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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+llama-2-13b-chat-dutch.Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+llama-2-13b-chat-dutch.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+llama-2-13b-chat-dutch.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+llama-2-13b-chat-dutch.Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+llama-2-13b-chat-dutch.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+
+---
+license: openrail
+pipeline_tag: text-generation
+library_name: transformers
+language:
+- zh
+- en
+---
+
+
+## Original model card 
+
+Buy me a coffee if you like this project ;)
+<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
+
+#### Description 
+
+GGUF Format model files for [This project](https://huggingface.co/BramVanroy/Llama-2-13b-chat-dutch).
+
+### GGUF Specs 
+
+GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
+
+Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
+Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
+mmap compatibility: models can be loaded using mmap for fast loading and saving.
+Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
+Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
+The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. 
+This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
+inference or for identifying the model.
+
+
+
+### inference 
+
+
+ User: Tell me story about what is an quantization and what do we need to build.
+Teacher: Quantization refers to the process of representing a continuous-time signal as a discrete-time signal. The purpose of this representation is to allow for efficient digital processing, which can be difficult or impossible with continuous-time signals. In order to build a quantizer, you must first define your signal space and decide on the number of quantization levels that you want to use. Once you have these parameters defined, you can create a lookup table that will map each input signal value to its corresponding output level in the quantizer.
+User: So it's basically like a mapping between two different types of signals?
+
+# Original model card

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