commit e6fe0d383e72d02e4f75cfae39be4db2bf935498 Author: ModelHub XC Date: Fri Jun 5 03:06:23 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: mradermacher/Mem-T-4B-i1-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..6a9e9e6 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,60 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx 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sha256:831ac324865d8687a76d82cea197489e576bb75397114edec860e395b96c810b +size 3872640 diff --git a/README.md b/README.md new file mode 100644 index 0000000..acd0208 --- /dev/null +++ b/README.md @@ -0,0 +1,85 @@ +--- +base_model: EdwinYue/Mem-T-4B +language: +- en +library_name: transformers +license: apache-2.0 +mradermacher: + readme_rev: 1 +quantized_by: mradermacher +--- +## About + + + + + + + + + +weighted/imatrix quants of https://huggingface.co/EdwinYue/Mem-T-4B + + + +***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mem-T-4B-i1-GGUF).*** + +static quants are available at https://huggingface.co/mradermacher/Mem-T-4B-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/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.3 | for the desperate | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.4 | mostly desperate | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.5 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.6 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.7 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.8 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.8 | very low quality | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q2_K.gguf) | i1-Q2_K | 1.9 | IQ3_XXS probably better | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.9 | lower quality | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.1 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.2 | IQ3_XS probably better | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ3_S.gguf) | i1-IQ3_S | 2.2 | beats Q3_K* | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ3_M.gguf) | i1-IQ3_M | 2.2 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.3 | IQ3_S probably better | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.5 | IQ3_M probably better | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.6 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q4_0.gguf) | i1-Q4_0 | 2.7 | fast, low quality | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.7 | prefer IQ4_XS | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.7 | optimal size/speed/quality | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.8 | fast, recommended | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q4_1.gguf) | i1-Q4_1 | 2.9 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 3.2 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 3.3 | | +| [GGUF](https://huggingface.co/mradermacher/Mem-T-4B-i1-GGUF/resolve/main/Mem-T-4B.i1-Q6_K.gguf) | i1-Q6_K | 3.7 | 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. + +