semvec/pypi-packages huggingface dataset:
chatharuhi 1.0.9
A language model that imitates the tone, personality and plot of characters like Haruhi Suzumiya to chat in a similar style.
Keywords: python,chatbot,LLM,role-playing,chromadb,luotuo


# ChatHaruhi 2.0
## 中文版本
ChatHaruhi: Reviving Anime Character in Reality via Large Language Model

ChatHaruhi 2.0由李诚和米唯实创建

chengli.thu@gmail.com, mws22@mails.tsinghua.edu.cn

米唯实是清华大学电子信息专业二年级研究生，将于明年毕业，正在寻找工作或博士职位。

项目主页 https://github.com/LC1332/Chat-Haruhi-Suzumiya

如果你使用了这个代码，请引用我们的论文：

@misc{li2023chatharuhi,
      title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model}, 
      author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},
      year={2023},
      eprint={2308.09597},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

## English version
ChatHaruhi: Reviving Anime Character in Reality via Large Language Model

ChatHaruhi 2.0, built by Cheng Li and Weishi Mi

chengli.thu@gmail.com, mws22@mails.tsinghua.edu.cn

Weishi Mi is a second-year graduate student at Tsinghua University, majoring in computer science.
Weishi Mi is pursuing a job or a PhD position, which who will be available next year

homepage https://github.com/LC1332/Chat-Haruhi-Suzumiya

ChatHaruhi is a chatbot that can revive anime characters in reality.
the 2.0 version was built by Cheng Li and Weishi Mi.

Please cite our paper if you use this code for research: 

@misc{li2023chatharuhi,
      title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model}, 
      author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},
      year={2023},
      eprint={2308.09597},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

# Update log

`1.0.9` Increase support for NaiveDB and Qwen .
`1.0.8` Increase support for OpenAI api base.
`1.0.7` Increase support for Ernie switch and Spark3.0.
`1.0.6` Increase support for Ernie and fix baichuanAPI.
`1.0.5` Increase support for baichuanAPI.
`1.0.4` Increase support for other kinds of embedding models.
`1.0.3` Increase support for baichuan2 and Ernie.
`1.0.2` Increase support for hugging face and GLM2.
`1.0.1` Increase support for GLMPro.
`1.0.0` First released on August 22, 2023.
rnasa 0.2.0
Gene Expression Level Calculator for RNA-seq
Requires Python: >=3.6

rnasa
=====

Gene Expression Level Calculator for RNA-seq

[![Test](https://github.com/dceoy/rnasa/actions/workflows/test.yml/badge.svg)](https://github.com/dceoy/rnasa/actions/workflows/test.yml)
[![Upload Python Package](https://github.com/dceoy/rnasa/actions/workflows/python-publish.yml/badge.svg)](https://github.com/dceoy/rnasa/actions/workflows/python-publish.yml)
[![CI to Docker Hub](https://github.com/dceoy/rnasa/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/dceoy/rnasa/actions/workflows/docker-publish.yml)

Installation
------------

```sh
$ pip install -U rnasa
```

Dependent commands:

- `pigz`
- `pbzip2`
- `bgzip`
- `samtools` (and `plot-bamstats`)
- `java`
- `fastqc`
- `trim_galore`
- `STAR`
- `rsem-prepare-reference`
- `rsem-refseq-extract-primary-assembly`
- `rsem-calculate-expression`

Docker image
------------

Pull the image from [Docker Hub](https://hub.docker.com/r/dceoy/rnasa/).

```sh
$ docker image pull dceoy/rnasa
```

Usage
-----

#### Calculate gene expression levels

| input files       | output files  |
|:-----------------:|:-------------:|
| FASTQ (Illumina)  | TSV (or GCT)  |


1.  Download and process resource data.

    ```sh
    $ rnasa download --genome=GRCh38 --dest-dir=/path/to/ref
    ```

2.  Calculate TPM (transcripts per million) values from FASTQ files.

    ```sh
    $ rnasa calculate \
        --workers=2 \
        --dest-dir=/path/to/output \
        /path/to/ref/GRCh38 \
        /path/to/sample1_fastq_prefix \
        /path/to/sample2_fastq_prefix \
        /path/to/sample3_fastq_prefix
    ```

    The command search for one (single-end) or two (paired-end) input FASTQ files by prefix.

    Standard workflow:
    1.  Trim adapters
        - `trim_galore`
    2.  Map reads and calculate TPM values
        - `STAR`
        - `rsem-calculate-expression`
    3.  Collect QC metrics
        - `fastqc`
        - `samtools`

3.  Extract TPM values from RSEM results files, and consolidate them into TSV files.

    ```sh
    $ rnasa extract --dest-dir=. /path/to/output/rsem
    ```

    If `--gct` is passed, `rnasa extract` creates output files in GCT format.

Run `rnasa --help` for more information.