模型量化是开源模型应用落地的重要手段之一。通过AWQ与GPTQ等量化方法的对比和实践应用,我们可以看到不同量化方法在性能保持、模型大小减小和推理速度提升等方面的差异。在实际应用中,我们应根据具体需求选择合适的量化方法,并通过不断优化量化参数和部署策略来充分发挥模型量化的优势。相关文章推荐 文心一言接入指南:通过...
如果你想同时利用CPU和GPU, GGUF是一个非常好的格式。 3、AWQ: Activation-aware Weight Quantization 除了上面两种以外,一种新格式是AWQ(激活感知权重量化),它是一种类似于GPTQ的量化方法。AWQ和GPTQ作为方法有几个不同之处,但最重要的是AWQ假设并非所有权重对LLM的性能都同等重要。 也就是说在量化过程中会...
本期code:https://github.com/chunhuizhang/llm_inference_serving/blob/main/tutorials/quantization/qlora_gptq_gguf_awq.ipynb https://github.com/chunhuizhang/llm_inference_serving/blob/main/tutorials/quantization/basics.ipynb 关于 llama3:BV15z42167yB,BV18E421A7TQ 关于bfloat16:BV1no4y1u7og 关于...
优化器量化(如:8-Bit Optimizers Via Block-Wise Quantization)也是用于训练场景;因此,本系列仅讨论权重、激活、KV Cache量化方案。 仅权重量化,如:W4A16、AWQ及GPTQ中的W4A16,W8A16(权重量化为INT8,激活仍为BF16或FP16) 权重、激活量化,如:SmoothQuant中的W8A8 KV Cache INT8 量化,LLM 推理时,为了避免...
vllm/model_executor/layers/quantization/__init__.py Comment on lines +83 to +85 from vllm_hpu_extension.awq_hpu import AWQHPUConfig from vllm_hpu_extension.gptq_hpu import GPTQHPUConfig Member mgoin Feb 28, 2025 Shouldn't this only be imported when we are hpu device? Sign ...
"csrc/quantization/aqlm/gemm_kernels.cu" "csrc/quantization/awq/gemm_kernels.cu" "csrc/quantization/marlin/marlin_cuda_kernel.cu" "csrc/quantization/gptq_marlin/gptq_marlin.cu" "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu" "csrc/custom_all_reduce.cu") endif() 18 changes: 18 ad...
^AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration ^GPTQ对称量化有一个小问题,即当x全部大于0时,xmin取值为0,导致scale=xmax/15,这会将xmax映射到23,截断到15会产生较大误差,具体讨论参考:https://github.com/AutoGPTQ/AutoGPTQ/issues/293 ^GPTQ还需要g_idx参数记录每行权...
parser.add_argument('--quantization', '-q', choices=['awq', None], default=None) parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1) parser.add_argument('--input-len', type=int, default=32) parser.add_argument('--output-len', type=int, default=128) pars...
To create a new 4-bit quantized model, you can leverage [AutoAWQ](https://github.com/casper-hansen/AutoAWQ). Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%. Quantization reduces the model's precision from BF16/FP16 to INT4...