--- base_model: ValiantLabs/Llama3.1-70B-ShiningValiant2 datasets: - sequelbox/Celestia - sequelbox/Spurline - sequelbox/Supernova language: - en library_name: transformers license: llama3.1 model_type: llama quantized_by: mradermacher tags: - shining-valiant - shining-valiant-2 - valiant - valiant-labs - llama - llama-3.1 - llama-3.1-instruct - llama-3.1-instruct-70b - llama-3 - llama-3-instruct - llama-3-instruct-70b - 70b - science - physics - biology - chemistry - compsci - computer-science - engineering - logic - rationality - advanced - expert - technical - conversational - chat - instruct --- ## About static quants of https://huggingface.co/ValiantLabs/Llama3.1-70B-ShiningValiant2 weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-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/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama3.1-70B-ShiningValiant2-GGUF/resolve/main/Llama3.1-70B-ShiningValiant2.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | 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.