--- base_model: anthracite-org/magnum-12b-v2.5-kto library_name: transformers quantized_by: InferenceIllusionist tags: - iMat - gguf - Mistral license: apache-2.0 --- # magnum-12b-v2.5-kto-iMat-GGUF > [!WARNING] >Important Note: Inferencing in llama.cpp has now been merged in [PR #8604](https://github.com/ggerganov/llama.cpp/pull/8604). Please ensure you are on release [b3438](https://github.com/ggerganov/llama.cpp/releases/tag/b3438) or newer. Text-generation-web-ui (Ooba) is also working as of 7/23. Kobold.cpp working as of [v1.71](https://github.com/LostRuins/koboldcpp/releases/tag/v1.71). Quantized from magnum-12b-v2.5-kto fp16 * Weighted quantizations were creating using fp16 GGUF and [groups_merged.txt](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) (special thanks to [Kalomaze](https://huggingface.co/kalomaze)) in 92 chunks and n_ctx=512 * Static fp16 will also be included in repo * For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747) * All quants are verified working prior to uploading to repo for your safety and convenience KL-Divergence Reference Chart (Click on image to view in full size) [](https://i.imgur.com/mV0nYdA.png) > [!TIP] >Quant-specific Tips: >* If you are getting a `cudaMalloc failed: out of memory` error, try passing an argument for lower context in llama.cpp, e.g. for 8k: `-c 8192` >* If you have all ampere generation or newer cards, you can use flash attention like so: `-fa` >* Provided Flash Attention is enabled you can also use quantized cache to save on VRAM e.g. for 8-bit: `-ctk q8_0 -ctv q8_0` Original model card can be found [here](https://huggingface.co/anthracite-org/magnum-12b-v2.5-kto)