Comments (4)
If this is true, it's very strange. I've coded the result so that it doesn't change.
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@GenTxt can you share your quantization code and model to us so that we can try to reproduce and figure out what went wrong.
Also you may try on the up-to-date commit in main branch, may be it can solve your problem.
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https://huggingface.co/kz919/gpt-neox-20b-8k-longtuning/tree/main
Converted above to safetensors with text generation webui script.
CUDA_VISIBLE_DEVICES="0" python quant_with_alpaca.py --pretrained_model_dir models/neox20b_8192_safe --quantized_model_dir 4bit_converted --bits 4 --group_size 128 --fast_tokenizer --save_and_reload
Old models deleted as current triton kernel can cause errors on refurbished 6000.
triton-lang/triton#1556
For the specific code above, this error:
Occurs on NVIDIA GeForce RTX 2080 Ti (similar to original 6000 - gpu1)
Doesn't occur on NVIDIA GeForce RTX 3090 (works fine on same - gpu0)
Quantized in latest cuda main and not encountering the error. False alarm. Closing here and carefully testing each update.
Thanks
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https://huggingface.co/kz919/gpt-neox-20b-8k-longtuning/tree/main
Converted above to safetensors with text generation webui script.
CUDA_VISIBLE_DEVICES="0" python quant_with_alpaca.py --pretrained_model_dir models/neox20b_8192_safe --quantized_model_dir 4bit_converted --bits 4 --group_size 128 --fast_tokenizer --save_and_reload
Old models deleted as current triton kernel can cause errors on refurbished 6000. openai/triton#1556
For the specific code above, this error:
Occurs on NVIDIA GeForce RTX 2080 Ti (similar to original 6000 - gpu1) Doesn't occur on NVIDIA GeForce RTX 3090 (works fine on same - gpu0)
Quantized in latest cuda main and not encountering the error. False alarm. Closing here and carefully testing each update.
Thanks
Hi, I've also tyied neox20b quantization, the inference speed I got is 16tokens/s, which isn't fast enough, may I have your results?
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Related Issues (20)
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