Upload finetuned Llama-2-7b models (#1)

- Upload finetuned Llama-2-7b models (7b3757455b7fc5b3609078aafee903218f4dd52a)


Co-authored-by: Denis Kuznedelev <SpiridonSunRotator@users.noreply.huggingface.co>

README.md

@@ -6,12 +6,15 @@ Selected evaluation results for this and other models:
 
 | Model      | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link                                                                 |
 |------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
-| Llama-2-7b | 1x16        | 6.31           | 2.4            | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf) |
-| Llama-2-7b (THIS) | 2x8         | 7.98           | 2.2            | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-2x8-hf)  |
+| Llama-2-7b | 1x16        | 5.92            | 2.4            | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf) |
+| Llama-2-7b (THIS) | 2x8         | 6.69           | 2.2            | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-2x8-hf)  |
 | Llama-2-7b | 8x8         | 7.83           | 2.2            | [Link](https://huggingface.co/BlackSamorez/Llama-2-7b-AQLM-2Bit-8x8-hf)  |
 | Llama-2-13b| 1x16        | 5.41           | 4.1            | [Link](https://huggingface.co/BlackSamorez/Llama-2-13b-AQLM-2Bit-1x16-hf)|
 | Llama-2-70b| 1x16        | 3.96           | 18.8           | [Link](https://huggingface.co/BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf)|
 | Llama-2-70b| 2x8         | 4.83           | 18.2           | [Link](https://huggingface.co/BlackSamorez/Llama-2-70b-AQLM-2Bit-2x8-hf) |
 | Mixtral-8x7b| 1x16       | 4.37           | 12.6            | [Link](https://huggingface.co/BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
 
+**UPD** (20.02.2024).
+We applied global finetuning on top of quantized model and improved results compared to first revision.
+
 To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM).