Split (1)

train · 843k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-26 06:27:38	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 496 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 06:27:10	card stringlengths 11 1.01M
mradermacher/Eurus-70b-sft-fixed-i1-GGUF	mradermacher	2024-05-06T05:00:08Z	84	2	transformers	[ "transformers", "gguf", "reasoning", "en", "dataset:openbmb/UltraInteract_sft", "dataset:stingning/ultrachat", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:Open-Orca/OpenOrca", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-12T07:14:07Z	--- base_model: jukofyork/Eurus-70b-sft-fixed datasets: - openbmb/UltraInteract_sft - stingning/ultrachat - openchat/openchat_sharegpt4_dataset - Open-Orca/OpenOrca language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - reasoning --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/jukofyork/Eurus-70b-sft-fixed <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-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/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurus-70b-sft-fixed-i1-GGUF/resolve/main/Eurus-70b-sft-fixed.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/CodeZero-7B-GGUF	mradermacher	2024-05-06T05:00:02Z	19	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "ResplendentAI/DaturaCookie_7B", "ResplendentAI/Flora_7B", "en", "base_model:bunnycore/CodeZero-7B", "base_model:quantized:bunnycore/CodeZero-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-12T07:47:27Z	--- base_model: bunnycore/CodeZero-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - ResplendentAI/DaturaCookie_7B - ResplendentAI/Flora_7B --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/bunnycore/CodeZero-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q2_K.gguf) \| Q2_K \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q3_K_S.gguf) \| Q3_K_S \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.IQ3_S.gguf) \| IQ3_S \| 4.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.IQ3_M.gguf) \| IQ3_M \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q3_K_M.gguf) \| Q3_K_M \| 4.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.IQ4_XS.gguf) \| IQ4_XS \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q4_K_S.gguf) \| Q4_K_S \| 5.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q4_K_M.gguf) \| Q4_K_M \| 5.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q5_K_S.gguf) \| Q5_K_S \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q5_K_M.gguf) \| Q5_K_M \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q6_K.gguf) \| Q6_K \| 7.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/CodeZero-7B-GGUF/resolve/main/CodeZero-7B.Q8_0.gguf) \| Q8_0 \| 9.7 \| 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. <!-- end -->
mradermacher/Orpomis-Prime-7B-it-GGUF	mradermacher	2024-05-06T04:59:54Z	16	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "kaist-ai/mistral-orpo-beta", "NousResearch/Hermes-2-Pro-Mistral-7B", "mistralai/Mistral-7B-Instruct-v0.2", "en", "base_model:saucam/Orpomis-Prime-7B-it", "base_model:quantized:saucam/Orpomis-Prime-7B-it", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-12T13:30:28Z	--- base_model: saucam/Orpomis-Prime-7B-it language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - kaist-ai/mistral-orpo-beta - NousResearch/Hermes-2-Pro-Mistral-7B - mistralai/Mistral-7B-Instruct-v0.2 --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/saucam/Orpomis-Prime-7B-it <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-it-GGUF/resolve/main/Orpomis-Prime-7B-it.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF	mradermacher	2024-05-06T04:59:51Z	20	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mpasila/Mistral-7B-Erebus-v3-Instruct-32k", "base_model:quantized:mpasila/Mistral-7B-Erebus-v3-Instruct-32k", "endpoints_compatible", "region:us" ]	null	2024-04-12T15:16:22Z	--- base_model: mpasila/Mistral-7B-Erebus-v3-Instruct-32k language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mpasila/Mistral-7B-Erebus-v3-Instruct-32k <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Erebus-v3-Instruct-32k-GGUF/resolve/main/Mistral-7B-Erebus-v3-Instruct-32k.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Orpomis-Prime-7B-GGUF	mradermacher	2024-05-06T04:59:39Z	3	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "en", "base_model:saucam/Orpomis-Prime-7B", "base_model:quantized:saucam/Orpomis-Prime-7B", "endpoints_compatible", "region:us" ]	null	2024-04-12T18:40:08Z	--- base_model: saucam/Orpomis-Prime-7B language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/saucam/Orpomis-Prime-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpomis-Prime-7B-GGUF/resolve/main/Orpomis-Prime-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/MysticNoromaidx-i1-GGUF	mradermacher	2024-05-06T04:59:36Z	57	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-12T19:23:42Z	--- base_model: Fredithefish/MysticNoromaidx language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/Fredithefish/MysticNoromaidx <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MysticNoromaidx-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/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ1_S.gguf) \| i1-IQ1_S \| 9.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ1_M.gguf) \| i1-IQ1_M \| 10.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 12.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 14.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ2_S.gguf) \| i1-IQ2_S \| 14.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ2_M.gguf) \| i1-IQ2_M \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q2_K.gguf) \| i1-Q2_K \| 17.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 18.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ3_S.gguf) \| i1-IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 20.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ3_M.gguf) \| i1-IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 22.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 24.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q4_0.gguf) \| i1-Q4_0 \| 26.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 26.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MysticNoromaidx-i1-GGUF/resolve/main/MysticNoromaidx.i1-Q6_K.gguf) \| i1-Q6_K \| 38.5 \| 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. <!-- end -->
mradermacher/MystixNoromaidx-GGUF	mradermacher	2024-05-06T04:59:31Z	37	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us" ]	null	2024-04-12T21:14:17Z	--- base_model: Fredithefish/MystixNoromaidx language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Fredithefish/MystixNoromaidx <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MystixNoromaidx-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/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q2_K.gguf) \| Q2_K \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.IQ3_XS.gguf) \| IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.IQ3_S.gguf) \| IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q3_K_S.gguf) \| Q3_K_S \| 20.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.IQ3_M.gguf) \| IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q3_K_M.gguf) \| Q3_K_M \| 22.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q3_K_L.gguf) \| Q3_K_L \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.IQ4_XS.gguf) \| IQ4_XS \| 25.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q4_K_S.gguf) \| Q4_K_S \| 26.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q4_K_M.gguf) \| Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q5_K_S.gguf) \| Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q5_K_M.gguf) \| Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q6_K.gguf) \| Q6_K \| 38.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-GGUF/resolve/main/MystixNoromaidx.Q8_0.gguf) \| Q8_0 \| 49.7 \| 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. <!-- end -->
mradermacher/Experiment26-7B-GGUF	mradermacher	2024-05-06T04:59:13Z	74	0	transformers	[ "transformers", "gguf", "chat", "en", "base_model:yam-peleg/Experiment26-7B", "base_model:quantized:yam-peleg/Experiment26-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-12T23:47:59Z	--- base_model: yam-peleg/Experiment26-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - chat --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/yam-peleg/Experiment26-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26-7B-GGUF/resolve/main/Experiment26-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/MystixNoromaidx-i1-GGUF	mradermacher	2024-05-06T04:59:10Z	50	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us" ]	null	2024-04-13T00:11:33Z	--- base_model: Fredithefish/MystixNoromaidx language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/Fredithefish/MystixNoromaidx <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MystixNoromaidx-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/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ1_S.gguf) \| i1-IQ1_S \| 9.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ1_M.gguf) \| i1-IQ1_M \| 10.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 12.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 14.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ2_S.gguf) \| i1-IQ2_S \| 14.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ2_M.gguf) \| i1-IQ2_M \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q2_K.gguf) \| i1-Q2_K \| 17.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 18.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ3_S.gguf) \| i1-IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 20.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ3_M.gguf) \| i1-IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 22.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 24.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q4_0.gguf) \| i1-Q4_0 \| 26.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 26.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MystixNoromaidx-i1-GGUF/resolve/main/MystixNoromaidx.i1-Q6_K.gguf) \| i1-Q6_K \| 38.5 \| 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. <!-- end -->
mradermacher/strix-rufipes-70b-GGUF	mradermacher	2024-05-06T04:59:05Z	102	0	transformers	[ "transformers", "gguf", "logic", "planning", "en", "base_model:ibivibiv/strix-rufipes-70b", "base_model:quantized:ibivibiv/strix-rufipes-70b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-13T01:54:59Z	--- base_model: ibivibiv/strix-rufipes-70b language: - en library_name: transformers license: llama2 quantized_by: mradermacher tags: - logic - planning --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ibivibiv/strix-rufipes-70b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/strix-rufipes-70b-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/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/strix-rufipes-70b-GGUF/resolve/main/strix-rufipes-70b.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| 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. <!-- end -->
mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF	mradermacher	2024-05-06T04:59:02Z	28	0	transformers	[ "transformers", "gguf", "Italian", "Mistral", "finetuning", "Text Generation", "it", "dataset:scribis/Wikipedia_it_Trame_Romanzi", "dataset:scribis/Corpus-Frasi-da-Opere-Letterarie", "dataset:scribis/Wikipedia-it-Trame-di-Film", "dataset:scribis/Wikipedia-it-Descrizioni-di-Dipinti", "dataset:scribis/Wikipedia-it-Mitologia-Greca", "base_model:scribis/Fantastica-7b-Instruct-0.2-Italian_merged", "base_model:quantized:scribis/Fantastica-7b-Instruct-0.2-Italian_merged", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-13T02:20:18Z	--- base_model: scribis/Fantastica-7b-Instruct-0.2-Italian_merged datasets: - scribis/Wikipedia_it_Trame_Romanzi - scribis/Corpus-Frasi-da-Opere-Letterarie - scribis/Wikipedia-it-Trame-di-Film - scribis/Wikipedia-it-Descrizioni-di-Dipinti - scribis/Wikipedia-it-Mitologia-Greca language: - it library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - Italian - Mistral - finetuning - Text Generation --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/scribis/Fantastica-7b-Instruct-0.2-Italian_merged <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Fantastica-7b-Instruct-0.2-Italian_merged-GGUF/resolve/main/Fantastica-7b-Instruct-0.2-Italian_merged.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Uoxudo_V2-GGUF	mradermacher	2024-05-06T04:58:52Z	76	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "base_model:TheHappyDrone/Uoxudo_V2", "base_model:quantized:TheHappyDrone/Uoxudo_V2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-13T02:48:10Z	--- base_model: TheHappyDrone/Uoxudo_V2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TheHappyDrone/Uoxudo_V2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Uoxudo_V2-GGUF/resolve/main/Uoxudo_V2.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/PiVoT-SUS-RP-GGUF	mradermacher	2024-05-06T04:58:49Z	21	0	transformers	[ "transformers", "gguf", "en", "base_model:maywell/PiVoT-SUS-RP", "base_model:quantized:maywell/PiVoT-SUS-RP", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-13T03:07:42Z	--- base_model: maywell/PiVoT-SUS-RP language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/maywell/PiVoT-SUS-RP <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/PiVoT-SUS-RP-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/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q2_K.gguf) \| Q2_K \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.IQ3_XS.gguf) \| IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q3_K_S.gguf) \| Q3_K_S \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.IQ3_S.gguf) \| IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.IQ3_M.gguf) \| IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q3_K_M.gguf) \| Q3_K_M \| 16.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q3_K_L.gguf) \| Q3_K_L \| 18.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.IQ4_XS.gguf) \| IQ4_XS \| 18.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q4_K_S.gguf) \| Q4_K_S \| 19.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q4_K_M.gguf) \| Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q5_K_S.gguf) \| Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q5_K_M.gguf) \| Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q6_K.gguf) \| Q6_K \| 28.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/PiVoT-SUS-RP-GGUF/resolve/main/PiVoT-SUS-RP.Q8_0.gguf) \| Q8_0 \| 36.6 \| 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. <!-- end -->
mradermacher/Pyrhea-72B-GGUF	mradermacher	2024-05-06T04:58:36Z	63	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "davidkim205/Rhea-72b-v0.5", "abacusai/Smaug-72B-v0.1", "en", "base_model:saucam/Pyrhea-72B", "base_model:quantized:saucam/Pyrhea-72B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-13T05:01:17Z	--- base_model: saucam/Pyrhea-72B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - davidkim205/Rhea-72b-v0.5 - abacusai/Smaug-72B-v0.1 --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/saucam/Pyrhea-72B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pyrhea-72B-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/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q2_K.gguf) \| Q2_K \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.IQ3_XS.gguf) \| IQ3_XS \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.IQ3_S.gguf) \| IQ3_S \| 31.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q3_K_S.gguf) \| Q3_K_S \| 31.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.IQ3_M.gguf) \| IQ3_M \| 33.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q3_K_M.gguf) \| Q3_K_M \| 35.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q3_K_L.gguf) \| Q3_K_L \| 38.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.IQ4_XS.gguf) \| IQ4_XS \| 39.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q4_K_S.gguf) \| Q4_K_S \| 41.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q4_K_M.gguf) \| Q4_K_M \| 43.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q5_K_S.gguf) \| Q5_K_S \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 51.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q6_K.gguf.part2of2) \| Q6_K \| 59.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pyrhea-72B-GGUF/resolve/main/Pyrhea-72B.Q8_0.gguf.part2of2) \| Q8_0 \| 76.9 \| 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. <!-- end -->
mradermacher/FashionGPT-70B-V1-i1-GGUF	mradermacher	2024-05-06T04:58:29Z	72	0	transformers	[ "transformers", "gguf", "en", "dataset:ehartford/samantha-data", "dataset:Open-Orca/OpenOrca", "dataset:jondurbin/airoboros-gpt4-1.4.1", "base_model:ICBU-NPU/FashionGPT-70B-V1", "base_model:quantized:ICBU-NPU/FashionGPT-70B-V1", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-13T10:49:37Z	--- base_model: ICBU-NPU/FashionGPT-70B-V1 datasets: - ehartford/samantha-data - Open-Orca/OpenOrca - jondurbin/airoboros-gpt4-1.4.1 language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/FashionGPT-70B-V1-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/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FashionGPT-70B-V1-i1-GGUF/resolve/main/FashionGPT-70B-V1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/MonaCeption-7B-SLERP-SFT-GGUF	mradermacher	2024-05-06T04:58:15Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:CultriX/MonaCeption-7B-SLERP-SFT", "base_model:quantized:CultriX/MonaCeption-7B-SLERP-SFT", "endpoints_compatible", "region:us" ]	null	2024-04-13T12:12:34Z	--- base_model: CultriX/MonaCeption-7B-SLERP-SFT language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/CultriX/MonaCeption-7B-SLERP-SFT <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MonaCeption-7B-SLERP-SFT-GGUF/resolve/main/MonaCeption-7B-SLERP-SFT.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/CreativeSmart-2x7B-GGUF	mradermacher	2024-05-06T04:58:07Z	21	1	transformers	[ "transformers", "gguf", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "Nexusflow/Starling-LM-7B-beta", "bunnycore/Chimera-Apex-7B", "en", "base_model:bunnycore/CreativeSmart-2x7B", "base_model:quantized:bunnycore/CreativeSmart-2x7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-13T14:05:37Z	--- base_model: bunnycore/CreativeSmart-2x7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - frankenmoe - merge - mergekit - lazymergekit - Nexusflow/Starling-LM-7B-beta - bunnycore/Chimera-Apex-7B --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/bunnycore/CreativeSmart-2x7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q4_K_M.gguf) \| Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q5_K_S.gguf) \| Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q5_K_M.gguf) \| Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q6_K.gguf) \| Q6_K \| 10.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q8_0.gguf) \| Q8_0 \| 13.8 \| 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. <!-- end -->
mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF	mradermacher	2024-05-06T04:57:56Z	88	0	transformers	[ "transformers", "gguf", "llama-2", "self-instruct", "distillation", "synthetic instruction", "en", "base_model:NousResearch/Nous-Hermes-Llama2-70b", "base_model:quantized:NousResearch/Nous-Hermes-Llama2-70b", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-13T18:27:11Z	--- base_model: NousResearch/Nous-Hermes-Llama2-70b language: - en library_name: transformers license: - mit quantized_by: mradermacher tags: - llama-2 - self-instruct - distillation - synthetic instruction --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/NousResearch/Nous-Hermes-Llama2-70b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-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/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Nous-Hermes-Llama2-70b-i1-GGUF/resolve/main/Nous-Hermes-Llama2-70b.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/Pyrhea-72B-i1-GGUF	mradermacher	2024-05-06T04:57:47Z	96	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "davidkim205/Rhea-72b-v0.5", "abacusai/Smaug-72B-v0.1", "en", "base_model:saucam/Pyrhea-72B", "base_model:quantized:saucam/Pyrhea-72B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-13T19:08:47Z	--- base_model: saucam/Pyrhea-72B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - davidkim205/Rhea-72b-v0.5 - abacusai/Smaug-72B-v0.1 --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/saucam/Pyrhea-72B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Pyrhea-72B-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/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 16.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 17.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 23.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 25.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q2_K.gguf) \| i1-Q2_K \| 27.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 33.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 35.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 38.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 41.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 43.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 51.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pyrhea-72B-i1-GGUF/resolve/main/Pyrhea-72B.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 59.4 \| 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. <!-- end -->
mradermacher/athene-noctua-13b-GGUF	mradermacher	2024-05-06T04:57:33Z	118	0	transformers	[ "transformers", "gguf", "logic", "reasoning", "en", "base_model:ibivibiv/athene-noctua-13b", "base_model:quantized:ibivibiv/athene-noctua-13b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-14T00:13:49Z	--- base_model: ibivibiv/athene-noctua-13b language: - en library_name: transformers license: llama2 quantized_by: mradermacher tags: - logic - reasoning --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ibivibiv/athene-noctua-13b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.IQ3_XS.gguf) \| IQ3_XS \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.IQ3_S.gguf) \| IQ3_S \| 5.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.IQ3_M.gguf) \| IQ3_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/athene-noctua-13b-GGUF/resolve/main/athene-noctua-13b.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. <!-- end -->
mradermacher/Mistral-22B-v0.2-GGUF	mradermacher	2024-05-06T04:57:26Z	31	1	transformers	[ "transformers", "gguf", "en", "base_model:Vezora/Mistral-22B-v0.2", "base_model:quantized:Vezora/Mistral-22B-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-14T00:33:11Z	--- base_model: Vezora/Mistral-22B-v0.2 language: - en library_name: transformers license: apache-2.0 no_imatrix: 'GGML_ASSERT: llama.cpp/ggml-quants.c:11239: grid_index >= 0' quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Vezora/Mistral-22B-v0.2 No imatrix quants will be coming from me, as the model overflowed after 180k tokens and llama.cpp crashed generating most quants with smaller training data. weighted/imatrix quants by bartowksi (with smaller training data) can be found at https://huggingface.co/bartowski/Mistral-22B-v0.2-GGUF <!-- provided-files --> ## 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/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q2_K.gguf) \| Q2_K \| 8.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.IQ3_S.gguf) \| IQ3_S \| 9.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.IQ3_M.gguf) \| IQ3_M \| 10.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 10.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 11.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 12.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 13.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 15.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 15.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q6_K.gguf) \| Q6_K \| 18.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-22B-v0.2-GGUF/resolve/main/Mistral-22B-v0.2.Q8_0.gguf) \| Q8_0 \| 23.7 \| 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. <!-- end -->
