--- base_model: chihoonlee10/chihoonlee-27B-SFT-V1.0 datasets: - open-ko-llm-leaderboard/KMMLU language: - ko - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - qwen3.5 - korean - sft - distillation - cot - kmmlu - k-ai-leaderboard --- ## About weighted/imatrix quants of https://huggingface.co/chihoonlee10/chihoonlee-27B-SFT-V1.0 ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#chihoonlee-27B-SFT-V1.0-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-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/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ1_M.gguf) | i1-IQ1_M | 7.7 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.5 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ2_S.gguf) | i1-IQ2_S | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ2_M.gguf) | i1-IQ2_M | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q2_K_S.gguf) | i1-Q2_K_S | 10.3 | very low quality | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q2_K.gguf) | i1-Q2_K | 10.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 11.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 12.1 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.2 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ3_S.gguf) | i1-IQ3_S | 12.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ3_M.gguf) | i1-IQ3_M | 12.7 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 14.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 15.2 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q4_0.gguf) | i1-Q4_0 | 15.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 15.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 16.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q4_1.gguf) | i1-Q4_1 | 17.2 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 18.8 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 19.3 | | | [GGUF](https://huggingface.co/mradermacher/chihoonlee-27B-SFT-V1.0-i1-GGUF/resolve/main/chihoonlee-27B-SFT-V1.0.i1-Q6_K.gguf) | i1-Q6_K | 22.2 | practically like static Q6_K | 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. 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.
