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NorskGPT-Llama3-8b-GGUF

GGUF
Norwegian
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NorskGPT
instruct
finetune
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metadata
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

This repo contains GGUF format model files for NorskGPT-Llama3-8b.

License

Attribution-NonCommercial-ShareAlike 4.0 International

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.

About GGUF

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

  • llama.cpp. The source project for GGUF. Offers a CLI and a server option.
  • text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
  • KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
  • GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
  • LM Studio, 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, a great web UI with many interesting and unique features, including a full model library for easy model selection.
  • 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, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
  • candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
  • 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.

Prompt template: ChatML 

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

Explanation of quantisation methods

Click to see details

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.

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.

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 for preparing an amazing README on how to use GGUF models