Split (1)

train · 788k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-05-30 12:29:02	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 460 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-30 12:28:50	card stringlengths 11 1.01M
gevaertlab/he2rna-paad-3	gevaertlab	2025-05-29T00:27:07Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:19:31Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-lusc-0	gevaertlab	2025-05-29T00:25:44Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:19:02Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-lihc-3	gevaertlab	2025-05-29T00:25:12Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:17:07Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-lihc-0	gevaertlab	2025-05-29T00:25:01Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:16:56Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-kirp-4	gevaertlab	2025-05-29T00:24:58Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:24:56Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-kirc-3	gevaertlab	2025-05-29T00:23:50Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:53Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-hnsc-2	gevaertlab	2025-05-29T00:23:35Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:21Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-gbm-4	gevaertlab	2025-05-29T00:22:40Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:02Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-gbm-2	gevaertlab	2025-05-29T00:22:35Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:14:57Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-coad-3	gevaertlab	2025-05-29T00:22:24Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:10:58Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-brca-3	gevaertlab	2025-05-29T00:21:37Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:10:45Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF	mradermacher	2025-05-29T00:18:06Z	54	0	transformers	[ "transformers", "gguf", "mergekit", "moe", "mixture of experts", "merge", "llama-3", "llama3", "en", "base_model:DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B", "base_model:quantized:DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-16T06:22:30Z	--- base_model: DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - moe - mixture of experts - merge - llama-3 - llama3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-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/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q2_K.gguf) \| Q2_K \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q3_K_S.gguf) \| Q3_K_S \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q3_K_M.gguf) \| Q3_K_M \| 12.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q3_K_L.gguf) \| Q3_K_L \| 13.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.IQ4_XS.gguf) \| IQ4_XS \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q4_K_S.gguf) \| Q4_K_S \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q4_K_M.gguf) \| Q4_K_M \| 15.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q5_K_S.gguf) \| Q5_K_S \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q5_K_M.gguf) \| Q5_K_M \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q6_K.gguf) \| Q6_K \| 20.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.Q8_0.gguf) \| Q8_0 \| 26.6 \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF	mradermacher	2025-05-29T00:17:42Z	95	0	transformers	[ "transformers", "gguf", "mergekit", "moe", "mixture of experts", "merge", "llama-3", "llama3", "en", "base_model:DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B", "base_model:quantized:DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-16T06:56:22Z	--- base_model: DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - moe - mixture of experts - merge - llama-3 - llama3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/L3-MOE-4x8B-Dark-Planet-Rising-25B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-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/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 6.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.8 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q2_K.gguf) \| i1-Q2_K \| 9.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 11.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 11.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 11.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 12.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 13.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q4_0.gguf) \| i1-Q4_0 \| 14.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 14.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 15.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4x8B-Dark-Planet-Rising-25B-i1-GGUF/resolve/main/L3-MOE-4x8B-Dark-Planet-Rising-25B.i1-Q6_K.gguf) \| i1-Q6_K \| 20.6 \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF	mradermacher	2025-05-29T00:14:25Z	65	0	transformers	[ "transformers", "gguf", "mergekit", "moe", "mixture of experts", "merge", "llama-3", "llama3", "en", "base_model:DavidAU/L3-MOE-4X8B-Grand-Horror-25B", "base_model:quantized:DavidAU/L3-MOE-4X8B-Grand-Horror-25B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-16T21:21:57Z	--- base_model: DavidAU/L3-MOE-4X8B-Grand-Horror-25B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - moe - mixture of experts - merge - llama-3 - llama3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/L3-MOE-4X8B-Grand-Horror-25B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-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/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 6.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.8 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q2_K.gguf) \| i1-Q2_K \| 9.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 11.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 11.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 11.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 12.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 13.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q4_0.gguf) \| i1-Q4_0 \| 14.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 14.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 15.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-i1-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.i1-Q6_K.gguf) \| i1-Q6_K \| 20.6 \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf	RichardErkhov	2025-05-29T00:10:46Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:42:22Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv13-layer-8 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv13-layer-8/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv13-layer-8.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv13-layer-8.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv13-layer-8.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv13-layer-8.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv13-layer-8.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: --- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
CeciGonSer/translation_pu_es_sintetico6	CeciGonSer	2025-05-29T00:09:02Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-29T00:06:59Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_2_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-29T00:06:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:10:19Z	--- 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. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-29T00:02:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:31:04Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_2_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-29T00:02:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:05:13Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_6_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-29T00:01:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:04:59Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_0_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-29T00:01:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:12:44Z	--- 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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mansoorhamidzadeh/qwen3-0.6b-persian	mansoorhamidzadeh	2025-05-29T00:01:14Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-29T00:01:10Z	--- library_name: transformers tags: - unsloth --- # 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_8_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:09:38Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_22_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:10:26Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_28_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T19:52:14Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_16_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:10:37Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_4_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:09:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF	mradermacher	2025-05-28T23:59:19Z	46	0	transformers	[ "transformers", "gguf", "en", "base_model:shirova-ai/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct", "base_model:quantized:shirova-ai/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-26T09:44:28Z	--- base_model: shirova-ai/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/shirova-ai/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q2_K.gguf) \| Q2_K \| 39.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 46.8 \| \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 51.9 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 56.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 58.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 61.6 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 65.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 74.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 76.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q6_K.gguf.part2of2) \| Q6_K \| 88.5 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/FineTuned-Meta-Llama-4-Scout-17B-16E-Instruct.Q8_0.gguf.part3of3) \| Q8_0 \| 114.6 \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
