--- base_model: Noodlz/QueenLiz-120B language: - en library_name: transformers quantized_by: mradermacher --- ## About static quants of https://huggingface.co/Noodlz/QueenLiz-120B weighted/imatrix quants are available at https://huggingface.co/mradermacher/QueenLiz-120B-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/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q2_K.gguf) | Q2_K | 44.6 | | | [GGUF](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_XS.gguf) | IQ3_XS | 49.6 | | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_S.gguf.part2of2) | Q3_K_S | 52.2 | | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_S.gguf.part2of2) | IQ3_S | 52.4 | beats Q3_K* | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_M.gguf.part2of2) | IQ3_M | 54.2 | | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_M.gguf.part2of2) | Q3_K_M | 58.2 | lower quality | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_L.gguf.part2of2) | Q3_K_L | 63.4 | | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_XS.gguf.part2of2) | IQ4_XS | 65.2 | | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_0.gguf.part2of2) | Q4_0 | 68.2 | fast, low quality | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_S.gguf.part2of2) | Q4_K_S | 68.7 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_NL.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_NL.gguf.part2of2) | IQ4_NL | 68.8 | prefer IQ4_XS | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_M.gguf.part2of2) | Q4_K_M | 72.6 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_S.gguf.part2of2) | Q5_K_S | 83.2 | | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_M.gguf.part2of2) | Q5_K_M | 85.4 | | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q6_K.gguf.part3of3) | Q6_K | 99.1 | very good quality | | [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q8_0.gguf.part3of3) | Q8_0 | 128.2 | fast, best quality | 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.