glider-gguf

High-performance quantized language model

CommonProductProgrammingGGUFQuantized Model

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.

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.

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.

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.

1. Choose the appropriate GGUF quantization version.
2. Run the following code to load the model: `from transformers import AutoModelForCausalLM; model = AutoModelForCausalLM.from_pretrained(\\\PatronusAI/glider-gguf\\\
gguf_file=\\\glider_{version_from_list}.gguf\\\)`.
3. Replace `{version_from_list}` with the desired quantization version
for example

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glider-gguf Visit Over Time

Monthly Visits

26103677

Bounce Rate

43.69%

Page per Visit

5.5

Visit Duration

00:04:43

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glider-gguf

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