par Qwen
Open source · 407k downloads · 165 likes
Qwen2 0.5B est un modèle de langage de base de petite taille (0,5 milliard de paramètres) issu de la série Qwen2, conçue pour offrir des performances élevées dans divers domaines comme la compréhension linguistique, la génération de texte, le raisonnement, les mathématiques et la programmation. Contrairement aux modèles spécialisés, il se distingue par sa polyvalence et sa capacité à rivaliser avec des modèles propriétaires sur des benchmarks variés, tout en restant accessible grâce à sa taille réduite. Bien qu'il ne soit pas optimisé pour une utilisation directe en génération de texte, il sert de point de départ idéal pour des adaptations ultérieures, comme l'affinage supervisé ou le renforcement par apprentissage par renforcement. Ses forces résident dans sa robustesse multilingue, couvrant aussi bien l'anglais, le chinois que d'autres langues, ainsi que dans son efficacité sur des tâches techniques comme le codage ou la résolution de problèmes mathématiques. Ce modèle incarne ainsi un équilibre entre performance et accessibilité, adapté aux développeurs et chercheurs souhaitant explorer des applications avancées sans recourir à des ressources computationnelles excessives.
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}
}