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train · 844k rows

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mradermacher/Guardian-Samantha-7b-slerp-GGUF	mradermacher	2024-05-06T05:36:25Z	6	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "llamas-community/LlamaGuard-7b", "ParthasarathyShanmugam/llama-2-7b-samantha", "en", "base_model:brichett/Guardian-Samantha-7b-slerp", "base_model:quantized:brichett/Guardian-Samantha-7b-slerp", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-29T17:39:09Z	--- base_model: brichett/Guardian-Samantha-7b-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - llamas-community/LlamaGuard-7b - ParthasarathyShanmugam/llama-2-7b-samantha --- ## About static quants of https://huggingface.co/brichett/Guardian-Samantha-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/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.IQ3_S.gguf) \| IQ3_S \| 3.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.IQ4_XS.gguf) \| IQ4_XS \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q4_0.gguf) \| Q4_0 \| 4.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.IQ4_NL.gguf) \| IQ4_NL \| 4.1 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q5_K_S.gguf) \| Q5_K_S \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q6_K.gguf) \| Q6_K \| 5.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Guardian-Samantha-7b-slerp-GGUF/resolve/main/Guardian-Samantha-7b-slerp.Q8_0.gguf) \| Q8_0 \| 7.4 \| 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/llamoe-8x1b-hermes-GGUF	mradermacher	2024-05-06T05:36:05Z	15	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-29T18:12:35Z	--- base_model: N8Programs/llamoe-8x1b-hermes language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About static quants of https://huggingface.co/N8Programs/llamoe-8x1b-hermes <!-- 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/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.IQ3_XS.gguf) \| IQ3_XS \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.IQ3_M.gguf) \| IQ3_M \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q3_K_M.gguf) \| Q3_K_M \| 3.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q3_K_L.gguf) \| Q3_K_L \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q4_0.gguf) \| Q4_0 \| 3.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.IQ4_NL.gguf) \| IQ4_NL \| 3.9 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q4_K_S.gguf) \| Q4_K_S \| 3.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q4_K_M.gguf) \| Q4_K_M \| 4.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q5_K_S.gguf) \| Q5_K_S \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q5_K_M.gguf) \| Q5_K_M \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q6_K.gguf) \| Q6_K \| 5.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-hermes-GGUF/resolve/main/llamoe-8x1b-hermes.Q8_0.gguf) \| Q8_0 \| 7.0 \| 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/llamoe-8x1b-GGUF	mradermacher	2024-05-06T05:36:02Z	22	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-29T18:44:17Z	--- base_model: N8Programs/llamoe-8x1b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About static quants of https://huggingface.co/N8Programs/llamoe-8x1b <!-- 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/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q2_K.gguf) \| Q2_K \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.IQ3_XS.gguf) \| IQ3_XS \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.IQ3_S.gguf) \| IQ3_S \| 3.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q3_K_S.gguf) \| Q3_K_S \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.IQ3_M.gguf) \| IQ3_M \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q3_K_L.gguf) \| Q3_K_L \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.IQ4_XS.gguf) \| IQ4_XS \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q4_0.gguf) \| Q4_0 \| 4.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.IQ4_NL.gguf) \| IQ4_NL \| 4.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q5_K_S.gguf) \| Q5_K_S \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llamoe-8x1b-GGUF/resolve/main/llamoe-8x1b.Q8_0.gguf) \| Q8_0 \| 7.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Wespeaker/wespeaker-voxceleb-ecapa-tdnn512	Wespeaker	2024-05-06T05:35:55Z	5	0	null	[ "onnx", "license:apache-2.0", "region:us" ]	null	2024-05-06T05:10:38Z	--- license: apache-2.0 ---
mradermacher/roleplay-mis_wes-GGUF	mradermacher	2024-05-06T05:35:50Z	225	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "HuggingFaceH4/mistral-7b-grok", "senseable/WestLake-7B-v2", "en", "base_model:ajay141/roleplay-mis_wes", "base_model:quantized:ajay141/roleplay-mis_wes", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-29T20:03:16Z	--- base_model: ajay141/roleplay-mis_wes language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - HuggingFaceH4/mistral-7b-grok - senseable/WestLake-7B-v2 --- ## About static quants of https://huggingface.co/ajay141/roleplay-mis_wes <!-- 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/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/roleplay-mis_wes-GGUF/resolve/main/roleplay-mis_wes.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/Customer-Support-Clown-Extended-GGUF	mradermacher	2024-05-06T05:35:47Z	134	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "arcee-ai/Clown-DPO-Extended", "mistralai/Mistral-7B-v0.1+predibase/customer_support", "en", "base_model:arcee-ai/Customer-Support-Clown-Extended", "base_model:quantized:arcee-ai/Customer-Support-Clown-Extended", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-29T20:11:49Z	--- base_model: arcee-ai/Customer-Support-Clown-Extended language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - arcee-ai/Clown-DPO-Extended - mistralai/Mistral-7B-v0.1+predibase/customer_support --- ## About static quants of https://huggingface.co/arcee-ai/Customer-Support-Clown-Extended <!-- 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/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Customer-Support-Clown-Extended-GGUF/resolve/main/Customer-Support-Clown-Extended.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/QueenLiz-120B-GGUF	mradermacher	2024-05-06T05:35:44Z	18	0	transformers	[ "transformers", "gguf", "en", "base_model:Noodlz/QueenLiz-120B", "base_model:quantized:Noodlz/QueenLiz-120B", "endpoints_compatible", "region:us" ]	null	2024-03-29T21:13:38Z	--- base_model: Noodlz/QueenLiz-120B language: - en library_name: transformers quantized_by: mradermacher --- ## About static quants of https://huggingface.co/Noodlz/QueenLiz-120B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/QueenLiz-120B-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/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q2_K.gguf) \| Q2_K \| 44.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_XS.gguf) \| IQ3_XS \| 49.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_S.gguf.part2of2) \| Q3_K_S \| 52.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_S.gguf.part2of2) \| IQ3_S \| 52.4 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ3_M.gguf.part2of2) \| IQ3_M \| 54.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 58.2 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 63.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 65.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_0.gguf.part2of2) \| Q4_0 \| 68.2 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 68.7 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_NL.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.IQ4_NL.gguf.part2of2) \| IQ4_NL \| 68.8 \| prefer IQ4_XS \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 72.6 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 83.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 85.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q6_K.gguf.part3of3) \| Q6_K \| 99.1 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/QueenLiz-120B-GGUF/resolve/main/QueenLiz-120B.Q8_0.gguf.part3of3) \| Q8_0 \| 128.2 \| 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/shqiponja-59b-v1-i1-GGUF	mradermacher	2024-05-06T05:35:13Z	3	0	transformers	[ "transformers", "gguf", "mergekit", "frankenstein", "merge", "en", "base_model:nisten/shqiponja-59b-v1", "base_model:quantized:nisten/shqiponja-59b-v1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-29T23:04:42Z	--- base_model: nisten/shqiponja-59b-v1 language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - mergekit - frankenstein - merge --- ## About weighted/imatrix quants of https://huggingface.co/nisten/shqiponja-59b-v1 only the first 40k tokens of my 160k token training data is used as the model overflowed (likely a problem with the model weights) <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/shqiponja-59b-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/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 13.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 14.5 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 16.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 18.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 20.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 22.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 23.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 24.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 26.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 26.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 29.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 31.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 32.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 34.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 34.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 36.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 41.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 42.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/shqiponja-59b-v1-i1-GGUF/resolve/main/shqiponja-59b-v1.i1-Q6_K.gguf) \| i1-Q6_K \| 49.0 \| 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 -->
brdemorin/Llama3_7b-custom_v2	brdemorin	2024-05-06T05:35:13Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-06T05:34:46Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: brdemorin - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/NeuralGanesha-7b-GGUF	mradermacher	2024-05-06T05:35:10Z	102	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Kukedlc/SomeModelsMerge-7b", "Kukedlc/MyModelsMerge-7b", "en", "base_model:Kukedlc/NeuralGanesha-7b", "base_model:quantized:Kukedlc/NeuralGanesha-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-29T23:18:08Z	--- base_model: Kukedlc/NeuralGanesha-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Kukedlc/SomeModelsMerge-7b - Kukedlc/MyModelsMerge-7b --- ## About static quants of https://huggingface.co/Kukedlc/NeuralGanesha-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/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralGanesha-7b-GGUF/resolve/main/NeuralGanesha-7b.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/Rhea-72b-v0.5-GGUF	mradermacher	2024-05-06T05:35:07Z	32	3	transformers	[ "transformers", "gguf", "en", "base_model:davidkim205/Rhea-72b-v0.5", "base_model:quantized:davidkim205/Rhea-72b-v0.5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-29T23:30:00Z	--- base_model: davidkim205/Rhea-72b-v0.5 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About static quants of https://huggingface.co/davidkim205/Rhea-72b-v0.5 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Rhea-72b-v0.5-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/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q2_K.gguf) \| Q2_K \| 31.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.IQ3_XS.gguf) \| IQ3_XS \| 34.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.IQ3_S.gguf) \| IQ3_S \| 35.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q3_K_S.gguf) \| Q3_K_S \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.IQ3_M.gguf) \| IQ3_M \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q3_K_M.gguf) \| Q3_K_M \| 39.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q3_K_L.gguf) \| Q3_K_L \| 42.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.IQ4_XS.gguf) \| IQ4_XS \| 43.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q4_0.gguf) \| Q4_0 \| 45.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.IQ4_NL.gguf) \| IQ4_NL \| 45.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q4_K_S.gguf) \| Q4_K_S \| 45.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q4_K_M.gguf) \| Q4_K_M \| 47.8 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 53.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 55.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q6_K.gguf.part2of2) \| Q6_K \| 63.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.Q8_0.gguf.part2of2) \| Q8_0 \| 80.6 \| fast, best quality \| \| [P1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.SOURCE.gguf.part1of6) [P2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.SOURCE.gguf.part2of6) [P3](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.SOURCE.gguf.part3of6) [P4](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.SOURCE.gguf.part4of6) [P5](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.SOURCE.gguf.part5of6) [P6](https://huggingface.co/mradermacher/Rhea-72b-v0.5-GGUF/resolve/main/Rhea-72b-v0.5.SOURCE.gguf.part6of6) \| SOURCE \| 289.3 \| source gguf, only provided when it was hard to come by \| 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 -->
