update graph (#1167)

Author: Qubitium

README.md

@@ -18,13 +18,12 @@
 * 01/01/2025 [1.5.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.5.1): 🎉 2025! Added `QuantizeConfig.device` to clearly define which device is used for quantization: default = `auto`. Non-quantized models are always loaded on cpu by-default and each layer is moved to `QuantizeConfig.device` during quantization to minimize vram usage. Compatibility fixes for `attn_implementation_autoset` in latest transformers. 
 * 12/23/2024 [1.5.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.5.0): Multi-modal (image-to-text) optimized quantization support has been added for Qwen 2-VL and Ovis 1.6-VL. Previous image-to-text model quantizations did not use image calibration data, resulting in less than optimal post-quantization results. Version 1.5.0 is the first release to provide a stable path for multi-modal quantization: only text layers are quantized.
 * 12/19/2024 [1.4.5](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.4.5): Windows 11 support added/validated. Ovis VL model support with image dataset calibration. Fixed `dynamic` loading. Reduced quantization vram usage. 
-* 12/15/2024 [1.4.2](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.4.2): MacOS `gpu` (Metal) and `cpu` (M+) support added/validated for inference and quantization. Cohere 2 model support added. 
-* 12/13/2024 [1.4.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.4.1): Added Qwen2-VL model support. `mse` quantization control exposed in `QuantizeConfig`. Monkey patch `patch_vllm()` and `patch_hf()` api added to allow Transformers/Optimum/PEFT and vLLM to correctly loaded GPTQModel quantized models while upstream PRs are in pending status. 
-
 
 <details>
 
 <summary>Archived News</summary>
+* 12/15/2024 [1.4.2](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.4.2): MacOS `gpu` (Metal) and `cpu` (M+) support added/validated for inference and quantization. Cohere 2 model support added. 
+* 12/13/2024 [1.4.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.4.1): Added Qwen2-VL model support. `mse` quantization control exposed in `QuantizeConfig`. Monkey patch `patch_vllm()` and `patch_hf()` api added to allow Transformers/Optimum/PEFT and vLLM to correctly loaded GPTQModel quantized models while upstream PRs are in pending status. 
 * 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. 
@@ -88,7 +87,7 @@ Public tests/papers and ModelCloud's internal tests have shown that GPTQ is on-p
 ## Quality: GPTQ 4bit (5.0 bpw) can match BF16:
 🤗 [ModelCloud quantized Vortex models on HF](https://huggingface.co/collections/ModelCloud/vortex-673743382af0a52b2a8b9fe2)
 
-![image](https://github.com/user-attachments/assets/713e00e4-d991-4917-aca1-0af35fe853c6)
+![image](https://github.com/user-attachments/assets/23901236-10c5-4435-ac2f-06cf2e097f1e)
 
 ## Model Support  
 | Model            |    |                |    |                  |    |            |    |    |   |