Accuracy results for scale estimation algorithm in docs. (openvinotoo…

…lkit#2706)

### Changes

Added scale estimation accuracy results to documentation.

docs/usage/post_training_compression/weights_compression/Usage.md

@@ -61,10 +61,11 @@ nncf_dataset = nncf.Dataset(data_source, transform_fn)
 compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_SYM, ratio=0.8, dataset=nncf_dataset) # model is openvino.Model object
 ```
 
-- Accuracy of the 4-bit compressed models also can be improved by using AWQ algorithm over data-based mixed-precision algorithm. It is capable to equalize some subset of weights to minimize difference between
+- Accuracy of the 4-bit compressed models also can be improved by using AWQ algorithm or Scale Estimation algorithm over data-based mixed-precision algorithm. It is capable to equalize some subset of weights to minimize difference between
 original precision and 4-bit.
-Below is the example how to compress 80% of layers to 4-bit integer with a default data-based mixed precision algorithm and AWQ.
-It requires to set `awq` to `True` additionally to data-based mixed-precision algorithm.
+Below is the example how to compress 80% of layers to 4-bit integer with a default data-based mixed precision algorithm and AWQ with Scale Estimation.
+It requires to set `awq` to `True` and `scale_estimation` to `True` additionally to data-based mixed-precision algorithm.
+Both algorithms, AWQ and Scale Estimation, can be enabled together or separately.
 
 ```python
 from datasets import load_dataset
@@ -114,7 +115,12 @@ input_shapes = get_input_shapes(model)
 nncf_dataset = Dataset(dataset, partial(transform_func, tokenizer=tokenizer,
                                                         input_shapes=input_shapes))
 
-model.model = compress_weights(model.model, mode=CompressWeightsMode.INT4_SYM, ratio=0.8, dataset=nncf_dataset, awq=True)
+model.model = compress_weights(model.model,
+                               mode=CompressWeightsMode.INT4_SYM,
+                               ratio=0.8,
+                               dataset=nncf_dataset,
+                               awq=True,
+                               scale_estimation=True)
 
 model.save_pretrained(...)
 ```
@@ -389,6 +395,90 @@ This modification applies only for patterns `MatMul-Multiply-MatMul` (for exampl
     </tr>
 </table>
 
+Here is the perplexity and accuracy with data-free and data-aware mixed-precision INT4-INT8 weight compression for different language models on the [lambada openai dataset](https://huggingface.co/datasets/EleutherAI/lambada_openai).
+`_scale` suffix refers to the data-aware mixed-precision with Scale Estimation algorithm.
+`r100` means that embeddings and lm_head have INT8 precision and all other linear layers have INT4 precision.
+<table>
+    <tr bgcolor='#B4B5BB'>
+        <td>Model</td>
+        <td>Mode</td>
+        <td>Acc (↑)</td>
+        <td>Ppl (↓)</td>
+    </tr>
+    <tr>
+        <td>stabilityai_stablelm-2-zephyr-1_6b</td>
+        <td>fp32</td>
+        <td>0.5925</td>
+        <td>6.3024</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs64_scale</td>
+        <td>0.5795</td>
+        <td>7.3245</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs64</td>
+        <td>0.5465</td>
+        <td>8.649</td>
+    </tr>
+    <tr>
+        <td>stable-zephyr-3b-dpo</td>
+        <td>fp32</td>
+        <td>0.6099</td>
+        <td>6.7151</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs64_scale</td>
+        <td>0.595</td>
+        <td>7.037</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs64</td>
+        <td>0.5639</td>
+        <td>9.349</td>
+    </tr>
+    <tr>
+        <td>microsoft_Phi-3-mini-4k-instruct</td>
+        <td>fp32</td>
+        <td>0.6839</td>
+        <td>4.1681</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs128_scale</td>
+        <td>0.6736</td>
+        <td>4.4711</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs128</td>
+        <td>0.6342</td>
+        <td>5.3419</td>
+    </tr>
+    <tr>
+        <td>mistralai_Mistral-7B-v0.1</td>
+        <td>fp32</td>
+        <td>0.7592</td>
+        <td>3.1898</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs128_scale</td>
+        <td>0.7479</td>
+        <td>3.3527</td>
+    </tr>
+    <tr>
+        <td></td>
+        <td>int4_sym_r100_gs128</td>
+        <td>0.7421</td>
+        <td>3.4932</td>
+    <t/r>
+</table>
+
 #### Limitations
 
 - The algorithm is supported for OpenVINO and PyTorch models.