baaaaaaaam/v6	baaaaaaaam	2024-05-06T05:35:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-06T03:19:18Z	--- 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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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/NeuralMaths-Experiment-7b-GGUF	mradermacher	2024-05-06T05:34:52Z	92	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "WizardLM/WizardMath-7B-V1.1", "mlabonne/NeuralDaredevil-7B", "Kukedlc/Neural4gsm8k", "Eric111/Mayo", "Kukedlc/NeuralSirKrishna-7b", "en", "base_model:Kukedlc/NeuralMaths-Experiment-7b", "base_model:quantized:Kukedlc/NeuralMaths-Experiment-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-30T02:04:55Z	--- base_model: Kukedlc/NeuralMaths-Experiment-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - WizardLM/WizardMath-7B-V1.1 - mlabonne/NeuralDaredevil-7B - Kukedlc/Neural4gsm8k - Eric111/Mayo - Kukedlc/NeuralSirKrishna-7b --- ## About static quants of https://huggingface.co/Kukedlc/NeuralMaths-Experiment-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/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralMaths-Experiment-7b-GGUF/resolve/main/NeuralMaths-Experiment-7b.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/NeuraRP-7B-slerp-GGUF	mradermacher	2024-05-06T05:34:16Z	4	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "mlabonne/NeuralHermes-2.5-Mistral-7B", "ChaoticNeutrals/BuRP_7B", "en", "base_model:stevez80/NeuraRP-7B-slerp", "base_model:quantized:stevez80/NeuraRP-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-30T07:04:45Z	--- base_model: stevez80/NeuraRP-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - mlabonne/NeuralHermes-2.5-Mistral-7B - ChaoticNeutrals/BuRP_7B --- ## About static quants of https://huggingface.co/stevez80/NeuraRP-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/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuraRP-7B-slerp-GGUF/resolve/main/NeuraRP-7B-slerp.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/ErebusNeuralSamir-7B-dare-ties-GGUF	mradermacher	2024-05-06T05:33:55Z	195	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "samir-fama/SamirGPT-v1", "mlabonne/NeuralHermes-2.5-Mistral-7B", "KoboldAI/Mistral-7B-Erebus-v3", "en", "base_model:stevez80/ErebusNeuralSamir-7B-dare-ties", "base_model:quantized:stevez80/ErebusNeuralSamir-7B-dare-ties", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-30T08:36:39Z	--- base_model: stevez80/ErebusNeuralSamir-7B-dare-ties language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - samir-fama/SamirGPT-v1 - mlabonne/NeuralHermes-2.5-Mistral-7B - KoboldAI/Mistral-7B-Erebus-v3 --- ## About static quants of https://huggingface.co/stevez80/ErebusNeuralSamir-7B-dare-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/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ErebusNeuralSamir-7B-dare-ties-GGUF/resolve/main/ErebusNeuralSamir-7B-dare-ties.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/StarFuse-7B-DARE-GGUF	mradermacher	2024-05-06T05:33:51Z	39	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "en", "base_model:hfghfghg/StarFuse-7B-DARE", "base_model:quantized:hfghfghg/StarFuse-7B-DARE", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-30T10:06:48Z	--- base_model: hfghfghg/StarFuse-7B-DARE language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit --- ## About static quants of https://huggingface.co/hfghfghg/StarFuse-7B-DARE <!-- 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/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/StarFuse-7B-DARE-GGUF/resolve/main/StarFuse-7B-DARE.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 -->
xinping/Mixtral-8x7B-instruction-v0.1_zh-GGUF	xinping	2024-05-06T05:33:37Z	1	0	adapter-transformers	[ "adapter-transformers", "gguf", "zh", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T08:33:12Z	--- license: apache-2.0 language: - zh - en library_name: adapter-transformers --- 安装llama.cpp 保存gguf文件路径：../Mixtral-8x7B-instruction-zh_V0.1.Q4_K_S.gguf linux系统下的测试：进入安装的llama.cpp根目录下，在命令行界面CLI下执行： CUDA_VISIBLE_DEVICES=0 ./main -m ../Mixtral-8x7B-instruction-zh_V0.1.Q4_K_S.gguf -n 2048 -p "今年是2024年，大后年是哪年？"
mradermacher/Neural-4-Wino-7b-GGUF	mradermacher	2024-05-06T05:33:19Z	368	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralFusion-7b-Dare-Ties", "paulml/OmniBeagleSquaredMBX-v3-7B-v2", "macadeliccc/MBX-7B-v3-DPO", "Kukedlc/Fasciculus-Arcuatus-7B-slerp", "liminerity/Neurotic-Jomainotrik-7b-slerp", "en", "base_model:Kukedlc/Neural-4-Wino-7b", "base_model:quantized:Kukedlc/Neural-4-Wino-7b", "endpoints_compatible", "region:us" ]	null	2024-03-30T13:08:10Z	--- base_model: Kukedlc/Neural-4-Wino-7b language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralFusion-7b-Dare-Ties - paulml/OmniBeagleSquaredMBX-v3-7B-v2 - macadeliccc/MBX-7B-v3-DPO - Kukedlc/Fasciculus-Arcuatus-7B-slerp - liminerity/Neurotic-Jomainotrik-7b-slerp --- ## About static quants of https://huggingface.co/Kukedlc/Neural-4-Wino-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/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.IQ4_NL.gguf) \| IQ4_NL \| 4.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Neural-4-Wino-7b-GGUF/resolve/main/Neural-4-Wino-7b.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 -->
DavidClark314/ppo-LunarLander-v2	DavidClark314	2024-05-06T05:33:18Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-06T04:56:23Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 259.55 +/- 14.71 name: mean_reward verified: false --- # ppo Agent playing LunarLander-v2 This is a trained model of a ppo agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mradermacher/Rhea-72b-v0.5-i1-GGUF	mradermacher	2024-05-06T05:33:16Z	62	4	transformers	[ "transformers", "gguf", "en", "base_model:davidkim205/Rhea-72b-v0.5", "base_model:quantized:davidkim205/Rhea-72b-v0.5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-30T13:33:40Z	--- base_model: davidkim205/Rhea-72b-v0.5 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About weighted/imatrix quants of https://huggingface.co/davidkim205/Rhea-72b-v0.5 the imatrix was calculated on a reduced 40k token set (the "quarter" set) as the full token set caused overflows in the model (likely a model bug) <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Rhea-72b-v0.5-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/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ1_S.gguf) \| i1-IQ1_S \| 20.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ1_M.gguf) \| i1-IQ1_M \| 21.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 24.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 26.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ2_S.gguf) \| i1-IQ2_S \| 27.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ2_M.gguf) \| i1-IQ2_M \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q2_K.gguf) \| i1-Q2_K \| 31.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 31.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 34.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ3_S.gguf) \| i1-IQ3_S \| 35.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 35.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ3_M.gguf) \| i1-IQ3_M \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 39.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 42.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 42.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 45.1 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q4_0.gguf) \| i1-Q4_0 \| 45.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 45.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 47.8 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 53.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 55.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Rhea-72b-v0.5-i1-GGUF/resolve/main/Rhea-72b-v0.5.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 63.4 \| 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/Mixtral_AI_Cyber_4.0-GGUF	mradermacher	2024-05-06T05:32:00Z	18	0	transformers	[ "transformers", "gguf", "biology", "chemistry", "medical", "en", "base_model:LeroyDyer/Mixtral_AI_Cyber_4.0", "base_model:quantized:LeroyDyer/Mixtral_AI_Cyber_4.0", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-31T00:09:59Z	--- base_model: LeroyDyer/Mixtral_AI_Cyber_4.0 language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - biology - chemistry - medical --- ## About static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_4.0 <!-- 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/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_4.0-GGUF/resolve/main/Mixtral_AI_Cyber_4.0.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/Saily_220B-GGUF	mradermacher	2024-05-06T05:31:50Z	0	0	transformers	[ "transformers", "en", "dataset:tiiuae/falcon-refinedweb", "dataset:EleutherAI/pile", "dataset:meta-math/MetaMathQA", "base_model:deepnight-research/Saily_220B", "base_model:finetune:deepnight-research/Saily_220B", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-03-31T02:09:11Z	--- base_model: deepnight-research/Saily_220B datasets: - tiiuae/falcon-refinedweb - EleutherAI/pile - meta-math/MetaMathQA language: - en library_name: transformers license: llama2 no_imatrix: 'GGML_ASSERT: llama.cpp/ggml.c:16553: i != GGML_HASHTABLE_FULL' quantized_by: mradermacher --- ## About static quants of https://huggingface.co/deepnight-research/Saily_220B <!-- 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 \| \|:-----\|:-----\|--------:\|:------\| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q2_K.gguf.part2of2) \| Q2_K \| 76.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ3_XS.gguf.part2of2) \| IQ3_XS \| 85.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_S.gguf.part2of2) \| Q3_K_S \| 90.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ3_S.gguf.part2of2) \| IQ3_S \| 90.4 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ3_M.gguf.part2of2) \| IQ3_M \| 93.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_M.gguf.part3of3) \| Q3_K_M \| 100.6 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_L.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_L.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q3_K_L.gguf.part3of3) \| Q3_K_L \| 109.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ4_XS.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ4_XS.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.IQ4_XS.gguf.part3of3) \| IQ4_XS \| 112.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_0.gguf.part3of3) \| Q4_0 \| 117.7 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_K_S.gguf.part3of3) \| Q4_K_S \| 118.6 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q4_K_M.gguf.part3of3) \| Q4_K_M \| 125.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q5_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q5_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q5_K_S.gguf.part3of3) \| Q5_K_S \| 143.8 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q5_K_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q5_K_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q5_K_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q5_K_M.gguf.part4of4) \| Q5_K_M \| 147.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q6_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q6_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q6_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q6_K.gguf.part4of4) \| Q6_K \| 171.4 \| very good quality \| \| [P1](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q8_0.gguf.part1of5) [P2](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q8_0.gguf.part2of5) [P3](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q8_0.gguf.part3of5) [P4](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q8_0.gguf.part4of5) [P5](https://huggingface.co/mradermacher/Saily_220B-GGUF/resolve/main/Saily_220B.Q8_0.gguf.part5of5) \| Q8_0 \| 221.8 \| 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/NeuralKuke-4-All-7b-GGUF	mradermacher	2024-05-06T05:31:24Z	177	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Kukedlc/Neural-4-ARC-7b", "Kukedlc/Neural-4-Wino-7b", "Kukedlc/NeuralSirKrishna-7b", "Kukedlc/Neural-4-QA-7b", "Kukedlc/Neural-4-Maths-7b", "en", "base_model:Kukedlc/NeuralKuke-4-All-7b", "base_model:quantized:Kukedlc/NeuralKuke-4-All-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-31T04:15:37Z	--- base_model: Kukedlc/NeuralKuke-4-All-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Kukedlc/Neural-4-ARC-7b - Kukedlc/Neural-4-Wino-7b - Kukedlc/NeuralSirKrishna-7b - Kukedlc/Neural-4-QA-7b - Kukedlc/Neural-4-Maths-7b --- ## About static quants of https://huggingface.co/Kukedlc/NeuralKuke-4-All-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/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKuke-4-All-7b-GGUF/resolve/main/NeuralKuke-4-All-7b.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_Cyber_3.1_SFT-GGUF	mradermacher	2024-05-06T05:31:17Z	25	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "music", "Cyber-Series", "en", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-31T04:46:29Z	--- base_model: LeroyDyer/Mixtral_AI_Cyber_3.1_SFT datasets: - WhiteRabbitNeo/WRN-Chapter-1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - music - Cyber-Series --- ## About static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_3.1_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_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_3.1_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_3.1_SFT.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/MultiverseEx26-7B-slerp-GGUF	mradermacher	2024-05-06T05:31:08Z	2	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "yam-peleg/Experiment26-7B", "MTSAIR/multi_verse_model", "en", "base_model:allknowingroger/MultiverseEx26-7B-slerp", "base_model:quantized:allknowingroger/MultiverseEx26-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-31T05:37:00Z	--- base_model: allknowingroger/MultiverseEx26-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - yam-peleg/Experiment26-7B - MTSAIR/multi_verse_model --- ## About static quants of https://huggingface.co/allknowingroger/MultiverseEx26-7B-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/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MultiverseEx26-7B-slerp-GGUF/resolve/main/MultiverseEx26-7B-slerp.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/Neurallaymons-7B-slerp-GGUF	mradermacher	2024-05-06T05:31:05Z	36	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Kukedlc/Neural-4-Maths-7b", "ABX-AI/Starfinite-Laymons-7B", "en", "base_model:allknowingroger/Neurallaymons-7B-slerp", "base_model:quantized:allknowingroger/Neurallaymons-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-31T07:01:46Z	--- base_model: allknowingroger/Neurallaymons-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Kukedlc/Neural-4-Maths-7b - ABX-AI/Starfinite-Laymons-7B --- ## About static quants of https://huggingface.co/allknowingroger/Neurallaymons-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/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Neurallaymons-7B-slerp-GGUF/resolve/main/Neurallaymons-7B-slerp.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/GodziLLa-30B-GGUF	mradermacher	2024-05-06T05:30:35Z	119	0	transformers	[ "transformers", "gguf", "merge", "mix", "cot", "en", "base_model:MayaPH/GodziLLa-30B", "base_model:quantized:MayaPH/GodziLLa-30B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-03-31T12:51:30Z	--- base_model: MayaPH/GodziLLa-30B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - merge - mix - cot --- ## About static quants of https://huggingface.co/MayaPH/GodziLLa-30B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/GodziLLa-30B-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/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.IQ3_XS.gguf) \| IQ3_XS \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.IQ3_S.gguf) \| IQ3_S \| 14.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q3_K_S.gguf) \| Q3_K_S \| 14.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.IQ3_M.gguf) \| IQ3_M \| 15.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q3_K_M.gguf) \| Q3_K_M \| 16.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q3_K_L.gguf) \| Q3_K_L \| 17.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.IQ4_XS.gguf) \| IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q4_0.gguf) \| Q4_0 \| 18.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q4_K_S.gguf) \| Q4_K_S \| 18.