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