shallow6414/sn11-w3-7-1	shallow6414	2025-05-28T23:58:18Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:27:33Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
shallow6414/sn11-w3-1-1	shallow6414	2025-05-28T23:58:15Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:27:27Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_18_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:26:12Z	--- 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. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_22_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:54Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_10_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:17Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_14_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:05:16Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:23:59Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_4_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:04:41Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_24_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:05:29Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_18_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:21:42Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_28_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T19:45:02Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:21:40Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_0_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:08Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:02:42Z	--- 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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Matelq-2/SmolLM2-FT-DPO	Matelq-2	2025-05-28T23:44:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "smol-course", "module_1", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:Matelq-2/SmolLM2-FT-MyDataset", "base_model:finetune:Matelq-2/SmolLM2-FT-MyDataset", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:37:38Z	--- base_model: Matelq-2/SmolLM2-FT-MyDataset library_name: transformers model_name: SmolLM2-FT-DPO tags: - generated_from_trainer - smol-course - module_1 - trl - dpo licence: license --- # Model Card for SmolLM2-FT-DPO This model is a fine-tuned version of [Matelq-2/SmolLM2-FT-MyDataset](https://huggingface.co/Matelq-2/SmolLM2-FT-MyDataset). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Matelq-2/SmolLM2-FT-DPO", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.7.0+cu118 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin GallouГ©dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
DVLe/DPO_Llama_v1	DVLe	2025-05-28T23:43:29Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:unsloth/Llama-3.2-1B", "base_model:finetune:unsloth/Llama-3.2-1B", "endpoints_compatible", "region:us" ]	null	2025-05-28T11:24:38Z	--- base_model: unsloth/Llama-3.2-1B library_name: transformers model_name: DPO_Llama_v1 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for DPO_Llama_v1 This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="DVLe/DPO_Llama_v1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ldv/huggingface/runs/icnflvke) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf	RichardErkhov	2025-05-28T23:37:49Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:07:18Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv18-3 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv18-3/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv18-3.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv18-3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv18-3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv18-3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv18-3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv18-3.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv18-3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv18-3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv18-3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv18-3.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv18-3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv18-3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv18-3.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv18-3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv18-3.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv18-3.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv18-3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv18-3.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv18-3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv18-3.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv18-3.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv18-3.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv18-3-gguf/blob/main/GPT2XL_RLLMv18-3.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: --- license: mit ---
fristrup/flan-t5-semantic-tagger-base-4bit	fristrup	2025-05-28T23:36:41Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text2text-generation	2025-05-28T23:36:06Z	--- 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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CeciGonSer/translation_pu_es_sintetico5	CeciGonSer	2025-05-28T23:32:48Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-28T23:28:04Z	--- 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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Saef/mistral_dp_new-lora_epoch-100	Saef	2025-05-28T23:31:22Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2025-05-28T23:30:54Z	--- base_model: mistralai/Mistral-7B-v0.1 library_name: peft --- # 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. 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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. (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] ### Framework versions - PEFT 0.13.2
jinx2321/mt5-1e4-paper-5	jinx2321	2025-05-28T23:30:11Z	0	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:jinx2321/mt5-1e4-paper", "base_model:finetune:jinx2321/mt5-1e4-paper", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-28T22:22:58Z	--- library_name: transformers license: apache-2.0 base_model: jinx2321/mt5-1e4-paper tags: - generated_from_trainer model-index: - name: mt5-1e4-paper-5 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. --> # mt5-1e4-paper-5 This model is a fine-tuned version of [jinx2321/mt5-1e4-paper](https://huggingface.co/jinx2321/mt5-1e4-paper) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
PhucNT2511/Unsloth-Llama-3.2-1B-FT-DPO	PhucNT2511	2025-05-28T23:29:52Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "smol-course", "module_1", "trl", "dpo", "arxiv:2305.18290", "base_model:unsloth/Llama-3.2-1B", "base_model:finetune:unsloth/Llama-3.2-1B", "endpoints_compatible", "region:us" ]	null	2025-05-28T18:57:11Z	--- base_model: unsloth/Llama-3.2-1B library_name: transformers model_name: Unsloth-Llama-3.2-1B-FT-DPO tags: - generated_from_trainer - smol-course - module_1 - trl - dpo licence: license --- # Model Card for Unsloth-Llama-3.2-1B-FT-DPO This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="PhucNT2511/Unsloth-Llama-3.2-1B-FT-DPO", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.5.1+cu121 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF	mradermacher	2025-05-28T23:24:40Z	178	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "gemma2", "en", "base_model:DavidAU/Gemma-The-Writer-Mighty-Sword-9B", "base_model:quantized:DavidAU/Gemma-The-Writer-Mighty-Sword-9B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-26T04:39:06Z	--- base_model: DavidAU/Gemma-The-Writer-Mighty-Sword-9B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - gemma2 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/Gemma-The-Writer-Mighty-Sword-9B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-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/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 5.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q4_0.gguf) \| i1-Q4_0 \| 5.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 5.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q4_1.gguf) \| i1-Q4_1 \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-Mighty-Sword-9B-i1-GGUF/resolve/main/Gemma-The-Writer-Mighty-Sword-9B.i1-Q6_K.gguf) \| i1-Q6_K \| 7.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
zhangchenxu/TinyV-1.5B	zhangchenxu	2025-05-28T23:17:42Z	190	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2505.14625", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-13T10:32:33Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-1.5B-Instruct-SFT-BigmathV_Simple_Balanced-LR1.0e-5-EPOCHS2 results: [] --- [TinyV]((https://arxiv.org/abs/2505.14625)) is a reward system for efficient RL post-training that detects false negatives in current rule-based verifiers and provides more accurate reward signals via a small LLM during RL training. Experiments show that TinyV incurs only 6% additional computational cost while significantly increasing both RL efficiency and final model performance. - 📄 [Technical Report](https://arxiv.org/abs/2505.14625) - Including false negative analysis and theotical insights behind TinyV - 💾 [Github Repo](https://github.com/uw-nsl/TinyV) - Access the complete pipeline for more efficient RL training via TinyV - 🤗 [HF Collection](https://huggingface.co/collections/zhangchenxu/tinyv-682d5840c7e309217df625df) - Training Data, Benchmarks, and Model Artifact This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on [zhangchenxu/TinyV_Training_Data_Balanced](https://huggingface.co/datasets/zhangchenxu/TinyV_Training_Data_Balanced) dataset. ### Overview ![TinyV Pipeline](fn_tinyv_combine.png) ### How to use it? Please refer to the codebase: [https://github.com/uw-nsl/TinyV](https://github.com/uw-nsl/TinyV) for details. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.0 - Datasets 3.2.0 - Tokenizers 0.21.0
