diff --git a/README.md b/README.md
index ed4f679..32764fb 100644
--- a/README.md
+++ b/README.md
@@ -45,7 +45,7 @@ pip install "pquant-ml[torch]"        # PyTorch backend
 | `PQAvgPool*D` | Average pooling layers |
 | `PQBatchNorm*D` | Batch normalization layers |
 | `PQDense` | Linear (fully connected) layer |
-| `PQActivation` | Activation layers: ReLU, Tanh, Leaky ReLU, GELU, Hard Tanh, or a user-provided activation function (Torch only) |
+| `PQActivation` | Activation layers: ReLU, Tanh, Leaky ReLU, GELU, Hard Tanh, Softmax or a user-provided activation function (Torch only) |
 | `MultiHeadAttention` | Multi-head attention layer |
 | `LayerNorm` | Layer normalization layer (currently Torch only) |
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 971c162..4d382c2 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -14,7 +14,7 @@
 project = "PQuantML"
 copyright = "2025, Roope Niemi"
 author = "Roope Niemi, Anastasiia Petrovych"
-release = "0.0.6"
+release = "0.1.0"
 version = release
 
 
diff --git a/docs/source/install.md b/docs/source/install.md
index 8fed3e6..f83a4cb 100644
--- a/docs/source/install.md
+++ b/docs/source/install.md
@@ -6,5 +6,5 @@ With TensorFlow backend:  `pip install pquant-ml[tensorflow]`.
 With PyTorch backend: `pip install pquant-ml[torch]`.
 
 ```{warning}
-PQuantML v0.0.6 requires `tensorflow>=2.17`, `mlflow>=2.0,<3.0`, and `python>=3.10,<=3.12`.
+PQuantML v0.1.0 requires `tensorflow>=2.17`, `mlflow>=2.0,<3.0`, and `python>=3.10,<=3.12`.
 ```
diff --git a/docs/source/reference.md b/docs/source/reference.md
index 53aafef..6ba16d5 100644
--- a/docs/source/reference.md
+++ b/docs/source/reference.md
@@ -203,7 +203,7 @@ Optionally, there is also FITCompress method implemented for PyTorch-only:
 - `PQAvgPool*D`: Average pooling layers.
 - `PQBatchNorm*D`: BatchNorm layers.
 - `PQDense`: Linear layer.
-- `PQActivation`: Activation layers (ReLU, Tanh, Leaky Relu, Gelu, Hard Tanh, or a user-provided activation function (Torch only) ).
+- `PQActivation`: Activation layers (ReLU, Tanh, Leaky Relu, Gelu, Hard Tanh, Softmax or a user-provided activation function (Torch only) ).
 - `MultiHeadAttention`: Multi-head attention layer.
 - `LayerNorm`: Layer normalization layer (Currently Torch only).
 
diff --git a/docs/source/status.md b/docs/source/status.md
index 276021b..6b93aa7 100644
--- a/docs/source/status.md
+++ b/docs/source/status.md
@@ -2,22 +2,22 @@
 
 This page tracks the development status of PQuantML features.
 
-## Release: v0.0.6
+## Release: v0.1.0
 
 | Feature                         | Status          | Notes |
 |---------------------------------|-----------------|-------|
-| Compression pipeline    | ✅ Complete     | Included in v0.0.6 |
+| Compression pipeline    | ✅ Complete     | Included in v0.1.0 |
 | Pruning methods (7 variants)    | ✅ Complete     | All documented |
 | Quantization (fixed-point)      | ✅ Complete     | Supports per-layer overrides |
 | HGQ support   |✅ Complete  |  Supports HGQ quantization |
-| hls4ml integration              | ✅ Complete   | Works in v0.0.6 |
+| hls4ml integration              | ✅ Complete   | Works in v0.1.0 |
 | FITCompress                     | ✅ Complete |  Supported in PyTorch only |
-| Model fit support | ✅ Complete | Works in v0.0.6 |
-| Alkaid converter support        | ⏳ Coming in v0.0.7 | Implemented in dev |
-| Onnx converter support          | ⏳ Coming in v0.0.7  | Implemented in dev |
-| Knowledge distillation  | ⏳ Coming in v0.0.7  | Implemented in dev |
-| Implementation of HGQ and pruning layers in Torch | ⏳ Coming in v0.0.7  | Implemented in dev |
-| Additional test coverage for pruning methods | ⏳ Will be in the next release | Implemented in dev |
+| Model fit support | ✅ Complete | Works in v0.1.0 |
+| Alkaid converter support        | ✅ Complete | Works in v0.1.0 |
+| Onnx converter support          | ✅ Complete | Works in v0.1.0 |
+| Knowledge distillation  | ⏳ In progress | Initial prototype available in v0.1.0 |
+| Implementation of HGQ and pruning layers in Torch  | ✅ Complete | Works in v0.1.0 |
+| Additional test coverage for pruning methods | ⏳ Will be in the next release | Due to the next release|
 | CI/CD pipeline                   | 🚧 Work in progress    | Due to the next release |
 | MDMM pruning algorithm metrics extension               | 🚧 Work in progress    | Due to the next release |
 | Documentation                   | 🚧 Improving    | Expanded monthly |