semvec/pypi-packages huggingface dataset:
halkoSVD 0.5.0
Fast Python/Cython implementation of the PCAone Halko algorithm
Requires Python: >=3.10

# Cython/Python implementation of Halko algorithm
This is a fast implementation of Halko algorithm in Python/Cython for genotype data. It takes binary PLINK format (*.bed, *.bim, *.fam) as input. For simplicity, mean imputation is performed for missing data.

It is inspired by the lovely *PCAone* software! Have a look [here](https://github.com/Zilong-Li/PCAone).

## Installation
```bash
# Option 1: Build and install via PyPI
pip install halkoSVD

# Option 2: Download source and install via pip
git clone https://github.com/Rosemeis/halkoSVD.git
cd halkoSVD
pip install .

# Option 3: Download source and install in a new Conda environment
git clone https://github.com/Rosemeis/halkoSVD.git
conda env create -f halkoSVD/environment.yml
conda activate halkoSVD
```
You can now run the program with the `halkoSVD` command.

## Quick usage
Provide `halkoSVD` with the file prefix of the PLINK files.
```bash
# Check help message of the program
halkoSVD -h

# Extract the top 10 PCs
halkoSVD --bfile input --threads 32 --pca 10 --out halko
```

### Options
* `--seed`, set random seed for reproducibility (42)
* `--power`, specify the number of power iterations (10)
* `--batch`, specify the batch size to process SNPs (8192)
* `--loadings`, save the SNP loadings
* `--raw`, only output eigenvectors without FID/IID
mcp-deepthinking 0.1.12
MCP server providing a 'deepthinking' tool powered by Groq LLMs for complex reasoning.
Requires Python: >=3.11
Keywords: deepthinking, llm, mcp

# mcp-deepthinking

An MCP (Model Context Protocol) server that provides a **deep thinking** tool powered by Groq Large Language Models (LLMs). This server enables complex reasoning, multi-step problem solving, and advanced planning capabilities through a simple API interface.

---

## Features

- **Deep Thinking Tool**: Exposes a tool named `deepthinking` for complex reasoning tasks.
- **Groq LLM Integration**: Utilizes Groq's high-performance language models via the Groq API.
- **Configurable Models**: Supports multiple Groq models, configurable via environment variables.
- **Async & Streaming Support**: Designed for efficient, asynchronous operation.
- **Easy Integration**: Compatible with any MCP client.

---

## Requirements

- **Python** >= 3.12
- **Groq API Key** (sign up at [https://console.groq.com/](https://console.groq.com/))
- Supported Groq models:
  - `deepseek-r1-distill-llama-70b` (default)
  - `deepseek-r1-distill-qwen-32b`
  - `qwen-qwq-32b`

---

## Installation

You can install dependencies using either `uv` (recommended) or `pip`.

### Using uv (recommended)

```bash
uv pip install -r requirements.txt
# or directly
uv pip install fastmcp>=0.4.1 langchain-groq>=0.3.2 mcp[cli]>=1.6.0 python-dotenv>=1.1.0
```

### Using pip

```bash
pip install fastmcp>=0.4.1 langchain-groq>=0.3.2 mcp[cli]>=1.6.0 python-dotenv>=1.1.0
```

---

## Configuration

Create a `.env` file in the project root with the following content:

```env
GROQ_API_KEY=your_groq_api_key_here
MODEL_ID=deepseek-r1-distill-llama-70b  # optional, defaults to this model
```

- `GROQ_API_KEY` (required): Your Groq API key.
- `MODEL_ID` (optional): One of the supported model IDs.

---

## Usage

Run the MCP server:

```bash
python server.py
```

This will start the server using **stdio** transport.

### Invoking the `deepthinking` tool

From an MCP-compatible client, you can invoke the `deepthinking` tool with a query string:

```python
response = mcp.call_tool("deepthinking", {"query": "Explain the theory of relativity step by step."})
print(response)
```

The tool will return a detailed reasoning response generated by the Groq LLM.

---

## Tool Details

### `deepthinking(query: str) -> str`

A tool that helps AI perform deep thinking (reasoning) processes. It can be used for complex problem solving, planning, multi-step reasoning, and more.

- **Arguments:**
  - `query` (str): The input prompt or question.
- **Returns:**
  - `str`: The generated reasoning response, ending with `</think>`.