samuelchristlie/SmolVLM2-2.2B-Instruct-GGUF	samuelchristlie	2025-05-28T23:17:02Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-28T23:09:58Z	--- license: apache-2.0 ---
alfatih1/BOS	alfatih1	2025-05-28T23:09:36Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-28T23:09:36Z	--- license: apache-2.0 ---
while0628/student_model_data8000_epoch24	while0628	2025-05-28T23:06:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:03:50Z	--- 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]
RedPandaBoy/ppo-Huggy	RedPandaBoy	2025-05-28T23:06:10Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-28T23:05:58Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: RedPandaBoy/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Videos-18-katrina-lim-kiffy-katrinalim768/Original.Video.18.katrina.lim.kiffy.katrinalim123.katrina.lim.tg.telegram	Videos-18-katrina-lim-kiffy-katrinalim768	2025-05-28T23:04:50Z	0	0	null	[ "region:us" ]	null	2025-05-28T23:04:22Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?V=video">🌐 CLICK HERE 🟢==►► WATCH NOW</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?V=video">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?V=video"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
greenwich157/phi4-base-telcollm-d-Q8_0-GGUF	greenwich157	2025-05-28T22:55:42Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:greenwich157/phi4-base-telcollm-d", "base_model:quantized:greenwich157/phi4-base-telcollm-d", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:54:40Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: greenwich157/phi4-base-telcollm-d --- # greenwich157/phi4-base-telcollm-d-Q8_0-GGUF This model was converted to GGUF format from [`greenwich157/phi4-base-telcollm-d`](https://huggingface.co/greenwich157/phi4-base-telcollm-d) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/greenwich157/phi4-base-telcollm-d) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo greenwich157/phi4-base-telcollm-d-Q8_0-GGUF --hf-file phi4-base-telcollm-d-q8_0.gguf -c 2048 ```
AmberYifan/Qwen2.5-14B-sft-spin-10k	AmberYifan	2025-05-28T22:55:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF", "base_model:finetune:AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:20:01Z	--- base_model: AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF library_name: transformers model_name: Qwen2.5-14B-sft-spin-10k tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen2.5-14B-sft-spin-10k This model is a fine-tuned version of [AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AmberYifan/Qwen2.5-14B-sft-spin-10k", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yifanwang/huggingface/runs/bzj0ouih) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
autoprogrammer/gsm_output_ste_lora_all_merged	autoprogrammer	2025-05-28T22:53:16Z	0	0	transformers	[ "transformers", "safetensors", "olmoe", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:46:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Vortex5/ChaosFlowerRP-24B-Q4_K_M-GGUF	Vortex5	2025-05-28T22:51:41Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Vortex5/ChaosFlowerRP-24B", "base_model:quantized:Vortex5/ChaosFlowerRP-24B", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:50:36Z	--- base_model: Vortex5/ChaosFlowerRP-24B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Vortex5/ChaosFlowerRP-24B-Q4_K_M-GGUF This model was converted to GGUF format from [`Vortex5/ChaosFlowerRP-24B`](https://huggingface.co/Vortex5/ChaosFlowerRP-24B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Vortex5/ChaosFlowerRP-24B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Vortex5/ChaosFlowerRP-24B-Q4_K_M-GGUF --hf-file chaosflowerrp-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Vortex5/ChaosFlowerRP-24B-Q4_K_M-GGUF --hf-file chaosflowerrp-24b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Vortex5/ChaosFlowerRP-24B-Q4_K_M-GGUF --hf-file chaosflowerrp-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Vortex5/ChaosFlowerRP-24B-Q4_K_M-GGUF --hf-file chaosflowerrp-24b-q4_k_m.gguf -c 2048 ```
aminedata/whisper-small-fr	aminedata	2025-05-28T22:49:20Z	15	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "fr", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-22T10:03:30Z	--- library_name: transformers language: - fr license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: whisper-small-fr-asla-200k 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. --> # whisper-small-fr-asla-200k This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
quickstep3621/dippy-g1-8-1	quickstep3621	2025-05-28T22:31:56Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:18:01Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf	BootesVoid	2025-05-28T22:24:17Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-28T22:24:15Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: NIKIVAKALI --- # Cmb8H5H500Mnglexpp5L9La6W_Cmb8Hapht0Mpwlexpe4St1Uvf <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `NIKIVAKALI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NIKIVAKALI", "lora_weights": "https://huggingface.co/BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf', weight_name='lora.safetensors') image = pipeline('NIKIVAKALI').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8h5h500mnglexpp5l9la6w_cmb8hapht0mpwlexpe4st1uvf/discussions) to add images that show off what you’ve made with this LoRA.
sdfgvdcv/phi3-dream-interpreter	sdfgvdcv	2025-05-28T22:15:45Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	2025-05-28T21:35:50Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft --- # 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. (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] ### Framework versions - PEFT 0.15.2
Moneerrashed/Gari_And_Luna_Voiceover_Collection	Moneerrashed	2025-05-28T22:14:44Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-05-06T02:15:38Z	--- license: mit --- Use This Model To Make A Voiceover With News Open, Talent Open, Segment, ID And Promo Here's A Link For Gradio https://huggingface.co/spaces/TheStinger/Ilaria_RVC And https://huggingface.co/spaces/Clebersla/RVC_V2_Huggingface_Version
Tashi-Projects/DZO_ASR_mms1ball	Tashi-Projects	2025-05-28T22:13:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-28T11:06:14Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: DZO_ASR_mms1ball 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. --> # DZO_ASR_mms1ball This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5484 - Wer: 0.3622 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\| \| 5.9857 \| 0.8782 \| 400 \| 1.1527 \| 0.6884 \| \| 1.3889 \| 1.7552 \| 800 \| 0.9422 \| 0.5880 \| \| 1.187 \| 2.6323 \| 1200 \| 0.8545 \| 0.5501 \| \| 1.1338 \| 3.5093 \| 1600 \| 0.7957 \| 0.5231 \| \| 1.0482 \| 4.3864 \| 2000 \| 0.7578 \| 0.4994 \| \| 1.0305 \| 5.2634 \| 2400 \| 0.7273 \| 0.4851 \| \| 0.9609 \| 6.1405 \| 2800 \| 0.7110 \| 0.4754 \| \| 0.9499 \| 7.0176 \| 3200 \| 0.6915 \| 0.4636 \| \| 0.9284 \| 7.8957 \| 3600 \| 0.6732 \| 0.4544 \| \| 0.9071 \| 8.7728 \| 4000 \| 0.6631 \| 0.4484 \| \| 0.8788 \| 9.6498 \| 4400 \| 0.6616 \| 0.4447 \| \| 0.8719 \| 10.5269 \| 4800 \| 0.6410 \| 0.4271 \| \| 0.8536 \| 11.4040 \| 5200 \| 0.6324 \| 0.4229 \| \| 0.8389 \| 12.2810 \| 5600 \| 0.6156 \| 0.4139 \| \| 0.8155 \| 13.1581 \| 6000 \| 0.6139 \| 0.4076 \| \| 0.8226 \| 14.0351 \| 6400 \| 0.6111 \| 0.4054 \| \| 0.8108 \| 14.9133 \| 6800 \| 0.5977 \| 0.3992 \| \| 0.7765 \| 15.7903 \| 7200 \| 0.5962 \| 0.3981 \| \| 0.8176 \| 16.6674 \| 7600 \| 0.5899 \| 0.3939 \| \| 0.7887 \| 17.5445 \| 8000 \| 0.5890 \| 0.3897 \| \| 0.761 \| 18.4215 \| 8400 \| 0.5807 \| 0.3812 \| \| 0.7703 \| 19.2986 \| 8800 \| 0.5763 \| 0.3866 \| \| 0.7602 \| 20.1756 \| 9200 \| 0.5717 \| 0.3782 \| \| 0.7642 \| 21.0527 \| 9600 \| 0.5643 \| 0.3744 \| \| 0.7558 \| 21.9308 \| 10000 \| 0.5686 \| 0.3746 \| \| 0.7238 \| 22.8079 \| 10400 \| 0.5643 \| 0.3716 \| \| 0.7797 \| 23.6850 \| 10800 \| 0.5620 \| 0.3691 \| \| 0.7402 \| 24.5620 \| 11200 \| 0.5528 \| 0.3665 \| \| 0.7404 \| 25.4391 \| 11600 \| 0.5508 \| 0.3659 \| \| 0.7125 \| 26.3161 \| 12000 \| 0.5567 \| 0.3650 \| \| 0.724 \| 27.1932 \| 12400 \| 0.5471 \| 0.3633 \| \| 0.7325 \| 28.0703 \| 12800 \| 0.5467 \| 0.3623 \| \| 0.7247 \| 28.9484 \| 13200 \| 0.5481 \| 0.3617 \| \| 0.7307 \| 29.8255 \| 13600 \| 0.5484 \| 0.3622 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
quickstep3621/dippy-g1-21-1	quickstep3621	2025-05-28T22:10:03Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:05:16Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0	BootesVoid	2025-05-28T22:10:01Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-28T22:09:56Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: A123 --- # Cmb8Gnk640Mfulexpo60Fakqn_Cmb8H01260Ml0Lexpahkqfss0 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `A123` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "A123", "lora_weights": "https://huggingface.co/BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0', weight_name='lora.safetensors') image = pipeline('A123').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8gnk640mfulexpo60fakqn_cmb8h01260ml0lexpahkqfss0/discussions) to add images that show off what you’ve made with this LoRA.