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-GGUF/resolve/main/GodziLLa-30B.Q8_0.gguf) \| Q8_0 \| 34.9 \| 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/BigLiberated-20B-V2-GGUF	mradermacher	2024-05-06T05:29:57Z	4	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:win10/BigLiberated-20B-V2", "base_model:quantized:win10/BigLiberated-20B-V2", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-31T15:47:35Z	--- base_model: win10/BigLiberated-20B-V2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About static quants of https://huggingface.co/win10/BigLiberated-20B-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/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q2_K.gguf) \| Q2_K \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.IQ3_XS.gguf) \| IQ3_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.IQ3_S.gguf) \| IQ3_S \| 10.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.IQ3_M.gguf) \| IQ3_M \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q3_K_M.gguf) \| Q3_K_M \| 11.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q3_K_L.gguf) \| Q3_K_L \| 12.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.IQ4_XS.gguf) \| IQ4_XS \| 12.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q4_0.gguf) \| Q4_0 \| 12.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q4_K_S.gguf) \| Q4_K_S \| 13.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q4_K_M.gguf) \| Q4_K_M \| 14.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q5_K_S.gguf) \| Q5_K_S \| 15.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q5_K_M.gguf) \| Q5_K_M \| 16.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q6_K.gguf) \| Q6_K \| 18.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BigLiberated-20B-V2-GGUF/resolve/main/BigLiberated-20B-V2.Q8_0.gguf) \| Q8_0 \| 22.3 \| 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/NeuralAlgo-7B-slerp-GGUF	mradermacher	2024-05-06T05:29:52Z	69	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "AurelPx/Percival_01-7b-slerp", "yam-peleg/Experiment26-7B", "en", "base_model:Kukedlc/NeuralAlgo-7B-slerp", "base_model:quantized:Kukedlc/NeuralAlgo-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-31T15:53:31Z	--- base_model: Kukedlc/NeuralAlgo-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - AurelPx/Percival_01-7b-slerp - yam-peleg/Experiment26-7B --- ## About static quants of https://huggingface.co/Kukedlc/NeuralAlgo-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/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q4_0.gguf) \| Q4_0 \| 4.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralAlgo-7B-slerp-GGUF/resolve/main/NeuralAlgo-7B-slerp.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/Melusine_103b-GGUF	mradermacher	2024-05-06T05:29:32Z	95	1	transformers	[ "transformers", "gguf", "rp", "erp", "chat", "miqu", "en", "base_model:MarsupialAI/Melusine_103b", "base_model:quantized:MarsupialAI/Melusine_103b", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-31T16:13:39Z	--- base_model: MarsupialAI/Melusine_103b language: - en library_name: transformers quantized_by: mradermacher tags: - rp - erp - chat - miqu --- ## About static quants of https://huggingface.co/MarsupialAI/Melusine_103b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Melusine_103b-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/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q2_K.gguf) \| Q2_K \| 38.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.IQ3_XS.gguf) \| IQ3_XS \| 42.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q3_K_S.gguf) \| Q3_K_S \| 44.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.IQ3_S.gguf) \| IQ3_S \| 45.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.IQ3_M.gguf) \| IQ3_M \| 46.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 50.0 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 56.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q4_0.gguf.part2of2) \| Q4_0 \| 58.5 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 59.0 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 62.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 71.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 73.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q6_K.gguf.part2of2) \| Q6_K \| 85.1 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Melusine_103b-GGUF/resolve/main/Melusine_103b.Q8_0.gguf.part3of3) \| Q8_0 \| 110.0 \| 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-13b-h-GGUF	mradermacher	2024-05-06T05:28:20Z	4	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-31T19:58:01Z	--- base_model: sanjay920/rubra-13b-h language: - en library_name: transformers quantized_by: mradermacher --- ## About static quants of https://huggingface.co/sanjay920/rubra-13b-h <!-- 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-13b-h-GGUF/resolve/main/rubra-13b-h.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.IQ3_M.gguf) \| IQ3_M \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.IQ4_XS.gguf) \| IQ4_XS \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q4_K_M.gguf) \| Q4_K_M \| 7.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q5_K_S.gguf) \| Q5_K_S \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q5_K_M.gguf) \| Q5_K_M \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q6_K.gguf) \| Q6_K \| 10.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/rubra-13b-h-GGUF/resolve/main/rubra-13b-h.Q8_0.gguf) \| Q8_0 \| 13.5 \| 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/KitchenSink_103b-GGUF	mradermacher	2024-05-06T05:27:53Z	74	1	transformers	[ "transformers", "gguf", "rp", "erp", "chat", "storywriting", "en", "base_model:MarsupialAI/KitchenSink_103b", "base_model:quantized:MarsupialAI/KitchenSink_103b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-03-31T23:00:20Z	--- base_model: MarsupialAI/KitchenSink_103b language: - en library_name: transformers license: llama2 quantized_by: mradermacher tags: - rp - erp - chat - storywriting --- ## About static quants of https://huggingface.co/MarsupialAI/KitchenSink_103b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/KitchenSink_103b-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/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q2_K.gguf) \| Q2_K \| 38.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.IQ3_XS.gguf) \| IQ3_XS \| 42.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q3_K_S.gguf) \| Q3_K_S \| 44.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.IQ3_S.gguf) \| IQ3_S \| 45.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.IQ3_M.gguf) \| IQ3_M \| 46.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 50.0 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 56.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 59.0 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 62.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 71.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 73.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q6_K.gguf.part2of2) \| Q6_K \| 85.1 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/KitchenSink_103b-GGUF/resolve/main/KitchenSink_103b.Q8_0.gguf.part3of3) \| Q8_0 \| 110.0 \| 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/MistralMath-7B-v0.1-GGUF	mradermacher	2024-05-06T05:27:16Z	22	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "WizardLM/WizardMath-7B-V1.1", "meta-math/MetaMath-Mistral-7B", "en", "base_model:nachoaristimuno/MistralMath-7B-v0.1", "base_model:quantized:nachoaristimuno/MistralMath-7B-v0.1", "endpoints_compatible", "region:us" ]	null	2024-04-01T03:34:59Z	--- base_model: nachoaristimuno/MistralMath-7B-v0.1 language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - WizardLM/WizardMath-7B-V1.1 - meta-math/MetaMath-Mistral-7B --- ## About static quants of https://huggingface.co/nachoaristimuno/MistralMath-7B-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/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MistralMath-7B-v0.1-GGUF/resolve/main/MistralMath-7B-v0.1.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/Helen-v1_7B-GGUF	mradermacher	2024-05-06T05:27:11Z	24	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "mistral", "roleplay", "en", "base_model:Virt-io/Helen-v1_7B", "base_model:quantized:Virt-io/Helen-v1_7B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-01T04:59:07Z	--- base_model: Virt-io/Helen-v1_7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - mistral - roleplay --- ## About static quants of https://huggingface.co/Virt-io/Helen-v1_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/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Helen-v1_7B-GGUF/resolve/main/Helen-v1_7B.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/lemonade-rebase-32k-7B-GGUF	mradermacher	2024-05-06T05:27:08Z	34	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:grimjim/lemonade-rebase-32k-7B", "base_model:quantized:grimjim/lemonade-rebase-32k-7B", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-01T05:09:10Z	--- base_model: grimjim/lemonade-rebase-32k-7B language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About static quants of https://huggingface.co/grimjim/lemonade-rebase-32k-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/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/lemonade-rebase-32k-7B-GGUF/resolve/main/lemonade-rebase-32k-7B.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/athena-120b-GGUF	mradermacher	2024-05-06T05:26:29Z	10	0	transformers	[ "transformers", "gguf", "merge", "en", "base_model:ibivibiv/athena-120b", "base_model:quantized:ibivibiv/athena-120b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-01T05:58:00Z	--- base_model: ibivibiv/athena-120b language: - en library_name: transformers license: llama2 quantized_by: mradermacher tags: - merge --- ## About static quants of https://huggingface.co/ibivibiv/athena-120b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/athena-120b-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/athena-120b-GGUF/resolve/main/athena-120b.Q2_K.gguf) \| Q2_K \| 45.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ3_XS.gguf.part2of2) \| IQ3_XS \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q3_K_S.gguf.part2of2) \| Q3_K_S \| 52.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ3_S.gguf.part2of2) \| IQ3_S \| 52.9 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ3_M.gguf.part2of2) \| IQ3_M \| 54.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 58.8 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 63.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 65.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 69.2 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 73.1 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 83.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 86.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q6_K.gguf.part3of3) \| Q6_K \| 99.6 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/athena-120b-GGUF/resolve/main/athena-120b.Q8_0.gguf.part3of3) \| Q8_0 \| 128.7 \| 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/LadybirdGonzo-7B-slerp-GGUF	mradermacher	2024-05-06T05:26:25Z	5	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "Badgids/Gonzo-Chat-7B", "bobofrut/ladybird-base-7B-v8", "en", "base_model:allknowingroger/LadybirdGonzo-7B-slerp", "base_model:quantized:allknowingroger/LadybirdGonzo-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-01T06:22:40Z	--- base_model: allknowingroger/LadybirdGonzo-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - Badgids/Gonzo-Chat-7B - bobofrut/ladybird-base-7B-v8 --- ## About static quants of https://huggingface.co/allknowingroger/LadybirdGonzo-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/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LadybirdGonzo-7B-slerp-GGUF/resolve/main/LadybirdGonzo-7B-slerp.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/Merak-7B-v4-GGUF	mradermacher	2024-05-06T05:26:06Z	50	0	transformers	[ "transformers", "gguf", "id", "en", "dataset:wikipedia", "dataset:Ichsan2895/OASST_Top1_Indonesian", "dataset:Ichsan2895/alpaca-gpt4-indonesian", "base_model:Ichsan2895/Merak-7B-v4", "base_model:quantized:Ichsan2895/Merak-7B-v4", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-01T06:35:32Z	--- base_model: Ichsan2895/Merak-7B-v4 datasets: - wikipedia - Ichsan2895/OASST_Top1_Indonesian - Ichsan2895/alpaca-gpt4-indonesian language: - id - en library_name: transformers license: cc-by-nc-sa-4.0 quantized_by: mradermacher --- ## About static quants of https://huggingface.co/Ichsan2895/Merak-7B-v4 <!-- 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/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q2_K.gguf) \| Q2_K \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.IQ3_XS.gguf) \| IQ3_XS \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q3_K_S.gguf) \| Q3_K_S \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.IQ3_S.gguf) \| IQ3_S \| 3.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.IQ3_M.gguf) \| IQ3_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q3_K_M.gguf) \| Q3_K_M \| 4.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q3_K_L.gguf) \| Q3_K_L \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.IQ4_XS.gguf) \| IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q4_K_S.gguf) \| Q4_K_S \| 4.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q4_K_M.gguf) \| Q4_K_M \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q5_K_S.gguf) \| Q5_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q5_K_M.gguf) \| Q5_K_M \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q6_K.gguf) \| Q6_K \| 6.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Merak-7B-v4-GGUF/resolve/main/Merak-7B-v4.Q8_0.gguf) \| Q8_0 \| 8.2 \| 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/MasherAI-v6-7B-GGUF	mradermacher	2024-05-06T05:25:55Z	4	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "base_model:mahiatlinux/MasherAI-v6-7B", "base_model:quantized:mahiatlinux/MasherAI-v6-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-01T08:00:28Z	--- base_model: mahiatlinux/MasherAI-v6-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About static quants of https://huggingface.co/mahiatlinux/MasherAI-v6-7B <!-- 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/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MasherAI-v6-7B-GGUF/resolve/main/MasherAI-v6-7B.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 -->
tsavage68/chat_550_STEPS_01beta_1e6_rate_CDPOSFT	tsavage68	2024-05-06T05:25:40Z	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	2024-05-06T05:14:30Z	--- base_model: tsavage68/chat_600STEPS_1e8rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: chat_550_STEPS_01beta_1e6_rate_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_550_STEPS_01beta_1e6_rate_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: 0.6716 - Rewards/chosen: -0.1192 - Rewards/rejected: -0.1802 - Rewards/accuracies: 0.5253 - Rewards/margins: 0.0610 - Logps/rejected: -20.6044 - Logps/chosen: -17.9469 - Logits/rejected: -0.6222 - Logits/chosen: -0.6220 ## 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: 550 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6925 \| 0.0977 \| 50 \| 0.6917 \| 0.0117 \| 0.0085 \| 0.4659 \| 0.0031 \| -18.7166 \| -16.6380 \| -0.6015 \| -0.6013 \| \| 0.6776 \| 0.1953 \| 100 \| 0.6812 \| -0.0371 \| -0.0646 \| 0.5253 \| 0.0275 \| -19.4479 \| -17.1259 \| -0.6242 \| -0.6241 \| \| 0.6927 \| 0.2930 \| 150 \| 0.6819 \| -0.0802 \| -0.1112 \| 0.5011 \| 0.0310 \| -19.9140 \| -17.5569 \| -0.6222 \| -0.6220 \| \| 0.6928 \| 0.3906 \| 200 \| 0.6776 \| -0.1032 \| -0.1444 \| 0.5033 \| 0.0412 \| -20.2463 \| -17.7865 \| -0.6050 \| -0.6048 \| \| 0.6937 \| 0.4883 \| 250 \| 0.6762 \| -0.0643 \| -0.1121 \| 0.5121 \| 0.0478 \| -19.9228 \| -17.3977 \| -0.6013 \| -0.6011 \| \| 0.6758 \| 0.5859 \| 300 \| 0.6717 \| -0.1055 \| -0.1663 \| 0.5231 \| 0.0608 \| -20.4645 \| -17.8094 \| -0.6301 \| -0.6299 \| \| 0.6696 \| 0.6836 \| 350 \| 0.6724 \| -0.1144 \| -0.1731 \| 0.5275 \| 0.0587 \| -20.5330 \| -17.8991 \| -0.6162 \| -0.6160 \| \| 0.6587 \| 0.7812 \| 400 \| 0.6711 \| -0.1221 \| -0.1842 \| 0.5297 \| 0.0621 \| -20.6441 \| -17.9756 \| -0.6249 \| -0.6247 \| \| 0.6755 \| 0.8789 \| 450 \| 0.6713 \| -0.1178 \| -0.1794 \| 0.5341 \| 0.0616 \| -20.5960 \| -17.9326 \| -0.6214 \| -0.6212 \| \| 0.6637 \| 0.9766 \| 500 \| 0.6712 \| -0.1188 \| -0.1808 \| 0.5253 \| 0.0620 \| -20.6100 \| -17.9427 \| -0.6222 \| -0.6220 \| \| 0.5575 \| 1.0742 \| 550 \| 0.6716 \| -0.1192 \| -0.1802 \| 0.5253 \| 0.0610 \| -20.6044 \| -17.9469 \| -0.6222 \| -0.6220 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.0 - Tokenizers 0.19.1
mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF	mradermacher	2024-05-06T05:25:33Z	23	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-01T09:23:52Z	--- base_model: LeroyDyer/Mixtral_AI_CyberBrain_SFT language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl --- ## About static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_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_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberBrain_SFT-GGUF/resolve/main/Mixtral_AI_CyberBrain_SFT.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/saily-13b-v0-GGUF	mradermacher	2024-05-06T05:24:55Z	90	0	transformers	[ "transformers", "gguf", "7B", "Saily", "DEEPNIGHT", "Llama", "Llama2", "en", "base_model:deepnight-research/saily-13b-v0", "base_model:quantized:deepnight-research/saily-13b-v0", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-01T13:39:23Z	--- base_model: deepnight-research/saily-13b-v0 language: - en library_name: transformers license: other quantized_by: mradermacher tags: - 7B - Saily - DEEPNIGHT - Llama - Llama2 --- ## About static quants of https://huggingface.co/deepnight-research/saily-13b-v0 <!-- 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/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q2_K.gguf) \| Q2_K \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.IQ3_XS.gguf) \| IQ3_XS \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.IQ3_S.gguf) \| IQ3_S \| 6.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q3_K_S.gguf) \| Q3_K_S \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.IQ3_M.gguf) \| IQ3_M \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q3_K_M.gguf) \| Q3_K_M \| 6.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q3_K_L.gguf) \| Q3_K_L \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.IQ4_XS.gguf) \| IQ4_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q4_K_S.gguf) \| Q4_K_S \| 7.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q4_K_M.gguf) \| Q4_K_M \| 8.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q5_K_S.gguf) \| Q5_K_S \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q5_K_M.gguf) \| Q5_K_M \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q6_K.gguf) \| Q6_K \| 11.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/saily-13b-v0-GGUF/resolve/main/saily-13b-v0.Q8_0.gguf) \| Q8_0 \| 14.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Yeet_51b_200k-GGUF	mradermacher	2024-05-06T05:24:43Z	106	0	transformers	[ "transformers", "gguf", "en", "base_model:MarsupialAI/Yeet_51b_200k", "base_model:quantized:MarsupialAI/Yeet_51b_200k", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-01T16:03:18Z	--- base_model: MarsupialAI/Yeet_51b_200k language: - en library_name: transformers license: other license_name: yi-other quantized_by: mradermacher --- ## About static quants of https://huggingface.co/MarsupialAI/Yeet_51b_200k <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Yeet_51b_200k-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/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q2_K.gguf) \| Q2_K \| 19.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ3_XS.gguf) \| IQ3_XS \| 21.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q3_K_S.gguf) \| Q3_K_S \| 22.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ3_S.gguf) \| IQ3_S \| 22.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ3_M.gguf) \| IQ3_M \| 23.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q3_K_M.gguf) \| Q3_K_M \| 25.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q3_K_L.gguf) \| Q3_K_L \| 27.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ4_XS.gguf) \| IQ4_XS \| 28.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q4_K_S.gguf) \| Q4_K_S \| 29.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q4_K_M.gguf) \| Q4_K_M \| 31.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q5_K_S.gguf) \| Q5_K_S \| 35.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q5_K_M.gguf) \| Q5_K_M \| 36.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q6_K.gguf) \| Q6_K \| 42.6 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q8_0.gguf.part2of2) \| Q8_0 \| 54.9 \| 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/penchant-7B-GGUF	mradermacher	2024-05-06T05:24:39Z	5	0	transformers	[ "transformers", "gguf", "en", "base_model:afoland/penchant-7B", "base_model:quantized:afoland/penchant-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-01T16:09:20Z	--- base_model: afoland/penchant-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About static quants of https://huggingface.co/afoland/penchant-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/penchant-7B-GGUF/resolve/main/penchant-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/penchant-7B-GGUF/resolve/main/penchant-7B.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/TimeMax-20B-GGUF	mradermacher	2024-05-06T05:24:37Z	2	0	transformers	[ "transformers", "gguf", "en", "base_model:R136a1/TimeMax-20B", "base_model:quantized:R136a1/TimeMax-20B", "endpoints_compatible", "region:us" ]	null	2024-04-01T16:14:35Z	--- base_model: R136a1/TimeMax-20B language: - en library_name: transformers quantized_by: mradermacher --- ## About static quants of https://huggingface.co/R136a1/TimeMax-20B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/TimeMax-20B-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/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q2_K.gguf) \| Q2_K \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ3_XS.gguf) \| IQ3_XS \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ3_S.gguf) \| IQ3_S \| 9.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q3_K_S.gguf) \| Q3_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ3_M.gguf) \| IQ3_M \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q3_K_M.gguf) \| Q3_K_M \| 10.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q3_K_L.gguf) \| Q3_K_L \| 10.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ4_XS.gguf) \| IQ4_XS \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q4_K_S.gguf) \| Q4_K_S \| 11.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q4_K_M.gguf) \| Q4_K_M \| 12.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q5_K_S.gguf) \| Q5_K_S \| 14.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q5_K_M.gguf) \| Q5_K_M \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q6_K.gguf) \| Q6_K \| 16.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q8_0.gguf) \| Q8_0 \| 21.5 \| 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/GodziLLa-30B-i1-GGUF	mradermacher	2024-05-06T05:24:23Z	108	1	transformers	[ "transformers", "gguf", "merge", "mix", "cot", "en", "base_model:MayaPH/GodziLLa-30B", "base_model:quantized:MayaPH/GodziLLa-30B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-01T19:31:22Z	--- base_model: MayaPH/GodziLLa-30B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - merge - mix - cot --- ## About weighted/imatrix quants of https://huggingface.co/MayaPH/GodziLLa-30B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/GodziLLa-30B-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/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 15.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GodziLLa-30B-i1-GGUF/resolve/main/GodziLLa-30B.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| 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/TimeMax-20B-i1-GGUF	mradermacher	2024-05-06T05:24:20Z	6	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "text-generation", "en", "base_model:R136a1/TimeMax-20B", "base_model:quantized:R136a1/TimeMax-20B", "endpoints_compatible", "region:us" ]	text-generation	2024-04-01T20:10:41Z	--- base_model: R136a1/TimeMax-20B language: - en library_name: transformers pipeline_tag: text-generation quantized_by: mradermacher tags: - mergekit - merge --- ## About weighted/imatrix quants of https://huggingface.co/R136a1/TimeMax-20B Only 50k tokens from my standard set have been used, as more caused an overflow. This is likely a problem with the model itself. <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/TimeMax-20B-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/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 4.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q2_K.gguf) \| i1-Q2_K \| 7.7 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 7.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 9.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 9.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 10.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 10.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q4_0.gguf) \| i1-Q4_0 \| 11.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 11.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 12.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 14.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q6_K.gguf) \| i1-Q6_K \| 16.7 \| 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/Stork-7B-slerp-GGUF	mradermacher	2024-05-06T05:24:18Z	6	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "bofenghuang/vigostral-7b-chat", "jpacifico/French-Alpaca-7B-Instruct-beta", "fr", "base_model:ntnq/Stork-7B-slerp", "base_model:quantized:ntnq/Stork-7B-slerp", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-01T20:27:02Z	--- base_model: ntnq/Stork-7B-slerp language: - fr library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - bofenghuang/vigostral-7b-chat - jpacifico/French-Alpaca-7B-Instruct-beta --- ## About static quants of https://huggingface.co/ntnq/Stork-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/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Stork-7B-slerp-GGUF/resolve/main/Stork-7B-slerp.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/Zebrafish-dare-7B-GGUF	mradermacher	2024-05-06T05:24:07Z	17	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:mlabonne/Zebrafish-dare-7B", "base_model:quantized:mlabonne/Zebrafish-dare-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-01T23:15:33Z	--- base_model: mlabonne/Zebrafish-dare-7B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About static quants of https://huggingface.co/mlabonne/Zebrafish-dare-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/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-dare-7B-GGUF/resolve/main/Zebrafish-dare-7B.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/Zebrafish-linear-7B-GGUF	mradermacher	2024-05-06T05:24:05Z	13	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:mlabonne/Zebrafish-linear-7B", "base_model:quantized:mlabonne/Zebrafish-linear-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-02T00:44:08Z	--- base_model: mlabonne/Zebrafish-linear-7B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About static quants of https://huggingface.co/mlabonne/Zebrafish-linear-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/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.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/MonarchPipe-7B-slerp-GGUF	mradermacher	2024-05-06T05:23:52Z	25	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1227", "mlabonne/AlphaMonarch-7B", "en", "base_model:ichigoberry/MonarchPipe-7B-slerp", "base_model:quantized:ichigoberry/MonarchPipe-7B-slerp", "license:cc-by-nc-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-02T02:11:17Z	--- base_model: ichigoberry/MonarchPipe-7B-slerp language: - en library_name: transformers license: cc-by-nc-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1227 - mlabonne/AlphaMonarch-7B --- ## About static quants of https://huggingface.co/ichigoberry/MonarchPipe-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/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.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 -->
Wespeaker/wespeaker-voxceleb-gemini-DFresnet114-LM	Wespeaker	2024-05-06T05:23:46Z	4	0	null	[ "onnx", "license:apache-2.0", "region:us" ]	null	2024-05-06T05:14:05Z	--- license: apache-2.0 ---
