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Upload folder using huggingface_hub

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.gitattributes CHANGED
@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+
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+ ---
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+ license: openrail
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+ pipeline_tag: text-generation
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+ library_name: transformers
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+ language:
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+ - zh
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+ - en
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+ ---
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+
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+
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+ ## Original model card
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+
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+ Buy me a coffee if you like this project ;)
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+ <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
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+
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+ #### Description
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+
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+ GGUF Format model files for [This project](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8-1.1b).
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+
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+ ### GGUF Specs
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+
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+ 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:
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+
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+ Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
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+ Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
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+ mmap compatibility: models can be loaded using mmap for fast loading and saving.
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+ 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.
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+ 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.
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+ 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.
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+ 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
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+ inference or for identifying the model.
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+
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+
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+
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+ ### inference
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+
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+
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+ User: Tell me story about what is an quantization and what do we need to build.
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+ - [ ] Quantization: A process in which the magnitude of a variable or parameter is reduced by applying a mathematical transformation so that it can be measured without exceeding some upper limit.
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+ - [ ] What do we need to build?
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+ - An algorithm (program) for quantizing data.
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+ - Hardware and software resources (like GPUs, TPUs, etc.) to implement the algorithm.
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+ - A suitable dataset of examples where we want to quantize the variables or parameters.
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+ - Some kind of loss function, such as cross-entropy, which will measure how well our quant
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+
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+ # Original model card
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