semvec/pypi-packages huggingface dataset:
crowdworks-weather 0.0.1
interlude0112 weather bot
Requires Python: >=3.10

○ 목적
- 자연어로 날씨에 대한 질의 답변을 해주는 패키지 구현

○ 사용하기

```python

from weather import weather_crowd

answer = weather_crowd.query("내일 서울 날씨는 어때?")

print(answer)
```
○ 테스트 하기

○ Package 만들기
chessdata-pyclient 0.0.4
Python library for interacting with the CHESS metadata service
Requires Python: >=3.8

# chessdata-pyclient
Python library for interacting with foxden / CHESS data services

# Installation
1. In an environment of your choosing, run `pip install chessdata-pyclient`
2. Set the environment variable `$REQUESTS_CA_BUNDLE` to a path to a CA bundle to use (for SSL).
3. Set `$PATH` to include the path to a `foxden` CLI executable (so that `chessdata-pyclient` can run `foxden token create <scope>`)
4. Set `$KRB5CCNAME` to be the full path to a file containing a valid kerberos ticket. It is up to the user to make sure the ticket is not expired.

# Examples
- Search the CHESS metadata database for records on tomography scans taken at ID3A:
  ```python
  from chessdata import query
  records = query('{"beamline":"3a" "technique":"tomography"}')
  ```
- Search the CHESS spec scans database for all scan records from the "pi-nnnn-x" BTR:
  ```python
  from chessdata import query
  records = query('{"btr": "pi-nnnn-x"}', url='https://foxden-scans.classe.cornell.edu:8390')
  ```
crowd-weather 0.0.2
interlude0112 weather bot
Requires Python: >=3.10

○ 목적
- 자연어로 날씨에 대한 질의 답변을 해주는 패키지 구현

○ 사용하기

```python

from weather import weather_crowd

answer = weather_crowd.query("내일 서울 날씨는 어때?")

print(answer)
```
○ 테스트 하기

○ Package 만들기
llm-finetuner 0.1.1
A package for fine-tuning and quantizing language models
Requires Python: >=3.7
Keywords: llm, finetuning, quantization

# Finetuner Module

### This module contains functions for fine-tuning language models, merging models, and uploading models to Hugging Face

1. train_model: Used for fine-tuning a language model on a specific dataset.
2. merge_model: Allows merging of a base model with a fine-tuned model.
3. upload_to_hf: Handles uploading models to the Hugging Face model hub.

# Quantise Module

### This module provides functionality for quantizing models and uploading the quantized versions

1. run_quantization: Performs quantization on a given model to reduce its size.
2. run_upload: Uploads a quantized model to Hugging Face.