quickstep3621/dippy-g1-2-1	quickstep3621	2025-05-28T22:09:48Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:17:00Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
bralynn/test1	bralynn	2025-05-28T22:05:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:50:51Z	--- library_name: transformers tags: - unsloth - trl - sft --- # 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. 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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. (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]
FormlessAI/c86c6b31-c0cc-4956-bdf1-66a2f7e35c22	FormlessAI	2025-05-28T21:56:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:jingyeom/seal3.1.6n_7b", "base_model:finetune:jingyeom/seal3.1.6n_7b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T17:40:58Z	--- base_model: jingyeom/seal3.1.6n_7b library_name: transformers model_name: c86c6b31-c0cc-4956-bdf1-66a2f7e35c22 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for c86c6b31-c0cc-4956-bdf1-66a2f7e35c22 This model is a fine-tuned version of [jingyeom/seal3.1.6n_7b](https://huggingface.co/jingyeom/seal3.1.6n_7b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="FormlessAI/c86c6b31-c0cc-4956-bdf1-66a2f7e35c22", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/nsx8j1za) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
cam-1000/MNLP_M2_rag_model	cam-1000	2025-05-28T21:52:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-18T21:00:48Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M2_mcqa_model2 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. --> # MNLP_M2_mcqa_model2 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5787 ## 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: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.6677 \| 1.0 \| 4380 \| 1.5840 \| \| 1.6558 \| 2.0 \| 8760 \| 1.5796 \| \| 1.6602 \| 3.0 \| 13140 \| 1.5785 \| \| 1.6553 \| 4.0 \| 17520 \| 1.5787 \| \| 1.6479 \| 5.0 \| 21900 \| 1.5787 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
Ainxz/phi3.5-pucv	Ainxz	2025-05-28T21:49:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-28T21:48:51Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ainxz - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-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)
morturr/Mistral-7B-v0.1-amazon-2025-05-28	morturr	2025-05-28T21:48:40Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2025-05-28T13:15:34Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: Mistral-7B-v0.1-amazon-2025-05-28 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. --> # Mistral-7B-v0.1-amazon-2025-05-28 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
kuds/rl-lunar-lander-dqn	kuds	2025-05-28T21:37:22Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "en", "model-index", "region:us" ]	reinforcement-learning	2024-06-10T22:25:34Z	--- library_name: stable-baselines3 language: - en tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: Lunar Lander results: - task: type: game-play # Required. Example: automatic-speech-recognition metrics: - type: mean_reward value: 225.25 name: mean_reward verified: false --- ## Finding Theta Blog Posts: - [Solving Gymnasium's Lunar Lander with Deep Q Learning (DQN)](https://www.findingtheta.com/blog/solving-gymnasiums-lunar-lander-with-deep-q-learning-dqn) - [Comparing how PPO, SAC, and DQN Perform on Gymnasium's Lunar Lander](https://www.findingtheta.com/blog/comparing-how-ppo-sac-and-dqn-perform-on-gymnasiums-lunar-lander)
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rsh-raj/node-commits_with_defn	rsh-raj	2025-05-28T21:26:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/codellama-7b-bnb-4bit", "base_model:adapter:unsloth/codellama-7b-bnb-4bit", "region:us" ]	null	2025-05-28T21:21:58Z	--- base_model: unsloth/codellama-7b-bnb-4bit library_name: peft --- # 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. 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TheGardener/KD-MLP-qwen2.5-0.41B-epoch-1st	TheGardener	2025-05-28T21:22:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:22:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Clean6/LegalQwen3-8B	Clean6	2025-05-28T21:15:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-28T21:14:08Z	--- base_model: unsloth/qwen3-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Clean6 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b-unsloth-bnb-4bit This qwen3 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)
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_12_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-28T21:14:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:11:40Z	--- 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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while0628/student_model_data8000_epoch10	while0628	2025-05-28T21:13:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:10:41Z	--- 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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yale-cultural-heritage/name-parser-model	yale-cultural-heritage	2025-05-28T21:10:50Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:yale-cultural-heritage/name-parser-model", "base_model:finetune:yale-cultural-heritage/name-parser-model", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-28T15:28:02Z	--- library_name: transformers license: apache-2.0 base_model: yale-cultural-heritage/name-parser-model tags: - generated_from_trainer metrics: - accuracy model-index: - name: name-parser-model 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. --> # name-parser-model This model is a fine-tuned version of [yale-cultural-heritage/name-parser-model](https://huggingface.co/yale-cultural-heritage/name-parser-model) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 0.9921 ## 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adafactor and the args are: No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 0.041 \| 3.1952 \| 1000 \| 0.0352 \| 0.9912 \| \| 0.0369 \| 6.3904 \| 2000 \| 0.0345 \| 0.9915 \| \| 0.0358 \| 9.5856 \| 3000 \| 0.0336 \| 0.9917 \| \| 0.0349 \| 12.7808 \| 4000 \| 0.0333 \| 0.9919 \| \| 0.0337 \| 15.9760 \| 5000 \| 0.0331 \| 0.9920 \| \| 0.0332 \| 19.1696 \| 6000 \| 0.0334 \| 0.9919 \| \| 0.0328 \| 22.3648 \| 7000 \| 0.0332 \| 0.9921 \| \| 0.0323 \| 25.56 \| 8000 \| 0.0333 \| 0.9921 \| \| 0.0318 \| 28.7552 \| 9000 \| 0.0333 \| 0.9921 \| \| 0.032 \| 31.9504 \| 10000 \| 0.0332 \| 0.9921 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