mradermacher/ValidateAI-3-33B-Ties-GGUF	mradermacher	2024-05-06T04:57:23Z	115	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "WizardLM/WizardCoder-33B-V1.1", "codefuse-ai/CodeFuse-DeepSeek-33B", "deepseek-ai/deepseek-coder-33b-instruct", "en", "base_model:arvindanand/ValidateAI-3-33B-Ties", "base_model:quantized:arvindanand/ValidateAI-3-33B-Ties", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-14T00:38:43Z	--- base_model: arvindanand/ValidateAI-3-33B-Ties language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - WizardLM/WizardCoder-33B-V1.1 - codefuse-ai/CodeFuse-DeepSeek-33B - deepseek-ai/deepseek-coder-33b-instruct --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/arvindanand/ValidateAI-3-33B-Ties <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q2_K.gguf) \| Q2_K \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.IQ3_XS.gguf) \| IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.IQ3_S.gguf) \| IQ3_S \| 14.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.IQ3_M.gguf) \| IQ3_M \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q3_K_M.gguf) \| Q3_K_M \| 16.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q3_K_L.gguf) \| Q3_K_L \| 17.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.IQ4_XS.gguf) \| IQ4_XS \| 18.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q4_K_S.gguf) \| Q4_K_S \| 19.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q5_K_S.gguf) \| Q5_K_S \| 23.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q5_K_M.gguf) \| Q5_K_M \| 23.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q6_K.gguf) \| Q6_K \| 27.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ValidateAI-3-33B-Ties-GGUF/resolve/main/ValidateAI-3-33B-Ties.Q8_0.gguf) \| Q8_0 \| 35.5 \| 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. <!-- end -->
mradermacher/rubra-11h-orpo-GGUF	mradermacher	2024-05-06T04:57:20Z	21	0	transformers	[ "transformers", "gguf", "en", "base_model:yingbei/rubra-11h-orpo", "base_model:quantized:yingbei/rubra-11h-orpo", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-14T02:25:49Z	--- base_model: yingbei/rubra-11h-orpo language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/yingbei/rubra-11h-orpo <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.IQ3_XS.gguf) \| IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.IQ3_S.gguf) \| IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.IQ3_M.gguf) \| IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/rubra-11h-orpo-GGUF/resolve/main/rubra-11h-orpo.Q8_0.gguf) \| Q8_0 \| 11.5 \| 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. <!-- end -->
mradermacher/vidalet-alpha-GGUF	mradermacher	2024-05-06T04:57:17Z	23	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-14T03:38:19Z	--- base_model: MarcOrfilaCarreras/vidalet-alpha language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/MarcOrfilaCarreras/vidalet-alpha <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q2_K.gguf) \| Q2_K \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.IQ3_XS.gguf) \| IQ3_XS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q3_K_S.gguf) \| Q3_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.IQ3_S.gguf) \| IQ3_S \| 1.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.IQ3_M.gguf) \| IQ3_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q3_K_M.gguf) \| Q3_K_M \| 1.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q3_K_L.gguf) \| Q3_K_L \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.IQ4_XS.gguf) \| IQ4_XS \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q4_K_S.gguf) \| Q4_K_S \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q4_K_M.gguf) \| Q4_K_M \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q5_K_S.gguf) \| Q5_K_S \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q5_K_M.gguf) \| Q5_K_M \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q6_K.gguf) \| Q6_K \| 2.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/vidalet-alpha-GGUF/resolve/main/vidalet-alpha.Q8_0.gguf) \| Q8_0 \| 2.8 \| 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. <!-- end -->
mradermacher/OpenCerebrum-2.0-7B-GGUF	mradermacher	2024-05-06T04:57:07Z	31	0	transformers	[ "transformers", "gguf", "open-source", "code", "math", "chemistry", "biology", "text-generation", "question-answering", "en", "base_model:Locutusque/OpenCerebrum-2.0-7B", "base_model:quantized:Locutusque/OpenCerebrum-2.0-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-04-14T04:08:05Z	--- base_model: Locutusque/OpenCerebrum-2.0-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - open-source - code - math - chemistry - biology - text-generation - question-answering --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Locutusque/OpenCerebrum-2.0-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenCerebrum-2.0-7B-GGUF/resolve/main/OpenCerebrum-2.0-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/OG-SQL-7B-GGUF	mradermacher	2024-05-06T04:56:58Z	19	0	transformers	[ "transformers", "gguf", "Text-to-sql", "en", "base_model:OneGate/OG-SQL-7B", "base_model:quantized:OneGate/OG-SQL-7B", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-14T07:17:11Z	--- base_model: OneGate/OG-SQL-7B language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher tags: - Text-to-sql --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/OneGate/OG-SQL-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OG-SQL-7B-GGUF/resolve/main/OG-SQL-7B.Q8_0.gguf) \| Q8_0 \| 7.3 \| 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. <!-- end -->
isaaclee/witness_count_mistral_train_run3	isaaclee	2024-05-06T04:56:54Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-06T02:31:46Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: witness_count_mistral_train_run3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # witness_count_mistral_train_run3 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF	mradermacher	2024-05-06T04:56:50Z	90	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "chemistry", "biology", "climate", "science", "philosophy", "nature", "ecology", "biomimicry", "fauna", "flora", "en", "dataset:Severian/Biomimicry", "dataset:emrgnt-cmplxty/sciphi-textbooks-are-all-you-need", "dataset:fmars/wiki_stem", "dataset:fblgit/tree-of-knowledge", "dataset:Severian/Bio-Design-Process", "base_model:Joseph717171/ANIMA-Phi-Neptune-Mistral-10.7B", "base_model:quantized:Joseph717171/ANIMA-Phi-Neptune-Mistral-10.7B", "license:artistic-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-14T08:05:55Z	--- base_model: Joseph717171/ANIMA-Phi-Neptune-Mistral-10.7B datasets: - Severian/Biomimicry - emrgnt-cmplxty/sciphi-textbooks-are-all-you-need - fmars/wiki_stem - fblgit/tree-of-knowledge - Severian/Bio-Design-Process language: - en library_name: transformers license: artistic-2.0 quantized_by: mradermacher tags: - mergekit - merge - chemistry - biology - climate - science - philosophy - nature - ecology - biomimicry - fauna - flora --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Joseph717171/ANIMA-Phi-Neptune-Mistral-10.7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.IQ3_XS.gguf) \| IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.IQ3_S.gguf) \| IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.IQ3_M.gguf) \| IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ANIMA-Phi-Neptune-Mistral-10.7B-GGUF/resolve/main/ANIMA-Phi-Neptune-Mistral-10.7B.Q8_0.gguf) \| Q8_0 \| 11.5 \| 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. <!-- end -->
mradermacher/aegolius-acadicus-34b-v3-GGUF	mradermacher	2024-05-06T04:56:38Z	67	0	transformers	[ "transformers", "gguf", "moe", "en", "base_model:ibivibiv/aegolius-acadicus-34b-v3", "base_model:quantized:ibivibiv/aegolius-acadicus-34b-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-14T09:27:12Z	--- base_model: ibivibiv/aegolius-acadicus-34b-v3 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ibivibiv/aegolius-acadicus-34b-v3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-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/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q2_K.gguf) \| Q2_K \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.IQ3_XS.gguf) \| IQ3_XS \| 14.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q3_K_S.gguf) \| Q3_K_S \| 15.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.IQ3_S.gguf) \| IQ3_S \| 15.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.IQ3_M.gguf) \| IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q3_K_M.gguf) \| Q3_K_M \| 17.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q3_K_L.gguf) \| Q3_K_L \| 18.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.IQ4_XS.gguf) \| IQ4_XS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q4_K_S.gguf) \| Q4_K_S \| 20.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q4_K_M.gguf) \| Q4_K_M \| 21.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q5_K_S.gguf) \| Q5_K_S \| 24.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q5_K_M.gguf) \| Q5_K_M \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q6_K.gguf) \| Q6_K \| 29.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/aegolius-acadicus-34b-v3-GGUF/resolve/main/aegolius-acadicus-34b-v3.Q8_0.gguf) \| Q8_0 \| 37.7 \| 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. <!-- end -->
mradermacher/Narwhal-7b-GGUF	mradermacher	2024-05-06T04:56:18Z	130	0	transformers	[ "transformers", "gguf", "llama", "orca", "stable", "stability", "bloke", "hf", "7b", "13b", "34b", "70b", "22b", "60b", "coding", "progaming", "logic", "deduction", "en", "base_model:Vezora/Narwhal-7b", "base_model:quantized:Vezora/Narwhal-7b", "endpoints_compatible", "region:us" ]	null	2024-04-14T13:29:39Z	--- base_model: Vezora/Narwhal-7b language: - en library_name: transformers quantized_by: mradermacher tags: - llama - orca - stable - stability - bloke - hf - 7b - 13b - 34b - 70b - 22b - 60b - coding - progaming - logic - deduction --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Vezora/Narwhal-7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Narwhal-7b-GGUF/resolve/main/Narwhal-7b.Q8_0.gguf) \| Q8_0 \| 7.3 \| 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. <!-- end -->
mradermacher/HBDN-MoE-4x7B-GGUF	mradermacher	2024-05-06T04:56:01Z	25	0	transformers	[ "transformers", "gguf", "en", "base_model:NeuroDonu/HBDN-MoE-4x7B", "base_model:quantized:NeuroDonu/HBDN-MoE-4x7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-14T15:44:51Z	--- base_model: NeuroDonu/HBDN-MoE-4x7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/NeuroDonu/HBDN-MoE-4x7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q2_K.gguf) \| Q2_K \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.IQ3_XS.gguf) \| IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.IQ3_S.gguf) \| IQ3_S \| 10.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.IQ3_M.gguf) \| IQ3_M \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q3_K_M.gguf) \| Q3_K_M \| 11.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q3_K_L.gguf) \| Q3_K_L \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.IQ4_XS.gguf) \| IQ4_XS \| 13.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q4_K_S.gguf) \| Q4_K_S \| 13.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q4_K_M.gguf) \| Q4_K_M \| 14.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q5_K_S.gguf) \| Q5_K_S \| 16.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q5_K_M.gguf) \| Q5_K_M \| 17.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q6_K.gguf) \| Q6_K \| 19.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/HBDN-MoE-4x7B-GGUF/resolve/main/HBDN-MoE-4x7B.Q8_0.gguf) \| Q8_0 \| 25.8 \| 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. <!-- end -->
mradermacher/34b-beta2-GGUF	mradermacher	2024-05-06T04:55:58Z	35	2	transformers	[ "transformers", "gguf", "en", "zh", "base_model:CausalLM/34b-beta2", "base_model:quantized:CausalLM/34b-beta2", "license:gpl-3.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-14T16:25:51Z	--- base_model: CausalLM/34b-beta2 language: - en - zh library_name: transformers license: gpl-3.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/CausalLM/34b-beta2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/34b-beta2-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/34b-beta2-GGUF/resolve/main/34b-beta2.Q2_K.gguf) \| Q2_K \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.IQ3_XS.gguf) \| IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q3_K_S.gguf) \| Q3_K_S \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.IQ3_S.gguf) \| IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.IQ3_M.gguf) \| IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q3_K_M.gguf) \| Q3_K_M \| 16.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q3_K_L.gguf) \| Q3_K_L \| 18.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.IQ4_XS.gguf) \| IQ4_XS \| 18.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q4_K_S.gguf) \| Q4_K_S \| 19.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q4_K_M.gguf) \| Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q5_K_S.gguf) \| Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q5_K_M.gguf) \| Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q6_K.gguf) \| Q6_K \| 28.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/34b-beta2-GGUF/resolve/main/34b-beta2.Q8_0.gguf) \| Q8_0 \| 36.6 \| 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. <!-- end -->