mradermacher/NeuralStock-7B-v2-GGUF	mradermacher	2024-05-06T05:23:35Z	1	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:Kukedlc/NeuralStock-7B-v2", "base_model:quantized:Kukedlc/NeuralStock-7B-v2", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-02T04:58:23Z	--- base_model: Kukedlc/NeuralStock-7B-v2 language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About static quants of https://huggingface.co/Kukedlc/NeuralStock-7B-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/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-v2-GGUF/resolve/main/NeuralStock-7B-v2.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/UNAversal-8x7B-v1beta-GGUF	mradermacher	2024-05-06T05:23:33Z	69	0	transformers	[ "transformers", "gguf", "UNA", "juanako", "mixtral", "MoE", "en", "base_model:fblgit/UNAversal-8x7B-v1beta", "base_model:quantized:fblgit/UNAversal-8x7B-v1beta", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-02T05:11:23Z	--- base_model: fblgit/UNAversal-8x7B-v1beta language: - en library_name: transformers license: cc-by-nc-sa-4.0 quantized_by: mradermacher tags: - UNA - juanako - mixtral - MoE --- ## About static quants of https://huggingface.co/fblgit/UNAversal-8x7B-v1beta <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-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/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q2_K.gguf) \| Q2_K \| 17.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ3_XS.gguf) \| IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ3_S.gguf) \| IQ3_S \| 20.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q3_K_S.gguf) \| Q3_K_S \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ3_M.gguf) \| IQ3_M \| 21.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q3_K_M.gguf) \| Q3_K_M \| 22.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q3_K_L.gguf) \| Q3_K_L \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ4_XS.gguf) \| IQ4_XS \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q4_K_S.gguf) \| Q4_K_S \| 27.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q4_K_M.gguf) \| Q4_K_M \| 28.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q5_K_S.gguf) \| Q5_K_S \| 32.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q5_K_M.gguf) \| Q5_K_M \| 33.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q6_K.gguf) \| Q6_K \| 38.6 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q8_0.gguf.part2of2) \| Q8_0 \| 49.8 \| 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/SatoshiNv5-GGUF	mradermacher	2024-05-06T05:23:28Z	55	0	transformers	[ "transformers", "gguf", "finance", "legal", "biology", "art", "en", "base_model:chrischain/SatoshiNv5", "base_model:quantized:chrischain/SatoshiNv5", "license:cc-by-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-02T06:23:06Z	--- base_model: chrischain/SatoshiNv5 language: - en library_name: transformers license: cc-by-2.0 quantized_by: mradermacher tags: - finance - legal - biology - art --- ## About static quants of https://huggingface.co/chrischain/SatoshiNv5 <!-- 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/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SatoshiNv5-GGUF/resolve/main/SatoshiNv5.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/bagel-dpo-20b-v04-GGUF	mradermacher	2024-05-06T05:23:25Z	206	2	transformers	[ "transformers", "gguf", "en", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:jondurbin/bagel-dpo-20b-v04", "base_model:quantized:jondurbin/bagel-dpo-20b-v04", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-02T06:31:44Z	--- base_model: jondurbin/bagel-dpo-20b-v04 datasets: - ai2_arc - allenai/ultrafeedback_binarized_cleaned - argilla/distilabel-intel-orca-dpo-pairs - jondurbin/airoboros-3.2 - codeparrot/apps - facebook/belebele - bluemoon-fandom-1-1-rp-cleaned - boolq - camel-ai/biology - camel-ai/chemistry - camel-ai/math - camel-ai/physics - jondurbin/contextual-dpo-v0.1 - jondurbin/gutenberg-dpo-v0.1 - jondurbin/py-dpo-v0.1 - jondurbin/truthy-dpo-v0.1 - LDJnr/Capybara - jondurbin/cinematika-v0.1 - WizardLM/WizardLM_evol_instruct_70k - glaiveai/glaive-function-calling-v2 - jondurbin/gutenberg-dpo-v0.1 - grimulkan/LimaRP-augmented - lmsys/lmsys-chat-1m - ParisNeo/lollms_aware_dataset - TIGER-Lab/MathInstruct - Muennighoff/natural-instructions - openbookqa - kingbri/PIPPA-shareGPT - piqa - Vezora/Tested-22k-Python-Alpaca - ropes - cakiki/rosetta-code - Open-Orca/SlimOrca - b-mc2/sql-create-context - squad_v2 - mattpscott/airoboros-summarization - migtissera/Synthia-v1.3 - unalignment/toxic-dpo-v0.2 - WhiteRabbitNeo/WRN-Chapter-1 - WhiteRabbitNeo/WRN-Chapter-2 - winogrande language: - en library_name: transformers license: other license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license license_name: internlm2-20b quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jondurbin/bagel-dpo-20b-v04 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-dpo-20b-v04-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/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q2_K.gguf) \| Q2_K \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ3_XS.gguf) \| IQ3_XS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q3_K_S.gguf) \| Q3_K_S \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ3_S.gguf) \| IQ3_S \| 9.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ3_M.gguf) \| IQ3_M \| 9.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q3_K_M.gguf) \| Q3_K_M \| 10.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q3_K_L.gguf) \| Q3_K_L \| 11.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ4_XS.gguf) \| IQ4_XS \| 11.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q4_K_S.gguf) \| Q4_K_S \| 12.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q4_K_M.gguf) \| Q4_K_M \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q5_K_S.gguf) \| Q5_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q5_K_M.gguf) \| Q5_K_M \| 14.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q6_K.gguf) \| Q6_K \| 17.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q8_0.gguf) \| Q8_0 \| 21.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.SOURCE.gguf) \| SOURCE \| 39.8 \| source gguf, only provided when it was hard to come by \| 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/Yeet_51b_200k-i1-GGUF	mradermacher	2024-05-06T05:23:14Z	30	0	transformers	[ "transformers", "gguf", "en", "base_model:MarsupialAI/Yeet_51b_200k", "base_model:quantized:MarsupialAI/Yeet_51b_200k", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-02T09:58:07Z	--- base_model: MarsupialAI/Yeet_51b_200k language: - en library_name: transformers license: other license_name: yi-other no_imatrix: 'IQ3_XXS GGML_ASSERT: llama.cpp/ggml-quants.c:11239: grid_index >= 0' quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/MarsupialAI/Yeet_51b_200k No more quants forthcoming, as llama.cpp crashes. <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Yeet_51b_200k-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/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q2_K.gguf) \| i1-Q2_K \| 19.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 22.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 25.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 27.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q4_0.gguf) \| i1-Q4_0 \| 29.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 29.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 31.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 35.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 36.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q6_K.gguf) \| i1-Q6_K \| 42.6 \| 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/NeuralSirKrishna-7b-DPO-GGUF	mradermacher	2024-05-06T05:23:09Z	4	0	transformers	[ "transformers", "gguf", "en", "base_model:Kukedlc/NeuralSirKrishna-7b-DPO", "base_model:quantized:Kukedlc/NeuralSirKrishna-7b-DPO", "endpoints_compatible", "region:us" ]	null	2024-04-02T10:50:14Z	--- base_model: Kukedlc/NeuralSirKrishna-7b-DPO language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Kukedlc/NeuralSirKrishna-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/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralSirKrishna-7b-DPO-GGUF/resolve/main/NeuralSirKrishna-7b-DPO.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/bagel-dpo-34b-v0.5-GGUF	mradermacher	2024-05-06T05:23:03Z	80	8	transformers	[ "transformers", "gguf", "en", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:jondurbin/bagel-dpo-34b-v0.5", "base_model:quantized:jondurbin/bagel-dpo-34b-v0.5", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-02T12:20:42Z	--- base_model: jondurbin/bagel-dpo-34b-v0.5 datasets: - ai2_arc - allenai/ultrafeedback_binarized_cleaned - argilla/distilabel-intel-orca-dpo-pairs - jondurbin/airoboros-3.2 - codeparrot/apps - facebook/belebele - bluemoon-fandom-1-1-rp-cleaned - boolq - camel-ai/biology - camel-ai/chemistry - camel-ai/math - camel-ai/physics - jondurbin/contextual-dpo-v0.1 - jondurbin/gutenberg-dpo-v0.1 - jondurbin/py-dpo-v0.1 - jondurbin/truthy-dpo-v0.1 - LDJnr/Capybara - jondurbin/cinematika-v0.1 - WizardLM/WizardLM_evol_instruct_70k - glaiveai/glaive-function-calling-v2 - jondurbin/gutenberg-dpo-v0.1 - grimulkan/LimaRP-augmented - lmsys/lmsys-chat-1m - ParisNeo/lollms_aware_dataset - TIGER-Lab/MathInstruct - Muennighoff/natural-instructions - openbookqa - kingbri/PIPPA-shareGPT - piqa - Vezora/Tested-22k-Python-Alpaca - ropes - cakiki/rosetta-code - Open-Orca/SlimOrca - b-mc2/sql-create-context - squad_v2 - mattpscott/airoboros-summarization - migtissera/Synthia-v1.3 - unalignment/toxic-dpo-v0.2 - WhiteRabbitNeo/WRN-Chapter-1 - WhiteRabbitNeo/WRN-Chapter-2 - winogrande language: - en library_name: transformers license: other license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE license_name: yi-license quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jondurbin/bagel-dpo-34b-v0.5 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-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/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q2_K.gguf) \| Q2_K \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.IQ3_XS.gguf) \| IQ3_XS \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q3_K_S.gguf) \| Q3_K_S \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.IQ3_S.gguf) \| IQ3_S \| 15.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.IQ3_M.gguf) \| IQ3_M \| 16.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q3_K_M.gguf) \| Q3_K_M \| 17.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q3_K_L.gguf) \| Q3_K_L \| 18.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.IQ4_XS.gguf) \| IQ4_XS \| 19.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q4_K_S.gguf) \| Q4_K_S \| 20.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q4_K_M.gguf) \| Q4_K_M \| 21.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q5_K_S.gguf) \| Q5_K_S \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q5_K_M.gguf) \| Q5_K_M \| 25.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q6_K.gguf) \| Q6_K \| 28.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF/resolve/main/bagel-dpo-34b-v0.5.Q8_0.gguf) \| Q8_0 \| 37.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/bagel-34b-v0.5-GGUF	mradermacher	2024-05-06T05:22:41Z	3	0	transformers	[ "transformers", "gguf", "en", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:jondurbin/bagel-34b-v0.5", "base_model:quantized:jondurbin/bagel-34b-v0.5", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-02T14:45:55Z	--- base_model: jondurbin/bagel-34b-v0.5 datasets: - ai2_arc - allenai/ultrafeedback_binarized_cleaned - argilla/distilabel-intel-orca-dpo-pairs - jondurbin/airoboros-3.2 - codeparrot/apps - facebook/belebele - bluemoon-fandom-1-1-rp-cleaned - boolq - camel-ai/biology - camel-ai/chemistry - camel-ai/math - camel-ai/physics - jondurbin/contextual-dpo-v0.1 - jondurbin/gutenberg-dpo-v0.1 - jondurbin/py-dpo-v0.1 - jondurbin/truthy-dpo-v0.1 - LDJnr/Capybara - jondurbin/cinematika-v0.1 - WizardLM/WizardLM_evol_instruct_70k - glaiveai/glaive-function-calling-v2 - jondurbin/gutenberg-dpo-v0.1 - grimulkan/LimaRP-augmented - lmsys/lmsys-chat-1m - ParisNeo/lollms_aware_dataset - TIGER-Lab/MathInstruct - Muennighoff/natural-instructions - openbookqa - kingbri/PIPPA-shareGPT - piqa - Vezora/Tested-22k-Python-Alpaca - ropes - cakiki/rosetta-code - Open-Orca/SlimOrca - b-mc2/sql-create-context - squad_v2 - mattpscott/airoboros-summarization - migtissera/Synthia-v1.3 - unalignment/toxic-dpo-v0.2 - WhiteRabbitNeo/WRN-Chapter-1 - WhiteRabbitNeo/WRN-Chapter-2 - winogrande language: - en library_name: transformers license: other license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE license_name: yi-license quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jondurbin/bagel-34b-v0.5 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-34b-v0.5-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/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q2_K.gguf) \| Q2_K \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.IQ3_XS.gguf) \| IQ3_XS \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q3_K_S.gguf) \| Q3_K_S \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.IQ3_S.gguf) \| IQ3_S \| 15.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.IQ3_M.gguf) \| IQ3_M \| 16.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q3_K_M.gguf) \| Q3_K_M \| 17.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q3_K_L.gguf) \| Q3_K_L \| 18.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.IQ4_XS.gguf) \| IQ4_XS \| 19.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q4_K_S.gguf) \| Q4_K_S \| 20.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q4_K_M.gguf) \| Q4_K_M \| 21.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q5_K_S.gguf) \| Q5_K_S \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q5_K_M.gguf) \| Q5_K_M \| 25.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q6_K.gguf) \| Q6_K \| 28.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-34b-v0.5-GGUF/resolve/main/bagel-34b-v0.5.Q8_0.gguf) \| Q8_0 \| 37.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
nuebaek/komt_mistral_mss_user_111_max_steps_80	nuebaek	2024-05-06T05:22:39Z	76	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-06T05:19:43Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/TripleMerge-7B-Ties-GGUF	mradermacher	2024-05-06T05:22:37Z	29	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "allknowingroger/MultiverseEx26-7B-slerp", "allknowingroger/limyClown-7B-slerp", "allknowingroger/LeeMerge-7B-slerp", "en", "base_model:allknowingroger/TripleMerge-7B-Ties", "base_model:quantized:allknowingroger/TripleMerge-7B-Ties", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-02T16:30:30Z	--- base_model: allknowingroger/TripleMerge-7B-Ties language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - allknowingroger/MultiverseEx26-7B-slerp - allknowingroger/limyClown-7B-slerp - allknowingroger/LeeMerge-7B-slerp --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/allknowingroger/TripleMerge-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/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TripleMerge-7B-Ties-GGUF/resolve/main/TripleMerge-7B-Ties.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 -->
