commit f25a427e1f2dab108cad7ebac1bed2906dc0a2e3 Author: ModelHub XC Date: Thu Jun 18 03:02:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..e47d71e --- /dev/null +++ b/.gitattributes @@ -0,0 +1,48 @@ +*.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 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+static quants of https://huggingface.co/mpasila/Ahma-SlimInstruct-V1-7B + + +weighted/imatrix quants are available at https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-i1-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/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q2_K.gguf) | Q2_K | 2.8 | | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q3_K_S.gguf) | Q3_K_S | 3.2 | | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.IQ4_XS.gguf) | IQ4_XS | 3.9 | | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.1 | fast on arm, low quality | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q4_K_S.gguf) | Q4_K_S | 4.1 | fast, recommended | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q4_K_M.gguf) | Q4_K_M | 4.4 | fast, recommended | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q5_K_S.gguf) | Q5_K_S | 5.0 | | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q5_K_M.gguf) | Q5_K_M | 5.1 | | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q6_K.gguf) | Q6_K | 5.8 | very good quality | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.Q8_0.gguf) | Q8_0 | 7.5 | fast, best quality | +| [GGUF](https://huggingface.co/mradermacher/Ahma-SlimInstruct-V0.1-7B-GGUF/resolve/main/Ahma-SlimInstruct-V0.1-7B.f16.gguf) | f16 | 14.1 | 16 bpw, overkill | + +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. + +