appvoid/merging-x3	appvoid	2024-05-06T04:55:33Z	139	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:merge:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:appvoid/palmer-002", "base_model:merge:appvoid/palmer-002", "base_model:appvoid/palmer-003", "base_model:merge:appvoid/palmer-003", "base_model:vihangd/DopeyTinyLlama-1.1B-v1", "base_model:merge:vihangd/DopeyTinyLlama-1.1B-v1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T04:51:03Z	--- base_model: - vihangd/DopeyTinyLlama-1.1B-v1 - TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T - appvoid/palmer-002 - appvoid/palmer-003 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [vihangd/DopeyTinyLlama-1.1B-v1](https://huggingface.co/vihangd/DopeyTinyLlama-1.1B-v1) * [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) * [appvoid/palmer-002](https://huggingface.co/appvoid/palmer-002) * [appvoid/palmer-003](https://huggingface.co/appvoid/palmer-003) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: appvoid/palmer-002 layer_range: [0, 5] - sources: - model: appvoid/palmer-003 layer_range: [3, 10] - sources: - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T layer_range: [7, 15] - sources: - model: vihangd/DopeyTinyLlama-1.1B-v1 layer_range: [11, 20] - sources: - model: appvoid/palmer-003 layer_range: [15, 21] merge_method: passthrough dtype: float16 ```
mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF	mradermacher	2024-05-06T04:55:29Z	30	0	transformers	[ "transformers", "gguf", "Riiid", "llama-2", "sheep-duck-llama-2", "en", "base_model:Riiid/sheep-duck-llama-2-70b-v1.1", "base_model:quantized:Riiid/sheep-duck-llama-2-70b-v1.1", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-14T21:46:13Z	--- base_model: Riiid/sheep-duck-llama-2-70b-v1.1 language: - en library_name: transformers license: llama2 quantized_by: mradermacher tags: - Riiid - llama-2 - sheep-duck-llama-2 --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-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/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| 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. <!-- end -->
smit2911/results	smit2911	2024-05-06T04:55:29Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-06T04:51:54Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/trootech/Fine%20tuning%20mistral%207B/runs/rniuh99d) # results This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.19.0 - Tokenizers 0.19.1
mradermacher/stairolz-70b-GGUF	mradermacher	2024-05-06T04:55:27Z	25	0	transformers	[ "transformers", "gguf", "en", "base_model:uncensorie/stairolz-70b", "base_model:quantized:uncensorie/stairolz-70b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-14T23:03:23Z	--- base_model: uncensorie/stairolz-70b language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/uncensorie/stairolz-70b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/stairolz-70b-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/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/stairolz-70b-GGUF/resolve/main/stairolz-70b.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| 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. <!-- end -->
mradermacher/NeuralStockFusion-7b-GGUF	mradermacher	2024-05-06T04:55:21Z	44	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Kukedlc/NeuralStockFusion-7b", "base_model:quantized:Kukedlc/NeuralStockFusion-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-14T23:15:19Z	--- base_model: Kukedlc/NeuralStockFusion-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Kukedlc/NeuralStockFusion-7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/llama-65b-instruct-GGUF	mradermacher	2024-05-06T04:55:07Z	24	0	transformers	[ "transformers", "gguf", "upstage", "llama", "instruct", "instruction", "en", "base_model:upstage/llama-65b-instruct", "base_model:quantized:upstage/llama-65b-instruct", "endpoints_compatible", "region:us" ]	null	2024-04-14T23:39:28Z	--- base_model: upstage/llama-65b-instruct language: - en library_name: transformers quantized_by: mradermacher tags: - upstage - llama - instruct - instruction --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/upstage/llama-65b-instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/llama-65b-instruct-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-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q2_K.gguf) \| Q2_K \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.IQ3_XS.gguf) \| IQ3_XS \| 26.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.IQ3_S.gguf) \| IQ3_S \| 28.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q3_K_S.gguf) \| Q3_K_S \| 28.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.IQ3_M.gguf) \| IQ3_M \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q3_K_M.gguf) \| Q3_K_M \| 31.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q3_K_L.gguf) \| Q3_K_L \| 34.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.IQ4_XS.gguf) \| IQ4_XS \| 35.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q4_K_S.gguf) \| Q4_K_S \| 37.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q4_K_M.gguf) \| Q4_K_M \| 39.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q5_K_S.gguf) \| Q5_K_S \| 45.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q5_K_M.gguf) \| Q5_K_M \| 46.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q6_K.gguf.part2of2) \| Q6_K \| 53.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-65b-instruct-GGUF/resolve/main/llama-65b-instruct.Q8_0.gguf.part2of2) \| Q8_0 \| 69.5 \| 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. <!-- end -->
mradermacher/K2S3-Mistral-7b-v1.48-GGUF	mradermacher	2024-05-06T04:54:27Z	5	0	transformers	[ "transformers", "gguf", "en", "ko", "base_model:Changgil/K2S3-Mistral-7b-v1.48", "base_model:quantized:Changgil/K2S3-Mistral-7b-v1.48", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-15T07:19:31Z	--- base_model: Changgil/K2S3-Mistral-7b-v1.48 language: - en - ko library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Changgil/K2S3-Mistral-7b-v1.48 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q2_K.gguf) \| Q2_K \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.IQ3_XS.gguf) \| IQ3_XS \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q3_K_M.gguf) \| Q3_K_M \| 3.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q3_K_L.gguf) \| Q3_K_L \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.IQ4_XS.gguf) \| IQ4_XS \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q4_K_S.gguf) \| Q4_K_S \| 4.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q5_K_S.gguf) \| Q5_K_S \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q5_K_M.gguf) \| Q5_K_M \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q6_K.gguf) \| Q6_K \| 6.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.48-GGUF/resolve/main/K2S3-Mistral-7b-v1.48.Q8_0.gguf) \| Q8_0 \| 7.9 \| 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. <!-- end -->
mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF	mradermacher	2024-05-06T04:54:10Z	33	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-15T09:35:27Z	--- base_model: LeroyDyer/Mixtral_AI_CyberTron_Ultra_SFT language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_CyberTron_Ultra_SFT <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberTron_Ultra_SFT-GGUF/resolve/main/Mixtral_AI_CyberTron_Ultra_SFT.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Einstein_x_Dolphin-GGUF	mradermacher	2024-05-06T04:54:03Z	5	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:bingbort/Einstein_x_Dolphin", "base_model:quantized:bingbort/Einstein_x_Dolphin", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-15T10:35:58Z	--- base_model: bingbort/Einstein_x_Dolphin language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/bingbort/Einstein_x_Dolphin <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Einstein_x_Dolphin-GGUF/resolve/main/Einstein_x_Dolphin.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/deepmoney-67b-chat-GGUF	mradermacher	2024-05-06T04:54:01Z	123	0	transformers	[ "transformers", "gguf", "en", "dataset:TriadParty/deepmoney-sft", "base_model:TriadParty/deepmoney-67b-chat", "base_model:quantized:TriadParty/deepmoney-67b-chat", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-15T10:52:11Z	--- base_model: TriadParty/deepmoney-67b-chat datasets: - TriadParty/deepmoney-sft language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TriadParty/deepmoney-67b-chat <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q2_K.gguf) \| Q2_K \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.IQ3_XS.gguf) \| IQ3_XS \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q3_K_S.gguf) \| Q3_K_S \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.IQ3_S.gguf) \| IQ3_S \| 29.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.IQ3_M.gguf) \| IQ3_M \| 30.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q3_K_M.gguf) \| Q3_K_M \| 32.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q3_K_L.gguf) \| Q3_K_L \| 35.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.IQ4_XS.gguf) \| IQ4_XS \| 36.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q4_K_S.gguf) \| Q4_K_S \| 38.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q4_K_M.gguf) \| Q4_K_M \| 40.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q5_K_S.gguf) \| Q5_K_S \| 46.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q5_K_M.gguf) \| Q5_K_M \| 47.8 \| \| \| [PART 1](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q6_K.gguf.part2of2) \| Q6_K \| 55.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/deepmoney-67b-chat-GGUF/resolve/main/deepmoney-67b-chat.Q8_0.gguf.part2of2) \| Q8_0 \| 71.7 \| 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. <!-- end -->
mradermacher/StableBeluga2-i1-GGUF	mradermacher	2024-05-06T04:53:57Z	96	0	transformers	[ "transformers", "gguf", "en", "dataset:conceptofmind/cot_submix_original", "dataset:conceptofmind/flan2021_submix_original", "dataset:conceptofmind/t0_submix_original", "dataset:conceptofmind/niv2_submix_original", "base_model:stabilityai/StableBeluga2", "base_model:quantized:stabilityai/StableBeluga2", "endpoints_compatible", "region:us" ]	null	2024-04-15T12:50:41Z	--- base_model: stabilityai/StableBeluga2 datasets: - conceptofmind/cot_submix_original - conceptofmind/flan2021_submix_original - conceptofmind/t0_submix_original - conceptofmind/niv2_submix_original language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/stabilityai/StableBeluga2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/StableBeluga2-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/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/Rava-2x7B-v0.1-GGUF	mradermacher	2024-05-06T04:53:49Z	4	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-15T17:23:17Z	--- base_model: Novin-AI/Rava-2x7B-v0.1 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Novin-AI/Rava-2x7B-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q6_K.gguf) \| Q6_K \| 10.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Rava-2x7B-v0.1-GGUF/resolve/main/Rava-2x7B-v0.1.Q8_0.gguf) \| Q8_0 \| 13.8 \| 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. <!-- end -->
mradermacher/Rava-3x7B-v0.1-GGUF	mradermacher	2024-05-06T04:53:44Z	2	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-15T20:14:16Z	--- base_model: Novin-AI/Rava-3x7B-v0.1 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Novin-AI/Rava-3x7B-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q2_K.gguf) \| Q2_K \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.IQ3_S.gguf) \| IQ3_S \| 8.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.IQ3_M.gguf) \| IQ3_M \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 9.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 10.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 11.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 12.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 13.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q6_K.gguf) \| Q6_K \| 15.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Rava-3x7B-v0.1-GGUF/resolve/main/Rava-3x7B-v0.1.Q8_0.gguf) \| Q8_0 \| 19.8 \| 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. <!-- end -->
