mirror of https://github.com/ikawrakow/ik_llama.cpp.git synced 2026-01-26 17:20:01 +00:00

Files

Kawrakow f369c6f921 Adding IQ4_KSS: 4.0 bpw quants (#89 )

* iq4_kss: WIP

* iq4_kss: CUDA dequantize works

So we can run perplexity. Sadly, the result does not look good
on the bpw vs quantization error plot.

* iq4_kss: slightly better quantization

* iq4_kss: another small quantization improvement

* iq4_kss: CUDA works

TG-128 performance is very decent with 131 t/s for LLaMA-3.1-8B.
In comparison, we have 123 t/s for q4_0 and 128 t/s for iq4_ks.
I.e., the reduced model size more than offsets the additional
bit fiddling required for iq4_kss.

* iq4_kss: new bit arrangement - CUDA and Zen4 work

Did not lose performance on CUDA. Zen4 is decent, but not great:
PP-512(LLaMA-3.1-8B) = 163 t/s.
TG-128 is of course better than other 4-bit quants due to smaller model size.
We get 14.5 t/s @ 8 threads.

* iq4_kss: ARM_NEON. Predictably very slow

* iq4_kss: Metal

PP is not too bad - just 10% slower than q4_0.
But TG is 30% slower, i.e., predictably bad.

* iq4_kss: somewhat faster Metal dot product

45.75 t/s -> 48.75 t/s.
Still 22% slower than q4_0

* iq4_kss: AVX2

Bad, but better than I expected.
PP-512(LLaMA-3.1-8B) = 167 t/s on the Ryzen-5950X.
I.e., with 32 AVX2 threads we get the performance of
16 Zen4 threads.

* iq4_kss: very slightly faster Metal dot product

48.7 t/s -> 49.3 t/s

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

2024-10-16 15:18:26 +03:00

CMakeLists.txt

build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809 )

2024-06-13 00:41:52 +01:00

quantize.cpp

Adding IQ4_KSS: 4.0 bpw quants (#89 )

2024-10-16 15:18:26 +03:00

README.md

Merge mainline llama.cpp (#3 )

2024-07-27 07:55:01 +02:00

tests.sh

build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809 )

2024-06-13 00:41:52 +01:00

README.md

quantize

You can also use the GGUF-my-repo space on Hugging Face to build your own quants without any setup.

Note: It is synced from llama.cpp main every 6 hours.

Example usage:

# obtain the official LLaMA model weights and place them in ./models
ls ./models
llama-2-7b tokenizer_checklist.chk tokenizer.model
# [Optional] for models using BPE tokenizers
ls ./models
<folder containing weights and tokenizer json> vocab.json
# [Optional] for PyTorch .bin models like Mistral-7B
ls ./models
<folder containing weights and tokenizer json>

# install Python dependencies
python3 -m pip install -r requirements.txt

# convert the model to ggml FP16 format
python3 convert_hf_to_gguf.py models/mymodel/

# quantize the model to 4-bits (using Q4_K_M method)
./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M

# update the gguf filetype to current version if older version is now unsupported
./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY

Run the quantized model:

# start inference on a gguf model
./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128

When running the larger models, make sure you have enough disk space to store all the intermediate files.

Memory/Disk Requirements

As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.

Model	Original size	Quantized size (Q4_0)
7B	13 GB	3.9 GB
13B	24 GB	7.8 GB
30B	60 GB	19.5 GB
65B	120 GB	38.5 GB

Quantization

Several quantization methods are supported. They differ in the resulting model disk size and inference speed.

(outdated)

Model	Measure	F16	Q4_0	Q4_1	Q5_0	Q5_1	Q8_0
7B	perplexity	5.9066	6.1565	6.0912	5.9862	5.9481	5.9070
7B	file size	13.0G	3.5G	3.9G	4.3G	4.7G	6.7G
7B	ms/tok @ 4th	127	55	54	76	83	72
7B	ms/tok @ 8th	122	43	45	52	56	67
7B	bits/weight	16.0	4.5	5.0	5.5	6.0	8.5
13B	perplexity	5.2543	5.3860	5.3608	5.2856	5.2706	5.2548
13B	file size	25.0G	6.8G	7.6G	8.3G	9.1G	13G
13B	ms/tok @ 4th	-	103	105	148	160	131
13B	ms/tok @ 8th	-	73	82	98	105	128
13B	bits/weight	16.0	4.5	5.0	5.5	6.0	8.5

k-quants
recent k-quants improvements and new i-quants

Llama 2 7B

Quantization	Bits per Weight (BPW)
Q2_K	3.35
Q3_K_S	3.50
Q3_K_M	3.91
Q3_K_L	4.27
Q4_K_S	4.58
Q4_K_M	4.84
Q5_K_S	5.52
Q5_K_M	5.68
Q6_K	6.56

Llama 2 13B

Quantization	Bits per Weight (BPW)
Q2_K	3.34
Q3_K_S	3.48
Q3_K_M	3.89
Q3_K_L	4.26
Q4_K_S	4.56
Q4_K_M	4.83
Q5_K_S	5.51
Q5_K_M	5.67
Q6_K	6.56

Llama 2 70B

Quantization	Bits per Weight (BPW)
Q2_K	3.40
Q3_K_S	3.47
Q3_K_M	3.85
Q3_K_L	4.19
Q4_K_S	4.53
Q4_K_M	4.80
Q5_K_S	5.50
Q5_K_M	5.65
Q6_K	6.56