semvec/pypi-packages huggingface dataset:
VegaliteJsonEnforcer 0.1
Python library for enforcing JSON schema to LLM outputs, specifically tailored for Vega-Lite visualization specifications.
License: MIT
Requires Python: >=3.10


# EVLLMPackage

EVLLMPackage is a Python library for enforcing JSON schema to LLM outputs, specifically tailored for Vega-Lite visualization specifications. It integrates functionalities for logits processing and schema validation, facilitating the generation of outputs that adhere to specific structural requirements.

## Installation

You can install EVLLMPackage using pip. It's recommended to use a virtual environment.

```bash
pip install EVLLMPackage
```

## Prerequisites

Before you begin, ensure you have Python 3.6 or later installed. Some features of this package require external libraries such as PyTorch, pydantic, and others listed in the `setup.py` file.

## Getting Started

Here's how to get started with EVLLMPackage.

### Importing the Package

```python
from evllm_package import EVLLM, create_processor_logit
```

### Using the EVLLM with Custom Parameters and Logits Processor

To use the Extended Very Large Language Model with custom configurations and integrate a logits processor:

```python
from evllm_package import EVLLM, create_processor_logit

# Assuming you've created a logits processor earlier
processor = create_processor_logit(model_id="your_model_id")

# Initialize the EVLLM with specific configurations
llm = EVLLM(
    model="your_model_id",
    trust_remote_code=True,  # Mandatory for models from Hugging Face, etc.
    max_new_tokens=1024,
    top_k=10,
    top_p=0.95,
    temperature=0.3,
    logits_processors=[processor.logits_processor]  # Integrating your logits processor
)

# Generate text using the configured model
output = llm.generate(["Your prompt here"])
print(output.text)
```

This setup demonstrates how to initialize the `EVLLM` class with a set of specific parameters that control the behavior of the language model, such as the generation length (`max_new_tokens`), randomness (`temperature`), and the logits processing (`logits_processors`). Remember to replace `"your_model_id"` with the actual model identifier you wish to use.

### Important Notes

- `trust_remote_code=True` is crucial when you're using models hosted on platforms like Hugging Face, as it allows the code necessary for the model to run to be executed.
- The `logits_processors` list expects instances of logits processors, which you can create using the `create_processor_logit` function demonstrated in earlier sections of this document.
- Ensure the `model_id` corresponds to a valid model that you have access to or is publicly available.

By following these instructions, you can leverage the EVLLMPackage to its full potential, customizing its behavior to fit your specific needs.
netunicorn-connector-aws 0.0.8
AWS connector for netunicorn
License: MIT
Requires Python: >=3.10
Keywords: netunicorn

# netunicorn-connector-aws
This is an AWS Fargate connector for netunicorn.

## How to use
This connector is supposed to be installed as a part of netunicorn-director-infrastructure package or container.

Install the package:
```bash
pip install netunicorn-connector-aws
```

Then, add the connector to the netunicorn-director-infrastructure configuration:
```yaml
  aws-fargate:  # unique name
    enabled: true
    module: "netunicorn.director.infrastructure.connectors.aws"  # where to import from
    class: "AWSFargate"  # class name
    config: "configuration-example.yaml"     # path to configuration file
```

Modify the configuration file to provide needed parameters (see [example](configuration-example.yaml)), such as
AWS credentials, region, etc.

## AWS configuration
You need to create an ECS cluster with Fargate as a provider type, e.g., according to this guide: https://docs.aws.amazon.com/AmazonECS/latest/userguide/create-cluster-console-v2.html

In the configuration file, please provide valid credentials, the correct region, and the cluster name.
pimterm 1.2.1
A simple Python package that helps you find the command you need.
License: MIT
Requires Python: >=3.6

# pimterm

pimterm can be used to find the linux command you need.
for example:

### using the short notation
```bash
pimterm c "how do I ping example.com?"
ping example.com
```

### using the long notation
```bash
pimterm command "how do i remove a file"
rm filename
```

## You can also ask reqular questions:

### using the short notation
```bash
pimterm q  "who is cleopatra?"
Cleopatra was the last active ruler of the Ptolemaic Kingdom of Egypt.
```

### using the long notation
```bash
pimterm question  "what is the capital of the netherlands?"
Amsterdam
```


### Notes

## intallation
Make sure to add your chatgpt key to your system

```bash
pimterm c "how do i add my chatgpt key to my linux instalation?"
echo "export OPENAI_API_KEY='YOUR_API_KEY'" >> ~/.bashrc
```




The ai will try to return only the command needed. Somtimes when the ai feels explenation is needed it will add it in plain text.