Remove mentions of Torchscript and quantization

Both are disabled in the initial release of `spacy-curated-transformers`.

website/docs/api/architectures.mdx

@@ -524,7 +524,6 @@ Construct an ALBERT transformer model.
 | `num_hidden_layers`            | Number of hidden layers. ~~int~~                                            |
 | `padding_idx`                  | Index of the padding meta-token. ~~int~~                                    |
 | `type_vocab_size`              | Type vocabulary size. ~~int~~                                               |
-| `torchscript`                  | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~  |
 | `mixed_precision`              | Use mixed-precision training. ~~bool~~                                      |
 | `grad_scaler_config`           | Configuration passed to the PyTorch gradient scaler. ~~dict~~               |
 | **CREATES**                    | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@@ -553,7 +552,6 @@ Construct a BERT transformer model.
 | `num_hidden_layers`            | Number of hidden layers. ~~int~~                                            |
 | `padding_idx`                  | Index of the padding meta-token. ~~int~~                                    |
 | `type_vocab_size`              | Type vocabulary size. ~~int~~                                               |
-| `torchscript`                  | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~  |
 | `mixed_precision`              | Use mixed-precision training. ~~bool~~                                      |
 | `grad_scaler_config`           | Configuration passed to the PyTorch gradient scaler. ~~dict~~               |
 | **CREATES**                    | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@@ -582,7 +580,6 @@ Construct a CamemBERT transformer model.
 | `num_hidden_layers`            | Number of hidden layers. ~~int~~                                            |
 | `padding_idx`                  | Index of the padding meta-token. ~~int~~                                    |
 | `type_vocab_size`              | Type vocabulary size. ~~int~~                                               |
-| `torchscript`                  | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~  |
 | `mixed_precision`              | Use mixed-precision training. ~~bool~~                                      |
 | `grad_scaler_config`           | Configuration passed to the PyTorch gradient scaler. ~~dict~~               |
 | **CREATES**                    | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@@ -611,7 +608,6 @@ Construct a RoBERTa transformer model.
 | `num_hidden_layers`            | Number of hidden layers. ~~int~~                                            |
 | `padding_idx`                  | Index of the padding meta-token. ~~int~~                                    |
 | `type_vocab_size`              | Type vocabulary size. ~~int~~                                               |
-| `torchscript`                  | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~  |
 | `mixed_precision`              | Use mixed-precision training. ~~bool~~                                      |
 | `grad_scaler_config`           | Configuration passed to the PyTorch gradient scaler. ~~dict~~               |
 | **CREATES**                    | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
@@ -641,7 +637,6 @@ Construct a XLM-RoBERTa transformer model.
 | `num_hidden_layers`            | Number of hidden layers. ~~int~~                                            |
 | `padding_idx`                  | Index of the padding meta-token. ~~int~~                                    |
 | `type_vocab_size`              | Type vocabulary size. ~~int~~                                               |
-| `torchscript`                  | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~  |
 | `mixed_precision`              | Use mixed-precision training. ~~bool~~                                      |
 | `grad_scaler_config`           | Configuration passed to the PyTorch gradient scaler. ~~dict~~               |
 | **CREATES**                    | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |

website/docs/api/cli.mdx

@@ -1054,18 +1054,6 @@ Token length range: [1, 8]
 ```
 </Accordion>
 
-## quantize {id="quantize",tag="command",version="3.6"}
-
-Quantize a curated transformers model to reduce its size.
-
-| Name           | Description                                                         |
-|----------------|---------------------------------------------------------------------|
-| `model_path`   | Model to quantize. ~~Path (positional)~~                            |
-| `output_path`  | Output directory to store quantized model in. ~~Path (positional)~~ |
-| `max_mse_loss` | Maximum MSE loss of quantized parameters. ~~float (option)~~        |
-| `skip_embeds`  | Do not quantize embeddings. ~~bool (option)~~                       |
-| `skip_linear`  | Do not quantize linear layers. ~~bool (option)~~                    |
-
 ## train {id="train",tag="command"}
 
 Train a pipeline. Expects data in spaCy's

website/docs/api/curated-transformer.mdx

@@ -25,7 +25,7 @@ work out-of-the-box.
 
 </Infobox>
 
-This Python package provides a curated set of transformer models for spaCy. It is focused on deep integration into spaCy and will support deployment-focused features such as distillation and quantization. Curated transformers currently supports the following model types:
+This Python package provides a curated set of transformer models for spaCy. It is focused on deep integration into spaCy and will support deployment-focused features such as distillation and quantization in the future. spaCy curated transformers currently supports the following model types:
 
 * ALBERT
 * BERT