mradermacher/kaori-34b-v4-GGUF	mradermacher	2024-05-06T04:53:34Z	4	0	transformers	[ "transformers", "gguf", "en", "base_model:KaeriJenti/kaori-34b-v4", "base_model:quantized:KaeriJenti/kaori-34b-v4", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-15T23:58:47Z	--- base_model: KaeriJenti/kaori-34b-v4 language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/KaeriJenti/kaori-34b-v4 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/kaori-34b-v4-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/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q2_K.gguf) \| Q2_K \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.IQ3_XS.gguf) \| IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q3_K_S.gguf) \| Q3_K_S \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.IQ3_S.gguf) \| IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.IQ3_M.gguf) \| IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q3_K_M.gguf) \| Q3_K_M \| 16.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q3_K_L.gguf) \| Q3_K_L \| 18.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.IQ4_XS.gguf) \| IQ4_XS \| 18.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q4_K_S.gguf) \| Q4_K_S \| 19.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q4_K_M.gguf) \| Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q5_K_S.gguf) \| Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q5_K_M.gguf) \| Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q6_K.gguf) \| Q6_K \| 28.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-GGUF/resolve/main/kaori-34b-v4.Q8_0.gguf) \| Q8_0 \| 36.6 \| 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. <!-- end -->
mradermacher/chronob-1.4-lin-70b-GGUF	mradermacher	2024-05-06T04:53:18Z	1	0	transformers	[ "transformers", "gguf", "en", "base_model:uncensorie/chronob-1.4-lin-70b", "base_model:quantized:uncensorie/chronob-1.4-lin-70b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-16T01:54:51Z	--- base_model: uncensorie/chronob-1.4-lin-70b language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/uncensorie/chronob-1.4-lin-70b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/chronob-1.4-lin-70b-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/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-GGUF/resolve/main/chronob-1.4-lin-70b.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| 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. <!-- end -->
mradermacher/kaori-34b-v4-i1-GGUF	mradermacher	2024-05-06T04:53:15Z	14	0	transformers	[ "transformers", "gguf", "en", "base_model:KaeriJenti/kaori-34b-v4", "base_model:quantized:KaeriJenti/kaori-34b-v4", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-16T01:55:21Z	--- base_model: KaeriJenti/kaori-34b-v4 language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/KaeriJenti/kaori-34b-v4 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/kaori-34b-v4-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/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ2_S.gguf) \| i1-IQ2_S \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q2_K.gguf) \| i1-Q2_K \| 12.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 13.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 15.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ3_S.gguf) \| i1-IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ3_M.gguf) \| i1-IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 18.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 18.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q4_0.gguf) \| i1-Q4_0 \| 19.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 19.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-34b-v4-i1-GGUF/resolve/main/kaori-34b-v4.i1-Q6_K.gguf) \| i1-Q6_K \| 28.3 \| 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. <!-- end -->
mradermacher/NeuralSOTA-7B-slerp-GGUF	mradermacher	2024-05-06T04:52:53Z	6	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralSoTa-7b-v0.1", "Kukedlc/NeuralSynthesis-7B-v0.3", "Kukedlc/NeuralSirKrishna-7b", "en", "base_model:Kukedlc/NeuralSOTA-7B-slerp", "base_model:quantized:Kukedlc/NeuralSOTA-7B-slerp", "endpoints_compatible", "region:us" ]	null	2024-04-16T05:40:06Z	--- base_model: Kukedlc/NeuralSOTA-7B-slerp language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralSoTa-7b-v0.1 - Kukedlc/NeuralSynthesis-7B-v0.3 - Kukedlc/NeuralSirKrishna-7b --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Kukedlc/NeuralSOTA-7B-slerp <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Inspire-7B-slerp-GGUF	mradermacher	2024-05-06T04:52:51Z	4	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:tvkkishore/Inspire-7B-slerp", "base_model:quantized:tvkkishore/Inspire-7B-slerp", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-16T06:08:27Z	--- base_model: tvkkishore/Inspire-7B-slerp language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/tvkkishore/Inspire-7B-slerp <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Chimera-7B-TIES-GGUF	mradermacher	2024-05-06T04:52:43Z	37	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "DreadPoor/Siren-7B-slerp", "S-miguel/The-Trinity-Coder-7B", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-16T07:05:14Z	--- base_model: DreadPoor/Chimera-7B-TIES language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - DreadPoor/Siren-7B-slerp - S-miguel/The-Trinity-Coder-7B --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/DreadPoor/Chimera-7B-TIES <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Chimera-7B-TIES-GGUF/resolve/main/Chimera-7B-TIES.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
appvoid/merging-x2	appvoid	2024-05-06T04:49:36Z	141	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:merge:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:appvoid/palmer-002", "base_model:merge:appvoid/palmer-002", "base_model:appvoid/palmer-003", "base_model:merge:appvoid/palmer-003", "base_model:vihangd/DopeyTinyLlama-1.1B-v1", "base_model:merge:vihangd/DopeyTinyLlama-1.1B-v1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T04:47:33Z	--- base_model: - appvoid/palmer-002 - TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T - vihangd/DopeyTinyLlama-1.1B-v1 - appvoid/palmer-003 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [appvoid/palmer-002](https://huggingface.co/appvoid/palmer-002) * [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) * [vihangd/DopeyTinyLlama-1.1B-v1](https://huggingface.co/vihangd/DopeyTinyLlama-1.1B-v1) * [appvoid/palmer-003](https://huggingface.co/appvoid/palmer-003) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: appvoid/palmer-002 layer_range: [0, 5] - sources: - model: appvoid/palmer-003 layer_range: [4, 10] - sources: - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T layer_range: [8, 15] - sources: - model: vihangd/DopeyTinyLlama-1.1B-v1 layer_range: [12, 20] - sources: - model: appvoid/palmer-003 layer_range: [16, 21] merge_method: passthrough dtype: float16 ```
Kimty/Sqlcoder_v3	Kimty	2024-05-06T04:46:53Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T04:43:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/Michel-13B-GGUF	mradermacher	2024-05-06T04:44:04Z	193	0	transformers	[ "transformers", "gguf", "en", "base_model:PotatoOff/Michel-13B", "base_model:quantized:PotatoOff/Michel-13B", "license:agpl-3.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-16T07:06:16Z	--- base_model: PotatoOff/Michel-13B language: - en library_name: transformers license: agpl-3.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/PotatoOff/Michel-13B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Michel-13B-GGUF/resolve/main/Michel-13B.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ3_XS.gguf) \| IQ3_XS \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ3_S.gguf) \| IQ3_S \| 5.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ3_M.gguf) \| IQ3_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Michel-13B-GGUF/resolve/main/Michel-13B.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. <!-- end -->
mradermacher/SeaMax-7B-GGUF	mradermacher	2024-05-06T04:43:44Z	43	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mpasila/SeaMax-7B", "base_model:quantized:mpasila/SeaMax-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-16T13:43:30Z	--- base_model: mpasila/SeaMax-7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mpasila/SeaMax-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SeaMax-7B-GGUF/resolve/main/SeaMax-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/kaori-72b-v1-i1-GGUF	mradermacher	2024-05-06T04:43:33Z	2	0	transformers	[ "transformers", "gguf", "en", "base_model:KaeriJenti/kaori-72b-v1", "base_model:quantized:KaeriJenti/kaori-72b-v1", "license:unknown", "endpoints_compatible", "region:us" ]	null	2024-04-16T17:29:26Z	--- base_model: KaeriJenti/kaori-72b-v1 language: - en library_name: transformers license: unknown quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/KaeriJenti/kaori-72b-v1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/kaori-72b-v1-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/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 17.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 26.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 28.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 30.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 34.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 36.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 39.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 41.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 45.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 53.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/kaori-72b-v1-i1-GGUF/resolve/main/kaori-72b-v1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 59.4 \| 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. <!-- end -->
mradermacher/mergekit-slerp-exkkzvd-GGUF	mradermacher	2024-05-06T04:43:31Z	2	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mergekit-community/mergekit-slerp-exkkzvd", "base_model:quantized:mergekit-community/mergekit-slerp-exkkzvd", "endpoints_compatible", "region:us" ]	null	2024-04-16T17:36:21Z	--- base_model: mergekit-community/mergekit-slerp-exkkzvd language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mergekit-community/mergekit-slerp-exkkzvd <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-slerp-exkkzvd-GGUF/resolve/main/mergekit-slerp-exkkzvd.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Lumina-3.5-GGUF	mradermacher	2024-05-06T04:43:23Z	39	0	transformers	[ "transformers", "gguf", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "en", "base_model:Ppoyaa/Lumina-3.5", "base_model:quantized:Ppoyaa/Lumina-3.5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-16T18:40:36Z	--- base_model: Ppoyaa/Lumina-3.5 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - frankenmoe - merge - mergekit - lazymergekit --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Ppoyaa/Lumina-3.5 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q2_K.gguf) \| Q2_K \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.IQ3_XS.gguf) \| IQ3_XS \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q3_K_S.gguf) \| Q3_K_S \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.IQ3_S.gguf) \| IQ3_S \| 8.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.IQ3_M.gguf) \| IQ3_M \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q3_K_M.gguf) \| Q3_K_M \| 9.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q3_K_L.gguf) \| Q3_K_L \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.IQ4_XS.gguf) \| IQ4_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q4_K_S.gguf) \| Q4_K_S \| 10.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q4_K_M.gguf) \| Q4_K_M \| 11.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q5_K_S.gguf) \| Q5_K_S \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q5_K_M.gguf) \| Q5_K_M \| 13.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q6_K.gguf) \| Q6_K \| 15.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF/resolve/main/Lumina-3.5.Q8_0.gguf) \| Q8_0 \| 19.8 \| 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. <!-- end -->
mradermacher/mergekit-ties-vjlpsxw-GGUF	mradermacher	2024-05-06T04:43:20Z	19	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mergekit-community/mergekit-ties-vjlpsxw", "base_model:quantized:mergekit-community/mergekit-ties-vjlpsxw", "endpoints_compatible", "region:us" ]	null	2024-04-16T19:30:06Z	--- base_model: mergekit-community/mergekit-ties-vjlpsxw language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mergekit-community/mergekit-ties-vjlpsxw <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mergekit-ties-vjlpsxw-GGUF/resolve/main/mergekit-ties-vjlpsxw.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Neversleep-11B-v0.1-GGUF	mradermacher	2024-05-06T04:43:09Z	4	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-16T22:47:53Z	--- base_model: crimsonjoo/Neversleep-11B-v0.1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/crimsonjoo/Neversleep-11B-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.IQ3_S.gguf) \| IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.IQ3_M.gguf) \| IQ3_M \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 6.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q6_K.gguf) \| Q6_K \| 9.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Neversleep-11B-v0.1-GGUF/resolve/main/Neversleep-11B-v0.1.Q8_0.gguf) \| Q8_0 \| 11.6 \| 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. <!-- end -->
