--- base_model: jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated language: - ar - de - en - es - fr - hi - id - it - pt - th - tl - vi library_name: transformers license: llama4 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - facebook - meta - pytorch - llama - llama4 --- ## About static quants of https://huggingface.co/jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-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/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 1.1 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.mmproj-f16.gguf) | mmproj-f16 | 1.9 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q2_K.gguf) | Q2_K | 39.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q3_K_S.gguf) | Q3_K_S | 46.8 | | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q3_K_M.gguf.part2of2) | Q3_K_M | 51.9 | lower quality | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q3_K_L.gguf.part2of2) | Q3_K_L | 56.1 | | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.IQ4_XS.gguf.part2of2) | IQ4_XS | 58.4 | | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q4_K_S.gguf.part2of2) | Q4_K_S | 61.6 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q4_K_M.gguf.part2of2) | Q4_K_M | 65.5 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q5_K_S.gguf.part2of2) | Q5_K_S | 74.4 | | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q5_K_M.gguf.part2of2) | Q5_K_M | 76.6 | | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q6_K.gguf.part2of2) | Q6_K | 88.5 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-GGUF/resolve/main/Llama-4-Scout-17B-16E-Instruct-abliterated.Q8_0.gguf.part3of3) | Q8_0 | 114.6 | 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. 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.