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Model: tclf90/qwen2.5-14b-instruct-awq
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Qwen LICENSE AGREEMENT
Qwen LICENSE AGREEMENT Release Date: September 19, 2024
By clicking to agree or by using or distributing any portion or element of the Qwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
1. Definitions
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d. "Third Parties" shall mean individuals or legal entities that are not under common control with us or you.
e. "Qwen" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by us.
f. "Materials" shall mean, collectively, Alibaba Cloud's proprietary Qwen and Documentation (and any portion thereof) made available under this Agreement.
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b. If you use the Materials or any outputs or results therefrom to create, train, fine-tune, or improve an AI model that is distributed or made available, you shall prominently display “Built with Qwen” or “Improved using Qwen” in the related product documentation.
6. Intellectual Property
a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
b. No trademark license is granted to use the trade names, trademarks, service marks, or product names of us, except as required to fulfill notice requirements under this Agreement or as required for reasonable and customary use in describing and redistributing the Materials.
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b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.

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---
license: qwen
license_name: Tongyi Qianwen LICENSE AGREEMENT
license_link: LICENSE
pipeline_tag: text-generation
tags:
- qwen2.5
- awq
- int4
- 量化修复
- vLLM
- sglang
---
# 通义千问2.5-14B-Instruct-AWQ-量化修复
原模型 [qwen/Qwen2.5-14B-Instruct](https://www.modelscope.cn/models/qwen/Qwen2.5-14B-Instruct)
### 【模型更新日期】
注:通过`snapshot_download`函数传入`revision=...`来下载指定的`tag`版本
```
2024-09-24
1. add group 128
```
### 【模型列表】
| tag | 文件大小 | 最近更新时间 |
|--------|---------|--------------|
| `g128` | `9.4GB` | `2024-09-24` |
### 【模型下载】
```python
from modelscope import snapshot_download
snapshot_download('tclf90/...', cache_dir="本地路径", revision='g128')
```
### 【修复内容】
1. 对GPTQ量化的校准做了额外优化减少模型的 `1.乱吐字`、`2.无限循环`、`3.长文能力丢失`等情况。
2. 相同技术移植至AWQ。
3. 有些推理框架的默认`top_k`与`top_p`较大,可以考虑减小对应数值,来获得更合理的模型输出。
### 【介绍】
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
### 【高并发RESTFul API推理】
方式1[vllm](https://github.com/vllm-project/vllm)
方式2[sglang](https://github.com/sgl-project/sglang)
目前推荐使用sglang进行部署相较于vllm, sglang于A100实测能有50%100%的吞吐增益。

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{
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