README.md

---
language:
- zh
- en
license: apache-2.0
model-index:
- name: tigerbot-70b-base
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 62.46
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-70b-base
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 83.61
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-70b-base
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 65.49
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-70b-base
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 52.76
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-70b-base
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 80.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-70b-base
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.76
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-70b-base
      name: Open LLM Leaderboard
---
<div style="width: 100%;">
    <p align="center" width="20%">
      <img src="http://x-pai.algolet.com/bot/img/logo_core.png" alt="TigerBot" width="20%", style="display: block; margin: auto;"></img>
    </p>
</div>
<p align="center">
<font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font>
</p>
<p align="center">
	💻<a href="https://github.com/TigerResearch/TigerBot" target="_blank">Github</a> • 🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a>
</p>

# 快速开始

- 方法1，通过transformers使用

  - 下载 TigerBot Repo

     ```shell
     git clone https://github.com/TigerResearch/TigerBot.git
     ```

  - 启动infer代码

    ```shell
    python infer.py --model_path TigerResearch/tigerbot-70b-base-v1 --model_type base
    ```

- 方法2:

  - 下载 TigerBot Repo
    
     ```shell
    git clone https://github.com/TigerResearch/TigerBot.git
    ```

  - 安装git lfs： `git lfs install`

  - 通过huggingface或modelscope平台下载权重
    ```shell
    git clone https://huggingface.co/TigerResearch/tigerbot-70b-base-v1
    git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-base-v1.git
    ```
    
  - 启动infer代码
    
    ```shell
    python infer.py --model_path tigerbot-70b-base-v1 --model_type base
    ```

------

# Quick Start

- Method 1, use through transformers

  - Clone TigerBot Repo

     ```shell
     git clone https://github.com/TigerResearch/TigerBot.git
     ```

  - Run infer script

    ```shell
    python infer.py --model_path TigerResearch/tigerbot-70b-base-v1 --model_type base
    ```

- Method 2:

  - Clone TigerBot Repo

    ```shell
    git clone https://github.com/TigerResearch/TigerBot.git
    ```

  - install git lfs： `git lfs install`

  - Download weights from huggingface or modelscope
    ```shell
    git clone https://huggingface.co/TigerResearch/tigerbot-70b-base-v1
    git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-base-v1.git
    ```
  
  - Run infer script
  
     ```shell
     python infer.py --model_path tigerbot-70b-base-v1 --model_type base
     ```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-70b-base)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 62.1   |
| ARC (25-shot)         | 62.46          |
| HellaSwag (10-shot)   | 83.61    |
| MMLU (5-shot)         | 65.49         |
| TruthfulQA (0-shot)   | 52.76   |
| Winogrande (5-shot)   | 80.19   |
| GSM8K (5-shot)        | 37.76        |
| DROP (3-shot)         | 52.45         |

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-70b-base)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |63.71|
|AI2 Reasoning Challenge (25-Shot)|62.46|
|HellaSwag (10-Shot)              |83.61|
|MMLU (5-Shot)                    |65.49|
|TruthfulQA (0-shot)              |52.76|
|Winogrande (5-shot)              |80.19|
|GSM8k (5-shot)                   |37.76|