librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-30 00:44:18	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 536 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-30 00:43:43	card stringlengths 11 1.01M
jordyvl/dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5	jordyvl	2023-07-30T04:43:54Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-29T22:45:24Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 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. --> # dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.3085 - Accuracy: 0.828 - Brier Loss: 0.3005 - Nll: 1.3339 - F1 Micro: 0.828 - F1 Macro: 0.8285 - Ece: 0.1391 - Aurc: 0.0474 ## 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 167 \| 5.3750 \| 0.61 \| 0.5591 \| 2.2520 \| 0.61 \| 0.5922 \| 0.1827 \| 0.1664 \| \| No log \| 2.0 \| 334 \| 5.0343 \| 0.7117 \| 0.4389 \| 1.9483 \| 0.7117 \| 0.7096 \| 0.1691 \| 0.0962 \| \| 5.4927 \| 3.0 \| 501 \| 4.8554 \| 0.7472 \| 0.3777 \| 1.6689 \| 0.7472 \| 0.7474 \| 0.1221 \| 0.0780 \| \| 5.4927 \| 4.0 \| 668 \| 4.7917 \| 0.76 \| 0.3524 \| 1.5715 \| 0.76 \| 0.7644 \| 0.0915 \| 0.0699 \| \| 5.4927 \| 5.0 \| 835 \| 4.7792 \| 0.765 \| 0.3461 \| 1.5348 \| 0.765 \| 0.7590 \| 0.0737 \| 0.0704 \| \| 4.6216 \| 6.0 \| 1002 \| 4.6378 \| 0.7993 \| 0.2954 \| 1.3769 \| 0.7993 \| 0.7995 \| 0.0546 \| 0.0559 \| \| 4.6216 \| 7.0 \| 1169 \| 4.8666 \| 0.771 \| 0.3359 \| 1.5727 \| 0.771 \| 0.7728 \| 0.0670 \| 0.0666 \| \| 4.6216 \| 8.0 \| 1336 \| 4.6834 \| 0.7897 \| 0.3047 \| 1.3537 \| 0.7897 \| 0.7914 \| 0.0531 \| 0.0564 \| \| 4.2978 \| 9.0 \| 1503 \| 4.6558 \| 0.7997 \| 0.2912 \| 1.3758 \| 0.7997 \| 0.7988 \| 0.0521 \| 0.0508 \| \| 4.2978 \| 10.0 \| 1670 \| 4.8214 \| 0.7923 \| 0.3144 \| 1.5316 \| 0.7923 \| 0.7928 \| 0.0815 \| 0.0561 \| \| 4.2978 \| 11.0 \| 1837 \| 4.8908 \| 0.7923 \| 0.3201 \| 1.4158 \| 0.7923 \| 0.7931 \| 0.0988 \| 0.0573 \| \| 4.1375 \| 12.0 \| 2004 \| 4.7703 \| 0.8093 \| 0.2971 \| 1.3642 \| 0.8093 \| 0.8097 \| 0.0812 \| 0.0514 \| \| 4.1375 \| 13.0 \| 2171 \| 4.8126 \| 0.806 \| 0.3039 \| 1.3759 \| 0.806 \| 0.8053 \| 0.0916 \| 0.0491 \| \| 4.1375 \| 14.0 \| 2338 \| 4.7875 \| 0.8063 \| 0.2990 \| 1.3712 \| 0.8062 \| 0.8065 \| 0.0904 \| 0.0481 \| \| 4.0665 \| 15.0 \| 2505 \| 4.7995 \| 0.805 \| 0.3016 \| 1.4133 \| 0.805 \| 0.8049 \| 0.0909 \| 0.0503 \| \| 4.0665 \| 16.0 \| 2672 \| 4.7712 \| 0.8075 \| 0.2957 \| 1.4018 \| 0.8075 \| 0.8082 \| 0.0880 \| 0.0484 \| \| 4.0665 \| 17.0 \| 2839 \| 4.7245 \| 0.812 \| 0.2886 \| 1.2816 \| 0.8120 \| 0.8139 \| 0.0831 \| 0.0464 \| \| 4.0204 \| 18.0 \| 3006 \| 4.8990 \| 0.8055 \| 0.3079 \| 1.3884 \| 0.8055 \| 0.8046 \| 0.1117 \| 0.0504 \| \| 4.0204 \| 19.0 \| 3173 \| 4.9286 \| 0.802 \| 0.3147 \| 1.3977 \| 0.802 \| 0.7995 \| 0.1078 \| 0.0522 \| \| 4.0204 \| 20.0 \| 3340 \| 4.9510 \| 0.8055 \| 0.3121 \| 1.4482 \| 0.8055 \| 0.8062 \| 0.1125 \| 0.0521 \| \| 3.9854 \| 21.0 \| 3507 \| 4.8837 \| 0.8033 \| 0.3082 \| 1.4528 \| 0.8033 \| 0.8022 \| 0.1052 \| 0.0502 \| \| 3.9854 \| 22.0 \| 3674 \| 5.0103 \| 0.813 \| 0.3069 \| 1.4217 \| 0.813 \| 0.8169 \| 0.1207 \| 0.0500 \| \| 3.9854 \| 23.0 \| 3841 \| 4.9602 \| 0.8093 \| 0.3091 \| 1.4672 \| 0.8093 \| 0.8103 \| 0.1187 \| 0.0494 \| \| 3.9599 \| 24.0 \| 4008 \| 4.8980 \| 0.8177 \| 0.2953 \| 1.3589 \| 0.8178 \| 0.8203 \| 0.1083 \| 0.0451 \| \| 3.9599 \| 25.0 \| 4175 \| 4.8753 \| 0.8145 \| 0.2932 \| 1.3219 \| 0.8145 \| 0.8140 \| 0.1054 \| 0.0460 \| \| 3.9599 \| 26.0 \| 4342 \| 4.9644 \| 0.8193 \| 0.3000 \| 1.4336 \| 0.8193 \| 0.8200 \| 0.1173 \| 0.0458 \| \| 3.9358 \| 27.0 \| 4509 \| 4.9648 \| 0.8203 \| 0.2985 \| 1.4117 \| 0.8203 \| 0.8197 \| 0.1132 \| 0.0471 \| \| 3.9358 \| 28.0 \| 4676 \| 5.0130 \| 0.8195 \| 0.3014 \| 1.4618 \| 0.8195 \| 0.8201 \| 0.1205 \| 0.0456 \| \| 3.9358 \| 29.0 \| 4843 \| 4.8974 \| 0.8255 \| 0.2874 \| 1.3041 \| 0.8255 \| 0.8258 \| 0.1097 \| 0.0421 \| \| 3.9151 \| 30.0 \| 5010 \| 4.9045 \| 0.8255 \| 0.2878 \| 1.2801 \| 0.8255 \| 0.8250 \| 0.1119 \| 0.0426 \| \| 3.9151 \| 31.0 \| 5177 \| 4.9720 \| 0.823 \| 0.2945 \| 1.3551 \| 0.823 \| 0.8240 \| 0.1212 \| 0.0439 \| \| 3.9151 \| 32.0 \| 5344 \| 4.9508 \| 0.826 \| 0.2913 \| 1.2669 \| 0.826 \| 0.8268 \| 0.1201 \| 0.0422 \| \| 3.9003 \| 33.0 \| 5511 \| 5.0336 \| 0.8237 \| 0.2991 \| 1.3443 \| 0.8237 \| 0.8240 \| 0.1243 \| 0.0433 \| \| 3.9003 \| 34.0 \| 5678 \| 4.9828 \| 0.8237 \| 0.2901 \| 1.2843 \| 0.8237 \| 0.8239 \| 0.1214 \| 0.0440 \| \| 3.9003 \| 35.0 \| 5845 \| 5.0256 \| 0.8287 \| 0.2920 \| 1.2961 \| 0.8287 \| 0.8291 \| 0.1232 \| 0.0422 \| \| 3.89 \| 36.0 \| 6012 \| 5.0229 \| 0.8283 \| 0.2922 \| 1.2471 \| 0.8283 \| 0.8283 \| 0.1236 \| 0.0432 \| \| 3.89 \| 37.0 \| 6179 \| 5.0835 \| 0.8285 \| 0.2936 \| 1.2892 \| 0.8285 \| 0.8289 \| 0.1254 \| 0.0442 \| \| 3.89 \| 38.0 \| 6346 \| 5.1148 \| 0.8287 \| 0.2946 \| 1.3106 \| 0.8287 \| 0.8282 \| 0.1287 \| 0.0427 \| \| 3.8846 \| 39.0 \| 6513 \| 5.1238 \| 0.827 \| 0.2954 \| 1.2964 \| 0.827 \| 0.8275 \| 0.1298 \| 0.0441 \| \| 3.8846 \| 40.0 \| 6680 \| 5.1481 \| 0.8307 \| 0.2950 \| 1.3136 \| 0.8308 \| 0.8311 \| 0.1277 \| 0.0453 \| \| 3.8846 \| 41.0 \| 6847 \| 5.1491 \| 0.8293 \| 0.2943 \| 1.2841 \| 0.8293 \| 0.8294 \| 0.1298 \| 0.0451 \| \| 3.881 \| 42.0 \| 7014 \| 5.1982 \| 0.829 \| 0.2969 \| 1.3111 \| 0.8290 \| 0.8292 \| 0.1331 \| 0.0459 \| \| 3.881 \| 43.0 \| 7181 \| 5.2041 \| 0.8283 \| 0.2970 \| 1.3427 \| 0.8283 \| 0.8283 \| 0.1327 \| 0.0465 \| \| 3.881 \| 44.0 \| 7348 \| 5.2310 \| 0.8297 \| 0.2985 \| 1.3351 \| 0.8297 \| 0.8303 \| 0.1346 \| 0.0471 \| \| 3.8796 \| 45.0 \| 7515 \| 5.2394 \| 0.83 \| 0.2999 \| 1.3308 \| 0.83 \| 0.8305 \| 0.1348 \| 0.0467 \| \| 3.8796 \| 46.0 \| 7682 \| 5.2632 \| 0.83 \| 0.2990 \| 1.3350 \| 0.83 \| 0.8304 \| 0.1355 \| 0.0471 \| \| 3.8796 \| 47.0 \| 7849 \| 5.2821 \| 0.828 \| 0.2998 \| 1.3354 \| 0.828 \| 0.8286 \| 0.1383 \| 0.0470 \| \| 3.8753 \| 48.0 \| 8016 \| 5.2949 \| 0.829 \| 0.2998 \| 1.3341 \| 0.8290 \| 0.8294 \| 0.1374 \| 0.0472 \| \| 3.8753 \| 49.0 \| 8183 \| 5.3026 \| 0.8287 \| 0.3004 \| 1.3281 \| 0.8287 \| 0.8293 \| 0.1382 \| 0.0474 \| \| 3.8753 \| 50.0 \| 8350 \| 5.3085 \| 0.828 \| 0.3005 \| 1.3339 \| 0.828 \| 0.8285 \| 0.1391 \| 0.0474 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
NasimB/cbt-log-rarity-seed	NasimB	2023-07-30T04:27:02Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-30T01:22:05Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: cbt-log-rarity-seed 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. --> # cbt-log-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1049 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3551 \| 0.29 \| 500 \| 5.3459 \| \| 5.0301 \| 0.58 \| 1000 \| 4.9295 \| \| 4.7058 \| 0.87 \| 1500 \| 4.6870 \| \| 4.4358 \| 1.17 \| 2000 \| 4.5471 \| \| 4.2948 \| 1.46 \| 2500 \| 4.4310 \| \| 4.1839 \| 1.75 \| 3000 \| 4.3262 \| \| 4.0867 \| 2.04 \| 3500 \| 4.2510 \| \| 3.8901 \| 2.33 \| 4000 \| 4.2098 \| \| 3.8635 \| 2.62 \| 4500 \| 4.1570 \| \| 3.8226 \| 2.91 \| 5000 \| 4.1006 \| \| 3.6452 \| 3.21 \| 5500 \| 4.0979 \| \| 3.5849 \| 3.5 \| 6000 \| 4.0657 \| \| 3.5624 \| 3.79 \| 6500 \| 4.0400 \| \| 3.475 \| 4.08 \| 7000 \| 4.0368 \| \| 3.3171 \| 4.37 \| 7500 \| 4.0306 \| \| 3.3088 \| 4.66 \| 8000 \| 4.0165 \| \| 3.3004 \| 4.95 \| 8500 \| 4.0034 \| \| 3.1584 \| 5.24 \| 9000 \| 4.0145 \| \| 3.1334 \| 5.54 \| 9500 \| 4.0146 \| \| 3.1257 \| 5.83 \| 10000 \| 4.0136 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
Yntec/theallysMixIV-verisimilar	Yntec	2023-07-30T04:12:07Z	489	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "TheAlly", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-21T18:11:23Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image - TheAlly --- # TheAlly's Mix IV: Verisimilar Original page: https://civitai.com/models/40369/theallys-mix-iv-verisimilar
