FalconLite2
Maintainer: amazon
49
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| Property | Value |
|---|---|
| Run this model | Run 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
FalconLite2 is a fine-tuned and quantized version of the Falcon 40B language model. It can process up to 24,000 token input sequences, leveraging 4-bit GPTQ quantization and adapted RotaryEmbedding. This allows FalconLite2 to consume 4 times less GPU memory than the original Falcon 40B model while still processing 10 times longer contexts. FalconLite2 evolves from the previous FalconLite model, with improvements to its fine-tuning and quantization.
Model Inputs and Outputs
Inputs
- Long input sequences up to 24,000 tokens
Outputs
- Processed and generated text based on the input
Capabilities
FalconLite2 is capable of handling much longer input contexts than the original Falcon 40B model. This makes it well-suited for applications that require processing and understanding of long-form content, such as topic retrieval, summarization, and question-answering.
What can I use it for?
The ability to process longer input sequences with lower memory usage makes FalconLite2 a good choice for deployment in resource-constrained environments, such as on a single AWS g5.12x instance. It can be used for applications that need to work with long-form content, like content search and retrieval, summarization of lengthy documents, and question-answering on complex topics.
Things to Try
You can experiment with FalconLite2 on tasks that require understanding and generating text from long input sequences, such as extracting key information from lengthy reports or articles, generating comprehensive summaries of complex topics, or building question-answering systems that can handle in-depth queries. The model's improved efficiency compared to the original Falcon 40B makes it an interesting option to explore for these types of 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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