Qwen2-7B-Instruct
Maintainer: Qwen
348
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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
The Qwen2-7B-Instruct is the 7 billion parameter instruction-tuned language model from the Qwen2 series of large language models developed by Qwen. Compared to state-of-the-art open-source language models like LLaMA and ChatGLM, the Qwen2 series has generally surpassed them in performance across a range of benchmarks targeting language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning.
The Qwen2 series includes models ranging from 0.5 to 72 billion parameters, with the Qwen2-7B-Instruct being one of the smaller yet capable instruction-tuned variants. It is based on the Transformer architecture with enhancements like SwiGLU activation, attention QKV bias, and group query attention. The model also uses an improved tokenizer that is adaptive to multiple natural languages and coding.
Model inputs and outputs
Inputs
- Text: The model can take text inputs of up to 131,072 tokens, enabling processing of extensive inputs.
Outputs
- Text: The model generates text outputs, which can be used for a variety of natural language tasks such as question answering, summarization, and creative writing.
Capabilities
The Qwen2-7B-Instruct model has shown strong performance across a range of benchmarks, including language understanding (MMLU, C-Eval), mathematics (GSM8K, MATH), coding (HumanEval, MBPP), and reasoning (BBH). It has demonstrated competitiveness against proprietary models in these areas.
What can I use it for?
The Qwen2-7B-Instruct model can be used for a variety of natural language processing tasks, such as:
- Question answering: The model can be used to answer questions on a wide range of topics, drawing upon its broad knowledge base.
- Summarization: The model can be used to generate concise summaries of long-form text, such as articles or reports.
- Creative writing: The model can be used to generate original text, such as stories, poems, or scripts, with its strong language generation capabilities.
- Coding assistance: The model's coding knowledge can be leveraged to help with tasks like code generation, explanation, and debugging.
Things to try
One interesting aspect of the Qwen2-7B-Instruct model is its ability to process long-form text inputs, thanks to its large context length of up to 131,072 tokens. This can be particularly useful for tasks that require understanding and reasoning over extensive information, such as academic papers, legal documents, or historical archives.
Another area to explore is the model's multilingual capabilities. As mentioned, the Qwen2 series, including the Qwen2-7B-Instruct, has been designed to be adaptive to multiple languages, which could make it a valuable tool for cross-lingual applications.
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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