---
title: tigerbot-7b-sft
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/tigerbot-7b-sft"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/tigerbot-7b-sft.md"
repository: AI-ModelScope/tigerbot-7b-sft
last_updated: 2023-06-08
license: "Apache License 2.0"
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - bloom
architectures:
  - BloomForCausalLM
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - ch
domain:
  - nlp
inference_backends:
  - "deploy_task text/emb"
  - "vllm 0.9.2"
downloads: 68
stars: 0
tags:
  - transformer
  - LLM
  - generation
---

# tigerbot-7b-sft

> tigerbot-7b-sft - AI-ModelScope 在 ModelScope 开源的模型。A cutting-edge foundation for your very own LLM. 🌐 TigerBot • 🤗 Hugging Face

AI-ModelScope/tigerbot-7b-sft 是 ModelScope 魔搭社区上的text-generation模型，采用 Apache License 2.0 许可，可用 deploy_task text/emb、vllm 0.9.2 部署。

- **Repository**: AI-ModelScope/tigerbot-7b-sft
- **License**: Apache License 2.0
- **Tasks**: text-generation
- **Inference backends**: deploy_task text/emb, vllm 0.9.2
- **Tags**: transformer, LLM, generation
- **Downloads**: 68
- **Stars**: 0
- **Last updated**: 2023-06-08

Source: https://www.modelscope.cn/models/AI-ModelScope/tigerbot-7b-sft

---

<div style="width: 100%; ">

    <img src="http://x-pai.algolet.com/bot/img/logo_core.png" alt="TigerBot" style="width: 20%; display: block; margin: auto;">

</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://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a>
</p>

## Github

https://github.com/TigerResearch/TigerBot

## Usage

```python
from modelscope.utils.constant import Tasks
from modelscope.pipelines import pipeline
pipe = pipeline(task=Tasks.text_generation, model='AI-ModelScope/tigerbot-7b-sft', model_revision='v1.0.0')
tok_ins = "\n\n### Instruction:\n"
tok_res = "\n\n### Response:\n"
prompt_input = tok_ins + "{instruction}" + tok_res

input_text = "What is the next number after this list: [1, 2, 3, 5, 8, 13, 21]"
input_text = prompt_input.format_map({'instruction': input_text})

result = pipe(input_text)
print(result)
```

输出

```
{'text': '34'}
```