mradermacher/NSK-128k-7B-slerp-GGUF	mradermacher	2024-05-06T04:43:06Z	44	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Nitral-AI/Nyan-Stunna-7B", "Nitral-AI/Kunocchini-7b-128k-test", "128k", "en", "base_model:AlekseiPravdin/NSK-128k-7B-slerp", "base_model:quantized:AlekseiPravdin/NSK-128k-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-16T23:27:35Z	--- base_model: AlekseiPravdin/NSK-128k-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Nitral-AI/Nyan-Stunna-7B - Nitral-AI/Kunocchini-7b-128k-test - 128k --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/AlekseiPravdin/NSK-128k-7B-slerp <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NSK-128k-7B-slerp-GGUF/resolve/main/NSK-128k-7B-slerp.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
rahul9699/wav2vec2-base-gig-demo-colab	rahul9699	2024-05-06T04:42:51Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-25T05:19:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/MeowGPT-3.5-GGUF	mradermacher	2024-05-06T04:42:45Z	91	0	transformers	[ "transformers", "gguf", "freeai", "conversational", "meowgpt", "gpt", "free", "opensource", "splittic", "ai", "en", "base_model:cutycat2000x/MeowGPT-3.5", "base_model:quantized:cutycat2000x/MeowGPT-3.5", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-17T03:15:54Z	--- base_model: cutycat2000x/MeowGPT-3.5 language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - freeai - conversational - meowgpt - gpt - free - opensource - splittic - ai --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/cutycat2000x/MeowGPT-3.5 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MeowGPT-3.5-GGUF/resolve/main/MeowGPT-3.5.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Wiz2Beagle-7b-v1-GGUF	mradermacher	2024-05-06T04:42:35Z	53	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "vortexmergekit", "amazingvince/Not-WizardLM-2-7B", "mlabonne/NeuralBeagle14-7B", "en", "base_model:eldogbbhed/Wiz2Beagle-7b-v1", "base_model:quantized:eldogbbhed/Wiz2Beagle-7b-v1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-17T05:25:21Z	--- base_model: eldogbbhed/Wiz2Beagle-7b-v1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - vortexmergekit - amazingvince/Not-WizardLM-2-7B - mlabonne/NeuralBeagle14-7B --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/eldogbbhed/Wiz2Beagle-7b-v1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Wiz2Beagle-7b-v1-GGUF/resolve/main/Wiz2Beagle-7b-v1.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/MoMo-70B-V1.1-i1-GGUF	mradermacher	2024-05-06T04:42:33Z	13	0	transformers	[ "transformers", "gguf", "en", "base_model:bongchoi/MoMo-70B-V1.1", "base_model:quantized:bongchoi/MoMo-70B-V1.1", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-17T05:37:47Z	--- base_model: bongchoi/MoMo-70B-V1.1 language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/bongchoi/MoMo-70B-V1.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MoMo-70B-V1.1-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/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MoMo-70B-V1.1-i1-GGUF/resolve/main/MoMo-70B-V1.1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/Dolph-Lund-Wizard-7B-GGUF	mradermacher	2024-05-06T04:42:23Z	50	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Noodlz/Dolph-Lund-Wizard-7B", "base_model:quantized:Noodlz/Dolph-Lund-Wizard-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-17T10:46:42Z	--- base_model: Noodlz/Dolph-Lund-Wizard-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Noodlz/Dolph-Lund-Wizard-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Dolph-Lund-Wizard-7B-GGUF/resolve/main/Dolph-Lund-Wizard-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/WizardLaker-7B-GGUF	mradermacher	2024-05-06T04:42:20Z	512	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Noodlz/WizardLaker-7B", "base_model:quantized:Noodlz/WizardLaker-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-17T10:56:45Z	--- base_model: Noodlz/WizardLaker-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Noodlz/WizardLaker-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLaker-7B-GGUF/resolve/main/WizardLaker-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/CeptrixBeagle-12B-MoE-GGUF	mradermacher	2024-05-06T04:42:17Z	75	0	transformers	[ "transformers", "gguf", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "allknowingroger/NeuralCeptrix-7B-slerp", "paulml/OmniBeagleSquaredMBX-v3-7B", "en", "base_model:allknowingroger/CeptrixBeagle-12B-MoE", "base_model:quantized:allknowingroger/CeptrixBeagle-12B-MoE", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-17T11:24:00Z	--- base_model: allknowingroger/CeptrixBeagle-12B-MoE language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - frankenmoe - merge - mergekit - lazymergekit - allknowingroger/NeuralCeptrix-7B-slerp - paulml/OmniBeagleSquaredMBX-v3-7B --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/allknowingroger/CeptrixBeagle-12B-MoE <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q4_K_M.gguf) \| Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q5_K_S.gguf) \| Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q5_K_M.gguf) \| Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q6_K.gguf) \| Q6_K \| 10.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/CeptrixBeagle-12B-MoE-GGUF/resolve/main/CeptrixBeagle-12B-MoE.Q8_0.gguf) \| Q8_0 \| 13.8 \| 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. <!-- end -->
mradermacher/MonaTrix-v4-7B-DPO-GGUF	mradermacher	2024-05-06T04:42:07Z	6	0	transformers	[ "transformers", "gguf", "en", "base_model:CultriX/MonaTrix-v4-7B-DPO", "base_model:quantized:CultriX/MonaTrix-v4-7B-DPO", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-17T16:10:46Z	--- base_model: CultriX/MonaTrix-v4-7B-DPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/CultriX/MonaTrix-v4-7B-DPO <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MonaTrix-v4-7B-DPO-GGUF/resolve/main/MonaTrix-v4-7B-DPO.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/KSI-RP-NSK-128k-7B-GGUF	mradermacher	2024-05-06T04:41:50Z	31	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "AlekseiPravdin/KukulStanta-InfinityRP-7B-slerp", "AlekseiPravdin/NSK-128k-7B-slerp", "en", "base_model:AlekseiPravdin/KSI-RP-NSK-128k-7B", "base_model:quantized:AlekseiPravdin/KSI-RP-NSK-128k-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-17T19:28:43Z	--- base_model: AlekseiPravdin/KSI-RP-NSK-128k-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - AlekseiPravdin/KukulStanta-InfinityRP-7B-slerp - AlekseiPravdin/NSK-128k-7B-slerp --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/AlekseiPravdin/KSI-RP-NSK-128k-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/KSI-RP-NSK-128k-7B-GGUF/resolve/main/KSI-RP-NSK-128k-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Sappho_V0.0.4-GGUF	mradermacher	2024-05-06T04:41:32Z	18	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Jakolo121/Sappho_V0.0.3", "VAGOsolutions/SauerkrautLM-7b-HerO", "en", "base_model:Jakolo121/Sappho_V0.0.4", "base_model:quantized:Jakolo121/Sappho_V0.0.4", "endpoints_compatible", "region:us" ]	null	2024-04-17T23:03:08Z	--- base_model: Jakolo121/Sappho_V0.0.4 language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Jakolo121/Sappho_V0.0.3 - VAGOsolutions/SauerkrautLM-7b-HerO --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Jakolo121/Sappho_V0.0.4 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Sappho_V0.0.4-GGUF/resolve/main/Sappho_V0.0.4.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Camel-Platypus2-70B-GGUF	mradermacher	2024-05-06T04:41:08Z	19	0	transformers	[ "transformers", "gguf", "en", "dataset:garage-bAInd/Open-Platypus", "base_model:garage-bAInd/Camel-Platypus2-70B", "base_model:quantized:garage-bAInd/Camel-Platypus2-70B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-18T06:59:36Z	--- base_model: garage-bAInd/Camel-Platypus2-70B datasets: - garage-bAInd/Open-Platypus language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/garage-bAInd/Camel-Platypus2-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Camel-Platypus2-70B-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/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Camel-Platypus2-70B-GGUF/resolve/main/Camel-Platypus2-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| 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. <!-- end -->
mradermacher/Boundary-4x7b-MoE-i1-GGUF	mradermacher	2024-05-06T04:40:55Z	75	0	transformers	[ "transformers", "gguf", "moe", "merge", "mergekit", "HuggingFaceH4/zephyr-7b-beta", "mistralai/Mistral-7B-Instruct-v0.2", "teknium/OpenHermes-2.5-Mistral-7B", "meta-math/MetaMath-Mistral-7B", "Mistral", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-18T08:30:12Z	--- base_model: NotAiLOL/Boundary-4x7b-MoE language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - merge - mergekit - HuggingFaceH4/zephyr-7b-beta - mistralai/Mistral-7B-Instruct-v0.2 - teknium/OpenHermes-2.5-Mistral-7B - meta-math/MetaMath-Mistral-7B - Mistral --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/NotAiLOL/Boundary-4x7b-MoE <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Boundary-4x7b-MoE-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/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q2_K.gguf) \| i1-Q2_K \| 8.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 10.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_S.gguf) \| i1-IQ3_S \| 10.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 11.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 12.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_0.gguf) \| i1-Q4_0 \| 13.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 13.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 14.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 16.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 17.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q6_K.gguf) \| i1-Q6_K \| 19.9 \| 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. <!-- end -->
mradermacher/Coxcomb-GGUF	mradermacher	2024-05-06T04:40:36Z	141	0	transformers	[ "transformers", "gguf", "en", "dataset:N8Programs/CreativeGPT", "base_model:N8Programs/Coxcomb", "base_model:quantized:N8Programs/Coxcomb", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-18T12:17:13Z	--- base_model: N8Programs/Coxcomb datasets: - N8Programs/CreativeGPT language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/N8Programs/Coxcomb <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Coxcomb-GGUF/resolve/main/Coxcomb.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Coxcomb-GGUF/resolve/main/Coxcomb.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Lila-70B-L2-GGUF	mradermacher	2024-05-06T04:40:25Z	2	0	transformers	[ "transformers", "gguf", "en", "base_model:Sao10K/Lila-70B-L2", "base_model:quantized:Sao10K/Lila-70B-L2", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-18T14:54:25Z	--- base_model: Sao10K/Lila-70B-L2 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Sao10K/Lila-70B-L2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Lila-70B-L2-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/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Lila-70B-L2-GGUF/resolve/main/Lila-70B-L2.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| 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. <!-- end -->
