Llama-3.1-8b-instruct_4bitgs64_hqq_calib
Maintainer: mobiuslabsgmbh
53
⛏️
| 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
The Llama-3.1-8b-instruct_4bitgs64_hqq_calib model is an AI model maintained by mobiuslabsgmbh that is a quantized version of the Meta-Llama-3.1-8B-Instruct model. It uses the HQQ quantization technique to reduce the model size to 4-bits with a group size of 64, resulting in significantly reduced memory usage compared to the original FP16 model. This model is available in both calibrated and uncalibrated versions.
Model inputs and outputs
Inputs
- This model takes in text as input, which can be used for a variety of language tasks such as open-ended conversation, question answering, and code generation.
Outputs
- The model generates text outputs, which can be used for tasks like text completion, summarization, and response generation.
Capabilities
The Llama-3.1-8b-instruct_4bitgs64_hqq_calib model is a highly capable language model that can be used for a variety of natural language processing tasks. It demonstrates strong performance on common benchmarks like ARC, HellaSwag, MMLU, TruthfulQA, and Winogrande, often outperforming other 4-bit quantized versions of the Llama 3.1 model.
What can I use it for?
This quantized Llama model can be useful for developers and researchers who need to deploy a powerful language model on resource-constrained devices or systems. The reduced memory footprint allows for faster inference times and lower hardware requirements, making it well-suited for applications like chatbots, virtual assistants, and code generation tools. Additionally, the calibrated version may be preferred for use cases that require more reliable and consistent outputs.
Things to try
One interesting aspect of this model is the ability to trade off memory usage and inference speed against output quality by selecting different quantization configurations. Developers can experiment with the HQQ 4-bit/gs-64 and AWQ 4-bit versions to find the optimal balance for their specific use case and hardware constraints.
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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