kristina-shemet/Fine-Tuned_Mistral-Instruct-V2_06-05	kristina-shemet	2024-05-06T05:22:22Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-05-06T05:22:02Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # 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. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/DevPearl-2x7B-GGUF	mradermacher	2024-05-06T05:22:20Z	127	1	transformers	[ "transformers", "gguf", "moe", "merge", "mergekit", "lazymergekit", "deepseek-ai/deepseek-coder-6.7b-instruct", "defog/sqlcoder-7b-2", "Python", "Javascript", "sql", "en", "base_model:louisbrulenaudet/DevPearl-2x7B", "base_model:quantized:louisbrulenaudet/DevPearl-2x7B", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-02T18:03:56Z	--- base_model: louisbrulenaudet/DevPearl-2x7B language: - en library_name: transformers license: cc-by-sa-4.0 quantized_by: mradermacher tags: - moe - merge - mergekit - lazymergekit - deepseek-ai/deepseek-coder-6.7b-instruct - defog/sqlcoder-7b-2 - Python - Javascript - sql --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/louisbrulenaudet/DevPearl-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/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q2_K.gguf) \| Q2_K \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.IQ3_XS.gguf) \| IQ3_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.IQ3_S.gguf) \| IQ3_S \| 5.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q3_K_S.gguf) \| Q3_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.IQ3_M.gguf) \| IQ3_M \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q3_K_M.gguf) \| Q3_K_M \| 5.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q3_K_L.gguf) \| Q3_K_L \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.IQ4_XS.gguf) \| IQ4_XS \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q4_K_S.gguf) \| Q4_K_S \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q4_K_M.gguf) \| Q4_K_M \| 7.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q5_K_S.gguf) \| Q5_K_S \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q5_K_M.gguf) \| Q5_K_M \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q6_K.gguf) \| Q6_K \| 9.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DevPearl-2x7B-GGUF/resolve/main/DevPearl-2x7B.Q8_0.gguf) \| Q8_0 \| 12.0 \| 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/airoboros-34b-3.3-GGUF	mradermacher	2024-05-06T05:21:59Z	60	0	transformers	[ "transformers", "gguf", "en", "dataset:jondurbin/airoboros-3.2", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:mattpscott/airoboros-summarization", "dataset:unalignment/toxic-dpo-v0.2", "base_model:jondurbin/airoboros-34b-3.3", "base_model:quantized:jondurbin/airoboros-34b-3.3", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-02T23:45:26Z	--- base_model: jondurbin/airoboros-34b-3.3 datasets: - jondurbin/airoboros-3.2 - bluemoon-fandom-1-1-rp-cleaned - boolq - jondurbin/gutenberg-dpo-v0.1 - LDJnr/Capybara - jondurbin/cinematika-v0.1 - glaiveai/glaive-function-calling-v2 - grimulkan/LimaRP-augmented - piqa - Vezora/Tested-22k-Python-Alpaca - mattpscott/airoboros-summarization - unalignment/toxic-dpo-v0.2 language: - en library_name: transformers license: other license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE license_name: yi-license quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jondurbin/airoboros-34b-3.3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/airoboros-34b-3.3-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/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q2_K.gguf) \| Q2_K \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ3_XS.gguf) \| IQ3_XS \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q3_K_S.gguf) \| Q3_K_S \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ3_S.gguf) \| IQ3_S \| 15.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ3_M.gguf) \| IQ3_M \| 16.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q3_K_M.gguf) \| Q3_K_M \| 17.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q3_K_L.gguf) \| Q3_K_L \| 18.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ4_XS.gguf) \| IQ4_XS \| 19.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q4_K_S.gguf) \| Q4_K_S \| 20.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q4_K_M.gguf) \| Q4_K_M \| 21.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q5_K_S.gguf) \| Q5_K_S \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q5_K_M.gguf) \| Q5_K_M \| 25.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q6_K.gguf) \| Q6_K \| 28.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q8_0.gguf) \| Q8_0 \| 37.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/bagel-dpo-20b-v04-i1-GGUF	mradermacher	2024-05-06T05:21:57Z	81	1	transformers	[ "transformers", "gguf", "en", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:jondurbin/bagel-dpo-20b-v04", "base_model:quantized:jondurbin/bagel-dpo-20b-v04", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-02T23:46:09Z	--- base_model: jondurbin/bagel-dpo-20b-v04 datasets: - ai2_arc - allenai/ultrafeedback_binarized_cleaned - argilla/distilabel-intel-orca-dpo-pairs - jondurbin/airoboros-3.2 - codeparrot/apps - facebook/belebele - bluemoon-fandom-1-1-rp-cleaned - boolq - camel-ai/biology - camel-ai/chemistry - camel-ai/math - camel-ai/physics - jondurbin/contextual-dpo-v0.1 - jondurbin/gutenberg-dpo-v0.1 - jondurbin/py-dpo-v0.1 - jondurbin/truthy-dpo-v0.1 - LDJnr/Capybara - jondurbin/cinematika-v0.1 - WizardLM/WizardLM_evol_instruct_70k - glaiveai/glaive-function-calling-v2 - jondurbin/gutenberg-dpo-v0.1 - grimulkan/LimaRP-augmented - lmsys/lmsys-chat-1m - ParisNeo/lollms_aware_dataset - TIGER-Lab/MathInstruct - Muennighoff/natural-instructions - openbookqa - kingbri/PIPPA-shareGPT - piqa - Vezora/Tested-22k-Python-Alpaca - ropes - cakiki/rosetta-code - Open-Orca/SlimOrca - b-mc2/sql-create-context - squad_v2 - mattpscott/airoboros-summarization - migtissera/Synthia-v1.3 - unalignment/toxic-dpo-v0.2 - WhiteRabbitNeo/WRN-Chapter-1 - WhiteRabbitNeo/WRN-Chapter-2 - winogrande language: - en library_name: transformers license: other license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license license_name: internlm2-20b quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-dpo-20b-v04 This uses only 95k tokens of my standard set, as the model overflowed with more. <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/bagel-dpo-20b-v04-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/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ2_M.gguf) \| i1-IQ2_M \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q2_K.gguf) \| i1-Q2_K \| 8.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 8.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 9.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ3_S.gguf) \| i1-IQ3_S \| 9.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ3_M.gguf) \| i1-IQ3_M \| 9.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 10.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 11.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 11.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q4_0.gguf) \| i1-Q4_0 \| 12.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 12.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 14.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF/resolve/main/bagel-dpo-20b-v04.i1-Q6_K.gguf) \| i1-Q6_K \| 17.1 \| 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-12.25B-Instruct-v0.2-GGUF	mradermacher	2024-05-06T05:21:54Z	17	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Joseph717171/Mistral-12.25B-Instruct-v0.2", "base_model:quantized:Joseph717171/Mistral-12.25B-Instruct-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T00:13:46Z	--- base_model: Joseph717171/Mistral-12.25B-Instruct-v0.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Joseph717171/Mistral-12.25B-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/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ3_M.gguf) \| IQ3_M \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 7.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q6_K.gguf) \| Q6_K \| 10.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q8_0.gguf) \| Q8_0 \| 13.5 \| 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/Irene-RP-v4-7B-GGUF	mradermacher	2024-05-06T05:21:45Z	35	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "roleplay", "mistral", "en", "base_model:Virt-io/Irene-RP-v4-7B", "base_model:quantized:Virt-io/Irene-RP-v4-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T00:56:57Z	--- base_model: Virt-io/Irene-RP-v4-7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - roleplay - mistral --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Virt-io/Irene-RP-v4-7B <!-- 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/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v4-7B-GGUF/resolve/main/Irene-RP-v4-7B.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/Pearl-34B-ties-GGUF	mradermacher	2024-05-06T05:21:42Z	19	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "jondurbin/bagel-dpo-34b-v0.2", "abacusai/MetaMath-Bagel-DPO-34B", "en", "base_model:louisbrulenaudet/Pearl-34B-ties", "base_model:quantized:louisbrulenaudet/Pearl-34B-ties", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T01:27:03Z	--- base_model: louisbrulenaudet/Pearl-34B-ties language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - jondurbin/bagel-dpo-34b-v0.2 - abacusai/MetaMath-Bagel-DPO-34B --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/louisbrulenaudet/Pearl-34B-ties <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pearl-34B-ties-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/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q2_K.gguf) \| Q2_K \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ3_XS.gguf) \| IQ3_XS \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q3_K_S.gguf) \| Q3_K_S \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ3_S.gguf) \| IQ3_S \| 15.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ3_M.gguf) \| IQ3_M \| 16.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q3_K_M.gguf) \| Q3_K_M \| 17.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q3_K_L.gguf) \| Q3_K_L \| 18.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ4_XS.gguf) \| IQ4_XS \| 19.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q4_K_S.gguf) \| Q4_K_S \| 20.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q4_K_M.gguf) \| Q4_K_M \| 21.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q5_K_S.gguf) \| Q5_K_S \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q5_K_M.gguf) \| Q5_K_M \| 25.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q6_K.gguf) \| Q6_K \| 28.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q8_0.gguf) \| Q8_0 \| 37.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/OverBloom-7b-GGUF	mradermacher	2024-05-06T05:21:29Z	4	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T03:57:02Z	--- base_model: nobita3921/OverBloom-7b language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/nobita3921/OverBloom-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/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.IQ3_S.gguf) \| IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.IQ3_M.gguf) \| IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OverBloom-7b-GGUF/resolve/main/OverBloom-7b.Q8_0.gguf) \| Q8_0 \| 8.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Pearl-34B-ties-i1-GGUF	mradermacher	2024-05-06T05:21:26Z	39	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "jondurbin/bagel-dpo-34b-v0.2", "abacusai/MetaMath-Bagel-DPO-34B", "en", "base_model:louisbrulenaudet/Pearl-34B-ties", "base_model:quantized:louisbrulenaudet/Pearl-34B-ties", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T04:15:00Z	--- base_model: louisbrulenaudet/Pearl-34B-ties language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - jondurbin/bagel-dpo-34b-v0.2 - abacusai/MetaMath-Bagel-DPO-34B --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/louisbrulenaudet/Pearl-34B-ties <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Pearl-34B-ties-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/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ1_S.gguf) \| i1-IQ1_S \| 8.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ2_S.gguf) \| i1-IQ2_S \| 11.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ2_M.gguf) \| i1-IQ2_M \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q2_K.gguf) \| i1-Q2_K \| 13.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 14.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 15.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ3_S.gguf) \| i1-IQ3_S \| 15.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ3_M.gguf) \| i1-IQ3_M \| 16.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 17.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 18.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 19.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q4_0.gguf) \| i1-Q4_0 \| 20.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 20.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 21.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 25.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF/resolve/main/Pearl-34B-ties.i1-Q6_K.gguf) \| i1-Q6_K \| 28.9 \| 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/neural-chat-7b-v3-GGUF	mradermacher	2024-05-06T05:21:16Z	29	0	transformers	[ "transformers", "gguf", "LLMs", "mistral", "Intel", "en", "dataset:Open-Orca/SlimOrca", "base_model:Intel/neural-chat-7b-v3", "base_model:quantized:Intel/neural-chat-7b-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-03T07:43:53Z	--- base_model: Intel/neural-chat-7b-v3 datasets: - Open-Orca/SlimOrca language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - LLMs - mistral - Intel --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Intel/neural-chat-7b-v3 <!-- 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/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-GGUF/resolve/main/neural-chat-7b-v3.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/LlaMixtral-MoE-16B-chat-GGUF	mradermacher	2024-05-06T05:21:12Z	5	0	transformers	[ "transformers", "gguf", "en", "base_model:AstraLLMs/LlaMixtral-MoE-16B-chat", "base_model:quantized:AstraLLMs/LlaMixtral-MoE-16B-chat", "endpoints_compatible", "region:us" ]	null	2024-04-03T08:23:36Z	--- base_model: AstraLLMs/LlaMixtral-MoE-16B-chat language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/AstraLLMs/LlaMixtral-MoE-16B-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/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q2_K.gguf) \| Q2_K \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.IQ3_XS.gguf) \| IQ3_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q3_K_S.gguf) \| Q3_K_S \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.IQ3_S.gguf) \| IQ3_S \| 7.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.IQ3_M.gguf) \| IQ3_M \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q3_K_M.gguf) \| Q3_K_M \| 7.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q3_K_L.gguf) \| Q3_K_L \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.IQ4_XS.gguf) \| IQ4_XS \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q4_K_S.gguf) \| Q4_K_S \| 9.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q4_K_M.gguf) \| Q4_K_M \| 9.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q5_K_S.gguf) \| Q5_K_S \| 11.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q5_K_M.gguf) \| Q5_K_M \| 11.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q6_K.gguf) \| Q6_K \| 13.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LlaMixtral-MoE-16B-chat-GGUF/resolve/main/LlaMixtral-MoE-16B-chat.Q8_0.gguf) \| Q8_0 \| 17.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/MultiVerse_70B-i1-GGUF	mradermacher	2024-05-06T05:21:09Z	35	0	transformers	[ "transformers", "gguf", "en", "base_model:MTSAIR/MultiVerse_70B", "base_model:quantized:MTSAIR/MultiVerse_70B", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-03T08:54:49Z	--- base_model: MTSAIR/MultiVerse_70B language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE license_name: qwen quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/MTSAIR/MultiVerse_70B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MultiVerse_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/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 18.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 19.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 23.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 25.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 26.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q2_K.gguf) \| i1-Q2_K \| 28.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 29.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 31.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 33.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 33.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 34.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 36.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 40.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 40.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q4_0.gguf) \| i1-Q4_0 \| 42.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 42.