.9G	3.5G	3.9G	2.7G
AWQ-LLama 7B	bits/weight	16.0	4.5	5.0	2.6

Llama2 7B (Build with CuBLAS)

Model	Measure	F16	Q4_0	Q4_1	Q2_K
Llama2 7B	perplexity	5.8664	6.0260	6.0656	6.4496
Llama2 7B	file size	12.9G	3.5G	3.9G	2.7G
Llama2 7B	bits/weight	16.0	4.5	5.0	2.6
AWQ-LLama2 7B	perplexity	5.8801	6.0054	5.9849	6.3650
AWQ-LLama2 7B	file size	12.9G	3.5G	3.9G	2.7G
AWQ-LLama2 7B	bits/weight	16.0	4.5	5.0	2.6

Mistral 7B v0.1 (Build with CuBLAS)

Model	Measure	F16	Q4_0	Q4_1	Q2_K
Mistral 7B	perplexity	5.6931	5.8202	5.8268	6.1645
Mistral 7B	file size	14.5G	4.1G	4.5G	3.1G
Mistral 7B	bits/weight	16.0	4.5	5.0	2.6
AWQ-Mistral 7B	perplexity	5.6934	5.8020	5.7691	6.0426
AWQ-Mistral 7B	file size	14.5G	4.1G	4.5G	3.1G
AWQ-Mistral 7B	bits/weight	16.0	4.5	5.0	2.6

MPT 7B (Build with OpenBLAS)

Model	Measure	F16	Q4_0	Q4_1	Q2_K
MPT 7B	perplexity	8.4369	8.7956	8.6265	11.4913
MPT 7B	file size	13.7G	3.9G	4.3G	2.8G
MPT 7B	bits/weight	16.0	4.5	5.0	2.6
AWQ-MPT 7B	perplexity	8.4944	8.7053	8.6750	10.2873
AWQ-MPT 7B	file size	13.7G	3.9G	4.3G	2.8G
AWQ-MPT 7B	bits/weight	16.0	4.5	5.0	2.6