Qwen-14B
Maintainer: Qwen
197
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
Qwen-14B is the 14B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-14B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-14B, Qwen-14B-Chat is released, a large-model-based AI assistant, which is trained with alignment techniques.
Qwen-14B features a large-scale high-quality training corpus of over 3 trillion tokens, covering Chinese, English, multilingual texts, code, and mathematics. It significantly surpasses existing open-source models of similar scale on multiple Chinese and English downstream evaluation tasks. Qwen-14B also uses a more comprehensive vocabulary of over 150K tokens, enabling users to directly enhance capabilities for certain languages without expanding the vocabulary.
Model inputs and outputs
Inputs
- Text: Qwen-14B accepts text input of up to 2048 tokens.
Outputs
- Text: Qwen-14B generates text output in response to the input.
Capabilities
Qwen-14B demonstrates competitive performance across a range of benchmarks. On the C-Eval Chinese evaluation, it achieves 69.8% zero-shot and 71.7% 5-shot accuracy, outperforming similarly-sized models. On MMLU, its zero-shot and 5-shot English evaluation accuracy reaches 64.6% and 66.5% respectively. Qwen-14B also performs well on coding tasks, scoring 43.9% on the HumanEval zero-shot benchmark, and 60.1% on the zero-shot GSM8K mathematics evaluation.
What can I use it for?
The large scale and broad capabilities of Qwen-14B make it suitable for a variety of natural language processing tasks. Potential use cases include:
- Content generation: Qwen-14B can be used to generate high-quality text on a wide range of topics, from creative writing to technical documentation.
- Conversational AI: Building on the Qwen-14B-Chat model, developers can create advanced chatbots and virtual assistants.
- Multilingual support: The model's comprehensive vocabulary allows it to handle multiple languages, enabling cross-lingual applications.
- Code generation and reasoning: Qwen-14B's strong performance on coding and math tasks makes it useful for programming-related applications.
Things to try
One interesting aspect of Qwen-14B is its ability to handle long-form text. By incorporating techniques like NTK-aware interpolation and LogN attention scaling, the model can maintain strong performance even on sequences up to 32,768 tokens long. Developers could explore leveraging this capability for tasks like long-form summarization or knowledge-intensive QA.
Another intriguing area to experiment with is Qwen-14B's tool usage capabilities. The model supports ReAct prompting, allowing it to interact with external plugins and APIs. This could enable the development of intelligent assistants that can seamlessly integrate diverse functionalities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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