AI

LLama

/lmg/

llama.cpp pull requests

cuBLAS: use host pinned memory and dequantize while copying

Add NVIDIA cuBLAS support (Merged now)

TextGeneration-webui pull requests

I think I made the Triton-GPTQ model work with the GPTQ-for-LLaMa (Triton branch) repository. (slower than non-triton)

Tried more Vicuna + superCOT lora and I am fairly impressed at how well it understands me for a 13B model in terms of followup instructions.

Models

q5 models (reddit)

q5 models

https://huggingface.co/Melbourne/Alpacino-30b-ggml/tree/main

Misc

https://github.com/stochasticai/xturing/tree/main/examples/int4_finetuning

https://wiki.installgentoo.com/wiki/Home_server#Expanding_Your_Storage

https://rentry.org/llama-tard-v2

https://rentry.org/llamaaids

https://hackmd.io/@reneil1337/alpaca

https://find.4chan.org/?q=AI+Dynamic+Storytelling+General

https://find.4chan.org/?q=AI+Chatbot+General

https://find.4chan.org/?q=%2Flmg%2F (local models general)

https://boards.4channel.org/g/thread/92400764#p92400764

https://rentry.org/llamaaids

https://files.catbox.moe/lvefgy.json

https://pytorch.org/hub/nvidia_deeplearningexamples_tacotron2/

python server.py --model llama-7b-4bit --wbits 4

python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128

https://github.com/qwopqwop200/GPTQ-for-LLaMa/issues/59 for installing with out of space error

https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model#4-bit-mode

https://github.com/pybind/pybind11/discussions/4566

https://lmsysvicuna.miraheze.org/wiki/How_to_use_Vicuna#Use_with_llama.cpp%3A

https://huggingface.co/anon8231489123/vicuna-13b-GPTQ-4bit-128g

Here's the uncucked Vicuna model (trained on the dataset that don't have the moralistic bullshit anymore) Too bad it's just the CPU quantized version

Vicuna generating its own prompts

should be worse than Q4_1 (which is QK=32) but there are several PRs in the work that should improve quantization accuracy in general

https://github.com/ggerganov/llama.cpp/pull/729

https://github.com/ggerganov/llama.cpp/pull/835

https://github.com/ggerganov/llama.cpp/pull/896

benchmark_threads.txt

https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g - python3 llama.py vicuna-AlekseyKorshuk-7B c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors vicuna-AlekseyKorshuk-7B-GPTQ-4bit-128g.safetensors

≈65% speedup of the AVX-512 implementation of ggml_vec_dot_q4_0() #933

"Speaking of which, for any 30b anons struggling with context size, I figured something out. If you use the Triton branch on WSL, go into GPTQ_loader.py and comment out make_quant_attn like so" from here

Just grab the CUDA branch of qwop's GPTQ for LLaMA (or Triton if you want to be a dickhole) or if you have webui installed, go into the folder for GPTQ. Make sure all the requirements are installed and run this line:

python llama.py /path/to30b c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors alpacino-4bit-128g.safetensors

And it'll run. For windows, obviously flip the slashes the right way. And for linux, you may need to add CUDA_VISIBLE_DEVICES=0 to the front of the command.

Benchmarks

Date	Interface	Model	GPTQ	Xformers?	HW	Load	Speed
	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g	GPTQ-for-LLaMa-triton	yes	240gb SSD, 16gb,desktop off	10.53	7.97 tokens/s
	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g	GPTQ-for-LLaMa-triton	No xformers	240gb SSD, 16gb,desktop off	10.22s	7.55 tokens/s
	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g	GPTQ-for-LLaMa-cuda	No xformers	240gb SSD, 16gb,desktop off	16.68s	4.03 tokens/s
	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g	GPTQ-for-LLaMa-cuda	yes	240gb SSD, 16gb,desktop off	9.34s	4.01 tokens/s
	text-gen	llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml	no	no	2TB SSD, 64gb	?	0.67 tokens/s
	text-gen	llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml	no	no	2TB SSD, 64gb, --threads 8	maybe 30s?	0.51 tokens/s
	text-gen	llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml	no	no	2TB SSD, 64gb, --threads 7		0.68 tokens/s
	text-gen	llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml	no	no	2TB SSD, 64gb, --threads 6		0.61 tokens/s
	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g-ggml	no	no	2TB SSD, 64gb		1.17 tokens/s
	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g	GPTQ-for-LLaMa-triton	yes	2TB SSD, 64gb, --pre_layer 25	45.69	0.25 tokens/s
	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g	GPTQ-for-LLaMa-triton	yes	2TB SSD, 64gb	36.47	9.63 tokens/s
	llama.cpp	llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml			2TB SSD, 64gb	10317.90 ms	1096.21 ms per token
	llama.cpp-modern-avx512	llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml			2TB SSD, 64gb	9288.69 ms	1049.03 ms per token
	llama.cpp-avx512-pr833	llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml			2TB SSD, 64gb	13864.06 ms	0.89 tokens/s, 820.68 ms per token
	text-gen	TheBloke-gpt4-alpaca-lora-30B-4bit-GGML/ggml-model-q4_0			2TB SSD, 64gb		0.78 tokens/s
	text-gen+avx512-pr833	TheBloke-gpt4-alpaca-lora-30B-4bit-GGML/ggml-model-q4_0			2TB SSD, 64gb		1.04 tokens/s
2023-04-24	text-gen	anon8231489123-vicuna-13b-GPTQ-4bit-128g	GPTQ-for-LLaMa-triton	yes	2TB SSD, 64gb, also running llama.cpp with another model	16.36	5.07 tokens/s
2023-04-26	koboldcpp	gozfarb-llama-30b-supercot-ggml/ggml-model-q4_0.bin	clblast	n/a	2TB SSD, 64gb, --threads 8		1073ms/T
2023-04-29	koboldcpp	Alpacino-30b-q4_0.bin	clblast	n/a	2TB SSD, 64gb		700ms/T