--- base_model: inclusionAI/SingGuard-4b language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About static quants of https://huggingface.co/inclusionAI/SingGuard-4b ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Sing-Guard-4b-GGUF).*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.6 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.mmproj-f16.gguf) | mmproj-f16 | 0.9 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q2_K.gguf) | Q2_K | 1.8 | | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q3_K_S.gguf) | Q3_K_S | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q3_K_L.gguf) | Q3_K_L | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.IQ4_XS.gguf) | IQ4_XS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q5_K_S.gguf) | Q5_K_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q5_K_M.gguf) | Q5_K_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q6_K.gguf) | Q6_K | 3.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Sing-Guard-4b-GGUF/resolve/main/Sing-Guard-4b.f16.gguf) | f16 | 8.2 | 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.
