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Merged
nroope merged 1 commit into from
Jul 31, 2026
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Modified version number and added layer supported in the new release #69

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -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) |

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2 changes: 1 addition & 1 deletion docs/source/conf.py
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Expand Up @@ -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


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2 changes: 1 addition & 1 deletion docs/source/install.md
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Expand Up @@ -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`.
```
2 changes: 1 addition & 1 deletion docs/source/reference.md
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Expand Up @@ -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).

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18 changes: 9 additions & 9 deletions docs/source/status.md
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Expand Up @@ -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 |
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