--- language: - en license: other tags: - text-generation-inference - transformers - unsloth - llama - trl - sft - TensorBlock - GGUF base_model: Kukedlc/LLama-3-8b-Maths datasets: - microsoft/orca-math-word-problems-200k ---
TensorBlock
[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## Kukedlc/LLama-3-8b-Maths - GGUF This repo contains GGUF format model files for [Kukedlc/LLama-3-8b-Maths](https://huggingface.co/Kukedlc/LLama-3-8b-Maths). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985). ## Our projects
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## Prompt template ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [LLama-3-8b-Maths-Q2_K.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes | | [LLama-3-8b-Maths-Q3_K_S.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q3_K_S.gguf) | Q3_K_S | 3.664 GB | very small, high quality loss | | [LLama-3-8b-Maths-Q3_K_M.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss | | [LLama-3-8b-Maths-Q3_K_L.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss | | [LLama-3-8b-Maths-Q4_0.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [LLama-3-8b-Maths-Q4_K_S.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss | | [LLama-3-8b-Maths-Q4_K_M.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended | | [LLama-3-8b-Maths-Q5_0.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [LLama-3-8b-Maths-Q5_K_S.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended | | [LLama-3-8b-Maths-Q5_K_M.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended | | [LLama-3-8b-Maths-Q6_K.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss | | [LLama-3-8b-Maths-Q8_0.gguf](https://huggingface.co/tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF/blob/main/LLama-3-8b-Maths-Q8_0.gguf) | Q8_0 | 8.541 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF --include "LLama-3-8b-Maths-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Kukedlc_LLama-3-8b-Maths-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```