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 45.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 52.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 60.9 \| 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/Synatra-7B-v0.3-RP-GGUF	mradermacher	2024-05-06T05:21:06Z	17	1	transformers	[ "transformers", "gguf", "ko", "base_model:maywell/Synatra-7B-v0.3-RP", "base_model:quantized:maywell/Synatra-7B-v0.3-RP", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T09:51:38Z	--- base_model: maywell/Synatra-7B-v0.3-RP language: - ko library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/maywell/Synatra-7B-v0.3-RP <!-- 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/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.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/neural-chat-7b-v3-1-GGUF	mradermacher	2024-05-06T05:20:58Z	90	0	transformers	[ "transformers", "gguf", "LLMs", "mistral", "Intel", "en", "dataset:Open-Orca/SlimOrca", "base_model:Intel/neural-chat-7b-v3-1", "base_model:quantized:Intel/neural-chat-7b-v3-1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-03T12:09:19Z	--- base_model: Intel/neural-chat-7b-v3-1 datasets: - Open-Orca/SlimOrca language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - LLMs - mistral - Intel --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Intel/neural-chat-7b-v3-1 <!-- 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/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/neural-chat-7b-v3-1-GGUF/resolve/main/neural-chat-7b-v3-1.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/pandafish-7b-GGUF	mradermacher	2024-05-06T05:20:56Z	9	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:ichigoberry/pandafish-7b", "base_model:quantized:ichigoberry/pandafish-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-03T12:13:43Z	--- base_model: ichigoberry/pandafish-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ichigoberry/pandafish-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/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.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/Eurus-7b-sft-GGUF	mradermacher	2024-05-06T05:20:27Z	130	0	transformers	[ "transformers", "gguf", "reasoning", "en", "dataset:openbmb/UltraInteract", "dataset:stingning/ultrachat", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:Open-Orca/OpenOrca", "base_model:pharaouk/Eurus-7b-sft", "base_model:quantized:pharaouk/Eurus-7b-sft", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T16:05:16Z	--- base_model: pharaouk/Eurus-7b-sft datasets: - openbmb/UltraInteract - stingning/ultrachat - openchat/openchat_sharegpt4_dataset - Open-Orca/OpenOrca language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - reasoning --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/pharaouk/Eurus-7b-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/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.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/Fireplace-34b-GGUF	mradermacher	2024-05-06T05:20:22Z	63	0	transformers	[ "transformers", "gguf", "fireplace", "function-calling", "code", "code-instruct", "conversational", "text-generation-inference", "valiant", "valiant-labs", "smaug", "yi", "yi-34b", "llama", "llama-2", "llama-2-chat", "34b", "en", "base_model:ValiantLabs/Fireplace-34b", "base_model:quantized:ValiantLabs/Fireplace-34b", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-03T17:29:23Z	--- base_model: ValiantLabs/Fireplace-34b language: - en library_name: transformers license: other license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE license_name: yi-license model_type: llama quantized_by: mradermacher tags: - fireplace - function-calling - code - code-instruct - conversational - text-generation-inference - valiant - valiant-labs - smaug - yi - yi-34b - llama - llama-2 - llama-2-chat - 34b --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ValiantLabs/Fireplace-34b <!-- 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/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q2_K.gguf) \| Q2_K \| 14.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.IQ3_XS.gguf) \| IQ3_XS \| 15.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q3_K_S.gguf) \| Q3_K_S \| 16.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.IQ3_S.gguf) \| IQ3_S \| 16.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.IQ3_M.gguf) \| IQ3_M \| 17.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q3_K_M.gguf) \| Q3_K_M \| 18.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q3_K_L.gguf) \| Q3_K_L \| 19.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.IQ4_XS.gguf) \| IQ4_XS \| 20.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q4_K_S.gguf) \| Q4_K_S \| 21.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q4_K_M.gguf) \| Q4_K_M \| 22.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q5_K_S.gguf) \| Q5_K_S \| 25.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q5_K_M.gguf) \| Q5_K_M \| 25.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q6_K.gguf) \| Q6_K \| 29.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Fireplace-34b-GGUF/resolve/main/Fireplace-34b.Q8_0.gguf) \| Q8_0 \| 38.0 \| 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/KittyNyanster-v1-GGUF	mradermacher	2024-05-06T05:20:17Z	191	2	transformers	[ "transformers", "gguf", "roleplay", "chat", "mistral", "en", "base_model:arlineka/KittyNyanster-v1", "base_model:quantized:arlineka/KittyNyanster-v1", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-03T18:30:14Z	--- base_model: arlineka/KittyNyanster-v1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - roleplay - chat - mistral --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/arlineka/KittyNyanster-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/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.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/KoSOLAR-10.7B-DPO-v1.0-GGUF	mradermacher	2024-05-06T05:20:15Z	1	0	transformers	[ "transformers", "gguf", "ko", "base_model:rrw-x2/KoSOLAR-10.7B-DPO-v1.0", "base_model:quantized:rrw-x2/KoSOLAR-10.7B-DPO-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T18:43:39Z	--- base_model: rrw-x2/KoSOLAR-10.7B-DPO-v1.0 language: - ko library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/rrw-x2/KoSOLAR-10.7B-DPO-v1.0 <!-- 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/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q2_K.gguf) \| Q2_K \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.IQ3_XS.gguf) \| IQ3_XS \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q3_K_S.gguf) \| Q3_K_S \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.IQ3_S.gguf) \| IQ3_S \| 5.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.IQ3_M.gguf) \| IQ3_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q3_K_M.gguf) \| Q3_K_M \| 5.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q3_K_L.gguf) \| Q3_K_L \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.IQ4_XS.gguf) \| IQ4_XS \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q4_K_S.gguf) \| Q4_K_S \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q4_K_M.gguf) \| Q4_K_M \| 7.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q5_K_S.gguf) \| Q5_K_S \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q5_K_M.gguf) \| Q5_K_M \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q6_K.gguf) \| Q6_K \| 9.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-DPO-v1.0-GGUF/resolve/main/KoSOLAR-10.7B-DPO-v1.0.Q8_0.gguf) \| Q8_0 \| 11.9 \| 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-7b-medical-assistance-GGUF	mradermacher	2024-05-06T05:20:12Z	17	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "base_model:Hdhsjfjdsj/mistral-7b-medical-assistance", "base_model:quantized:Hdhsjfjdsj/mistral-7b-medical-assistance", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-03T18:55:27Z	--- base_model: Hdhsjfjdsj/mistral-7b-medical-assistance language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Hdhsjfjdsj/mistral-7b-medical-assistance <!-- 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-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.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/pandafish-dt-7b-GGUF	mradermacher	2024-05-06T05:20:10Z	64	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "CultriX/MergeCeption-7B-v3", "en", "base_model:ichigoberry/pandafish-dt-7b", "base_model:quantized:ichigoberry/pandafish-dt-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-03T19:03:45Z	--- base_model: ichigoberry/pandafish-dt-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - CultriX/MergeCeption-7B-v3 --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ichigoberry/pandafish-dt-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/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.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/KunoichiVerse-7B-GGUF	mradermacher	2024-05-06T05:19:51Z	28	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:Ppoyaa/KunoichiVerse-7B", "base_model:quantized:Ppoyaa/KunoichiVerse-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T21:48:59Z	--- base_model: Ppoyaa/KunoichiVerse-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Ppoyaa/KunoichiVerse-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/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.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/bagel-20b-v04-GGUF	mradermacher	2024-05-06T05:19:46Z	65	1	transformers	[ "transformers", "gguf", "en", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:jondurbin/bagel-20b-v04", "base_model:quantized:jondurbin/bagel-20b-v04", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T22:08:35Z	--- base_model: jondurbin/bagel-20b-v04 datasets: - ai2_arc - allenai/ultrafeedback_binarized_cleaned - argilla/distilabel-intel-orca-dpo-pairs - jondurbin/airoboros-3.2 - codeparrot/apps - facebook/belebele - bluemoon-fandom-1-1-rp-cleaned - boolq - camel-ai/biology - camel-ai/chemistry - camel-ai/math - camel-ai/physics - jondurbin/contextual-dpo-v0.1 - jondurbin/gutenberg-dpo-v0.1 - jondurbin/py-dpo-v0.1 - jondurbin/truthy-dpo-v0.1 - LDJnr/Capybara - jondurbin/cinematika-v0.1 - WizardLM/WizardLM_evol_instruct_70k - glaiveai/glaive-function-calling-v2 - jondurbin/gutenberg-dpo-v0.1 - grimulkan/LimaRP-augmented - lmsys/lmsys-chat-1m - ParisNeo/lollms_aware_dataset - TIGER-Lab/MathInstruct - Muennighoff/natural-instructions - openbookqa - kingbri/PIPPA-shareGPT - piqa - Vezora/Tested-22k-Python-Alpaca - ropes - cakiki/rosetta-code - Open-Orca/SlimOrca - b-mc2/sql-create-context - squad_v2 - mattpscott/airoboros-summarization - migtissera/Synthia-v1.3 - unalignment/toxic-dpo-v0.2 - WhiteRabbitNeo/WRN-Chapter-1 - WhiteRabbitNeo/WRN-Chapter-2 - winogrande language: - en library_name: transformers license: other license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license license_name: internlm2-20b quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jondurbin/bagel-20b-v04 <!-- 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/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q2_K.gguf) \| Q2_K \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ3_XS.gguf) \| IQ3_XS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q3_K_S.gguf) \| Q3_K_S \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ3_S.gguf) \| IQ3_S \| 9.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ3_M.gguf) \| IQ3_M \| 9.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q3_K_M.gguf) \| Q3_K_M \| 10.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q3_K_L.gguf) \| Q3_K_L \| 11.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ4_XS.gguf) \| IQ4_XS \| 11.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q4_K_S.gguf) \| Q4_K_S \| 12.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q4_K_M.gguf) \| Q4_K_M \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q5_K_S.gguf) \| Q5_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q5_K_M.gguf) \| Q5_K_M \| 14.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q6_K.gguf) \| Q6_K \| 17.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q8_0.gguf) \| Q8_0 \| 21.7 \| 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/Mixtral_AI_CyberLAW-GGUF	mradermacher	2024-05-06T05:19:43Z	108	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "Cyber-Series", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-03T22:34:07Z	--- base_model: LeroyDyer/Mixtral_AI_CyberLAW language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - Cyber-Series --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_CyberLAW <!-- 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_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_CyberLAW-GGUF/resolve/main/Mixtral_AI_CyberLAW.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/StarMonarch-7B-GGUF	mradermacher	2024-05-06T05:19:34Z	71	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:Ppoyaa/StarMonarch-7B", "base_model:quantized:Ppoyaa/StarMonarch-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-04T00:13:47Z	--- base_model: Ppoyaa/StarMonarch-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Ppoyaa/StarMonarch-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/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.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/Llama-3-70B-Instruct-norefusal-GGUF	mradermacher	2024-05-06T05:19:27Z	29	2	transformers	[ "transformers", "gguf", "en", "base_model:theo77186/Llama-3-70B-Instruct-norefusal", "base_model:quantized:theo77186/Llama-3-70B-Instruct-norefusal", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-05T19:41:12Z	--- base_model: theo77186/Llama-3-70B-Instruct-norefusal language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/theo77186/Llama-3-70B-Instruct-norefusal <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-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-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.IQ3_XS.gguf) \| IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.IQ3_S.gguf) \| IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.IQ3_M.gguf) \| IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70B-Instruct-norefusal-GGUF/resolve/main/Llama-3-70B-Instruct-norefusal.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/dragonwar-7b-s1-GGUF	mradermacher	2024-05-06T05:18:44Z	11	0	transformers	[ "transformers", "gguf", "unsloth", "book", "en", "base_model:maldv/dragonwar-7b-s1", "base_model:quantized:maldv/dragonwar-7b-s1", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-04T05:42:53Z	--- base_model: maldv/dragonwar-7b-s1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - unsloth - book --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/maldv/dragonwar-7b-s1 <!-- 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/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/dragonwar-7b-s1-GGUF/resolve/main/dragonwar-7b-s1.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/IsenHumourAI-GGUF	mradermacher	2024-05-06T05:18:38Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "base_model:jberni29/IsenHumourAI", "base_model:quantized:jberni29/IsenHumourAI", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-04T06:46:50Z	--- base_model: jberni29/IsenHumourAI language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jberni29/IsenHumourAI <!