imatrix
Qwen3.8-27B-iMatrix-NVFP4-MTP-GGUFQwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUFQwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUFQwen3.6-27B-Heretic-Uncensored-FINETUNE-NEO-CODE-Di-IMatrix-MAX-GGUFQwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUFOpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-ggufGLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUFQwen3.8-27B-Uncensored-Cyber-agentic-imatrix-GGUF
Datasets
All datasets matching “imatrix”imatrix-calibration
Importance Matrix Calibration Datasets
This repository provides calibration datasets used to generate importance matrices (imatrix), which are required to minimize errors when quantizing models with LLaMA C++.
The llama-imatrix program cannot handle parquet files directly and thus requires them to be converted into text format first. There are many ways to do this but a simple approach is to use DuckDB with the following command: duckdb -noheader -ascii -c "SELECT content FROM… See the full description on the dataset page: https://huggingface.co/datasets/eaddario/imatrix-calibration.imatrix-storageimatrixQwQ-32B-abliterated-131k-GGUF-Yarn-Imatrix
QwQ-32B-Abliterated-131k-GGUF-Yarn-Imatrix
High-Fidelity Semantic Simulation & Orchestration AI Model
Will this pass the random stupid benchmarks that exist today? I don't know, nor care. I don't need my local AI model to know some random city capital of a foreign country. I need a local AI model that can simulate with high semantic fidelity. Why? Because your AI may be able to spit random facts. I want an AI that knows when to Google facts. I want an AI that tracks hundreds of… See the full description on the dataset page: https://huggingface.co/datasets/magiccodingman/QwQ-32B-abliterated-131k-GGUF-Yarn-Imatrix.imatrix
Input files for generating the Importance Matrix
Which file to use for generating the importance matrix
Not all importance matrices are equal. The best results are obtained when using a source file similar to the
training data. Size also matters: the bigger the model (eg: 70b vs 13b) and the higher the quant (eg: q6k_ vs iq3_xs),
the bigger the source file needs to be to make an impact. Multiple input files can be combined if needed;
for example:
cat multilingual.txt… See the full description on the dataset page: https://huggingface.co/datasets/froggeric/imatrix.imatrix-from-wiki-trainThis repository contains importance matrix datasets for use with the improved quantization methods recently added to llama.cpp.
The importance matrix has been computed using wiki.train.raw as training data.
Hope the file names are self-explanatory.
To use, after cloning this repo, for e.g. Mixtral-8x7B and Q4_K_M quantization, use
./quantize --imatrix path_to_repo/mixtral-8x7b.imatrix path_to_model ggml-model-q4k-m.gguf Q4_K_M
ik_llama_imatrix_converterWebGPU-reka-flash-3-21b-reasoning-uncensored-max-neo-imatrix-ggufWebGPU-qwen3-jan-v1-4b-grand-horror-day1-to-day7-evolved-imatrix-ggufqwen3-6-40b-claude-4-6-opus-deckard-heretic-uncensored-thinking-neo-code-di-imatrix-maWebGPU-qwen3-6-27b-heretic-uncensored-finetune-neo-code-di-imatrix-max-ggufWebGPU-glm-4-7-flash-uncensored-heretic-neo-code-imatrix-max-gguf