AnnaelleMyriam/MNLP_M2_dpo_model	AnnaelleMyriam	2025-05-28T21:04:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:02:50Z	--- 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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mohammed-orabi2/qwen-poetry-arabic-lora	mohammed-orabi2	2025-05-28T21:00:26Z	0	0	peft	[ "peft", "safetensors", "base_model:Qwen/Qwen3-1.7B", "base_model:adapter:Qwen/Qwen3-1.7B", "region:us" ]	null	2025-05-28T20:41:48Z	--- base_model: Qwen/Qwen3-1.7B library_name: peft --- ## Model Card for Model ID Model ID: mohammed-orabi2/qwen-poetry-lora2 --- ## Model Details Model Description: This is a LoRA fine-tuned version of the `Qwen/Qwen3-1.7B` model, specifically trained to generate Arabic poetic responses in a conversational format. It was trained on a dataset of 1,000 synthetic Arabic poetry dialogues, each containing a user query and a poetic response. Developed by: Mohammed Orabi Shared by : mohammed-orabi2 Model type: Causal Language Model with LoRA adaptation Language(s) (NLP): Arabic License: Apache 2.0 (inherits from Qwen3-1.7B) Finetuned from model : Qwen/Qwen3-1.7B Model Sources Repository: [https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2](https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2) --- ## Uses Direct Use: This model can be used for generating Arabic poetry in response to user queries, particularly in cultural, educational, or creative chatbot applications. Downstream Use : * Poetry recommendation systems * Arabic literature generation tools * Creative writing assistants Out-of-Scope Use: * Non-Arabic generation tasks * Factual or knowledge-based QA tasks * Sensitive or safety-critical environments --- ## Bias, Risks, and Limitations The model was fine-tuned on synthetic poetic data and may: * Favor specific poetic structures * Fail on factual, political, or philosophical prompts * Generate romantic or metaphorical content that could be misinterpreted in serious contexts Users should avoid relying on this model for objective or critical outputs. --- ## Recommendations Users (both direct and downstream) should be aware of the creative, poetic intent of this model. For factual content, use general-purpose LLMs. Evaluate outputs manually before publishing or broadcasting. --- ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", device_map="auto", torch_dtype=torch.float16) model = PeftModel.from_pretrained(base_model, "mohammed-orabi2/qwen-poetry-arabic-lora") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-1.7B") prompt = "اكتب لي بيت شعر عن النجاح." chat = [{"role": "user", "content": prompt}] formatted_prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) output = model.generate(inputs, max_new_tokens=100) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` --- ## Training Details Training Data: 1,000 synthetic Arabic poetic dialogues (prompt + poetic response) generated programmatically. Preprocessing :** * Applied Qwen chat template * Tokenized using Qwen3-1.7B tokenizer with padding/truncation Training Hyperparameters: * Epochs: 5 * Batch size: 2 * Max length: 1024 * Learning rate: 2e-4 * LoRA config: r=8, alpha=16, dropout=0.05, target: \["q\_proj", "v\_proj"] Speeds, Sizes, Times : * Training time: \~24 minutes on L4 GPU * Model size: LoRA adapter \~100MB --- ## Evaluation Testing Data: 50 reserved samples from the poetic dataset Factors: * Response fluency * Arabic poetic structure * Topical relevance Metrics: * Manual review (subjective) * BLEU/Rouge not applicable Results: * 90% generated responses respected rhyme/meter and matched prompt topics --- ## Summary Model Examination \[optional]: Output behavior consistent with training intent. Performs well within poetic use-case boundaries. --- ## Environmental Impact Hardware Type: NVIDIA L4 Hours used: \~0.4 hours (24 minutes) Cloud Provider: Google Colab Compute Region: US (GCP default) Carbon Emitted: Estimated \~0.2 kg CO2e --- ## Technical Specifications Model Architecture and Objective: Transformer decoder (CausalLM) + LoRA injection Compute Infrastructure: Google Colab Hardware: NVIDIA L4 (24 mins) Software: * Transformers 4.x * PEFT 0.15.2 * Accelerate 0.25+ --- ## Citation BibTeX: ```bibtex @misc{qwenpoetry2025, author = {Mohammed Orabi}, title = {Qwen Arabic Poetry LoRA}, year = {2025}, howpublished = {\url{https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2}} } ``` APA: Mohammed Orabi. (2025). Qwen Arabic Poetry LoRA \[Model]. Hugging Face. [https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2](https://huggingface.co/mohammed-orabi2/qwen-poetry-lora2) --- ## Glossary * LoRA: Low-Rank Adaptation, a method for efficient model fine-tuning * CausalLM: Causal Language Modeling, predicts the next token in a sequence --- ## More Information For support or feedback, please open an issue on the Hugging Face repo or contact via Hugging Face profile. ## Model Card Authors Mohammed Orabi ## Model Card Contact [https://huggingface.co/mohammed-orabi2](https://huggingface.co/mohammed-orabi2) --- ## Framework versions * Transformers: 4.x * PEFT: 0.15.2 * Datasets: latest * Accelerate: 0.25+
VIDEO-Alana-Flores-18/Original.Video.Leaked.Alana.Flores.Foto.Viral.X.Original.Video.Alana.Flores	VIDEO-Alana-Flores-18	2025-05-28T20:57:16Z	0	0	null	[ "region:us" ]	null	2025-05-28T20:56:44Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?nsu"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?nhu">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?nsu">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?nsu"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
while0628/student_model_epoch100	while0628	2025-05-28T20:46:32Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:43:38Z	--- 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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CodeAtCMU/Qwen3-1.7B_full_sft_mixed_data_120K	CodeAtCMU	2025-05-28T20:45:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:43:32Z	--- 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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GigiTrottola/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-peaceful_agile_falcon	GigiTrottola	2025-05-28T20:44:19Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am peaceful agile falcon", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:45:56Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-peaceful_agile_falcon tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am peaceful agile falcon - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-peaceful_agile_falcon This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="GigiTrottola/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-peaceful_agile_falcon", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
