README.md
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1 ---
2 base_model: Qwen/Qwen2.5-14B-Instruct
3 language:
4 - en
5 library_name: transformers
6 license: apache-2.0
7 license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-AWQ/blob/main/LICENSE
8 pipeline_tag: text-generation
9 tags:
10 - chat
11 ---
12 # Qwen2.5-14B-Instruct-AWQ
13
14 ## Introduction
15
16 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:
17
18 - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
19 - 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.
20 - **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
21 - **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
22
23 **This repo contains the AWQ-quantized 4-bit instruction-tuned 72B Qwen2.5 model**, which has the following features:
24 - Type: Causal Language Models
25 - Training Stage: Pretraining & Post-training
26 - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
27 - Number of Parameters: 14.7B
28 - Number of Paramaters (Non-Embedding): 13.1B
29 - Number of Layers: 48
30 - Number of Attention Heads (GQA): 40 for Q and 8 for KV
31 - Context Length: Full 131,072 tokens and generation 8192 tokens
32 - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
33 - Quantization: AWQ 4-bit
34
35 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/).
36
37 ## Requirements
38
39 The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
40
41 With `transformers<4.37.0`, you will encounter the following error:
42 ```
43 KeyError: 'qwen2'
44 ```
45
46 Also check out our [AWQ documentation](https://qwen.readthedocs.io/en/latest/quantization/awq.html) for more usage guide.
47
48 ## Quickstart
49
50 Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
51
52 ```python
53 from transformers import AutoModelForCausalLM, AutoTokenizer
54 model_name = "Qwen/Qwen2.5-14B-Instruct-AWQ"
55 model = AutoModelForCausalLM.from_pretrained(
56 model_name,
57 torch_dtype="auto",
58 device_map="auto"
59 )
60 tokenizer = AutoTokenizer.from_pretrained(model_name)
61 prompt = "Give me a short introduction to large language model."
62 messages = [
63 {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
64 {"role": "user", "content": prompt}
65 ]
66 text = tokenizer.apply_chat_template(
67 messages,
68 tokenize=False,
69 add_generation_prompt=True
70 )
71 model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
72 generated_ids = model.generate(
73 **model_inputs,
74 max_new_tokens=512
75 )
76 generated_ids = [
77 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
78 ]
79 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
80 ```
81
82 ### Processing Long Texts
83
84 The current `config.json` is set for context length up to 32,768 tokens.
85 To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
86
87 For supported frameworks, you could add the following to `config.json` to enable YaRN:
88 ```json
89 {
90 ...,
91 "rope_scaling": {
92 "factor": 4.0,
93 "original_max_position_embeddings": 32768,
94 "type": "yarn"
95 }
96 }
97 ```
98
99 For deployment, we recommend using vLLM.
100 Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
101 Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
102 We advise adding the `rope_scaling` configuration only when processing long contexts is required.
103
104 ## Evaluation & Performance
105
106 Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
107
108 For quantized models, the benchmark results against the original bfloat16 models can be found [here](https://qwen.readthedocs.io/en/latest/benchmark/quantization_benchmark.html)
109
110 For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
111
112 ## Citation
113
114 If you find our work helpful, feel free to give us a cite.
115
116 ```
117 @misc{qwen2.5,
118 title = {Qwen2.5: A Party of Foundation Models},
119 url = {https://qwenlm.github.io/blog/qwen2.5/},
120 author = {Qwen Team},
121 month = {September},
122 year = {2024}
123 }
124 @article{qwen2,
125 title={Qwen2 Technical Report},
126 author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
127 journal={arXiv preprint arXiv:2407.10671},
128 year={2024}
129 }
130 ```