semvec/pypi-packages huggingface dataset:
mcp-math-tools 0.1.0
A collection of MCP-compatible math tools (matrix, quadratic, eigen)
License: MIT
Requires Python: >=3.8

# mcp_math_tools

A set of MCP-compatible math tools for LLMs and AI agents.

### Tools Included:

- Matrix multiplication
- Quadratic equation solver (symbolic with sympy)
- Eigenvalue and eigenvector solver
moshi-mlx 0.3.0
Moshi is moshi, but running on macOS
License: MIT
Requires Python: >=3.10

# Moshi - MLX

See the [top-level README.md][main_repo] for more information on Moshi.

[Moshi][moshi] is a speech-text foundation model and full-duplex spoken dialogue framework.
It uses [Mimi][moshi], a state-of-the-art streaming neural audio codec. Mimi operates at a framerate of 12.5 Hz, and compresses
24 kHz audio down to 1.1 kbps, in a fully streaming manner (latency of 80ms, the frame size), yet performs better than existing, non-streaming, codec.

This is the MLX implementation for Moshi. For Mimi, this uses our Rust based implementation through the Python binding provided in `rustymimi`, available in the [rust/](https://github.com/kyutai-labs/moshi/tree/main/rust) folder of our main repository.

## Requirements

You will need at least Python 3.10, we recommend Python 3.12.

```bash
pip install moshi_mlx  # moshi MLX, from PyPI, best with Python 3.12.
# Or the bleeding edge versions for Moshi and Moshi-MLX.
pip install -e "git+https://git@github.com/kyutai-labs/moshi#egg=moshi_mlx&subdirectory=moshi_mlx"
```
We have tested the MLX version with MacBook Pro M3.

If you are not using Python 3.12, you might get an error when installing
`moshi_mlx` or `rustymimi` (which `moshi_mlx` depends on). Then,you will need to install the [Rust toolchain](https://rustup.rs/), or switch to Python 3.12.

## Usage


Once you have installed `moshi_mlx`, you can run
```bash
python -m moshi_mlx.local -q 4   # weights quantized to 4 bits
python -m moshi_mlx.local -q 8   # weights quantized to 8 bits
# And using a different pretrained model:
python -m moshi_mlx.local -q 4 --hf-repo kyutai/moshika-mlx-q4
python -m moshi_mlx.local -q 8 --hf-repo kyutai/moshika-mlx-q8
# be careful to always match the `-q` and `--hf-repo` flag.
```

This uses a command line interface, which is barebone. It does not perform any echo cancellation,
nor does it try to compensate for a growing lag by skipping frames.

You can use `--hf-repo` to select a different pretrained model, by setting the proper Hugging Face repository.
See [the model list](https://github.com/kyutai-labs/moshi?tab=readme-ov-file#models) for a reference of the available models.

Alternatively you can use `python -m moshi_mlx.local_web` to use
the web UI, the connection is via http, at [localhost:8998](http://localhost:8998).


## License

The present code is provided under the MIT license.

## Citation

If you use either Mimi or Moshi, please cite the following paper,

```
@techreport{kyutai2024moshi,
    author = {Alexandre D\'efossez and Laurent Mazar\'e and Manu Orsini and Am\'elie Royer and
			  Patrick P\'erez and Herv\'e J\'egou and Edouard Grave and Neil Zeghidour},
    title = {Moshi: a speech-text foundation model for real-time dialogue},
    institution = {Kyutai},
    year={2024},
    month={September},
    url={http://kyutai.org/Moshi.pdf},
}
```

[moshi]: https://kyutai.org/Moshi.pdf
[main_repo]: https://github.com/kyutai-labs/moshi
pyeutils 0.0.1
NCBI E-utilities Client
License: MIT

pyeutils
========

**pyeutils** is a lightweight Python wrapper for the NCBI E-utilities (Entrez Programming Utilities) API. It simplifies
programmatic access to the vast array of biomedical data available from NCBI, including PubMed, Gene,
Protein, Nucleotide, and more.

NCBI’s E-utilities are a powerful but low-level REST interface. `pyeutils` abstracts the complexity and
provides a more Pythonic way to interact with the API.

Features
--------

- Convenient interface to NCBI’s Entrez databases
- Supports key E-utilities: `esearch`, `efetch`, `esummary`
- `epost`, `elink`, `egquery`, and `espell` not yet implemented
- Handles request throttling and query parameters
- Returns structured Python objects (e.g., dicts, lists, or parsed XML/JSON)
- Compatible with Python 3.9+

Installation
------------

Install via pip:

.. code-block:: bash

   pip install pyeutils

Quick Start
-----------

.. code-block:: python

   import pyeutils
   client = pyeutils.NcbiEutils(API_TOKEN)

   result = client.esearch('assembly', 'GCF_000005845.2')
   print(result.id_list)

Documentation
-------------

none yet

License
-------

MIT License. See `LICENSE` file for details.

Support
-------

For questions, bugs, or feature requests, open an issue at:
https://github.com/Fxe/pyeutils/issues