semvec/pypi-packages huggingface dataset:
soraya-slog 1.0.1
A simple Python structured logging library that writes JSON-formatted output to stdout
Requires Python: >=3.7

# soraya_slog

soraya_slog is a simple Python structured logging library that writes
JSON-formatted output to stdout. The output format is compatible with
that defined for [Google Cloud Logging](log-format). The library is
designed to be used with projects related to Soraya, an experiment in
conversational AI that can be found at the [SliceOfFile](slice-of-file)
GitHub organization.

## Installation

soraya_slog can be installed using pip:

```bash
pip install soraya-slog
```

## Usage

Using soraya_slog is very simple. You just need to import the library
and call the log method with the message, severity level and additional
data. The severity levels are compatible with those for Google Cloud
Logging, and are as follows:

- `DEBUG`
- `INFO`
- `NOTICE`
- `WARNING`
- `ERROR`
- `CRITICAL`
- `ALERT`
- `EMERGENCY`

Here's an example.

```python
import soraya_slog
soraya_slog.log('ERROR', 'This is an error message', { 'additional': 'data' })
```

You might also prefer to use any of the convenient methods available for
each severity level, such as in the following example.

```python
import soraya_slog
soraya_slog.error('This is an error message', { 'additional': 'data' })
```

## License

soraya_slog is licensed under The Unlicense. See the LICENSE file for
more information.

[log-format]: https://cloud.google.com/logging/docs/reference/v2/rest/v2/LogEntry
[slice-of-file]: https://github.com/SliceOfFile
smoothquant 0.0.1.dev0
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Keywords: smoothquant

# SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

> SmoothQuant enables an INT8 quantization of both weights and activations for all the matrix multiplications in LLMs, including OPT-175B, BLOOM-176B, GLM-130B, and MT-NLG 530B.
SE-lib 0.195
Systems Engineering Library (SElib)
Requires Python: >=3.7

# Systems Engineering Library (SElib)
SElib Version .193

Copyright (c) 2022-2023 Ray Madachy

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.