commit 8fce21628da24ad5eb14294b7bd811e3c402cd07 Author: ModelHub XC Date: Mon May 11 15:03:47 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..955cfa9 --- /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 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https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-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/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ1_S.gguf) | i1-IQ1_S | 0.3 | for the desperate | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ1_M.gguf) | i1-IQ1_M | 0.3 | mostly desperate | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.3 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.3 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ2_S.gguf) | i1-IQ2_S | 0.3 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ2_M.gguf) | i1-IQ2_M | 0.3 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.3 | very low quality | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.3 | lower quality | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ3_S.gguf) | i1-IQ3_S | 0.4 | beats Q3_K* | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.4 | IQ3_XS probably better | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ3_M.gguf) | i1-IQ3_M | 0.4 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.4 | IQ3_S probably better | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.4 | IQ3_M probably better | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.4 | prefer IQ4_XS | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q4_0.gguf) | i1-Q4_0 | 0.4 | fast, low quality | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.4 | optimal size/speed/quality | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q4_1.gguf) | i1-Q4_1 | 0.5 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.5 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | | +| [GGUF](https://huggingface.co/mradermacher/Hunyuan-0.5B-Pretrain-i1-GGUF/resolve/main/Hunyuan-0.5B-Pretrain.i1-Q6_K.gguf) | i1-Q6_K | 0.5 | 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. + +