NasimB/bnc_spoken_cbt_rarity-seed	NasimB	2023-07-30T03:39:52Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-30T00:49:35Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc_spoken_cbt_rarity-seed 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. --> # bnc_spoken_cbt_rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1218 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.366 \| 0.29 \| 500 \| 5.3305 \| \| 5.0478 \| 0.59 \| 1000 \| 4.9325 \| \| 4.7159 \| 0.88 \| 1500 \| 4.6882 \| \| 4.4541 \| 1.17 \| 2000 \| 4.5478 \| \| 4.3142 \| 1.46 \| 2500 \| 4.4363 \| \| 4.2068 \| 1.76 \| 3000 \| 4.3334 \| \| 4.0948 \| 2.05 \| 3500 \| 4.2611 \| \| 3.9065 \| 2.34 \| 4000 \| 4.2156 \| \| 3.8793 \| 2.63 \| 4500 \| 4.1613 \| \| 3.8461 \| 2.93 \| 5000 \| 4.1146 \| \| 3.6533 \| 3.22 \| 5500 \| 4.1112 \| \| 3.5945 \| 3.51 \| 6000 \| 4.0788 \| \| 3.5863 \| 3.81 \| 6500 \| 4.0459 \| \| 3.4902 \| 4.1 \| 7000 \| 4.0491 \| \| 3.3324 \| 4.39 \| 7500 \| 4.0429 \| \| 3.3243 \| 4.68 \| 8000 \| 4.0290 \| \| 3.3156 \| 4.98 \| 8500 \| 4.0193 \| \| 3.1646 \| 5.27 \| 9000 \| 4.0316 \| \| 3.1471 \| 5.56 \| 9500 \| 4.0313 \| \| 3.1498 \| 5.85 \| 10000 \| 4.0303 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
zbulrush/controlnet-sd-xl-1.0-lineart	zbulrush	2023-07-30T03:35:56Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-30T03:35:56Z	--- license: creativeml-openrail-m ---
roman10/distilbert-base-uncased-finetuned-p1-en	roman10	2023-07-30T03:31:12Z	71	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-30T03:27:40Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: roman10/distilbert-base-uncased-finetuned-p1-en results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # roman10/distilbert-base-uncased-finetuned-p1-en This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.4677 - Validation Loss: 2.8371 - Epoch: 1 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -999, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.3261 \| 3.5044 \| 0 \| \| 3.4677 \| 2.8371 \| 1 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.1 - Datasets 2.13.1 - Tokenizers 0.13.3
bochen0909/a2c-PandaReachDense-v2	bochen0909	2023-07-30T02:52:16Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-30T02:50:09Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.60 +/- 0.85 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
RyanDA/QuickDraw-model	RyanDA	2023-07-30T02:29:06Z	4	0	keras	[ "keras", "license:bigscience-openrail-m", "region:us" ]	null	2023-07-30T01:50:55Z	--- license: bigscience-openrail-m --- # QuickDraw Model This model was trained on the quickdraw dataset from a selected list of doodles: ["Apple","Cell Phone","Chair","Hot Air Balloon","Jail","Ladder","Line","Spider","Windmill","Zigzag"]
