FalconLite
Maintainer: amazon
173
🛠️
| 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
FalconLite is a quantized version of the Falcon 40B SFT OASST-TOP1 model, capable of processing long input sequences while consuming 4x less GPU memory. By utilizing 4-bit GPTQ quantization and adapted dynamic NTK RotaryEmbedding, FalconLite achieves a balance between latency, accuracy, and memory efficiency. With the ability to process 5x longer contexts than the original model, FalconLite is useful for applications such as topic retrieval, summarization, and question-answering. It can be deployed on a single AWS g5.12x instance with TGI 0.9.2, making it suitable for resource-constrained environments.
Model inputs and outputs
Inputs
- Text data: FalconLite can process long input sequences up to 11K tokens.
Outputs
- Text generation: The model generates text in response to the input.
Capabilities
FalconLite can handle long input sequences, making it useful for applications like topic retrieval, summarization, and question-answering. Its ability to process 5x longer contexts than the original Falcon 40B model while consuming 4x less GPU memory demonstrates its efficiency and memory-friendliness.
What can I use it for?
FalconLite can be used in resource-constrained environments for applications that require high performance and the ability to handle long input sequences. This could include tasks like:
- Content summarization
- Question-answering
- Topic retrieval
- Generating responses to long prompts
The model's efficiency and memory-friendly design make it suitable for deployment on a single AWS g5.12x instance, which can be beneficial for businesses or organizations with limited computing resources.
Things to try
One interesting aspect of FalconLite is its use of 4-bit GPTQ quantization and dynamic NTK RotaryEmbedding. These techniques allow the model to balance latency, accuracy, and memory efficiency, making it a versatile tool for a variety of natural language processing tasks.
You could experiment with FalconLite by trying different prompts and evaluating its performance on tasks like question-answering or summarization. Additionally, you could explore how the model's quantization and specialized embedding techniques impact its behavior and outputs compared to other language models.
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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