qwen-14b-chat
Maintainer: nomagick
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| Paper Link | View on Arxiv |
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Model Overview
qwen-14b-chat is a Transformer-based large language model developed by nomagick, a researcher at Alibaba Cloud. It is the 14 billion parameter version of the Qwen series of large language models, which also includes qwen-1.8b, qwen-7b, and qwen-72b. Like other models in the Qwen series, qwen-14b-chat was pretrained on a large corpus of web texts, books, and code.
qwen-14b-chat is an AI assistant model, meaning it was further trained using alignment techniques like supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to make it better at open-ended dialogue and task-completion. Similar to models like chatglm3-6b and chatglm2-6b, qwen-14b-chat can engage in natural conversations, answer questions, and help with a variety of tasks.
The qwen-14b base model was trained on over 3 trillion tokens of multilingual data, giving it broad knowledge and capabilities. The qwen-14b-chat model builds on this to become a versatile AI assistant, able to chat, create content, extract information, summarize, translate, code, solve math problems, and more. It can also use tools, act as an agent, and even function as a code interpreter.
Model Inputs and Outputs
Inputs
- Prompt: The text prompt to be completed by the model. This should be formatted in the "chatml" format used by the Qwen models, which includes special tokens like
<|im_start|>and<|im_end|>to delineate different conversational turns. - Top P: The top-p sampling parameter, which controls the amount of diversity in the generated text.
- Max Tokens: The maximum number of new tokens to generate.
- Temperature: The temperature parameter, which controls the randomness of the generated text.
Outputs
- The model outputs a list of strings, where each string represents a continuation of the input prompt. The output is generated in a streaming fashion, so the full response can be observed incrementally.
Capabilities
qwen-14b-chat can engage in open-ended dialogue, answer questions, and assist with a variety of tasks like content creation, information extraction, summarization, translation, coding, and math problem solving. It also has the ability to use external tools, act as an agent, and function as a code interpreter.
In benchmarks, qwen-14b-chat has demonstrated strong performance on tasks like MMLU, C-Eval, GSM8K, HumanEval, and long-context understanding, often outperforming other large language models of comparable size. It has also shown impressive capabilities when it comes to tool usage and code generation.
What Can I Use It For?
qwen-14b-chat is a versatile AI assistant that can be used for a wide range of applications. Some potential use cases include:
- AI-powered chatbots and virtual assistants: Use
qwen-14b-chatto build conversational AI agents that can engage in natural dialogue, answer questions, and assist with tasks. - Content creation: Leverage
qwen-14b-chatto generate articles, stories, scripts, and other types of written content. - Language understanding and translation: Utilize
qwen-14b-chat's multilingual capabilities for tasks like text classification, sentiment analysis, and language translation. - Code generation and programming assistance: Integrate
qwen-14b-chatinto your development workflow to generate code, explain programming concepts, and debug issues. - Research and education: Use
qwen-14b-chatas a tool for exploring language models, testing new AI techniques, and educating students about large language models.
Things to Try
Some interesting things to try with qwen-14b-chat include:
- Exploring the model's ability to follow different system prompts:
qwen-14b-chathas been trained on a diverse set of system prompts, allowing it to roleplay, change its language style, and adjust its behavior to suit different tasks. - Integrating the model with external tools and APIs: Take advantage of
qwen-14b-chat's strong tool usage capabilities by connecting it to various APIs and services through the ReAct prompting approach. - Pushing the model's limits on long-context understanding: The techniques used to extend the context length of
qwen-14b-chat, such as NTK-aware interpolation and LogN attention scaling, make it well-suited for tasks that require processing long passages of text. - Experimenting with the quantized versions of the model: The Int4 and Int8 quantized models of
qwen-14b-chatoffer improved inference speed and reduced memory usage, while maintaining near-lossless performance.
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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