SG-AI-TEAMM/qwen-poetry-lora2	SG-AI-TEAMM	2025-05-28T20:40:00Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen3-1.7B", "base_model:adapter:Qwen/Qwen3-1.7B", "region:us" ]	null	2025-05-28T20:39:53Z	--- base_model: Qwen/Qwen3-1.7B library_name: peft --- # 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. 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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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Wissem29Z/FineTuningQwen3BTG	Wissem29Z	2025-05-28T20:39:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation", "conversational", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T18:34:03Z	--- library_name: transformers pipeline_tag: text-generation --- # 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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allenai/OLMo-2-0425-1B	allenai	2025-05-28T20:35:45Z	22,371	47	transformers	[ "transformers", "safetensors", "olmo2", "text-generation", "en", "arxiv:2501.00656", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-17T22:45:45Z	--- license: apache-2.0 language: - en library_name: transformers --- ## Model Details <img alt="OLMo Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmo2/olmo.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'"> # Model Card for OLMo 2 1B We introduce OLMo 2 1B, the smallest model in the OLMo 2 family. OLMo 2 was pre-trained on [OLMo-mix-1124](https://huggingface.co/datasets/allenai/olmo-mix-1124) and uses [Dolmino-mix-1124](https://huggingface.co/datasets/allenai/dolmino-mix-1124) for mid-training. OLMo 2 is the latest in a series of Open Language Models designed to enable the science of language models. We have released all code, checkpoints, logs, and associated training details on [GitHub](https://github.com/allenai/OLMo). \| Size \| Training Tokens \| Layers \| Hidden Size \| Attention Heads \| Context Length \| \|------\|--------\|---------\|-------------\|-----------------\|----------------\| \| [OLMo 2-1B](https://huggingface.co/allenai/OLMo-2-0425-1B) \| 4 Trillion \| 16 \| 2048 \| 16 \| 4096 \| \| [OLMo 2-7B](https://huggingface.co/allenai/OLMo-2-1124-7B) \| 4 Trillion \| 32 \| 4096 \| 32 \| 4096 \| \| [OLMo 2-13B](https://huggingface.co/allenai/OLMo-2-1124-13B) \| 5 Trillion \| 40 \| 5120 \| 40 \| 4096 \| \| [OLMo 2-32B](https://huggingface.co/allenai/OLMo-2-0325-32B) \| 6 Trillion \| 64 \| 5120 \| 40 \| 4096 \| The core models released in this batch include the following: \| Stage \| OLMo 2 1B \| OLMo 2 7B \| OLMo 2 13B \| OLMo 2 32B \| \|------------------------\|--------------------------------------------------------------------------------------------------------\|---------------------------------------------------------------------------------------------------------\|---------------------------------------------------------------------------------------------------------\|----------------------------------------------------------------------------------------------------------\| \| Base Model \| [allenai/OLMo-2-0425-1B](https://huggingface.co/allenai/OLMo-2-0425-1B) \| [allenai/OLMo-2-1124-7B](https://huggingface.co/allenai/OLMo-2-1124-7B) \| [allenai/OLMo-2-1124-13B](https://huggingface.co/allenai/OLMo-2-1124-13B) \| [allenai/OLMo-2-0325-32B](https://huggingface.co/allenai/OLMo-2-0325-32B) \| \| SFT \| [allenai/OLMo-2-0425-1B-SFT](https://huggingface.co/allenai/OLMo-2-0425-1B-SFT) \| [allenai/OLMo-2-1124-7B-SFT](https://huggingface.co/allenai/OLMo-2-1124-7B-SFT) \| [allenai/OLMo-2-1124-13B-SFT](https://huggingface.co/allenai/OLMo-2-1124-13B-SFT) \| [allenai/OLMo-2-0325-32B-SFT](https://huggingface.co/allenai/OLMo-2-0325-32B-SFT) \| \| DPO \| [allenai/OLMo-2-0425-1B-DPO](https://huggingface.co/allenai/OLMo-2-0425-1B-DPO) \| [allenai/OLMo-2-1124-7B-DPO](https://huggingface.co/allenai/OLMo-2-1124-7B-DPO) \| [allenai/OLMo-2-1124-13B-DPO](https://huggingface.co/allenai/OLMo-2-1124-13B-DPO) \| [allenai/OLMo-2-0325-32B-DPO](https://huggingface.co/allenai/OLMo-2-0325-32B-DPO) \| \| Final Models (RLVR)\| [allenai/OLMo-2-0425-1B-Instruct](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct) \| [allenai/OLMo-2-1124-7B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct) \| [allenai/OLMo-2-1124-13B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct) \| [allenai/OLMo-2-0325-32B-Instruct](https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct) \| \| Reward Model (RM) \| \| [allenai/OLMo-2-1124-7B-RM](https://huggingface.co/allenai/OLMo-2-1124-7B-RM) \|(Same as 7B) \| \| ## Installation OLMo 2 1B is supported in transformers v4.48 or higher: ```bash pip install transformers>=4.48 ``` If using vLLM, you will need to install from the main branch until v0.7.4 is released. Please ## Inference You can use OLMo with the standard HuggingFace transformers library: ```python from transformers import AutoModelForCausalLM, AutoTokenizer olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B") tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-0425-1B") message = ["Language modeling is "] inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False) # optional verifying cuda # inputs = {k: v.to('cuda') for k,v in inputs.items()} # olmo = olmo.to('cuda') response = olmo.generate(inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95) print(tokenizer.batch_decode(response, skip_special_tokens=True)[0]) >> 'Language modeling is a key component of any text-based application, but its effectiveness...' ``` For faster performance, you can quantize the model using the following method: ```python AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", torch_dtype=torch.float16, load_in_8bit=True) # Requires bitsandbytes ``` The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using: ```python inputs.input_ids.to('cuda') ``` We have released checkpoints for these models. For pretraining, the naming convention is `stage1-stepXXX-tokensYYYB`. For checkpoints with ingredients of the soup, the naming convention is `stage2-ingredientN-stepXXX-tokensYYYB` To load a specific model revision with HuggingFace, simply add the argument `revision`: ```bash olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", revision="stage1-step140000-tokens294B") ``` Or, you can access all the revisions for the models via the following code snippet: ```python from huggingface_hub import list_repo_refs out = list_repo_refs("allenai/OLMo-2-0425-1B") branches = [b.name for b in out.branches] ``` ### Fine-tuning Model fine-tuning can be done from the final checkpoint (the `main` revision of this model) or many intermediate checkpoints. Two recipes for tuning are available. 1. Fine-tune with the OLMo repository: ```bash torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \ --data.paths=[{path_to_data}/input_ids.npy] \ --data.label_mask_paths=[{path_to_data}/label_mask.npy] \ --load_path={path_to_checkpoint} \ --reset_trainer_state ``` For more documentation, see the [GitHub README](https://github.com/allenai/OLMo/). 