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:
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.
./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}
}
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