Llama.cpp imatrix quantizations of Qwen/Qwen2.5-72B-Instruct

Using llama.cpp commit 3ad5451 for quantization.

All quants were made using the imatrix option and Bartowski's calibration file.

Perplexity table (the lower the better)

Quant	Size (MB)	PPL	Size (%)	Accuracy (%)	PPL error rate
IQ1_S	21639	7.6552	15.60	67.82	0.11
IQ1_M	22640	7.2982	16.32	71.14	0.10
IQ2_XXS	24309	6.3958	17.53	81.18	0.09
IQ2_XS	25804	6.0909	18.61	85.25	0.08
IQ2_S	26644	6.0318	19.21	86.08	0.08
IQ2_M	27979	5.7589	20.17	90.16	0.08
Q2_K_S	28199	5.9731	20.33	86.93	0.08
Q2_K	28430	5.9188	20.50	87.72	0.08
IQ3_XXS	30369	5.5227	21.90	94.01	0.07
IQ3_XS	31320	5.4357	22.58	95.52	0.07
IQ3_S	32890	5.3782	23.72	96.54	0.07
Q3_K_S	32890	5.4492	23.72	95.28	0.07
IQ3_M	33858	5.3550	24.41	96.96	0.07
Q3_K_M	35952	5.4069	25.92	96.03	0.07
Q3_K_L	37675	5.4116	27.17	95.94	0.07
IQ4_XS	37869	5.2776	27.31	98.38	0.07
IQ4_NL	39401	5.2747	28.41	98.43	0.07
Q4_0	39466	5.2998	28.46	97.97	0.07
Q4_K_S	41856	5.2535	30.18	98.83	0.07
Q4_1	43580	5.2801	31.42	98.33	0.07
Q4_K_M	45219	5.2478	32.60	98.94	0.07
Q5_0	47984	5.2160	34.60	99.54	0.07
Q5_K_S	48995	5.2242	35.33	99.39	0.07
Q5_K_M	51925	5.2182	37.44	99.50	0.07
Q5_1	52099	5.2212	37.57	99.44	0.07
Q6_K	61366	5.1952	44.25	99.94	0.07
Q8_0	73683	5.1944	53.13	99.96	0.07
F16	138685	5.1922	100	100	0.07

Qwen2.5-72B-Instruct

Introduction

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.

This repo contains the instruction-tuned 72B Qwen2.5 model, which has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Number of Parameters: 72.7B
Number of Paramaters (Non-Embedding): 70.0B
Number of Layers: 80
Number of Attention Heads (GQA): 64 for Q and 8 for KV
Context Length: Full 131,072 tokens and generation 8192 tokens
- Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts.

For more details, please refer to our blog, GitHub, and Documentation.

Requirements

The code of Qwen2.5 has been in the latest Hugging face transformers and we advise you to use the latest version of transformers.

With transformers<4.37.0, you will encounter the following error:

KeyError: 'qwen2'

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-72B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Processing Long Texts

The current config.json is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.

For supported frameworks, you could add the following to config.json to enable YaRN:

{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}

For deployment, we recommend using vLLM. Please refer to our Documentation for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required.

Evaluation & Performance

Detailed evaluation results are reported in this 📑 blog.

For requirements on GPU memory and the respective throughput, see results here.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}

@article{qwen2,
      title={Qwen2 Technical Report}, 
      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},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}

ThomasBaruzier
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Qwen2.5-72B-Instruct-GGUF