[Docs] Update author from NeuralMagic to vLLM

setup.py

@@ -86,8 +86,8 @@ def localversion_func(version: ScmVersion) -> str:
         "local_scheme": localversion_func,
         "version_file": "src/llmcompressor/version.py",
     },
-    author="Neuralmagic, Inc.",
-    author_email="support@neuralmagic.com",
+    author="The vLLM Project",
+    author_email="vllm-questions@lists.berkeley.edu",
     description=(
         "A library for compressing large language models utilizing the "
         "latest techniques and research in the field for both "
@@ -102,7 +102,7 @@ def localversion_func(version: ScmVersion) -> str:
         "huggingface, compressors, compression, quantization, pruning, "
         "sparsity, optimization, model optimization, model compression, "
     ),
-    license="Apache",
+    license="Apache 2.0",
     url="https://github.com/vllm-project/llm-compressor",
     include_package_data=True,
     package_dir={"": "src"},

src/llmcompressor/modifiers/README.md

@@ -35,7 +35,7 @@ One-shot algorithm that quantizes weights, input activations and/or output activ
 calculating a range from weights or calibration data. All data is quantized to the closest 
 bin using a scale and (optional) zero point. This basic quantization algorithm is 
 suitable for FP8 quantization. A variety of quantization schemes are supported via the 
-[compressed-tensors](https://github.com/neuralmagic/compressed-tensors) library. 
+[compressed-tensors](https://github.com/vllm-project/compressed-tensors) library. 
 
 ### [GPTQ](./gptq/base.py)
 One-shot algorithm that uses calibration data to select the ideal bin for weight quantization.