--- base_model: huihui-ai/Huihui4-8B-A4B-v2 datasets: - huihui-ai/GLM-5.1-Multilingual-STEM language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - sft - Moe - Pruned --- ## About static quants of https://huggingface.co/huihui-ai/Huihui4-8B-A4B-v2 ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Huihui4-8B-A4B-v2-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-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/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.9 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.mmproj-f16.gguf) | mmproj-f16 | 1.3 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q2_K.gguf) | Q2_K | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q3_K_S.gguf) | Q3_K_S | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q3_K_M.gguf) | Q3_K_M | 4.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q3_K_L.gguf) | Q3_K_L | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.IQ4_XS.gguf) | IQ4_XS | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q4_K_S.gguf) | Q4_K_S | 5.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q4_K_M.gguf) | Q4_K_M | 5.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q5_K_S.gguf) | Q5_K_S | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q5_K_M.gguf) | Q5_K_M | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q6_K.gguf) | Q6_K | 7.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.Q8_0.gguf) | Q8_0 | 8.7 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Huihui4-8B-A4B-v2-GGUF/resolve/main/Huihui4-8B-A4B-v2.f16.gguf) | f16 | 16.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  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.
