Comments (4)
Hey! Just tried running the notebook (in the offload_per_layer = 5
setting) and everything works for me. Have you tinkered with the original notebook in any way? If not, try restarting the session and running it again starting with the model initialization cell.
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Hey!
Have you managed to solve this issue? If no, can you please provide some more information:
- On which cell exactly did the notebook crush with
offload_per_layer = 4
? - How many of RAM and GPU VRAM did you have in Google Colab?
Note that our demo notebook should run normally in Google Colab with offload_per_layer = 4
, but will crush sometimes with offload_per_layer = 5
. The latter option is made for local run with low VRAM.
from mixtral-offloading.
hqq_aten package not installed. HQQBackend.ATEN backend will not work unless you install the hqq_aten lib in hqq/kernels.
Hqq_aten is not required as we have custom triton kernels for GEMV.
from mixtral-offloading.
hqq_aten package not installed. HQQBackend.ATEN backend will not work unless you install the hqq_aten lib in hqq/kernels.
from mixtral-offloading.
Related Issues (16)
- Enhancing the Efficacy of MoE Offloading with Speculative Prefetching Strategies
- Mixtral OffLoading/GGUF/ExLlamaV2, which approach to use? HOT 1
- How to use the offloading in my MoE model? HOT 4
- Doesn't work HOT 10
- Can it run on multi-GPU? HOT 10
- Can it run with LlamaIndex?
- Is it possible to finetune this on a custom dataset? HOT 7
- CUDA OOM errors in wsl2
- need mixtral offload for NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
- hqq_aten package not installed. HOT 1
- Run without quantization HOT 9
- 4bit-3bit model produces gibberish when plugged into demo
- Run on second GPU (torch.device("cuda:1")) HOT 1
- Update Requirements.txt
- exl2 HOT 2
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