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Glider Gguf
Overview :
PatronusAI/glider-gguf is a high-performance quantized language model based on the Hugging Face platform, utilizing the GGUF format, and supporting multiple quantization versions such as BF16, Q8_0, Q5_K_M, and Q4_K_M. This model is built on the phi3 architecture and comprises 3.82 billion parameters. Its main strengths are efficient computational performance and a compact model size, ideal for scenarios requiring rapid inference and low resource consumption. Background information indicates that this model is provided by PatronusAI and is suited for developers and enterprises needing natural language processing and text generation capabilities.
Target Users :
The target audience includes researchers, developers, and enterprise users in the natural language processing (NLP) field, who require an efficient and cost-effective solution for text generation and language understanding tasks. PatronusAI/glider-gguf is particularly suited for scenarios that demand quick deployment and operation of NLP tasks, thanks to its efficient inference capabilities and smaller model size.
Use Cases
Used for building chatbots to provide a seamless conversation experience.
In text generation applications, generate news articles or social media content.
As part of a search engine, enhance semantic understanding and optimize search results.
Features
Supports various GGUF quantization versions, including BF16, Q8_0, Q5_K_M, and Q4_K_M.
Based on the phi3 architecture with 3.82 billion parameters, suitable for large-scale language models.
Easy integration with existing projects via the AutoModelForCausalLM.from_pretrained interface.
Supports fast inference, making it suitable for both online and offline applications.
Compact model size facilitates deployment in resource-constrained environments.
Active community; for issues, contact Darshan Deshpande or Rebecca Qian.
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