--- base_model: iselabvn/Tini-Cybersec-8B-A1B datasets: - AlicanKiraz0/Cybersecurity-Dataset-Heimdall-v1.1 - AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1 - Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset - nohurry/Opus-4.6-Reasoning-3000x-filtered - Jackrong/DeepSeek-V4-Distill-8000x - Jackrong/Qwen3.5-reasoning-700x language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - cybersecurity - reasoning - cot - liquid-foundation-model - lfm - instruction-tuning - sft --- ## About static quants of https://huggingface.co/iselabvn/Tini-Cybersec-8B-A1B ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Tini-Cybersec-8B-A1B-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/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q3_K_S.gguf) | Q3_K_S | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q3_K_L.gguf) | Q3_K_L | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.IQ4_XS.gguf) | IQ4_XS | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q4_K_S.gguf) | Q4_K_S | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q4_K_M.gguf) | Q4_K_M | 5.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q5_K_S.gguf) | Q5_K_S | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q5_K_M.gguf) | Q5_K_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q6_K.gguf) | Q6_K | 7.1 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.Q8_0.gguf) | Q8_0 | 9.1 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Tini-Cybersec-8B-A1B-GGUF/resolve/main/Tini-Cybersec-8B-A1B.f16.gguf) | f16 | 17.0 | 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.
