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. 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.
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
The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install transformers>=4.37.0, or you might 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
1from transformers import AutoModelForCausalLM, AutoTokenizer
2device ="cuda"# the device to load the model onto34model = AutoModelForCausalLM.from_pretrained(5"Qwen/Qwen2-0.5B-Instruct",6 torch_dtype="auto",7 device_map="auto"8)9tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")1011prompt ="Give me a short introduction to large language model."12messages =[13{"role":"system","content":"You are a helpful assistant."},14{"role":"user","content": prompt}15]16text = tokenizer.apply_chat_template(17 messages,18 tokenize=False,19 add_generation_prompt=True20)21model_inputs = tokenizer([text], return_tensors="pt").to(device)2223generated_ids = model.generate(24 model_inputs.input_ids,25 max_new_tokens=51226)27generated_ids =[28 output_ids[len(input_ids):]for input_ids, output_ids inzip(model_inputs.input_ids, generated_ids)29]3031response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Evaluation
We briefly compare Qwen2-0.5B-Instruct with Qwen1.5-0.5B-Chat. The results are as follows:
Datasets
Qwen1.5-0.5B-Chat
Qwen2-0.5B-Instruct
Qwen1.5-1.8B-Chat
Qwen2-1.5B-Instruct
MMLU
35.0
37.9
43.7
52.4
HumanEval
9.1
17.1
25.0
37.8
GSM8K
11.3
40.1
35.3
61.6
C-Eval
37.2
45.2
55.3
63.8
IFEval (Prompt Strict-Acc.)
14.6
20.0
16.8
29.0
Citation
If you find our work helpful, feel free to give us a cite.
