--- base_model: LeroyDyer/Mixtral_AI_CyberCoder_7b datasets: - WhiteRabbitNeo/WRN-Chapter-1 - WhiteRabbitNeo/WRN-Chapter-2 - CyberNative/Code_Vulnerability_Security_DPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge - code - art - Cyber-Series --- ## About weighted/imatrix quants of https://huggingface.co/LeroyDyer/Mixtral_AI_CyberCoder_7b static quants are available at https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-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/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberCoder_7b-i1-GGUF/resolve/main/Mixtral_AI_CyberCoder_7b.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | 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. 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.