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Hi all, thank you for your interests in our work!
In our speed benchmark, we based on the generation stage starting from zero context and measured the median latency of generating 2048 tokens, since evaluating speed using the WikiText2 perplexity evaluation may not accurately reflect the practical usage of LLM.
And it's necessary to replace some inefficient FP16 kernels (such as layernorm and rotary position embedding) in the HuggingFace implementation when testing the speed. At present, we have not released a version for speed testing yet. Please stay tuned for our further updates.
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Related Issues (20)
- reproduce Llama2 7b failure : RuntimeError: The expanded size of the tensor (4608) must match the existing size (4096) at non-singleton dimension 3. Target sizes: [65, 32, 512, 4608]. Tensor sizes: [65, 1, 512, 4096] HOT 3
- RuntimeError: Unknown Layout in CUDA Kernel Execution
- Use awq to quantize Deepseek-coder-33B-instruct model
- run_awq.<locals>.Catcher.forward() error
- KeyError: 'llava_llama' HOT 1
- Error while generating real quantized weights for VILA
- Weight int4 quantization, but actually it is int16 HOT 4
- Possible Bug in "_search_module_scale" Function
- AWQ for non-transformer layers?
- Out of memory in Jetson Orin NX 8GB
- Inquiry about Minimum GPU Requirements HOT 1
- when q-group-size = -1,the code will not run
- Weight Packing Format
- illegal memory access when input tokens < 8
- Grok-1 AWQ
- can awq support 3-bit,2-bit, 8-bit quantization? HOT 1
- awq_inference_engine is missing from source, so quantizing custom models fails HOT 2
- Support for Qwen models HOT 2
- AWQ for non-Transfomer Implementation HOT 3
- Error while Quantizing OWLv2 model
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