2. Further fine-tuning support is being developing in AI2's Open Instruct repository. Details are [here](https://github.com/allenai/open-instruct). ### Model Description - Developed by: Allen Institute for AI (Ai2) - Model type: a Transformer style autoregressive language model. - Language(s) (NLP): English - License: The code and model are released under Apache 2.0. - Contact: Technical inquiries: `[email protected]`. Press: `[email protected]` - Date cutoff: Dec. 2023. ### Model Sources - Project Page: https://allenai.org/olmo - Repositories: - Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo - Evaluation code: https://github.com/allenai/OLMo-Eval - Further fine-tuning code: https://github.com/allenai/open-instruct - Paper: https://arxiv.org/abs/2501.00656 ## Evaluation Core model results for OLMo 2 1B are found below. \| Instruct Model \| Avg \| FLOP×10²³ \| AE2 \| BBH \| DROP \| GSM8K \| IFE \| MATH \| MMLU \| Safety \| PQA \| TQA \| \|------------------------\|------\|-----------\|------\|------\|------\|-------\|------\|------\|------\|--------\|------\|------\| \| Closed API models \| \| \| \| \| \| \| \| \| \| \| \| \| \| GPT-3.5 Turbo 0125 \| 60.5 \| n/a \| 38.7 \| 66.6 \| 70.2 \| 74.3 \| 66.9 \| 41.2 \| 70.2 \| 69.1 \| 45.0 \| 62.9 \| \| GPT 4o Mini 0724 \| 65.7 \| n/a \| 49.7 \| 65.9 \| 36.3 \| 83.0 \| 83.5 \| 67.9 \| 82.2 \| 84.9 \| 39.0 \| 64.8 \| \| Open weights models 1-1.7B Parameters \| \| \| \| \| \| \| \| \| \| \| \| \| \| SmolLM2 1.7B \| 34.2 \| 1.1 \| 5.8 \| 39.8 \| 30.9 \| 45.3 \| 51.6 \| 20.3 \| 34.3 \| 52.4 \| 16.4 \| 45.3 \| \| Gemma 3 1B \| 38.3 \| 1.2 \| 20.4 \| 39.4 \| 25.1 \| 35.0 \| 60.6 \| 40.3 \| 38.9 \| 70.2 \| 9.6 \| 43.8 \| \| Llama 3.1 1B \| 39.3 \| 6.7 \| 10.1 \| 40.2 \| 32.2 \| 45.4 \| 54.0 \| 21.6 \| 46.7 \| 87.2 \| 13.8 \| 41.5 \| \| Qwen 2.5 1.5B \| 41.7 \| 1.7 \| 7.4 \| 45.8 \| 13.4 \| 66.2 \| 44.2 \| 40.6 \| 59.7 \| 77.6 \| 15.5 \| 46.5 \| \| Fully-open models \| \| \| \| \| \| \| \| \| \| \| \| \| \| OLMo 1B 0724 \| 24.4 \| 0.22 \| 2.4 \| 29.9 \| 27.9 \| 10.8 \| 25.3 \| 2.2 \| 36.6 \| 52.0 \| 12.1 \| 44.3 \| \| OLMo 2 1B \| 42.7 \| 0.35 \| 9.1 \| 35.0 \| 34.6 \| 68.3 \| 70.1 \| 20.7 \| 40.0 \| 87.6 \| 12.9 \| 48.7 \| ## Model Details ### Training \| \| OLMo 2 1B \| OLMo 2 7B \| OLMo 2 13B \| OLMo 2 32B** \| \|-------------------\|------------\|------------\|------------\|------------\| \| Pretraining Stage 1 \| 4 trillion tokens<br>(1 epoch) \| 4 trillion tokens<br>(1 epoch) \| 5 trillion tokens<br>(1.2 epochs) \| 6 trillion tokens<br>(1.5 epochs) \| \| Pretraining Stage 2 \| 50B tokens \| 50B tokens (3 runs)<br>merged \| 100B tokens (3 runs)<br>300B tokens (1 run)<br>merged \| 100B tokens (3 runs)<br>300B tokens (1 run)<br>merged \| \| Post-training \| SFT+DPO+GRPO<br>([preference mix](https://huggingface.co/datasets/allenai/olmo-2-0425-1b-preference-mix)) \| SFT + DPO + PPO<br>([preference mix](https://huggingface.co/datasets/allenai/olmo-2-1124-7b-preference-mix)) \| SFT + DPO + PPO<br>([preference mix](https://huggingface.co/datasets/allenai/olmo-2-1124-13b-preference-mix)) \| SFT + DPO + GRPO<br>([preference mix](https://huggingface.co/datasets/allenai/olmo-2-32b-pref-mix-v1)) \| #### Stage 1: Initial Pretraining - Dataset: [OLMo-mix-1124](https://huggingface.co/datasets/allenai/olmo-mix-1124) (3.9T tokens) - Coverage: 95%+ of total pretraining budget - 1B Model: ~1 epoch #### Stage 2: Mid-training - Dataset: Dolmino-Mix-1124 - One training mix: - 50B tokens - Mix composition: 50% high-quality web data + academic/Q&A/instruction/math content #### Model Merging - 1B Model: only 1 version is trained on a 50B mix (ingredient 3), we did not merge. Ingredients 1 and 2 are just exploratory runs. ## Bias, Risks, and Limitations Like any base or fine-tuned language model, AI can be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified. ## Citation ``` @misc{olmo20242olmo2furious, title={{2 OLMo 2 Furious}}, author={Team OLMo and Pete Walsh and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Shane Arora and Akshita Bhagia and Yuling Gu and Shengyi Huang and Matt Jordan and Nathan Lambert and Dustin Schwenk and Oyvind Tafjord and Taira Anderson and David Atkinson and Faeze Brahman and Christopher Clark and Pradeep Dasigi and Nouha Dziri and Michal Guerquin and Hamish Ivison and Pang Wei Koh and Jiacheng Liu and Saumya Malik and William Merrill and Lester James V. Miranda and Jacob Morrison and Tyler Murray and Crystal Nam and Valentina Pyatkin and Aman Rangapur and Michael Schmitz and Sam Skjonsberg and David Wadden and Christopher Wilhelm and Michael Wilson and Luke Zettlemoyer and Ali Farhadi and Noah A. Smith and Hannaneh Hajishirzi}, year={2024}, eprint={2501.00656}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.00656}, } ``` ## Model Card Contact For errors in this model card, contact `[email protected]`.
chasepkelly/jason-harris1	chasepkelly	2025-05-28T20:33:47Z	0	0	null	[ "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "region:us" ]	null	2025-05-28T20:29:26Z	--- base_model: - black-forest-labs/FLUX.1-dev ---
CodeAtCMU/Qwen3-0.6B_full_sft_mixed_data_120K	CodeAtCMU	2025-05-28T20:28:26Z	37	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T21:10:26Z	--- 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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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. 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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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CodeAtCMU/Qwen3-0.6B_full_sft_natural_language_data_120K	CodeAtCMU	2025-05-28T20:28:11Z	12	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T21:09:43Z	--- 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. 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shallow6414/sn11-w3-7	shallow6414	2025-05-28T20:27:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:27:33Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
fatihcihan/merged_finetuned_llama3.2_1b	fatihcihan	2025-05-28T20:26:14Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:23:28Z	--- 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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snaeppi/Qwen3-0.6B-Base-W8A8-MNLP	snaeppi	2025-05-28T20:24:02Z	1	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:08:37Z	--- 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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CodeAtCMU/Llama-3.1-8B_full_sft_natural_language_data_120K	CodeAtCMU	2025-05-28T20:21:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T20:18:27Z	--- 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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ServiceNow-AI/Apriel-5B-Base	ServiceNow-AI	2025-05-28T20:17:49Z	395	32	transformers	[ "transformers", "safetensors", "apriel", "text-generation", "custom_code", "en", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2025-03-13T17:28:31Z	--- library_name: transformers language: - en license: mit --- # Apriel-5B `/ˈɑː.pri.əl/` ## Table of Contents 1. [Model Summary](#model-summary) 2. [Evaluation](#evaluation) 3. [Intended Use](#intended-use) 4. [Limitations](#limitations) 5. [Security and Responsible Use](#security-and-responsible-use) 6. [License](#license) 7. [Citation](#citation) ## Model Summary Apriel is a family of models built for versatility, offering high throughput and efficiency across a wide range of tasks. ### Apriel-5B-Base Apriel-5B-base is a decoder-only transformer trained on 4.5T+ tokens of data. It is the first release in the Apriel model family, designed to support research on foundation models. Apriel-5B-base achieves strong performance across common benchmarks for models under 5B parameters. ### Apriel-5B-Instruct [Apriel-5B-Instruct](https://huggingface.co/ServiceNow-AI/Apriel-5B-Instruct) is built on top of [Apriel-5B-base](https://huggingface.co/ServiceNow-AI/Apriel-5B-base) using continual pretraining (CPT), supervised finetuning (SFT), and post-training alignment with DPO and RLVR. Both CPT and SFT stages involved training multiple domain-biased variants with overlapping datasets (e.g., instruction, code, math). These