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Fail to appropriately disclose to end users any known dangers of your AI system \nPlease report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)" language: - code library_name: transformers license: llama2 quantized_by: mradermacher tags: - facebook - meta - pytorch - llama - llama-2 - abliterated - uncensored --- ## About static quants of https://huggingface.co/huihui-ai/CodeLlama-13b-Instruct-hf-abliterated weighted/imatrix quants are available at https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-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/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/CodeLlama-13b-Instruct-hf-abliterated-GGUF/resolve/main/CodeLlama-13b-Instruct-hf-abliterated.Q8_0.gguf) | Q8_0 | 13.9 | 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.