mradermacher/chronob-1.4-lin-70b-i1-GGUF	mradermacher	2024-05-06T04:40:14Z	28	0	transformers	[ "transformers", "gguf", "en", "base_model:uncensorie/chronob-1.4-lin-70b", "base_model:quantized:uncensorie/chronob-1.4-lin-70b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-18T16:17:36Z	--- base_model: uncensorie/chronob-1.4-lin-70b language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/uncensorie/chronob-1.4-lin-70b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/chronob-1.4-lin-70b-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/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/chronob-1.4-lin-70b-i1-GGUF/resolve/main/chronob-1.4-lin-70b.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/tulu-2-70b-i1-GGUF	mradermacher	2024-05-06T04:40:12Z	15	0	transformers	[ "transformers", "gguf", "en", "dataset:allenai/tulu-v2-sft-mixture", "base_model:allenai/tulu-2-70b", "base_model:quantized:allenai/tulu-2-70b", "endpoints_compatible", "region:us" ]	null	2024-04-18T16:17:36Z	--- base_model: allenai/tulu-2-70b datasets: - allenai/tulu-v2-sft-mixture language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/allenai/tulu-2-70b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/tulu-2-70b-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/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/tulu-2-70b-i1-GGUF/resolve/main/tulu-2-70b.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/mistral-7b-orpo-v5.0-GGUF	mradermacher	2024-05-06T04:39:59Z	68	0	transformers	[ "transformers", "gguf", "alignment-handbook", "trl", "orpo", "generated_from_trainer", "en", "dataset:argilla/Capybara-Preferences", "base_model:orpo-explorers/mistral-7b-orpo-v5.0", "base_model:quantized:orpo-explorers/mistral-7b-orpo-v5.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-18T21:50:10Z	--- base_model: orpo-explorers/mistral-7b-orpo-v5.0 datasets: - argilla/Capybara-Preferences language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - orpo - generated_from_trainer - trl - orpo - generated_from_trainer --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/orpo-explorers/mistral-7b-orpo-v5.0 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v5.0-GGUF/resolve/main/mistral-7b-orpo-v5.0.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/BRisa-7B-Instruct-v0.2-GGUF	mradermacher	2024-05-06T04:39:56Z	10	1	transformers	[ "transformers", "gguf", "JJhooww/Mistral-7B-v0.2-Base_ptbr", "J-LAB/BRisa", "en", "base_model:J-LAB/BRisa-7B-Instruct-v0.2", "base_model:quantized:J-LAB/BRisa-7B-Instruct-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-18T22:09:02Z	--- base_model: J-LAB/BRisa-7B-Instruct-v0.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - JJhooww/Mistral-7B-v0.2-Base_ptbr - J-LAB/BRisa --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/J-LAB/BRisa-7B-Instruct-v0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BRisa-7B-Instruct-v0.2-GGUF/resolve/main/BRisa-7B-Instruct-v0.2.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF	mradermacher	2024-05-06T04:39:54Z	98	0	transformers	[ "transformers", "gguf", "moe", "merge", "mergekit", "NousResearch/Hermes-2-Pro-Mistral-7B", "Nexusflow/Starling-LM-7B-beta", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-18T23:56:27Z	--- base_model: NotAiLOL/Boundary-Hermes-Chat-2x7B-MoE language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - merge - mergekit - NousResearch/Hermes-2-Pro-Mistral-7B - Nexusflow/Starling-LM-7B-beta --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/NotAiLOL/Boundary-Hermes-Chat-2x7B-MoE <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q4_K_M.gguf) \| Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q5_K_S.gguf) \| Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q5_K_M.gguf) \| Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q6_K.gguf) \| Q6_K \| 10.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Hermes-Chat-2x7B-MoE-GGUF/resolve/main/Boundary-Hermes-Chat-2x7B-MoE.Q8_0.gguf) \| Q8_0 \| 13.8 \| 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. <!-- end -->
mradermacher/Admiral-Llama-3-8B-GGUF	mradermacher	2024-05-06T04:39:46Z	53	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "alpaca", "en", "dataset:vicgalle/alpaca-gpt4", "base_model:mayacinka/Admiral-Llama-3-8B", "base_model:quantized:mayacinka/Admiral-Llama-3-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T06:39:36Z	--- base_model: mayacinka/Admiral-Llama-3-8B datasets: - vicgalle/alpaca-gpt4 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - alpaca --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mayacinka/Admiral-Llama-3-8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Admiral-Llama-3-8B-GGUF/resolve/main/Admiral-Llama-3-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->
mradermacher/MermaidMoE-19B-GGUF	mradermacher	2024-05-06T04:39:39Z	16	0	transformers	[ "transformers", "gguf", "en", "base_model:TroyDoesAI/MermaidMoE-19B", "base_model:quantized:TroyDoesAI/MermaidMoE-19B", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T06:42:11Z	--- base_model: TroyDoesAI/MermaidMoE-19B language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TroyDoesAI/MermaidMoE-19B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q2_K.gguf) \| Q2_K \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.IQ3_XS.gguf) \| IQ3_XS \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q3_K_S.gguf) \| Q3_K_S \| 8.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.IQ3_S.gguf) \| IQ3_S \| 8.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.IQ3_M.gguf) \| IQ3_M \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q3_K_M.gguf) \| Q3_K_M \| 9.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q3_K_L.gguf) \| Q3_K_L \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.IQ4_XS.gguf) \| IQ4_XS \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q4_K_S.gguf) \| Q4_K_S \| 11.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q4_K_M.gguf) \| Q4_K_M \| 11.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q5_K_S.gguf) \| Q5_K_S \| 13.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q5_K_M.gguf) \| Q5_K_M \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q6_K.gguf) \| Q6_K \| 15.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MermaidMoE-19B-GGUF/resolve/main/MermaidMoE-19B.Q8_0.gguf) \| Q8_0 \| 20.5 \| 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. <!-- end -->
mradermacher/Mermaid-Llama-3-8B-GGUF	mradermacher	2024-05-06T04:39:37Z	50	0	transformers	[ "transformers", "gguf", "en", "base_model:TroyDoesAI/Mermaid-Llama-3-8B", "base_model:quantized:TroyDoesAI/Mermaid-Llama-3-8B", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T07:06:01Z	--- base_model: TroyDoesAI/Mermaid-Llama-3-8B language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TroyDoesAI/Mermaid-Llama-3-8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-3-8B-GGUF/resolve/main/Mermaid-Llama-3-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->
mradermacher/Configurable-Llama-3-8B-v0.2-GGUF	mradermacher	2024-05-06T04:39:31Z	62	1	transformers	[ "transformers", "gguf", "en", "dataset:vicgalle/configurable-system-prompt-multitask", "base_model:vicgalle/Configurable-Llama-3-8B-v0.2", "base_model:quantized:vicgalle/Configurable-Llama-3-8B-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T10:42:34Z	--- base_model: vicgalle/Configurable-Llama-3-8B-v0.2 datasets: - vicgalle/configurable-system-prompt-multitask language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/vicgalle/Configurable-Llama-3-8B-v0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Configurable-Llama-3-8B-v0.2-GGUF/resolve/main/Configurable-Llama-3-8B-v0.2.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->
mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF	mradermacher	2024-05-06T04:39:29Z	30	1	transformers	[ "transformers", "gguf", "llama", "llama-3", "ko", "en", "dataset:MarkrAI/KoCommercial-Dataset", "base_model:PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct", "base_model:quantized:PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T10:42:56Z	--- base_model: PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct datasets: - MarkrAI/KoCommercial-Dataset language: - ko - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - llama - llama-3 --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Waktaverse-Llama-3-KO-8B-Instruct-GGUF/resolve/main/Waktaverse-Llama-3-KO-8B-Instruct.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->
mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF	mradermacher	2024-05-06T04:39:26Z	3	0	transformers	[ "transformers", "gguf", "en", "base_model:anik424/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2", "base_model:quantized:anik424/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T11:15:26Z	--- base_model: anik424/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/anik424/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B_toxic-removed-dpo-v0.2.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF	mradermacher	2024-05-06T04:39:24Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:irthpe/OpenHermes-2.5-Mistral-7B-toxic", "base_model:quantized:irthpe/OpenHermes-2.5-Mistral-7B-toxic", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T11:29:56Z	--- base_model: irthpe/OpenHermes-2.5-Mistral-7B-toxic language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/irthpe/OpenHermes-2.5-Mistral-7B-toxic <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-toxic-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-toxic.Q8_0.gguf) \| Q8_0 \| 7.8 \| 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. <!-- end -->
mradermacher/Llama-3-DARE-8B-GGUF	mradermacher	2024-05-06T04:39:21Z	90	8	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:mlabonne/Llama-3-DARE-8B", "base_model:quantized:mlabonne/Llama-3-DARE-8B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T11:38:08Z	--- base_model: mlabonne/Llama-3-DARE-8B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mlabonne/Llama-3-DARE-8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-DARE-8B-GGUF/resolve/main/Llama-3-DARE-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->
mradermacher/Mermaid-Solar-GGUF	mradermacher	2024-05-06T04:39:18Z	1	0	transformers	[ "transformers", "gguf", "en", "base_model:TroyDoesAI/Mermaid-Solar", "base_model:quantized:TroyDoesAI/Mermaid-Solar", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T11:59:42Z	--- base_model: TroyDoesAI/Mermaid-Solar language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TroyDoesAI/Mermaid-Solar <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.IQ3_XS.gguf) \| IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.IQ3_S.gguf) \| IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.IQ3_M.gguf) \| IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid-Solar-GGUF/resolve/main/Mermaid-Solar.Q8_0.gguf) \| Q8_0 \| 11.5 \| 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. <!-- end -->
mradermacher/Skadi-Mixtral-v1-GGUF	mradermacher	2024-05-06T04:39:13Z	2	0	transformers	[ "transformers", "gguf", "merge", "en", "base_model:Sao10K/Skadi-Mixtral-v1", "base_model:quantized:Sao10K/Skadi-Mixtral-v1", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T12:41:18Z	--- base_model: Sao10K/Skadi-Mixtral-v1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Sao10K/Skadi-Mixtral-v1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Skadi-Mixtral-v1-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/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q2_K.gguf) \| Q2_K \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.IQ3_XS.gguf) \| IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.IQ3_S.gguf) \| IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q3_K_S.gguf) \| Q3_K_S \| 20.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.IQ3_M.gguf) \| IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q3_K_M.gguf) \| Q3_K_M \| 22.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q3_K_L.gguf) \| Q3_K_L \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.IQ4_XS.gguf) \| IQ4_XS \| 25.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q4_K_S.gguf) \| Q4_K_S \| 26.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q4_K_M.gguf) \| Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q5_K_S.gguf) \| Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q5_K_M.gguf) \| Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q6_K.gguf) \| Q6_K \| 38.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Skadi-Mixtral-v1-GGUF/resolve/main/Skadi-Mixtral-v1.Q8_0.gguf) \| Q8_0 \| 49.7 \| 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. <!-- end -->
