Instructions to use TigerResearch/tigerbot-180b-chat-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TigerResearch/tigerbot-180b-chat-v2 with Transformers:

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

pipe = pipeline("text-generation", model="TigerResearch/tigerbot-180b-chat-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TigerResearch/tigerbot-180b-chat-v2")
model = AutoModelForCausalLM.from_pretrained("TigerResearch/tigerbot-180b-chat-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TigerResearch/tigerbot-180b-chat-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TigerResearch/tigerbot-180b-chat-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-180b-chat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TigerResearch/tigerbot-180b-chat-v2

SGLang

How to use TigerResearch/tigerbot-180b-chat-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-180b-chat-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-180b-chat-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-180b-chat-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-180b-chat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TigerResearch/tigerbot-180b-chat-v2 with Docker Model Runner:
```
docker model run hf.co/TigerResearch/tigerbot-180b-chat-v2
```

i4never commited on Dec 4, 2023

Commit

1f2a711

1 Parent(s): 10cf389

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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-180b-chat-v2
+    ```
+- 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-180b-chat-v2
+    git clone https://www.modelscope.cn/TigerResearch/tigerbot-180b-chat-v2.git
+    ```
+  - 启动infer代码
+    ```shell
+    python infer.py --model_path tigerbot-180b-chat-v2
+    ```
+------
+# 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-180b-chat-v2
+    ```
+- 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-180b-chat-v2
+    git clone https://www.modelscope.cn/TigerResearch/tigerbot-180b-chat-v2.git
+    ```
+  - Run infer script
+     ```shell
+     python infer.py --model_path tigerbot-180b-chat-v2
+     ```