Instructions to use TigerResearch/tigerbot-70b-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TigerResearch/tigerbot-70b-base-v1 with Transformers:

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

pipe = pipeline("text-generation", model="TigerResearch/tigerbot-70b-base-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TigerResearch/tigerbot-70b-base-v1")
model = AutoModelForCausalLM.from_pretrained("TigerResearch/tigerbot-70b-base-v1")

Inference
Local Apps Settings

vLLM

How to use TigerResearch/tigerbot-70b-base-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TigerResearch/tigerbot-70b-base-v1"
# 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-70b-base-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TigerResearch/tigerbot-70b-base-v1

SGLang

How to use TigerResearch/tigerbot-70b-base-v1 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-70b-base-v1" \
    --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-70b-base-v1",
		"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-70b-base-v1" \
        --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-70b-base-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TigerResearch/tigerbot-70b-base-v1 with Docker Model Runner:
```
docker model run hf.co/TigerResearch/tigerbot-70b-base-v1
```

TigerBot

A cutting-edge foundation for your very own LLM.

💻Github • 🌐 TigerBot • 🤗 Hugging Face

快速开始

方法1，通过transformers使用

下载 TigerBot Repo

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

启动infer代码

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

方法2:

下载 TigerBot Repo

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

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

python infer.py --model_path tigerbot-70b-base-v1 --model_type base

Quick Start

Method 1, use through transformers

Clone TigerBot Repo

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

Run infer script

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

Method 2:

Clone TigerBot Repo

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

install git lfs： git lfs install

Download weights from huggingface or modelscope

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

python infer.py --model_path tigerbot-70b-base-v1 --model_type base

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

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

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