Model reference · open weights
Qwen2 is an open-weight language model from Qwen. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | Qwen |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 494M |
| Context | 128k tokens |
| Runs with | transformers |
| Released | 2024-05-31 |
| Popularity | 796k downloads / month |
| Licence | Open weights |
About
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 0.5B Qwen2 base language 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.
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.
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'
We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc.
The datasets for evaluation include:
English Tasks: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot)
Coding Tasks: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript)
Math Tasks: GSM8K (4-shot), MATH (4-shot)
Chinese Tasks: C-Eval(5-shot), CMMLU (5-shot)
Multilingual Tasks: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot)
| Datasets | Phi-2 | Gemma-2B | MiniCPM | Qwen1.5-1.8B | Qwen2-0.5B | Qwen2-1.5B |
|---|---|---|---|---|---|---|
| #Non-Emb Params | 2.5B | 2.0B | 2.4B | 1.3B | 0.35B | 1.3B |
| MMLU | 52.7 | 42.3 | 53.5 | 46.8 | 45.4 | 56.5 |
| MMLU-Pro | - | 15.9 | - | - | 14.7 | 21.8 |
| Theorem QA | - | - | - | - | 8.9 | 15.0 |
| HumanEval | 47.6 | 22.0 | 50.0 | 20.1 | 22.0 | 31.1 |
| MBPP | 55.0 | 29.2 | 47.3 | 18.0 | 22.0 | 37.4 |
| GSM8K | 57.2 | 17.7 | 53.8 | 38.4 | 36.5 | 58.5 |
| MATH | 3.5 | 11.8 | 10.2 | 10.1 | 10.7 | 21.7 |
| BBH | 43.4 | 35.2 | 36.9 | 24.2 | 28.4 | 37.2 |
| HellaSwag | 73.1 | 71.4 | 68.3 | 61.4 | 49.3 | 66.6 |
| Winogrande | 74.4 | 66.8 | - | 60.3 | 56.8 | 66.2 |
| ARC-C | 61.1 | 48.5 | - | 37.9 | 31.5 | 43.9 |
| TruthfulQA | 44.5 | 33.1 | - | 39.4 | 39.7 | 45.9 |
| C-Eval | 23.4 | 28.0 | 51.1 | 59.7 | 58.2 | 70.6 |
| CMMLU | 24.2 | - | 51.1 | 57.8 | 55.1 | 70.3 |
If you find our work helpful, feel free to give us a cite.
@article{qwen2,
title={Qwen2 Technical Report},
year={2024}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys qwen2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (qwen2 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"qwen2","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.