Bump to v0.1.6 (#139)

README.md

@@ -19,7 +19,7 @@
 AutoAWQ is an easy-to-use package for 4-bit quantized models. AutoAWQ speeds up models by 2x while reducing memory requirements by 3x compared to FP16. AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs.  AutoAWQ was created and improved upon from the [original work](https://github.com/mit-han-lab/llm-awq) from MIT.
 
 *Latest News* 🔥
-- [2023/11] AutoAWQ has been merged into 🤗 transformers. Example found in: [examples/basic_transformers](examples/basic_transformers.py).
+- [2023/11] AutoAWQ has been merged into 🤗 transformers. Now includes CUDA 12.1 wheels.
 - [2023/10] Mistral (Fused Modules), Bigcode, Turing support, Memory Bug Fix (Saves 2GB VRAM)
 - [2023/09] 1.6x-2.5x speed boost on fused models (now including MPT and Falcon).
 - [2023/09] Multi-GPU support, bug fixes, and better benchmark scripts available

awq/__init__.py

-__version__ = "0.1.5"
+__version__ = "0.1.6"
 from awq.models.auto import AutoAWQForCausalLM
\ No newline at end of file

setup.py

@@ -14,7 +14,7 @@ except Exception as ex:
     raise RuntimeError("Your system must have an Nvidia GPU for installing AutoAWQ")
 
 common_setup_kwargs = {
-    "version": f"0.1.5+cu{CUDA_VERSION}",
+    "version": f"0.1.6+cu{CUDA_VERSION}",
     "name": "autoawq",
     "author": "Casper Hansen",
     "license": "MIT",