semvec/pypi-packages huggingface dataset:
llama-deck 1.0.0.post1
Llama-Deck is a command-line tool for quickly managing and experimenting with multiple versions of llama inference implementations. It can help you quickly filter and download different llama implementations) and llama2-like transformer-based LLM models. We also provide some images based on some implementations, which can be easily deploy and run through our tool. Inspired by llama2.c project.
Keywords: python, llama, llama2, LLM, llama2.c, llama2.java, docker, llama-deck

# Llama Deck

**Llama Deck** is a command-line tool for quickly managing and experimenting with multiple versions of llama inference implementations. It can help you quickly filter and download [different llama implementations](https://github.com/xxxbf0222/LlamaDeck?tab=readme-ov-file#available-repositories) and [llama2-like transformer-based LLM models](https://github.com/xxxbf0222/LlamaDeck?tab=readme-ov-file#available-models). We also provide [some images](https://github.com/xxxbf0222/LlamaDeck?tab=readme-ov-file#available-images) based on some implementations, which can be easily deploy and run through our tool. 

Inspired by [llama2.c project](https://github.com/karpathy/llama2.c) and forked from [llama-shepherd-cli](https://github.com/mikepapadim/llama-shepherd-cli).

## Code Repository
For more detailed information and usage please access:

 [Llama-Deck-Repository](https://github.com/xxxbf0222/LlamaDeck).
dyncl 0.0.0
Contrastive estimation of nonlinear latent dynamics
Requires Python: >=3.10

# Self-supervised contrastive learning performs non-linear system identification

[[website](https://dynamical-inference.ai/dyncl)] [[pre-print](/#)]

This repo contains the code for the `DynCL` algorithm presented in "Self-supervised contrastive learning performs non-linear system identification".

**We will open source the code upon publication of our pre-print**. Stay tuned!
If you want to get notified about the code release, make sure to `watch 🕶️` the repo!
In case you need early access to the codebase (for benchmarking/comparisons, application of DynCL to a dataset, etc.), please send an email to [Steffen Schneider](mailto:steffen.schneider@helmholtz-munich.de).

## Summary

Self-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains. It has been argued that these successes can be attributed to a link between SSL and identifiable representation learning: Temporal structure and auxiliary variables ensure that latent representations are related to the true underlying generative factors of the data. Here, we deepen this connection and show that SSL can perform system identification in latent space. We propose a new model to uncover linear, switching linear and non-linear dynamics under a non-linear observation model, give theoretical guarantees and validate them empirically.

<img width="840" alt="image" src="https://github.com/user-attachments/assets/6cb94bd0-2f24-4164-ae7c-353c4d8554ba">

<img width="858" alt="image" src="https://github.com/user-attachments/assets/ede39332-4b8a-4728-9a23-8983c0534d68">

## Reference

```
@article{gozalezlaizschmidt2024dyncl,
  author = {González Laiz, Rodrigo and Schmidt, Tobias and Schneider, Steffen},
  title={Self-supervised contrastive learning performs non-linear system identification},
  journal={CoRR},
  year={2024},
  month={October},
  url={/#}
}
```
tether-agent 1.1.5
Standardised MQTT+MessagePack for inter-process communication
Requires Python: >=3.11

# Python Tether agent

This module contains a Tether agent for Python.  
It has been tested with Python 3.11 (the same version currently used by TouchDesigner)

## Installation

```
pip install tether_agent
```

## Usage

- Create the Tether Agent instance
- Connect
- Create Inputs and/or Outputs
- Define a message handler function if you expect to receive messages, and add it to each Input Plug
- Call publish on the Output Plug each time you want to publish something (payload automatically encoded as messagepack)

Here is an example script that outputs an ever increasing digit every second and listens for it as well:

```
from time import sleep
from tether_agent import TetherAgent

def on_message(message):
    (topic, payload) = message
    print("*** Received from topic " + topic + ": " + str(payload))

agent = TetherAgent("test", "abc123")

in_plug = agent.create_input("test")
in_plug.add_listener(on_message)
out_plug = agent.create_output("test")

# Don't forget to connect!
agent.connect("127.0.0.1", 1883, "tether", "sp_ceB0ss!")

value = 0

while True:
    if agent.get_is_connected():
        sleep(1)
        out_plug.publish(None if value <= 0 else value)
        value += 1
```

## Developer / packager notes
Create (if necessary) and activate a virtual environment for building/publishing, using pyenv and virtualenv:
```
pyenv virtualenv tether_build
pyenv activate tether_build
```

Upgrade (if necessary) Python Packaging build:
```
pip install --upgrade build
```

Actually build:
```
rm -rf dist
python -m build
```

Install Twine (distribution uploading):
```
pip install --upgrade twine
```

Upload to TEST pypi
```
python -m twine upload --repository testpypi dist/*
```

Upload to ACTUAL pypi
```
python -m twine upload dist/*
```

Install from TEST pypi
```
pip install install --index-url https://test.pypi.org/simple/ tether_agent
```tettether