This repository contains GGUF format model for Qwen2.5-3B-Instruct, compiled using Big-Endian. Every model has been verified to work on IBM z16 Mainframe.

Provided Files

Name	Quant Method	Bits	Size	Use Case
qwen2.5-3b-instruct-be.Q3_K_S.gguf	Q3_K_S	3	1.4G	very small, high quality loss
qwen2.5-3b-instruct-be.Q3_K_M.gguf	Q3_K_M	3	1.5G	very small, high quality loss
qwen2.5-3b-instruct-be.Q3_K_L.gguf	Q3_K_L	3	1.6G	small, substantial quality loss
qwen2.5-3b-instruct-be.Q4_0.gguf	Q4_0	4	1.7G	legacy; small, very high quality loss - prefer using Q3_K_M
qwen2.5-3b-instruct-be.Q4_K_S.gguf	Q4_K_S	4	1.8G	small, greater quality loss
qwen2.5-3b-instruct-be.Q4_K_M.gguf	Q4_K_M	4	1.8G	medium, balanced quality - recommended
qwen2.5-3b-instruct-be.Q5_0.gguf	Q5_0	5	2.1G	legacy; medium, balanced quality - prefer using Q4_K_M
qwen2.5-3b-instruct-be.Q5_K_S.gguf	Q5_K_S	5	2.1G	large, low quality loss - recommended
qwen2.5-3b-instruct-be.Q5_K_M.gguf	Q5_K_M	5	2.1G	large, very low quality loss - recommended
qwen2.5-3b-instruct-be.Q6_K.gguf	Q6_K	6	2.4G	very large, extremely low quality loss
qwen2.5-3b-instruct-be.Q8_0.gguf	Q8_0	8	3.1G	very large, extremely low quality loss - not recommended

Original Model Card: Qwen2.5-3B-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 3B Qwen2.5 model, which has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
Number of Parameters: 3.09B
Number of Paramaters (Non-Embedding): 2.77B
Number of Layers: 36
Number of Attention Heads (GQA): 16 for Q and 2 for KV
Context Length: Full 32,768 tokens and generation 8192 tokens

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.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-3B-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]

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}
}

taronaeo/Qwen2.5-3B-Instruct-BE-GGUF

Qwen2.5-3B Instruct Big-Endian - GGUF (Verified for IBM Z & LinuxONE Mainframes)

Description