README.md

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# 🚀 bloomvn-0.5b-ppo-GGUF

### Optimized quantized models for efficient inference

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## 📋 Overview

A collection of optimized GGUF quantized models derived from [bloomvn-0.5b-ppo](https://huggingface.co/BlossomsAI/BloomVN-0.5B-ppo), providing various performance-quality tradeoffs.

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## 💎 Model Variants

| Variant | Use Case | Download |
|---------|-----------|------------|
| base | The base model is suitable for applications where model size is not a concern, and high accuracy is required. It can be used for tasks such as text generation, language translation, and text summarization. This model is in FP16 format, providing a good balance between size and performance. | [📥](https://huggingface.co/Vuanhngo11/bloomvn-0.5b-ppo-gguf/resolve/main/base.gguf)
| q2_k | The q2_k variant is ideal for extremely constrained environments where model size is a significant concern, such as embedded systems or low-end mobile devices. Although it is highly compressed, it still maintains a reasonable level of accuracy, making it suitable for simple language tasks. This 2-bit quantized model is a good choice when storage space is limited. | [📥](https://huggingface.co/Vuanhngo11/bloomvn-0.5b-ppo-gguf/resolve/main/q2_k.gguf)
| q3_k_m | The q3_k_m variant is designed for memory-limited devices that require a balance between model size and accuracy. This 3-bit quantized model is very compressed, making it suitable for mid-range mobile devices or systems with limited storage capacity. It is a good choice for applications where a moderate level of accuracy is required, such as language understanding or text classification. | [📥](https://huggingface.co/Vuanhngo11/bloomvn-0.5b-ppo-gguf/resolve/main/q3_k_m.gguf)

## 🤝 Contributors

Developed with ❤️ by [BlossomAI](https://huggingface.co/BlossomsAI)

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