tsavage68/chat_400_STEPS_05beta_1e6rate_CDPOSFT	tsavage68	2024-05-06T04:39:10Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/chat_600STEPS_1e8rate_SFT", "base_model:finetune:tsavage68/chat_600STEPS_1e8rate_SFT", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T04:35:40Z	--- base_model: tsavage68/chat_600STEPS_1e8rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: chat_400_STEPS_05beta_1e6rate_CDPOSFT results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # chat_400_STEPS_05beta_1e6rate_CDPOSFT This model is a fine-tuned version of [tsavage68/chat_600STEPS_1e8rate_SFT](https://huggingface.co/tsavage68/chat_600STEPS_1e8rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6853 - Rewards/chosen: -0.1288 - Rewards/rejected: -0.2807 - Rewards/accuracies: 0.5143 - Rewards/margins: 0.1518 - Logps/rejected: -19.3633 - Logps/chosen: -17.0123 - Logits/rejected: -0.5890 - Logits/chosen: -0.5888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6871 \| 0.0977 \| 50 \| 0.6897 \| 0.0517 \| 0.0417 \| 0.4352 \| 0.0100 \| -18.7185 \| -16.6512 \| -0.6010 \| -0.6009 \| \| 0.6399 \| 0.1953 \| 100 \| 0.6728 \| -0.1560 \| -0.2548 \| 0.5099 \| 0.0989 \| -19.3116 \| -17.0666 \| -0.6090 \| -0.6089 \| \| 0.752 \| 0.2930 \| 150 \| 0.6985 \| -0.1949 \| -0.2845 \| 0.4505 \| 0.0896 \| -19.3710 \| -17.1445 \| -0.5936 \| -0.5934 \| \| 0.713 \| 0.3906 \| 200 \| 0.6945 \| -0.1538 \| -0.2727 \| 0.4923 \| 0.1188 \| -19.3473 \| -17.0623 \| -0.5881 \| -0.5879 \| \| 0.7476 \| 0.4883 \| 250 \| 0.6974 \| -0.1319 \| -0.2605 \| 0.5165 \| 0.1286 \| -19.3230 \| -17.0185 \| -0.5854 \| -0.5852 \| \| 0.6906 \| 0.5859 \| 300 \| 0.6883 \| -0.1320 \| -0.2782 \| 0.5165 \| 0.1461 \| -19.3583 \| -17.0187 \| -0.5910 \| -0.5909 \| \| 0.6808 \| 0.6836 \| 350 \| 0.6861 \| -0.1290 \| -0.2784 \| 0.5077 \| 0.1494 \| -19.3587 \| -17.0125 \| -0.5888 \| -0.5887 \| \| 0.6476 \| 0.7812 \| 400 \| 0.6853 \| -0.1288 \| -0.2807 \| 0.5143 \| 0.1518 \| -19.3633 \| -17.0123 \| -0.5890 \| -0.5888 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.0 - Tokenizers 0.19.1
mradermacher/Franziska-Mixtral-v1-i1-GGUF	mradermacher	2024-05-06T04:39:07Z	12	0	transformers	[ "transformers", "gguf", "en", "base_model:Sao10K/Franziska-Mixtral-v1", "base_model:quantized:Sao10K/Franziska-Mixtral-v1", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T14:04:16Z	--- base_model: Sao10K/Franziska-Mixtral-v1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/Sao10K/Franziska-Mixtral-v1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Franziska-Mixtral-v1-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/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 9.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 10.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 12.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 14.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 14.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 17.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 18.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 20.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 22.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 24.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 26.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 26.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Franziska-Mixtral-v1-i1-GGUF/resolve/main/Franziska-Mixtral-v1.i1-Q6_K.gguf) \| i1-Q6_K \| 38.5 \| 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. <!-- end -->
mradermacher/OkapiLlama-3-dpo-GGUF	mradermacher	2024-05-06T04:39:02Z	47	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "dpo", "en", "dataset:mlabonne/orpo-dpo-mix-40k", "base_model:mayacinka/OkapiLlama-3-dpo", "base_model:quantized:mayacinka/OkapiLlama-3-dpo", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T16:55:53Z	--- base_model: mayacinka/OkapiLlama-3-dpo datasets: - mlabonne/orpo-dpo-mix-40k language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - dpo --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mayacinka/OkapiLlama-3-dpo <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OkapiLlama-3-dpo-GGUF/resolve/main/OkapiLlama-3-dpo.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->
mradermacher/Aetheria-L2-70B-GGUF	mradermacher	2024-05-06T04:38:56Z	36	0	transformers	[ "transformers", "gguf", "llama", "llama 2", "en", "base_model:royallab/Aetheria-L2-70B", "base_model:quantized:royallab/Aetheria-L2-70B", "endpoints_compatible", "region:us" ]	null	2024-04-19T17:01:01Z	--- base_model: royallab/Aetheria-L2-70B language: - en library_name: transformers quantized_by: mradermacher tags: - llama - llama 2 --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/royallab/Aetheria-L2-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Aetheria-L2-70B-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/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Aetheria-L2-70B-GGUF/resolve/main/Aetheria-L2-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| 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. <!-- end -->
mradermacher/cyber-risk-llama-3-8b-GGUF	mradermacher	2024-05-06T04:38:48Z	104	4	transformers	[ "transformers", "gguf", "finance", "supervision", "cyber risk", "cybersecurity", "cyber threats", "SFT", "LoRA", "A100GPU", "en", "dataset:Vanessasml/cybersecurity_32k_instruction_input_output", "base_model:Vanessasml/cyber-risk-llama-3-8b", "base_model:quantized:Vanessasml/cyber-risk-llama-3-8b", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T18:47:31Z	--- base_model: Vanessasml/cyber-risk-llama-3-8b datasets: - Vanessasml/cybersecurity_32k_instruction_input_output language: - en library_name: transformers quantized_by: mradermacher tags: - finance - supervision - cyber risk - cybersecurity - cyber threats - SFT - LoRA - A100GPU --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Vanessasml/cyber-risk-llama-3-8b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/cyber-risk-llama-3-8b-GGUF/resolve/main/cyber-risk-llama-3-8b.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->
mradermacher/Llama-3-13B-GGUF	mradermacher	2024-05-06T04:38:34Z	14	2	transformers	[ "transformers", "gguf", "en", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T22:21:33Z	--- base_model: Replete-AI/Llama-3-13B language: - en library_name: transformers license: other license_link: https://llama.meta.com/llama3/license/ license_name: llama-3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Replete-AI/Llama-3-13B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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-3-13B-GGUF/resolve/main/Llama-3-13B.Q2_K.gguf) \| Q2_K \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.IQ3_XS.gguf) \| IQ3_XS \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q3_K_S.gguf) \| Q3_K_S \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.IQ3_S.gguf) \| IQ3_S \| 6.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.IQ3_M.gguf) \| IQ3_M \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q3_K_M.gguf) \| Q3_K_M \| 6.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q3_K_L.gguf) \| Q3_K_L \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.IQ4_XS.gguf) \| IQ4_XS \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q4_K_S.gguf) \| Q4_K_S \| 7.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q4_K_M.gguf) \| Q4_K_M \| 8.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q5_K_S.gguf) \| Q5_K_S \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q5_K_M.gguf) \| Q5_K_M \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q6_K.gguf) \| Q6_K \| 11.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-13B-GGUF/resolve/main/Llama-3-13B.Q8_0.gguf) \| Q8_0 \| 14.2 \| 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. <!-- end -->
mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF	mradermacher	2024-05-06T04:38:32Z	21	0	transformers	[ "transformers", "gguf", "moe", "merge", "mergekit", "NousResearch/Nous-Hermes-2-SOLAR-10.7B", "upstage/SOLAR-10.7B-Instruct-v1.0", "llama", "Llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T23:30:43Z	--- base_model: NotAiLOL/Boundary-Solar-Chat-2x10.7B-MoE language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - merge - mergekit - NousResearch/Nous-Hermes-2-SOLAR-10.7B - upstage/SOLAR-10.7B-Instruct-v1.0 - llama - Llama --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/NotAiLOL/Boundary-Solar-Chat-2x10.7B-MoE <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q2_K.gguf) \| Q2_K \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.IQ3_XS.gguf) \| IQ3_XS \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q3_K_S.gguf) \| Q3_K_S \| 8.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.IQ3_S.gguf) \| IQ3_S \| 8.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.IQ3_M.gguf) \| IQ3_M \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q3_K_M.gguf) \| Q3_K_M \| 9.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q3_K_L.gguf) \| Q3_K_L \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.IQ4_XS.gguf) \| IQ4_XS \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q4_K_S.gguf) \| Q4_K_S \| 11.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q4_K_M.gguf) \| Q4_K_M \| 11.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q5_K_S.gguf) \| Q5_K_S \| 13.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q5_K_M.gguf) \| Q5_K_M \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q6_K.gguf) \| Q6_K \| 15.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Boundary-Solar-Chat-2x10.7B-MoE-GGUF/resolve/main/Boundary-Solar-Chat-2x10.7B-MoE.Q8_0.gguf) \| Q8_0 \| 20.5 \| 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. <!-- end -->
mradermacher/Platypus2-70B-i1-GGUF	mradermacher	2024-05-06T04:38:29Z	295	2	transformers	[ "transformers", "gguf", "en", "dataset:garage-bAInd/Open-Platypus", "base_model:garage-bAInd/Platypus2-70B", "base_model:quantized:garage-bAInd/Platypus2-70B", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-20T01:49:29Z	--- base_model: garage-bAInd/Platypus2-70B datasets: - garage-bAInd/Open-Platypus language: - en library_name: transformers license: cc-by-nc-sa-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/garage-bAInd/Platypus2-70B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Platypus2-70B-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/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Platypus2-70B-i1-GGUF/resolve/main/Platypus2-70B.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| 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. <!-- end -->
mradermacher/deepseek-llm-67b-chat-GGUF	mradermacher	2024-05-06T04:38:27Z	143	0	transformers	[ "transformers", "gguf", "en", "base_model:deepseek-ai/deepseek-llm-67b-chat", "base_model:quantized:deepseek-ai/deepseek-llm-67b-chat", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-20T02:06:26Z	--- base_model: deepseek-ai/deepseek-llm-67b-chat language: - en library_name: transformers license: other license_link: LICENSE license_name: deepseek quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/deepseek-llm-67b-chat-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/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q2_K.gguf) \| Q2_K \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.IQ3_XS.gguf) \| IQ3_XS \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q3_K_S.gguf) \| Q3_K_S \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.IQ3_S.gguf) \| IQ3_S \| 29.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.IQ3_M.gguf) \| IQ3_M \| 30.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q3_K_M.gguf) \| Q3_K_M \| 32.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q3_K_L.gguf) \| Q3_K_L \| 35.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.IQ4_XS.gguf) \| IQ4_XS \| 36.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q4_K_S.gguf) \| Q4_K_S \| 38.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q4_K_M.gguf) \| Q4_K_M \| 40.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q5_K_S.gguf) \| Q5_K_S \| 46.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q5_K_M.gguf) \| Q5_K_M \| 47.8 \| \| \| [PART 1](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q6_K.gguf.part2of2) \| Q6_K \| 55.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/deepseek-llm-67b-chat-GGUF/resolve/main/deepseek-llm-67b-chat.Q8_0.gguf.part2of2) \| Q8_0 \| 71.7 \| 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. <!-- end -->
mradermacher/Aura_L3_8B-GGUF	mradermacher	2024-05-06T04:38:16Z	98	0	transformers	[ "transformers", "gguf", "en", "base_model:ResplendentAI/Aura_L3_8B", "base_model:quantized:ResplendentAI/Aura_L3_8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-20T03:48:07Z	--- base_model: ResplendentAI/Aura_L3_8B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ResplendentAI/Aura_L3_8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| 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. <!-- end -->

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