README.md

---
base_model: bineric/NorskGPT-Llama3-8b
tags:
  - llama
  - NorskGPT
  - instruct
  - finetune
language:
  - no

license: cc-by-nc-sa-4.0
---
# NorskGPT-Llama-3-8b-v0.1

This model is a Norwegian variant of 
Meta-Llama-3-8B, fine-tuned on a carefully selected mix of Norwegian instruction pairs. The model is tuned to understand and generate text in Norwegain. 


## Intended Use

This model is free to use for personal and research use. However a commercial license is required for commerical applications. 
This model can be used as an assistant-like chat. Try it out :)


## Prompt Template

```
<|im_start|>system
Du er NorskGPT ....<|im_end|>
<|im_start|>user
Hei<|im_end|>
<|im_start|>assistant
Hei, hva kan jeg hjelpe deg med?<|im_end|>
```
<!-- description start -->
## Description

This repo contains GGUF format model files for [NorskGPT-Llama3-8b](https://huggingface.co/bineric/NorskGPT-Llama3-8b).

## License
[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)


 This model is free to use for personal and research use. However a commercial license is required for commerical applications.
 
 You are free to:

    Share — copy and redistribute the material in any medium or format
    Adapt — remix, transform, and build upon the material
    The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

    Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
    NonCommercial — You may not use the material for commercial purposes .
    ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
    No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.


<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF

Here is an incomplete list of clients and libraries that are known to support GGUF:

* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

<!-- README_GGUF.md-about-gguf end -->

## Prompt template: ChatML

```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

```

<!-- prompt-template end -->




## Explanation of quantisation methods

<details>
  <summary>Click to see details</summary>

The new methods available are:

* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->

<!-- README_GGUF.md-provided-files start -->
## Provided files

| Name | Quant method | Bits |  Use case |
| ---- | ----  | ---- | ----- |
| NorskGPT-Llama3-8b.Q2_K.gguf | Q2_K | 2 |  significant quality loss - not recommended for most purposes |
| NorskGPT-Llama3-8b.Q3_K_S.gguf | Q3_K_S | 3 |  very small, high quality loss |
| NorskGPT-Llama3-8b.Q3_K_M.gguf| Q3_K_M | 3 |  very small, high quality loss |
| NorskGPT-Llama3-8b.Q3_K_L.gguf | Q3_K_L | 3 |  small, substantial quality loss |
| NorskGPT-Llama3-8b.Q4_0.gguf| Q4_0 | 4 |  legacy; small, very high quality loss - prefer using Q3_K_M |
| NorskGPT-Llama3-8b.Q4_K_S.gguf | Q4_K_S | 4 |  small, greater quality loss |
| NorskGPT-Llama3-8b.Q4_K_M.gguf | Q4_K_M | 4 |  medium, balanced quality - recommended |
| NorskGPT-Llama3-8b.Q5_0.gguf | Q5_0 | 5 |  legacy; medium, balanced quality - prefer using Q4_K_M |
| NorskGPT-Llama3-8b.Q5_K_S.gguf | Q5_K_S | 5 |  large, low quality loss - recommended |
| NorskGPT-Llama3-8b.Q5_K_M.gguf | Q5_K_M | 5 |  large, very low quality loss - recommended |
| NorskGPT-Llama3-8b.Q6_K.gguf| Q6_K | 6 |  very large, extremely low quality loss |
| NorskGPT-Llama3-8b.Q8_0.gguf | Q8_0 | 8 |  very large, extremely low quality loss - not recommended |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.



<!-- README_GGUF.md-provided-files end -->

<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files

**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

* LM Studio
* LoLLMS Web UI
* Faraday.dev

  Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models