|
|
|
--- |
|
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/GEITje-7B-ultra). |
|
|
|
### 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. |
|
|
|
AI Assistent: Zeker, ik kan u een vereenvoudigd overzicht geven van wat een kwantificatie in de context van machine learning inhoudt, en hoe je een simpel model kunt bouwen dat gebruikmaakt van kwantificatie. |
|
|
|
Kwantificatie (quantization) is het proces waarbij numerieke waarden, zoals de inputs van een neuron in een neuraal netwerk, vertegenwoordigd worden door discrete representaties met een beperkt aantal mogelijke waarden. Dit |
|
|
|
# Original model card |
|
|
