From 0b8c56e8ebd9cf2b639b8307073472f9d7a12da0 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 4 Jun 2026 22:55:17 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: mradermacher/QAi-2.0-i1-GGUF Source: Original Platform --- .gitattributes | 60 +++++++++++++++++++++++++++ QAi-2.0.i1-IQ1_M.gguf | 3 ++ QAi-2.0.i1-IQ1_S.gguf | 3 ++ QAi-2.0.i1-IQ2_M.gguf | 3 ++ QAi-2.0.i1-IQ2_S.gguf | 3 ++ QAi-2.0.i1-IQ2_XS.gguf | 3 ++ QAi-2.0.i1-IQ2_XXS.gguf | 3 ++ QAi-2.0.i1-IQ3_M.gguf | 3 ++ QAi-2.0.i1-IQ3_S.gguf | 3 ++ QAi-2.0.i1-IQ3_XS.gguf | 3 ++ QAi-2.0.i1-IQ3_XXS.gguf | 3 ++ QAi-2.0.i1-IQ4_NL.gguf | 3 ++ QAi-2.0.i1-IQ4_XS.gguf | 3 ++ QAi-2.0.i1-Q2_K.gguf | 3 ++ QAi-2.0.i1-Q2_K_S.gguf | 3 ++ QAi-2.0.i1-Q3_K_L.gguf | 3 ++ QAi-2.0.i1-Q3_K_M.gguf | 3 ++ 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/dev/null +++ b/QAi-2.0.imatrix.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d67b6148e7f44be100a671a195ff378bcec2a8a8f16835b87e35881484042aa5 +size 2065888 diff --git a/README.md b/README.md new file mode 100644 index 0000000..7fcf27e --- /dev/null +++ b/README.md @@ -0,0 +1,90 @@ +--- +base_model: QwexGP/QAi-2.0 +language: +- en +library_name: transformers +license: apache-2.0 +mradermacher: + readme_rev: 1 +quantized_by: mradermacher +tags: +- text-generation-inference +- transformers +- unsloth +- qwen2 +--- +## About + + + + + + + + + +weighted/imatrix quants of https://huggingface.co/QwexGP/QAi-2.0 + + + +***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#QAi-2.0-i1-GGUF).*** + +static quants are available at https://huggingface.co/mradermacher/QAi-2.0-GGUF +## Usage + +If you are unsure how to use GGUF files, refer to one of [TheBloke's +READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for +more details, including on how to concatenate multi-part files. + +## Provided Quants + +(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) + +| Link | Type | Size/GB | Notes | +|:-----|:-----|--------:|:------| +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ3_M.gguf) | i1-IQ3_M | 0.9 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.9 | IQ3_S probably better | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.0 | IQ3_M probably better | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.0 | optimal size/speed/quality | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.1 | fast, recommended | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.2 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.2 | | +| [GGUF](https://huggingface.co/mradermacher/QAi-2.0-i1-GGUF/resolve/main/QAi-2.0.i1-Q6_K.gguf) | i1-Q6_K | 1.4 | practically like static Q6_K | + +Here is a handy graph by ikawrakow comparing some lower-quality quant +types (lower is better): + +![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) + +And here are Artefact2's thoughts on the matter: +https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 + +## FAQ / Model Request + +See https://huggingface.co/mradermacher/model_requests for some answers to +questions you might have and/or if you want some other model quantized. + +## Thanks + +I thank my company, [nethype GmbH](https://www.nethype.de/), for letting +me use its servers and providing upgrades to my workstation to enable +this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. + +