Instructions to use TigerResearch/tigerbot-13b-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TigerResearch/tigerbot-13b-base-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TigerResearch/tigerbot-13b-base-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TigerResearch/tigerbot-13b-base-v2")
model = AutoModelForCausalLM.from_pretrained("TigerResearch/tigerbot-13b-base-v2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TigerResearch/tigerbot-13b-base-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TigerResearch/tigerbot-13b-base-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TigerResearch/tigerbot-13b-base-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TigerResearch/tigerbot-13b-base-v2

SGLang

How to use TigerResearch/tigerbot-13b-base-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TigerResearch/tigerbot-13b-base-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TigerResearch/tigerbot-13b-base-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TigerResearch/tigerbot-13b-base-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TigerResearch/tigerbot-13b-base-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TigerResearch/tigerbot-13b-base-v2 with Docker Model Runner:
```
docker model run hf.co/TigerResearch/tigerbot-13b-base-v2
```

tigerbot-13b-base-v2 / README.md

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	---
	license: apache-2.0
	language:
	- zh
	- en
	---

	<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-13b-base-v2 --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-13b-base-v2
	git clone https://www.modelscope.cn/TigerResearch/tigerbot-13b-base-v2.git
	```

	- 启动infer代码

	```shell
	python infer.py --model_path tigerbot-13b-base-v2 --model_type base --max_generate_length 64
	```

	------

	# 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-13b-base-v2 --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-13b-base-v2
	git clone https://www.modelscope.cn/TigerResearch/tigerbot-13b-base-v2.git
	```

	- Run infer script

	```shell
	python infer.py --model_path tigerbot-13b-base-v2 --model_type base --max_generate_length 64
	```
	# [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-13b-base)

	\| Metric \| Value \|
	\|-----------------------\|---------------------------\|
	\| Avg. \| 52.11 \|
	\| ARC (25-shot) \| 53.84 \|
	\| HellaSwag (10-shot) \| 77.05 \|
	\| MMLU (5-shot) \| 53.57 \|
	\| TruthfulQA (0-shot) \| 44.06 \|
	\| Winogrande (5-shot) \| 74.98 \|
	\| GSM8K (5-shot) \| 17.06 \|
	\| DROP (3-shot) \| 44.21 \|