Comments (6)
The neural network requires 200 GB of video memory to run. Have you even looked into the details?
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The neural network requires 200 GB of video memory to run. Have you even looked into the details?
I'm not trying to retrain the model, I'm trying to use it.
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There is no difference.
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GPU is 1660, 6gb vram. Is there anything I can do about it or have I wasted a few weeks?
You may try to use huggingface-accelerate https://github.com/huggingface/accelerate https://github.com/huggingface/accelerate/blob/main/src/accelerate/big_modeling.py
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GPU is 1660, 6gb vram. Is there anything I can do about it or have I wasted a few weeks?
You may try to use huggingface-accelerate https://github.com/huggingface/accelerate https://github.com/huggingface/accelerate/blob/main/src/accelerate/big_modeling.py
Can you tell me more about how to load such a large model on the 1060?
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@Aspector1 by the way. Did you use docker to run it?
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Related Issues (20)
- Привет HOT 2
- How did you used LAMB optimizer with ZeRO CPU offload? HOT 2
- Run on networked nodes
- AWS HOT 1
- Could you share the md5 value for those checkpoints? HOT 2
- Can it be launched on usual VPS? For example, 6 CPU 16 RAM (usual chips) HOT 2
- Would it be possible to run the model on single A100 (40GB) or 2xV100 (32GB) ? HOT 2
- No mention of `bfloat16` in source, and yet weights are `bfloat16`
- NCCL error HOT 1
- PCI x1 or PCI x16 for GPU
- Is there any plans for making cloud service? HOT 1
- Has anyone deployed it on 10x 3090 ? Or any similar configuration? HOT 1
- Provide pruned version for weaker hardware HOT 2
- Citation bibtex? HOT 2
- Request to Open "Russian Pile" Dataset for Public Access
- How to use it with LangChain? HOT 2
- Timeout on 8 x RTX A6000 HOT 2
- Why usage ssh-agent and openssh-client package in docker
- gguf / mlx format?
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