were then merged to form a more general-purpose model before alignment. The final model is aligned for instruction following, reasoning, and safety-aware dialogue. <img src="https://huggingface.co/ServiceNow-AI/Apriel-4.8B-base/resolve/main/eval_vs_latency.png" alt="graph" width="400"/> The y-axis shows average downstream benchmark scores. Throughput (x-axis) was measured using [vLLM](https://github.com/vllm-project/vllm) with batch size 8, 256 input tokens, and 32 output tokens. ### How to Use ```bash pip install transformers ``` #### Running the Base model ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "ServiceNow-AI/Apriel-5B-Base" device = "cuda" # or "cpu" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16).to(device) inputs = tokenizer.encode("Snow is", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 9664.14 MB ``` #### Running the Instruct model ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "ServiceNow-AI/Apriel-5B-Instruct" tokenizer = AutoTokenizer.from_pretrained(checkpoint) device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained( checkpoint, torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32 ).to(device) messages = [ {"role": "system", "content": "You are a helpful AI assistant that provides accurate and concise information."}, {"role": "user", "content": "Tell me about artificial intelligence"} ] input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(input_text, return_tensors="pt").to(device) generation_params = { "max_new_tokens": 512, "temperature": 0.2, "top_p": 0.9, "do_sample": True } outputs = model.generate(inputs, generation_params) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Chat Template ``` <\|system\|> System message here (optional) <\|end\|> <\|user\|> User message here <\|end\|> <\|assistant\|> Assistant response here <\|end\|> ``` If no system message is provided, the model inserts a blank system prompt to maintain format structure. The model supports structured interaction patterns, including tool calling and reasoning steps for more advanced workflows. ## Evaluation Evaluations were conducted using [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness) and [evalchemy](https://github.com/mlfoundations/evalchemy). ### Apriel-5B-Base \| Task Name \| Apriel-5B-Base \| OLMo-2-1124-7B \| Llama-3.1-8B \| Mistral-Nemo-Base-2407 \| \|---------------------\|------------------\|----------------\|--------------\|-------------------------\| \| Average \| 58.7 \| 58.71 \| 61.72 \| 66.01 \| \| ARC Challenge \| 56.7 \| 62.7 \| 58.2 \| 62.9 \| \| ARC Easy \| 82.4 \| 86.0 \| 85.7 \| 86.7 \| \| MMMLU \| 44.5 \| 35.3 \| 47.4 \| 54.7 \| \| Global MMLU \| 57.4 \| 52.4 \| 61.1 \| 68.4 \| \| GSM8k \| 64.2 \| 63.2 \| 54.8 \| 58.5 \| \| HellaSwag \| 74.4 \| 80.5 \| 78.8 \| 82.7 \| \| MUSR \| 39.1 \| 39.6 \| 38.0 \| 39.9 \| \| MBPP \| 27.6 \| 22.4 \| 46.0 \| 54.6 \| \| MMLU \| 61.3 \| 63.9 \| 66.0 \| 69.6 \| \| PIQA \| 78.9 \| 81.1 \| 81.2 \| 82.1 \| ### Apriel-5B-Instruct \| Task Name \| Apriel-5B-Instruct \| OLMo-2-1124-7B-Instruct \| Llama-3.1-8B-Instruct \| Mistral-Nemo-Instruct-2407 \| \|--------------\|--------------------\|--------------------------\|------------------------\|----------------------------\| \| Average \| 49.64 \| 43.91 \| 52.60 \| 48.63 \| \| ARC Challenge \| 59.04 \| 61.45 \| 64.25 \| 66.38 \| \| GSM8k \| 80.36 \| 79.68 \| 82.63 \| 77.63 \| \| Hellaswag \| 74.52 \| 80.21 \| 78.43 \| 81.71 \| \| BBH \| 39.82 \| 39.95 \| 50.86 \| 50.06 \| \| GPQA \| 28.36 \| 27.85 \| 29.19 \| 29.45 \| \| IF Eval \| 80.78 \| 72.64 \| 79.67 \| 62.85 \| \| MMLU Pro \| 29.19 \| 26.57 \| 37.74 \| 35.09 \| \| MUSR \| 36.77 \| 34.39 \| 38.36 \| 39.02 \| \| MBPP \| 45.80 \| 28.00 \| 59.00 \| 57.60 \| \| TruthfulQA \| 56.09 \| 56.46 \| 55.05 \| 57.69 \| \| Winogrande \| 62.35 \| 65.35 \| 67.01 \| 70.01 \| \| Minerva Math \| 39.80 \| 9.96 \| 36.72 \| 21.46 \| \| MATH500 \| 53.00 \| 31.4 \| 45.80 \| 34.40 \| \| AMC23 \| 29.00 \| 16.4 \| 21.00 \| 11.50 \| \| MixEval Hard \| 29.70 \| 28.40 \| 43.30 \| 34.60 \| ## Intended Use The Apriel family of models are designed for a variety of general-purpose instruction tasks, including: - Question answering and information retrieval - Content generation and summarization - Code assistance and generation - Logical reasoning and multi-step tasks - Creative writing and ideation They are not intended for use in safety-critical applications without human oversight or in scenarios requiring guaranteed factual accuracy. ## Limitations - Factual accuracy: May produce incorrect, misleading, or outdated content. Outputs should be verified before use in critical contexts. - Bias: May reflect societal, cultural, or systemic biases present in training data. - Ethics: Do not use the model to produce harmful, unlawful, or unethical content. - Language: Strongest performance is in English. Output quality may degrade in underrepresented languages. - Critical use: Not suitable for medical, legal, financial, or other high-risk applications without safeguards. ## Security and Responsible Use Security Responsibilities: Deployers and users are strongly encouraged to align their security practices with established frameworks and regulatory guidelines such as the EU AI Act and the NIST AI Risk Management Framework (RMF). Guidelines for Deployers: - Regularly conduct robustness assessments to identify and mitigate adversarial inputs. - Implement validation and filtering processes to prevent harmful or biased outputs. - Continuously perform data privacy checks to guard against unintended data leaks. - Document and communicate the model's limitations, intended usage, and known security risks to all end-users. - Schedule periodic security reviews and updates to address emerging threats and vulnerabilities. Guidelines for Users: - Follow established security policies and usage guidelines provided by deployers. - Protect and manage sensitive information when interacting with the model. - Report anomalies, suspicious behavior, or unsafe outputs to deployers or developers. - Maintain human oversight and apply judgment to mitigate potential security or ethical risks during interactions. Disclaimer: Users accept responsibility for securely deploying, managing, and using this open-source LLM. The model is provided "as-is," without explicit or implied warranty regarding security or fitness for any specific application or environment. ## Pretraining ### Model - Architecture: Transformer decoder with grouped-query attention and YARN rotary embeddings - Tokens: 4.5T - Precision: bfloat16 - Knowledge cutoff: April 2024 ### Hardware - Compute: 480 × H100 GPUs - GPU-hours: ~91,000 H100-hours ### Software - Training stack: [Fast-LLM](https://github.com/ServiceNow/Fast-LLM) ## License MIT ## Citation ```bibtex @misc{Apriel-small-language-models, author = {Slam labs team}, title = {{Apriel - a Family of performant small language models}}, howpublished = {https://huggingface.co/ServiceNow-AI/Apriel-5B-Instruct}, publisher = {SLAM - ServiceNow Language Models Lab} year = {2025} } ```

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