Create README.md

README.md

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+---
+library_name: transformers
+base_model: OEvortex/lite-hermes
+inference: false
+language:
+- en
+license: mit
+tags:
+- HelpingAI
+- lite
+- code
+---
+
+#### Description 
+
+Optimize your engagement with [This project](https://huggingface.co/OEvortex/OEvortex/HelpingAI-unvelite) by seamlessly integrating GGUF Format model files.
+Please Subscribe to my youtube channel [OEvortex](https://youtube.com/@OEvortex)
+### GGUF Technical Specifications 
+
+Delve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features:
+
+**Single-file Deployment:** Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information.
+
+**Extensibility:** Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models.
+
+**mmap Compatibility:** Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow.
+
+**User-Friendly:** Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries.
+
+**Full Information:** A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data.
+
+The differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification.
+
+
+**QUANTIZATION_METHODS:**
+
+| Method | Quantization | Advantages | Trade-offs |
+|---|---|---|---|
+| q2_k | 2-bit integers | Significant model size reduction | Minimal impact on accuracy |
+| q3_k_l | 3-bit integers | Balance between model size reduction and accuracy preservation | Moderate impact on accuracy |
+| q3_k_m | 3-bit integers | Enhanced accuracy with mixed precision | Increased computational complexity |
+| q3_k_s | 3-bit integers | Improved model efficiency with structured pruning | Reduced accuracy |
+| q4_0 | 4-bit integers | Significant model size reduction | Moderate impact on accuracy |
+| q4_1 | 4-bit integers | Enhanced accuracy with mixed precision | Increased computational complexity |
+| q4_k_m | 4-bit integers | Optimized model size and accuracy with mixed precision and structured pruning | Reduced accuracy |
+| q4_k_s | 4-bit integers | Improved model efficiency with structured pruning | Reduced accuracy |
+| q5_0 | 5-bit integers | Balance between model size reduction and accuracy preservation | Moderate impact on accuracy |
+| q5_1 | 5-bit integers | Enhanced accuracy with mixed precision | Increased computational complexity |
+| q5_k_m | 5-bit integers | Optimized model size and accuracy with mixed precision and structured pruning | Reduced accuracy |
+| q5_k_s | 5-bit integers | Improved model efficiency with structured pruning | Reduced accuracy |
+| q6_k | 6-bit integers | Balance between model size reduction and accuracy preservation | Moderate impact on accuracy |
+| q8_0 | 8-bit integers | Significant model size reduction | Minimal impact on accuracy |
+