Update documentation (#3703)

### Changes

Update the documentation to reflect the current status of ONNX backend
support.

Author: andrey-churkin

README.md

@@ -44,7 +44,7 @@ learning frameworks.
 | Compression algorithm                                                                                    | OpenVINO  | PyTorch   | TorchFX   | TensorFlow    | ONNX          |
 | :------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: | :-----------: |
 | [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Experimental | Supported     | Supported     |
-| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md)               | Supported | Supported | Experimental | Not supported | Not supported |
+| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md)               | Supported | Supported | Experimental | Not supported | Supported |
 | [Activation Sparsity](./src/nncf/experimental/torch/sparsify_activations/ActivationSparsity.md)          | Not supported | Experimental | Not supported| Not supported| Not supported |
 
 ### Training-Time Compression Algorithms
@@ -409,9 +409,9 @@ A list of notebooks demonstrating OpenVINO conversion and inference together wit
 | [LLM Instruction Following](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-question-answering)                                                                                                                                                                                                                   |                Weight Compression                 | OpenVINO  |                      NLP, Instruction Following                      |
 | [LLM Chat Bots](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-chatbot)                                                                                                                                                                                                                                          |                Weight Compression                 | OpenVINO  |                            NLP, Chat Bot                             |
 
-### Post-Training Quantization Examples
+### Post-Training Quantization and Weight Compression Examples
 
-Compact scripts demonstrating quantization and corresponding inference speed boost:
+Compact scripts demonstrating quantization/weight compression and corresponding inference speed boost:
 
 | Example Name                                                                                                                             |              Compression Algorithm               |  Backend   |         Domain         |
 |:-----------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------:|:----------:|:----------------------:|
@@ -424,6 +424,12 @@ Compact scripts demonstrating quantization and corresponding inference speed boo
 | [TorchFX Resnet18](./examples/post_training_quantization/torch_fx/resnet18/README.md)                                                    |            Post-Training Quantization            |  TorchFX   |  Image Classification  |
 | [TensorFlow MobileNetV2](./examples/post_training_quantization/tensorflow/mobilenet_v2/README.md)                                        |            Post-Training Quantization            | TensorFlow |  Image Classification  |
 | [ONNX MobileNetV2](./examples/post_training_quantization/onnx/mobilenet_v2/README.md)                                                    |            Post-Training Quantization            |    ONNX    |  Image Classification  |
+| [ONNX YOLOv8 QwAС](./examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/README.md)                           | Post-Training Quantization with Accuracy Control |    ONNX    |    Object Detection    |
+| [ONNX TinyLlama WC](./examples/llm_compression/onnx/tiny_llama/README.md)                                                                |                Weight Compression                |    ONNX    |           LLM          |
+| [TorchFX TinyLlama WC](./examples/llm_compression/torch_fx/tiny_llama/README.md)                                                         |                Weight Compression                |  TorchFX   |           LLM          |
+| [OpenVINO TinyLlama WC](./examples/llm_compression/openvino/tiny_llama/README.md)                                                        |                Weight Compression                |  OpenVINO  |           LLM          |
+| [OpenVINO TinyLlama WC with HS](./examples/llm_compression/openvino/tiny_llama_find_hyperparams/README.md)                               |  Weight Compression with Hyperparameters Search  |  OpenVINO  |           LLM          |
+| [ONNX TinyLlama WC with SE](./examples/llm_compression/onnx/tiny_llama_scale_estimation/README.md)                                       |     Weight Compression with Scale Estimation     |    ONNX    |           LLM          |
 
 ### Quantization-Aware Training Examples
 

docs/Algorithms.md

@@ -7,7 +7,7 @@
   - Signed and unsigned
   - Per tensor/per channel
   - Each backend support export to the OpenVINO format
-- [Weights compression](./usage/post_training_compression/weights_compression/Usage.md) (OpenVINO, PyTorch, TorchFX)
+- [Weights compression](./usage/post_training_compression/weights_compression/Usage.md) (OpenVINO, PyTorch, TorchFX, ONNX)
   - Symmetric 8 bit compression mode
   - Symmetric and asymmetric 4 bit compression mode
   - NF4 compression mode

docs/PyPiPublishing.md

@@ -41,7 +41,7 @@ For more information about NNCF, see:
 | Compression algorithm                                                                                    | OpenVINO  | PyTorch   | TorchFX   | TensorFlow    | ONNX          |
 | :------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: | :-----------: |
 | [Post-Training Quantization](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Experimental | Supported     | Supported     |
-| [Weights Compression](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/post_training_compression/weights_compression/Usage.md)               | Supported | Supported | Experimental | Not supported | Not supported |
+| [Weights Compression](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/post_training_compression/weights_compression/Usage.md)               | Supported | Supported | Experimental | Not supported | Supported |
 | [Activation Sparsity](https://github.com/openvinotoolkit/nncf/blob/develop/src/nncf/experimental/torch/sparsify_activations/ActivationSparsity.md)          | Not supported | Experimental | Not supported| Not supported| Not supported |
 
 ### Training-Time Compression Algorithms

docs/usage/post_training_compression/weights_compression/Usage.md

@@ -17,7 +17,7 @@
 
 The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). The algorithm compresses weights for Linear, Convolution and Embedding layers.
 
-[OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run Weights Compression with. PyTorch and Torch FX are also supported.
+[OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run Weights Compression with. PyTorch, ONNX and Torch FX are also supported.
 
 ### Supported modes
 
@@ -696,7 +696,7 @@ Accuracy/footprint trade-off for `microsoft/Phi-3-mini-4k-instruct`:
 
 ### Limitations
 
-- The algorithm is supported for OpenVINO, PyTorch and Torch FX models.
+- The algorithm is supported for OpenVINO, PyTorch, ONNX and Torch FX models.
 - The compression applies in-place.
 - The compressed model is not trainable.
 - INT4_SYM, INT4_ASYM, NF4 and E2M1 modes, grouped quantization and mixed precision selection is available for OpenVINO backend only.