Qwen1.5-110B
Maintainer: Qwen
80
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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
Qwen1.5-110B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include 9 model sizes ranging from 0.5B to 110B parameters, significant performance improvement in chat models, multilingual support, and stable support of 32K context length. The Qwen1.5-0.5B, Qwen1.5-110B-Chat, Qwen1.5-32B, Qwen1.5-72B, and Qwen1.5-0.5B-Chat models are some of the other variants in the Qwen1.5 series.
Model inputs and outputs
Qwen1.5-110B is a language model that takes text as input and generates text as output. The model is based on the Transformer architecture with improvements such as SwiGLU activation, attention QKV bias, group query attention, and a mixture of sliding window attention and full attention. It also has an improved tokenizer adaptive to multiple natural languages and codes.
Inputs
- Text sequences
- Prompts for generating text
Outputs
- Continuation of the input text
- Novel text generated based on the input prompt
Capabilities
Qwen1.5-110B demonstrates strong performance in open-ended text generation tasks, such as writing stories, generating responses in dialogues, and summarizing information. The model's large size and multilingual capabilities enable it to handle a wide range of language understanding and generation tasks across multiple languages.
What can I use it for?
Qwen1.5-110B can be used for various NLP applications, such as content creation, language translation, question answering, and task-oriented dialogue systems. The model's flexible size options and post-training capabilities allow users to fine-tune or adapt it to specific use cases. For example, users can apply techniques like supervised finetuning, reinforcement learning from human feedback, or continued pretraining to further improve the model's performance on their target tasks.
Things to try
One interesting aspect of Qwen1.5-110B is its ability to handle code-switching and multilingual content. Users can experiment with providing prompts that mix multiple languages or include programming code to see how the model responds. Additionally, the model's large context length support enables applications that require long-form text generation or summarization.
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