Update README and version following 0.5.0 release (AutoGPTQ#397)

README.md

@@ -62,13 +62,12 @@ For perplexity comparison, you can turn to [here](https://github.com/qwopqwop200
 ## Installation
 
 ### Quick Installation
-You can install the latest stable release of AutoGPTQ from pip with pre-built wheels compatible with PyTorch 2.0.1:
+You can install the latest stable release of AutoGPTQ from pip with pre-built wheels compatible with PyTorch 2.1 and PyTorch nightly:
 
-* For CUDA 11.7: `pip install auto-gptq`
+* For CUDA 12.1: `pip install auto-gptq`
 * For CUDA 11.8: `pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/`
-* For RoCm 5.4.2: `pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/rocm542/`
-
-**Warning:** These wheels are not expected to work on PyTorch nightly. Please install AutoGPTQ from source when using PyTorch nightly.
+* For RoCm 5.6.1: `pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/rocm561/`
+* For RoCm 5.7.1: `pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/rocm571/`
 
 AutoGPTQ can be installed with the Triton dependency with `pip install auto-gptq[triton]` in order to be able to use the Triton backend (currently only supports linux, no 3-bits quantization).
 
@@ -92,7 +91,7 @@ You can set `BUILD_CUDA_EXT=0` to disable pytorch extension building, but this i
 To install from source for AMD GPUs supporting RoCm, please specify the `ROCM_VERSION` environment variable. Example:
 
 ```
-ROCM_VERSION=5.6 pip install -v .
+ROCM_VERSION=5.6 pip install -v -e .
 ```
 
 The compilation can be speeded up by specifying the `PYTORCH_ROCM_ARCH` variable ([reference](https://github.com/pytorch/pytorch/blob/7b73b1e8a73a1777ebe8d2cd4487eb13da55b3ba/setup.py#L132)) in order to build for a single target device, for example `gfx90a` for MI200 series devices.

README_zh.md

@@ -15,6 +15,8 @@
     </p>
 </h4>
 
+Note: The English README is likely to be more up to date.
+
 ## 通向 v1.0.0 之路
 
 嗨，社区的伙伴们，好久不见！很抱歉这段时间由于个人原因，我没能以较高的频率来更新这个项目。过去几周对我的职业生涯规划而言意义重大。在不久前，我正式告别了毕业后便加入两年之久的创业团队，非常感谢团队的领导和同事们给予我的信任与指导，让我能够在两年时间里飞速地成长；同时也十分感激团队允许我自 AutoGPTQ 项目创立以来一直无偿使用内部的 A100 GPU 服务器集群以完成各项实验与性能测评。（当然今后是无法继续使用了，因此**若有新的硬件赞助我将感激不尽**！）过去的两年里，我在这个团队中担任算法工程师的角色，负责基于大语言模型的对话系统架构设计与开发，我们曾成功推出一款名为 gemsouls 的产品，但不幸的是它已经停止运营。而现在，这个团队即将推出一款名为 [modelize](https://www.beta.modelize.ai/) 的新产品，**这是一个大模型原生的 AI 智能体平台，用户可以使用多个 AI 智能体搭建一个高度自动化的团队，让它们在工作流中相互合作，高效完成复杂的项目。**
@@ -331,4 +333,4 @@ print(
 - 特别感谢 **Elias Frantar**， **Saleh Ashkboos**， **Torsten Hoefler** 和 **Dan Alistarh** 提出 **GPTQ** 算法并开源[代码](https://github.com/IST-DASLab/gptq)。
 - 特别感谢 **qwopqwop200**， 本项目中涉及到模型量化的代码主要参考自 [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda)。
 
-[![Star History Chart](https://api.star-history.com/svg?repos=PanQiwei/AutoGPTQ&type=Date)](https://star-history.com/#PanQiWei/AutoGPTQ&Date)
+[![Star History Chart](https://api.star-history.com/svg?repos=PanQiwei/AutoGPTQ&type=Date)](https://star-history.com/#PanQiWei/AutoGPTQ&Date)

auto_gptq/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.5.0.dev0"
+__version__ = "0.6.0.dev0"
 from .modeling import BaseQuantizeConfig
 from .modeling import AutoGPTQForCausalLM
 from .utils.peft_utils import get_gptq_peft_model

setup.py

@@ -10,7 +10,7 @@
 os.environ["CXX"] = "g++"
 
 common_setup_kwargs = {
-    "version": "0.5.0.dev0",
+    "version": "0.6.0.dev0",
     "name": "auto_gptq",
     "author": "PanQiWei",
     "description": "An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.",