Information
GPT4-X-Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.This was made using Chansung's GPT4-Alpaca Lora
Update 05.26.2023
Updated the ggml quantizations to be compatible with the latest version of llamacpp (again).
What's included
GPTQ: 2 quantized versions. One quantized --true-sequential and act-order optimizations, and the other was quantized using --true-sequential --groupsize 128 optimizations
GGML: 3 quantized versions. One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1.
GPU/GPTQ Usage
To use with your GPU using GPTQ pick one of the .safetensors along with all of the .jsons and .model files.
Oobabooga: If you require further instruction, see here and here
KoboldAI: If you require further instruction, see here
CPU/GGML Usage
To use your CPU using GGML(Llamacpp) you only need the single .bin ggml file.
Oobabooga: If you require further instruction, see here
KoboldAI: If you require further instruction, see here
Training Parameters
- num_epochs=10
- cutoff_len=512
- group_by_length
- lora_target_modules='[q_proj,k_proj,v_proj,o_proj]'
- lora_r=16
- micro_batch_size=8
Benchmarks
--true-sequential --act-order
Wikitext2: 4.481280326843262
Ptb-New: 8.539161682128906
C4-New: 6.451964855194092
Note: This version does not use --groupsize 128, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
--true-sequential --groupsize 128
Wikitext2: 4.285132884979248
Ptb-New: 8.34856128692627
C4-New: 6.292652130126953
Note: This version uses --groupsize 128, resulting in better evaluations. However, it consumes more VRAM.