--- base_model: rombodawg/rombos_Llama3-8B-Instruct-Replete-Adapted datasets: - Replete-AI/code_bagel_hermes-2.5 - Replete-AI/code_bagel - Replete-AI/OpenHermes-2.5-Uncensored - teknium/OpenHermes-2.5 - layoric/tiny-codes-alpaca - glaiveai/glaive-code-assistant-v3 - ajibawa-2023/Code-290k-ShareGPT - TIGER-Lab/MathInstruct - chargoddard/commitpack-ft-instruct-rated - iamturun/code_instructions_120k_alpaca - ise-uiuc/Magicoder-Evol-Instruct-110K - cognitivecomputations/dolphin-coder - nickrosh/Evol-Instruct-Code-80k-v1 - coseal/CodeUltraFeedback_binarized - glaiveai/glaive-function-calling-v2 - CyberNative/Code_Vulnerability_Security_DPO - jondurbin/airoboros-2.2 - camel-ai - lmsys/lmsys-chat-1m - CollectiveCognition/chats-data-2023-09-22 - CoT-Alpaca-GPT4 - WizardLM/WizardLM_evol_instruct_70k - WizardLM/WizardLM_evol_instruct_V2_196k - teknium/GPT4-LLM-Cleaned - GPTeacher - OpenGPT - meta-math/MetaMathQA - Open-Orca/SlimOrca - garage-bAInd/Open-Platypus - anon8231489123/ShareGPT_Vicuna_unfiltered - Unnatural-Instructions-GPT4 language: - en library_name: transformers license: other license_link: https://llama.meta.com/llama3/license/ license_name: llama-3 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama --- ## About weighted/imatrix quants of https://huggingface.co/rombodawg/rombos_Llama3-8B-Instruct-Replete-Adapted static quants are available at https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-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/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/rombos_Llama3-8B-Instruct-Replete-Adapted-i1-GGUF/resolve/main/rombos_Llama3-8B-Instruct-Replete-Adapted.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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.