---

## License

MIT License

---

## Contact

For questions or support, please contact the maintainer.
gguf-core 0.0.66
a simple way to interact llama with gguf

### GGUF core
[<img src="https://raw.githubusercontent.com/calcuis/gguf-core/master/gguf.gif" width="128" height="128">](https://github.com/calcuis/gguf-core)
[![Static Badge](https://img.shields.io/badge/core-release-orange?logo=github)](https://github.com/calcuis/gguf-core/releases)

This package is a GGUF (GPT-Generated Unified Format) file caller.
#### install the caller via pip/pip3 (once only):
```
pip install gguf-core
```
#### update the caller (if not in the latest version) by:
```
pip install gguf-core --upgrade
```
### user manual
This is a cmd-based (command line) package, you can find the user manual by adding the flag -h or --help.
```
gguf -h
```
#### check current version
```
gguf -v
```
#### cli connector
with command-line interface
```
gguf c
```
#### gui connector
with graphical user interface
```
gguf g
```
#### vision connector (beta)
with vision model supported
```
gguf v
```
#### interface selector
selection menu for connector interface(s) above
```
gguf i
```
#### metadata reader
read model metadata for detail(s)
```
gguf r
```
or try model analyzor (beta)
```
gguf a
```
GGUF file(s) in the current directory will automatically be detected by the caller.
#### get feature
get GGUF from URL; clone/download it to the current directory
```
gguf get [url]
```
#### sample model list
You can either use the get feature above or opt a sample GGUF straight from the sample list by:
```
gguf s
```
#### gguf splitter
Split gguf into part(s) by:
```
gguf split
```
#### gguf merger
Merge all gguf into one by:
```
gguf merge
```
#### prompt generator (beta)
Generate bulk prompt/descriptor(s) by:
```
gguf prompt
```
#### gguf comfy (beta)
Download ComfyUI GGUF portable package with gguf node by:
```
gguf comfy
```
#### gguf node (beta)
Download gguf node only by:
```
gguf node
```
#### gguf pack (beta)
Download gguf pack by:
```
gguf pack
```
#### pdf analyzor (beta)
You can now load your PDF file(s) straight into the model for generating digested summary; try it out by:
```
gguf p
```
#### wav analyzor (offline)
You can speak/talk straight to GGUF right away; prompt WAV(s) into the model for feedback; try it out by:
```
gguf f
```
#### wav analyzor (online)
more accurate (via google Open api); Internet access needed; try it out by:
```
gguf o
```
#### launch to page/container (gguf.org)
```
gguf org
```
#### launch to page/container (gguf.io)
```
gguf io
```
#### launch to page/container (gguf.us)
```
gguf us
```
oteapi-dlite 1.0.0
DLite plugin for Open Translation Environment (OTE) API.
Requires Python: ~=3.10
Keywords: OTE, OTEAPI, LITE, OTEAPI-DLITE

# OTEAPI DLite Plugin

[![DOI](https://zenodo.org/badge/455070691.svg)](https://zenodo.org/badge/latestdoi/455070691)

All strategies implemented in this plugin (except for `create_collection`) assumes that you have the UUID of a DLite collection with the key `collection_id` in the `session`:

```python
collection_id = session["collection_id"]
coll = dlite.get_collection(collection_id)
```

A DLite collection stores references to DLite instances and relations between them as RDF triples.
Hence, the collection is a knowledge base for the current use case.

In order to make it easy retrieve the collection id when executing a pipeline, the `get()` method of all filters in this plugin should return the `collection_id`.

Further reading:

- [OTEAPI Core Documentation](https://emmc-asbl.github.io/oteapi-core)
- [OTEAPI Services Documentation](https://github.com/EMMC-ASBL/oteapi-services)
- [DLite](https://github.com/SINTEF/dlite)

A list and documentation for all the strategies in this plugin can be found [here](https://EMMC-ASBL.github.io/oteapi-dlite/latest/all_strategies).

## License and copyright

The OTEAPI DLite Plugin is released under the [MIT license](LICENSE) with copyright &copy; SINTEF.

## Acknowledgment

OTEAPI DLite Plugin has been created via the [cookiecutter](https://cookiecutter.readthedocs.io/) [template for OTEAPI plugins](https://github.com/EMMC-ASBL/oteapi-plugin-template).

OTEAPI DLite Plugin has been supported by the following projects:

- __OntoTrans__ (2020-2024) that receives funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no. 862136.

- __VIPCOAT__ (2021-2025) receives funding from the European Union’s Horizon 2020 Research and Innovation Programme - DT-NMBP-11-2020 Open Innovation Platform for Materials Modelling, under Grant Agreement no: 952903.

- __OpenModel__ (2021-2025) receives funding from the European Union’s Horizon 2020 Research and Innovation Programme - DT-NMBP-11-2020 Open Innovation Platform for Materials Modelling, under Grant Agreement no: 953167.