From 97acd5e277e8f7587f0db3c4453c0acfa3c150ed Mon Sep 17 00:00:00 2001 From: Michael Gschwind <61328285+mikekgfb@users.noreply.github.com> Date: Thu, 6 Mar 2025 19:07:08 -0800 Subject: [PATCH] Update README.md fix various typos Signed-off-by: Michael Gschwind <61328285+mikekgfb@users.noreply.github.com> --- examples/quantization/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/quantization/README.md b/examples/quantization/README.md index a31a55e7fb0c..219426f66322 100644 --- a/examples/quantization/README.md +++ b/examples/quantization/README.md @@ -58,7 +58,7 @@ Checkpoint saved in `output_dir` can be directly passed to `trtllm-build`. - model_dir: Hugging Face model path. - qformat: Specify the quantization algorithm applied to the checkpoint. - - nvfp4: Weights are quantized to NVFP4 block-wise with size 16. Actiavation global scale are calibrated. + - nvfp4: Weights are quantized to NVFP4 block-wise with size 16. Activation global scale are calibrated. - fp8: Weights are quantized to FP8 tensor wise. Activation ranges are calibrated tensor wise. - int8_sq: Weights are smoothed and quantized to INT8 channel wise. Activation ranges are calibrated tensor wise. - int4_awq: Weights are re-scaled and block-wise quantized to INT4. Block size is specified by `awq_block_size`. @@ -99,7 +99,7 @@ Checkpoint saved in `output_dir` can be directly passed to `trtllm-build`. ### Building Arguments: -There are several arguments for building stage which related to quantizaion. +There are several arguments for the building stage which relate to quantization. - use_fp8_context_fmha: This is Hopper-only feature. Use FP8 Gemm to calculate the attention operation. ```python @@ -170,7 +170,7 @@ There will be another file about per-layer quantization information named `quant } ``` -TensorRT-LLM will automatically read `quant_cfg.json` after recogniziong the `MIXED_PRECISION` quantization method in `config.json`. All the specific algorithm keeps the same as what in `quantization` filed before. If some layers are not listed, they'll be treated as no quantization. +TensorRT-LLM will automatically read `quant_cfg.json` after recogniziong the `MIXED_PRECISION` quantization method in `config.json`. All the specific algorithm keeps the same as what in `quantization` field before. If some layers are not listed, they'll be treated as no quantization. ## APIs