Information
GPT4-X-Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.
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
hereKoboldAI: If you require further instruction, see
hereCPU/GGML Usage
To use your CPU using GGML(Llamacpp) you only need the single .bin ggml file.
Oobabooga: If you require further instruction, see
hereKoboldAI: If you require further instruction, see
hereTraining 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.