-- 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/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.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/talosian-7b-GGUF	mradermacher	2024-05-06T05:18:30Z	162	3	transformers	[ "transformers", "gguf", "en", "base_model:jspr/talosian-7b", "base_model:quantized:jspr/talosian-7b", "endpoints_compatible", "region:us" ]	null	2024-04-04T07:46:01Z	--- base_model: jspr/talosian-7b language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/jspr/talosian-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/talosian-7b-GGUF/resolve/main/talosian-7b.Q2_K.gguf) \| Q2_K \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.IQ3_S.gguf) \| IQ3_S \| 3.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.IQ3_M.gguf) \| IQ3_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q3_K_M.gguf) \| Q3_K_M \| 4.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q6_K.gguf) \| Q6_K \| 6.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/talosian-7b-GGUF/resolve/main/talosian-7b.Q8_0.gguf) \| Q8_0 \| 8.2 \| 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/MistarlingMaid-2x7B-base-GGUF	mradermacher	2024-05-06T05:18:27Z	74	0	transformers	[ "transformers", "gguf", "en", "base_model:dawn17/MistarlingMaid-2x7B-base", "base_model:quantized:dawn17/MistarlingMaid-2x7B-base", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-04T08:20:09Z	--- base_model: dawn17/MistarlingMaid-2x7B-base language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/dawn17/MistarlingMaid-2x7B-base <!-- 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/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_XS.gguf) \| IQ3_XS \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_S.gguf) \| IQ3_S \| 5.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_M.gguf) \| IQ3_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_M.gguf) \| Q3_K_M \| 6.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ4_XS.gguf) \| IQ4_XS \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q4_K_S.gguf) \| Q4_K_S \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q5_K_M.gguf) \| Q5_K_M \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.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/MythoMax-L2-Kimiko-v2-13b-GGUF	mradermacher	2024-05-06T05:18:21Z	40	1	transformers	[ "transformers", "gguf", "en", "base_model:Undi95/MythoMax-L2-Kimiko-v2-13b", "base_model:quantized:Undi95/MythoMax-L2-Kimiko-v2-13b", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-04T09:08:24Z	--- base_model: Undi95/MythoMax-L2-Kimiko-v2-13b language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Undi95/MythoMax-L2-Kimiko-v2-13b <!-- 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/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q2_K.gguf) \| Q2_K \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ3_XS.gguf) \| IQ3_XS \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ3_S.gguf) \| IQ3_S \| 6.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q3_K_S.gguf) \| Q3_K_S \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ3_M.gguf) \| IQ3_M \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q3_K_M.gguf) \| Q3_K_M \| 6.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q3_K_L.gguf) \| Q3_K_L \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.IQ4_XS.gguf) \| IQ4_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q4_K_S.gguf) \| Q4_K_S \| 7.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q4_K_M.gguf) \| Q4_K_M \| 8.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q5_K_S.gguf) \| Q5_K_S \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q5_K_M.gguf) \| Q5_K_M \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q6_K.gguf) \| Q6_K \| 11.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MythoMax-L2-Kimiko-v2-13b-GGUF/resolve/main/MythoMax-L2-Kimiko-v2-13b.Q8_0.gguf) \| Q8_0 \| 14.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/UNAversal-8x7B-v1beta-i1-GGUF	mradermacher	2024-05-06T05:18:09Z	61	1	transformers	[ "transformers", "gguf", "UNA", "juanako", "mixtral", "MoE", "en", "base_model:fblgit/UNAversal-8x7B-v1beta", "base_model:quantized:fblgit/UNAversal-8x7B-v1beta", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-04T09:42:55Z	--- base_model: fblgit/UNAversal-8x7B-v1beta language: - en library_name: transformers license: cc-by-nc-sa-4.0 quantized_by: mradermacher tags: - UNA - juanako - mixtral - MoE --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/fblgit/UNAversal-8x7B-v1beta <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-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/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ1_S.gguf) \| i1-IQ1_S \| 10.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ1_M.gguf) \| i1-IQ1_M \| 11.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 12.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 14.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_S.gguf) \| i1-IQ2_S \| 14.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_M.gguf) \| i1-IQ2_M \| 15.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q2_K.gguf) \| i1-Q2_K \| 17.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 18.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_S.gguf) \| i1-IQ3_S \| 20.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 20.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_M.gguf) \| i1-IQ3_M \| 21.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 22.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 24.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 25.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q4_0.gguf) \| i1-Q4_0 \| 26.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 27.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 28.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 32.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 33.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q6_K.gguf) \| i1-Q6_K \| 38.6 \| 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/Mixtral_AI_Cyber_Matrix_2_0-GGUF	mradermacher	2024-05-06T05:18:04Z	37	2	transformers	[ "transformers", "gguf", "mergekit", "megamerge", "code", "Cyber-Series", "en", "dataset:Open-Orca/OpenOrca", "dataset:cognitivecomputations/dolphin", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:gate369/Alpaca-Star", "dataset:gate369/alpaca-star-ascii", "base_model:LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0", "base_model:quantized:LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-04T10:11:11Z	--- base_model: LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0 datasets: - Open-Orca/OpenOrca - cognitivecomputations/dolphin - WhiteRabbitNeo/WRN-Chapter-2 - WhiteRabbitNeo/WRN-Chapter-1 - gate369/Alpaca-Star - gate369/alpaca-star-ascii language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - mergekit - megamerge - code - Cyber-Series --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_Matrix_2_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/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ3_XS.gguf) \| IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ3_S.gguf) \| IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ3_M.gguf) \| IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.IQ4_XS.gguf) \| IQ4_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q4_K_S.gguf) \| Q4_K_S \| 4.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q5_K_M.gguf) \| Q5_K_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_Matrix_2_0-GGUF/resolve/main/Mixtral_AI_Cyber_Matrix_2_0.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/Mistral-10.7B-Instruct-v0.2-GGUF	mradermacher	2024-05-06T05:17:59Z	1	0	transformers	[ "transformers", "gguf", "en", "base_model:ddh0/Mistral-10.7B-Instruct-v0.2", "base_model:quantized:ddh0/Mistral-10.7B-Instruct-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-04T12:08:09Z	--- base_model: ddh0/Mistral-10.7B-Instruct-v0.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ddh0/Mistral-10.7B-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/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q2_K.gguf) \| Q2_K \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_S.gguf) \| IQ3_S \| 4.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_M.gguf) \| IQ3_M \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 5.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 6.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 6.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q6_K.gguf) \| Q6_K \| 9.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.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/megatron_2.1_MoE_2x7B-GGUF	mradermacher	2024-05-06T05:17:51Z	1	0	transformers	[ "transformers", "gguf", "moe", "merge", "en", "base_model:Eurdem/megatron_2.1_MoE_2x7B", "base_model:quantized:Eurdem/megatron_2.1_MoE_2x7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-04T12:13:09Z	--- base_model: Eurdem/megatron_2.1_MoE_2x7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - merge --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Eurdem/megatron_2.1_MoE_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/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ3_XS.gguf) \| IQ3_XS \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ3_S.gguf) \| IQ3_S \| 5.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ3_M.gguf) \| IQ3_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q3_K_M.gguf) \| Q3_K_M \| 6.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.IQ4_XS.gguf) \| IQ4_XS \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q4_K_S.gguf) \| Q4_K_S \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q5_K_M.gguf) \| Q5_K_M \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/megatron_2.1_MoE_2x7B-GGUF/resolve/main/megatron_2.1_MoE_2x7B.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/athena-120b-i1-GGUF	mradermacher	2024-05-06T05:17:47Z	15	0	transformers	[ "transformers", "gguf", "merge", "en", "base_model:ibivibiv/athena-120b", "base_model:quantized:ibivibiv/athena-120b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-04-04T12:24:29Z	--- base_model: ibivibiv/athena-120b language: - en library_name: transformers license: llama2 quantized_by: mradermacher tags: - merge --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/ibivibiv/athena-120b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/athena-120b-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/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 26.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 28.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 32.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 36.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 38.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 41.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q2_K.gguf) \| i1-Q2_K \| 45.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 47.1 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q3_K_S.gguf.part2of2) \| i1-Q3_K_S \| 52.7 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ3_S.gguf.part2of2) \| i1-IQ3_S \| 52.9 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ3_M.gguf.part2of2) \| i1-IQ3_M \| 54.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q3_K_M.gguf.part2of2) \| i1-Q3_K_M \| 58.8 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q3_K_L.gguf.part2of2) \| i1-Q3_K_L \| 63.9 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-IQ4_XS.gguf.part2of2) \| i1-IQ4_XS \| 65.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 68.9 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 69.2 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 73.1 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 83.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 86.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/athena-120b-i1-GGUF/resolve/main/athena-120b.i1-Q6_K.gguf.part3of3) \| i1-Q6_K \| 99.6 \| 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/CatNyanster-34b-i1-GGUF	mradermacher	2024-05-06T05:17:35Z	4	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us" ]	null	2024-04-04T15:33:46Z	--- base_model: arlineka/CatNyanster-34b language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/arlineka/CatNyanster-34b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/CatNyanster-34b-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/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 8.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 11.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q2_K.gguf) \| i1-Q2_K \| 13.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 14.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 15.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 15.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 16.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 17.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 18.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 19.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q4_0.gguf) \| i1-Q4_0 \| 20.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 20.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 21.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 24.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 25.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CatNyanster-34b-i1-GGUF/resolve/main/CatNyanster-34b.i1-Q6_K.gguf) \| i1-Q6_K \| 28.9 \| 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/Mermaid_11.5B-GGUF	mradermacher	2024-05-06T05:17:13Z	19	0	transformers	[ "transformers", "gguf", "en", "base_model:TroyDoesAI/Mermaid_11.5B", "base_model:quantized:TroyDoesAI/Mermaid_11.5B", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-04T19:16:21Z	--- base_model: TroyDoesAI/Mermaid_11.5B language: - en library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/TroyDoesAI/Mermaid_11.5B <!-- 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_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q2_K.gguf) \| Q2_K \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ3_XS.gguf) \| IQ3_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q3_K_S.gguf) \| Q3_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ3_S.gguf) \| IQ3_S \| 5.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ3_M.gguf) \| IQ3_M \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q3_K_M.gguf) \| Q3_K_M \| 6.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q3_K_L.gguf) \| Q3_K_L \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ4_XS.gguf) \| IQ4_XS \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q4_K_S.gguf) \| Q4_K_S \| 7.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q4_K_M.gguf) \| Q4_K_M \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q5_K_S.gguf) \| Q5_K_S \| 8.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q5_K_M.gguf) \| Q5_K_M \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q6_K.gguf) \| Q6_K \| 9.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q8_0.gguf) \| Q8_0 \| 12.7 \| 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 -->

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