update setup.py

setup.py

@@ -6,14 +6,17 @@
 with open("requirements.txt") as f:
     requirements = f.read().splitlines()
 
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
 
 setuptools.setup(
     name="mlx-transformers",
     version="0.0.1",
     author="Ogundepo Odunayo",
     author_email="[email protected]",
     description=description,
-    long_description=description,
+    long_description=long_description,
     long_description_content_type="text/markdown",
     url="https://github.com/ToluClassics/mlx-transformers",
     install_requires=requirements,

src/mlx_transformers.egg-info/PKG-INFO

@@ -0,0 +1,95 @@
+Metadata-Version: 2.1
+Name: mlx-transformers
+Version: 0.0.1
+Summary: MLX transformers is a machine learning framework with similar Interface to Huggingface transformers using MLX core as backend.
+Home-page: https://github.com/ToluClassics/mlx-transformers
+Author: Ogundepo Odunayo
+Author-email: [email protected]
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: MIT License
+Requires-Python: >=3.10
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: huggingface-hub==0.22.2
+Requires-Dist: mlx==0.8.1
+Requires-Dist: numpy==1.26.4
+Requires-Dist: safetensors==0.4.3
+Requires-Dist: sentencepiece==0.2.0
+Requires-Dist: tokenizers==0.19.1
+Requires-Dist: torch==2.2.2
+Requires-Dist: tqdm==4.66.2
+Requires-Dist: transformers==4.40.0
+
+# MLX Transformers
+
+[![PyPI](https://img.shields.io/pypi/v/mlx-transformers?color=red)](https://pypi.org/project/mlx-transformers/)
+
+
+MLX Transformers is a library that provides model implementation in MLX. It uses a similar model interface as HuggingFace Transformers and provides a way to load and use models in Apple Silicon devices. Implemented models have the same modules 
+
+MLX transformers is currently only available for infernce on Apple Silicon devices. Training support will be added in the future.
+
+# Installation
+
+This library is available on PyPI and can be installed using pip:
+
+```bash
+pip install mlx-transformers
+```
+
+
+## Quick Tour
+
+A list of the available models can be found in the `mlx_transformers.models` module and are also listed in the [section below](#available-model-architectures). The following example demonstrates how to load a model and use it for inference:
+
+- First you need to download and convert the model checkpoint to MLX format
+    To do this from huggingface
+
+    ```python
+
+    from transformers import BertModel
+    from mlx_transformers.models.utils import convert
+
+    model_name_or_path = "bert-base-uncased"
+    mlx_checkpoint = "bert-base-uncased.npz"
+
+    convert("bert-base-uncased", "bert-base-uncased.npz", BertModel)
+    ```
+    This will download the model checkpoint from huggingface and convert it to MLX format.
+
+- Now you can load the model using MLX transformers in few lines of code
+
+    ```python
+    from transformers import BertConfig, BertTokenizer
+    from mlx_transformers.models import BertModel as MLXBertModel
+
+    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    config = BertConfig.from_pretrained("bert-base-uncased")
+    model = MLXBertModel(config)
+
+    sample_input = "Hello, world!"
+    inputs = tokenizer(sample_input, return_tensors="np")
+    outputs = model(**inputs)
+    ```
+
+
+## Available Models
+
+The following models have been ported to MLX Transformers from Huggingface for inference:
+
+1. Bert
+2. Roberta
+3. XLMRoberta
+4. M2M100
+
+## Examples
+
+Coming soon...
+
+## Benchmarks
+
+Coming soon...
+
+## Contributions
+
+Contributions to MLX transformers are welcome. See the contributing documentation for instructions on setting up a development environment.

src/mlx_transformers.egg-info/SOURCES.txt

@@ -0,0 +1,24 @@
+CONTRIBUTING.md
+LICENSE
+MANIFEST.in
+README.md
+pyproject.toml
+requirements.txt
+setup.py
+src/mlx_transformers/__init__.py
+src/mlx_transformers.egg-info/PKG-INFO
+src/mlx_transformers.egg-info/SOURCES.txt
+src/mlx_transformers.egg-info/dependency_links.txt
+src/mlx_transformers.egg-info/requires.txt
+src/mlx_transformers.egg-info/top_level.txt
+src/mlx_transformers/models/__init__.py
+src/mlx_transformers/models/bert.py
+src/mlx_transformers/models/clip.py
+src/mlx_transformers/models/m2m_100.py
+src/mlx_transformers/models/modelling_outputs.py
+src/mlx_transformers/models/roberta.py
+src/mlx_transformers/models/utils.py
+src/mlx_transformers/models/xlm_roberta.py
+tests/test_bert.py
+tests/test_roberta.py
+tests/test_xlm_roberta.py

src/mlx_transformers.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

src/mlx_transformers.egg-info/requires.txt

@@ -0,0 +1,9 @@
+huggingface-hub==0.22.2
+mlx==0.8.1
+numpy==1.26.4
+safetensors==0.4.3
+sentencepiece==0.2.0
+tokenizers==0.19.1
+torch==2.2.2
+tqdm==4.66.2
+transformers==4.40.0

src/mlx_transformers.egg-info/top_level.txt

@@ -0,0 +1 @@
+mlx_transformers