Comments (2)
I'd also like to know how to do this. It seems the primary bottleneck is how fast the layers can be fed to the GPU. My copy load is at 80% while GPU load is at 10%. Is there a way to improve this somehow? I assume if we can get the layers quantized down to 1/4th the size it would be almost 4x faster.
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It seems that GPTQ-4bit model is already supported in this project. https://github.com/qwopqwop200/GPTQ-for-LLaMa
this is meant for the bare weights
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Related Issues (20)
- "model parallel group is not initialized" when loading model HOT 2
- How to generate Bible data to LLAMA? HOT 4
- How to off model training on the runtime?
- Incomplete answer with GPTSQLStructStoreIndex compared to ChainLang HOT 2
- Exception RuntimeError: at::cuda::blas::gemm: not implemented HOT 1
- Is example-chat.py ready to use GPU?
- Feature Request - Ways to add context to conversation from beginning. HOT 1
- Question - perform tasks HOT 1
- Running this llama-chat successfully, but with repetitive progress bars, is this normal?
- To shield the annoying progress bar, we found a way HOT 1
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- The generation will stop at ":" keyword. HOT 1
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- FineTuning the last layer of Model
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- [Feature Request] Support InternLM
- Train model using GPU
- Can anyone suggest the best prompt for codellama13b model for converting the sql query to postgresql query
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