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	Update README for mlx-examples repo
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| # mlxbert | ||||
|  | ||||
| A BERT implementation in Apple's new MLX framework. | ||||
| An implementation of BERT [(Devlin, et al., 2019)](https://aclanthology.org/N19-1423/) within mlx. | ||||
|  | ||||
| ## Dependency Installation | ||||
| ## Downloading and Converting Weights | ||||
|  | ||||
| ```sh | ||||
| poetry install --no-root | ||||
| ``` | ||||
|  | ||||
| If you don't want to do that, simply make sure you have the following dependencies installed: | ||||
|  | ||||
| - `mlx` | ||||
| - `transformers` | ||||
| - `numpy` | ||||
|  | ||||
| ## Download and Convert | ||||
| The `convert.py` script relies on `transformers` to download the weights, and exports them as a single `.npz` file. | ||||
|  | ||||
| ``` | ||||
| python convert.py \ | ||||
| @@ -24,7 +14,7 @@ python convert.py \ | ||||
|  | ||||
| ## Run the Model | ||||
|  | ||||
| Right now, this is just a test to show tha the outputs from mlx and huggingface don't change all that much. | ||||
| In order to run the model, and have it forward inference on a batch of examples: | ||||
|  | ||||
| ```sh | ||||
| python model.py \ | ||||
| @@ -60,9 +50,3 @@ Which will show: | ||||
|   [ 0.946011    0.13582966 -0.29456618 ...  0.00868565 -0.90271175 | ||||
|    -0.27854213]]] | ||||
| ``` | ||||
|  | ||||
| ## To do's | ||||
|  | ||||
| - [x] fix position encodings | ||||
| - [x] bert large and cased variants loaded | ||||
| - [x] example usage | ||||
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