prepare for 1.4.5 release (#962)

* prepare for 1.4.5 release

* Update README.md

README.md

@@ -16,12 +16,13 @@
 * 12/10/2024 [1.4.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.4.0) `EvalPlus` harness integration merged upstream. We now support both `lm-eval` and `EvalPlus`. Added pure torch `Torch` kernel. Refactored `Cuda` kernel to be `DynamicCuda` kernel. `Triton` kernel now auto-padded for max model support. `Dynamic` quantization now supports both positive `+:`:default, and `-:` negative matching which allows matched modules to be skipped entirely for quantization. Fixed auto-`Marlin` kerenl selection. Added auto-kernel fallback for unsupported kernel/module pairs. Lots of internal refractor and cleanup in-preparation for transformers/optimum/peft upstream PR merge. Deprecated the saving of `Marlin` weight format since `Marlin` supports auto conversion of `gptq` format to `Marlin` during runtime. 
 
 * 11/29/2024 [1.3.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.3.1) Olmo2 model support. Intel XPU acceleration via IPEX. Model sharding Transformer compat fix due to api deprecation in HF. Removed triton dependency. Triton kernel now optionally dependent on triton pkg. 
-* 11/26/2024 [1.3.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.3.0) Zero-Day Hymba model support. Removed `tqdm` and `rogue` dependency. 
-* 11/24/2024 [1.2.3](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.2.3) HF GLM model support. ClearML logging integration. Use `device-smi` and replace `gputil` + `psutil` depends. Fixed model unit tests. 
 
 <details>
 
 <summary>Archived News:</summary>
+* 11/26/2024 [1.3.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.3.0) Zero-Day Hymba model support. Removed `tqdm` and `rogue` dependency. 
+* 11/24/2024 [1.2.3](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.2.3) HF GLM model support. ClearML logging integration. Use `device-smi` and replace `gputil` + `psutil` depends. Fixed model unit tests. 
+
 * 11/11/2024 🚀 [1.2.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.2.1) Meta MobileLLM model support added. `lm-eval[gptqmodel]` integration merged upstream. Intel/IPEX cpu inference merged replacing QBits (deprecated). Auto-fix/patch ChatGLM-3/GLM-4 compat with latest transformers. New `.load()` and `.save()` api. 
 
 * 10/29/2024 🚀 [1.1.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.1.0) IBM Granite model support. Full auto-buildless wheel install from pypi. Reduce max cpu memory usage by >20% during quantization. 100% CI model/feature coverage. 
@@ -79,7 +80,7 @@ Public tests/papers and ModelCloud's internal tests have shown that GPTQ is on-p
 * 🚀 40% faster `packing` stage in quantization (Llama 3.1 8B). 50% faster PPL calculations (OPT).
 
 ## Quality: GPTQModel 4bit can match BF16:
-🤗 [ModelCloud quantized ultra-high recovery vortex-series models on HF](https://huggingface.co/collections/ModelCloud/vortex-673743382af0a52b2a8b9fe2)
+🤗 [ModelCloud quantized Vortex models on HF](https://huggingface.co/collections/ModelCloud/vortex-673743382af0a52b2a8b9fe2)
 
 ![image](https://github.com/user-attachments/assets/7b2db012-b8af-4d19-a25d-7023cef19220)
 
@@ -184,8 +185,8 @@ GPTQModel inference is integrated into both [lm-eval](https://github.com/Eleuthe
 We highly recommend avoid using `ppl` and use `lm-eval`/`evalplus` to validate post-quantization model quality. `ppl` should only be used for regression tests and is not a good indicator of model output quality.  
 
 ```
-# gptqmodel is integrated into lm-eval >= v0.4.6
-pip install lm-eval>=0.4.6
+# gptqmodel is integrated into lm-eval >= v0.4.7
+pip install lm-eval>=0.4.7
 ```
 
 ```

setup.py

@@ -277,7 +277,7 @@ def run(self):
         'ipex': ["intel_extension_for_pytorch>=2.5.0"],
         'auto_round': ["auto_round>=0.3"],
         'logger': ["clearml", "random_word", "plotly"],
-        'eval': ["lm_eval>=0.4.6", "evalplus>=0.3.1"],
+        'eval': ["lm_eval>=0.4.7", "evalplus>=0.3.1"],
         'triton': ["triton>=2.0.0"]
     },
     include_dirs=include_dirs,