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.

How to Use (Modified from Git README)

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 qwen2-0_5b-instruct-q8_0.llamafile

If you're on Windows, rename the file by adding ".exe" on the end.
Run the llamafile. e.g.:

./qwen2-0_5b-instruct-q8_0.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 0.5B Instruct GGUF Llamafiles

Model creator: Qwen
Quantized GGUF files used: Qwen/Qwen2-0.5B-Instruct-GGUF
- Commit message "Update README.md"
- Commit hash 198f08841147e5196a6a69bd0053690fb1fd3857
LlamaFile version used: Mozilla-Ocho/llamafile
- Commit message "Merge pull request #687 from Xydane/main Add Support for DeepSeek-R1 models"
- Commit hash 29b5f27172306da39a9c70fe25173da1b1564f82
.args content format (example):

-m
qwen2-0_5b-instruct-q8_0.gguf
...

(Following is original model card for Qwen2 0.5B Instruct GGUF)

Qwen2-0.5B-Instruct-GGUF

Introduction

Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model (57B-A14B). This repo contains the instruction-tuned 0.5B Qwen2 model.

Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.

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

In this repo, we provide quantized models in the GGUF formats, including q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k and q8_0.

Model Details

Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.

Training details

We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.

Requirements

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.

How to use

Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use huggingface-cli (pip install huggingface_hub) as shown below:

huggingface-cli download Qwen/Qwen2-0.5B-Instruct-GGUF qwen2-0_5b-instruct-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False

To run Qwen2, you can use llama-cli (the previous main) or llama-server (the previous server). We recommend using the llama-server as it is simple and compatible with OpenAI API. For example:

./llama-server -m qwen2-0_5b-instruct-q5_k_m.gguf -ngl 24 -fa

(Note: -ngl 24 refers to offloading 24 layers to GPUs, and -fa refers to the use of flash attention.)

Then it is easy to access the deployed service with OpenAI API:

python

import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
    api_key = "sk-no-key-required"
)

completion = client.chat.completions.create(
    model="qwen",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "tell me something about michael jordan"}
    ]
)
print(completion.choices[0].message.content)

If you choose to use llama-cli, pay attention to the removal of -cml for the ChatML template. Instead you should use --in-prefix and --in-suffix to tackle this problem.

bash

./llama-cli -m qwen2-0_5b-instruct-q5_k_m.gguf \
  -n 512 -co -i -if -f prompts/chat-with-qwen.txt \
  --in-prefix "<|im_start|>user\n" \
  --in-suffix "<|im_end|>\n<|im_start|>assistant\n" \
  -ngl 24 -fa

Evaluation

We implement perplexity evaluation using wikitext following the practice of llama.cpp with ./llama-perplexity (the previous ./perplexity). In the following we report the PPL of GGUF models of different sizes and different quantization levels.

Size	fp16	q8_0	q6_k	q5_k_m	q5_0	q4_k_m	q4_0	q3_k_m	q2_k	iq1_m
0.5B	15.11	15.13	15.14	15.24	15.40	15.36	16.28	15.70	16.74	-
1.5B	10.43	10.43	10.45	10.50	10.56	10.61	10.79	11.08	13.04	-
7B	7.93	7.94	7.96	7.97	7.98	8.02	8.19	8.20	10.58	-
57B-A14B	6.81	6.81	6.83	6.84	6.89	6.99	7.02	7.43	-	-
72B	5.58	5.58	5.59	5.59	5.60	5.61	5.66	5.68	5.91	6.75

Citation

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

@article{qwen2,
  title={Qwen2 Technical Report},
  year={2024}
}

Bojun-Feng/Qwen2-0.5B-Instruct-GGUF-llamafile

Qwen2 0.5B Instruct GGUF - llamafile

Run LLMs locally with a single file - No installation required!