jartine's LLM work is generously supported by a grant from mozilla
Qwen2.5 0.5B Instruct GGUF - llamafile
Run LLMs locally with a single file - No installation required!
All you need is download a file and run it.
Our goal is to make open source large language models much more
accessible to both developers and end users. We're doing that by
combining llama.cpp with Cosmopolitan Libc into one
framework that collapses all the complexity of LLMs down to
a single-file executable (called a "llamafile") that runs
locally on most computers, with no installation.
The easiest way to try it for yourself is to download our example llamafile.
With llamafile, all inference happens locally; no data ever leaves your computer.
Download the llamafile.
Open your computer's terminal.
If you're using macOS, Linux, or BSD, you'll need to grant permission
for your computer to execute this new file. (You only need to do this
once.)
chmod +x qwen2.5-0.5b-instruct-q5_k_m.llamafile
If you're on Windows, rename the file by adding ".exe" on the end.
Run the llamafile. e.g.:
./qwen2.5-0.5b-instruct-q5_k_m.llamafile
Your browser should open automatically and display a chat interface.
(If it doesn't, just open your browser and point it at http://localhost:8080.)
When you're done chatting, return to your terminal and hit
Control-C to shut down llamafile.
Please note that LlamaFile is still under active development. Some methods may be not be compatible with the most recent documents.
Settings for Qwen2.5 0.5B Instruct GGUF Llamafiles
(Following is original model card for Qwen2.5 0.5B Instruct GGUF)
Qwen2.5-0.5B-Instruct-GGUF
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 0.5B Qwen2.5 model in the GGUF Format, 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: 0.49B
Number of Paramaters (Non-Embedding): 0.36B
Number of Layers: 24
Number of Attention Heads (GQA): 14 for Q and 2 for KV
Context Length: Full 32,768 tokens and generation 8192 tokens
We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp.
In the following demonstration, we assume that you are running commands under the repository llama.cpp.
Since cloning the entire repo may be inefficient, you can manually download the GGUF file that you need or use huggingface-cli:
For users, to achieve chatbot-like experience, it is recommended to commence in the conversation mode:
shell
1./llama-cli -m <gguf-file-path>\2 -co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."\3 -fa -ngl 80 -n 512
Evaluation & Performance
Detailed evaluation results are reported in this 📑 blog.
For quantized models, the benchmark results against the original bfloat16 models can be found here
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}
}