bochen0909/a2c-AntBulletEnv-v0	bochen0909	2023-07-30T01:51:53Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-30T01:47:58Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1006.82 +/- 316.92 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Ahsankhan123/taxi-v3	Ahsankhan123	2023-07-30T01:46:43Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-30T01:46:42Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Ahsankhan123/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Lexic0n/segformer-b0-finetuned-human-parsing	Lexic0n	2023-07-30T01:39:16Z	170	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2023-07-30T01:38:09Z	--- license: other tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-human-parsing 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. --> # segformer-b0-finetuned-human-parsing This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9476 - Mean Iou: 0.0726 - Mean Accuracy: 0.1221 - Overall Accuracy: 0.3575 - Accuracy Background: nan - Accuracy Hat: 0.0048 - Accuracy Hair: 0.4813 - Accuracy Sunglasses: 0.0 - Accuracy Upper-clothes: 0.9405 - Accuracy Skirt: 0.0000 - Accuracy Pants: 0.0631 - Accuracy Dress: 0.1031 - Accuracy Belt: 0.0 - Accuracy Left-shoe: 0.0011 - Accuracy Right-shoe: 0.0010 - Accuracy Face: 0.4406 - Accuracy Left-leg: 0.0291 - Accuracy Right-leg: 0.0 - Accuracy Left-arm: 0.0 - Accuracy Right-arm: 0.0001 - Accuracy Bag: 0.0114 - Accuracy Scarf: 0.0 - Iou Background: 0.0 - Iou Hat: 0.0043 - Iou Hair: 0.4221 - Iou Sunglasses: 0.0 - Iou Upper-clothes: 0.3239 - Iou Skirt: 0.0000 - Iou Pants: 0.0559 - Iou Dress: 0.0728 - Iou Belt: 0.0 - Iou Left-shoe: 0.0011 - Iou Right-shoe: 0.0009 - Iou Face: 0.3872 - Iou Left-leg: 0.0271 - Iou Right-leg: 0.0 - Iou Left-arm: 0.0 - Iou Right-arm: 0.0001 - Iou Bag: 0.0106 - Iou Scarf: 0.0 ## 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Hat \| Accuracy Hair \| Accuracy Sunglasses \| Accuracy Upper-clothes \| Accuracy Skirt \| Accuracy Pants \| Accuracy Dress \| Accuracy Belt \| Accuracy Left-shoe \| Accuracy Right-shoe \| Accuracy Face \| Accuracy Left-leg \| Accuracy Right-leg \| Accuracy Left-arm \| Accuracy Right-arm \| Accuracy Bag \| Accuracy Scarf \| Iou Background \| Iou Hat \| Iou Hair \| Iou Sunglasses \| Iou Upper-clothes \| Iou Skirt \| Iou Pants \| Iou Dress \| Iou Belt \| Iou Left-shoe \| Iou Right-shoe \| Iou Face \| Iou Left-leg \| Iou Right-leg \| Iou Left-arm \| Iou Right-arm \| Iou Bag \| Iou Scarf \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:------------:\|:-------------:\|:-------------------:\|:----------------------:\|:--------------:\|:--------------:\|:--------------:\|:-------------:\|:------------------:\|:-------------------:\|:-------------:\|:-----------------:\|:------------------:\|:-----------------:\|:------------------:\|:------------:\|:--------------:\|:--------------:\|:-------:\|:--------:\|:--------------:\|:-----------------:\|:---------:\|:---------:\|:---------:\|:--------:\|:-------------:\|:--------------:\|:--------:\|:------------:\|:-------------:\|:------------:\|:-------------:\|:-------:\|:---------:\| \| 2.5768 \| 0.4 \| 20 \| 2.7812 \| 0.0726 \| 0.1332 \| 0.2876 \| nan \| 0.0178 \| 0.3204 \| 0.0004 \| 0.5548 \| 0.0004 \| 0.2555 \| 0.2373 \| 0.0 \| 0.0103 \| 0.0003 \| 0.5637 \| 0.0287 \| 0.0302 \| 0.0001 \| 0.0008 \| 0.2435 \| 0.0 \| 0.0 \| 0.0166 \| 0.2759 \| 0.0001 \| 0.2781 \| 0.0004 \| 0.1710 \| 0.1295 \| 0.0 \| 0.0098 \| 0.0003 \| 0.3251 \| 0.0260 \| 0.0248 \| 0.0001 \| 0.0007 \| 0.0491 \| 0.0 \| \| 2.2093 \| 0.8 \| 40 \| 2.5166 \| 0.0563 \| 0.1052 \| 0.3288 \| nan \| 0.0 \| 0.1994 \| 0.0 \| 0.9447 \| 0.0015 \| 0.0435 \| 0.1164 \| 0.0 \| 0.0008 \| 0.0000 \| 0.4655 \| 0.0007 \| 0.0003 \| 0.0 \| 0.0 \| 0.0153 \| 0.0 \| 0.0 \| 0.0 \| 0.1946 \| 0.0 \| 0.3037 \| 0.0015 \| 0.0417 \| 0.0842 \| 0.0 \| 0.0008 \| 0.0000 \| 0.3726 \| 0.0007 \| 0.0003 \| 0.0 \| 0.0 \| 0.0124 \| 0.0 \| \| 1.8804 \| 1.2 \| 60 \| 2.0209 \| 0.0632 \| 0.1110 \| 0.3374 \| nan \| 0.0087 \| 0.3724 \| 0.0 \| 0.9475 \| 0.0014 \| 0.0162 \| 0.0528 \| 0.0 \| 0.0001 \| 0.0008 \| 0.4257 \| 0.0561 \| 0.0001 \| 0.0 \| 0.0 \| 0.0055 \| 0.0 \| 0.0 \| 0.0077 \| 0.3472 \| 0.0 \| 0.3086 \| 0.0014 \| 0.0156 \| 0.0403 \| 0.0 \| 0.0001 \| 0.0008 \| 0.3597 \| 0.0515 \| 0.0001 \| 0.0 \| 0.0 \| 0.0052 \| 0.0 \| \| 1.8776 \| 1.6 \| 80 \| 2.0016 \| 0.0665 \| 0.1154 \| 0.3454 \| nan \| 0.0056 \| 0.4172 \| 0.0 \| 0.9412 \| 0.0000 \| 0.0490 \| 0.0697 \| 0.0 \| 0.0002 \| 0.0006 \| 0.4349 \| 0.0329 \| 0.0000 \| 0.0 \| 0.0000 \| 0.0100 \| 0.0 \| 0.0 \| 0.0048 \| 0.3791 \| 0.0 \| 0.3138 \| 0.0000 \| 0.0438 \| 0.0542 \| 0.0 \| 0.0002 \| 0.0006 \| 0.3608 \| 0.0304 \| 0.0000 \| 0.0 \| 0.0000 \| 0.0093 \| 0.0 \| \| 1.8471 \| 2.0 \| 100 \| 1.9476 \| 0.0726 \| 0.1221 \| 0.3575 \| nan \| 0.0048 \| 0.4813 \| 0.0 \| 0.9405 \| 0.0000 \| 0.0631 \| 0.1031 \| 0.0 \| 0.0011 \| 0.0010 \| 0.4406 \| 0.0291 \| 0.0 \| 0.0 \| 0.0001 \| 0.0114 \| 0.0 \| 0.0 \| 0.0043 \| 0.4221 \| 0.0 \| 0.3239 \| 0.0000 \| 0.0559 \| 0.0728 \| 0.0 \| 0.0011 \| 0.0009 \| 0.3872 \| 0.0271 \| 0.0 \| 0.0 \| 0.0001 \| 0.0106 \| 0.0 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
HachiML/mpt-7b-instruct-ja-qlora	HachiML	2023-07-30T01:23:44Z	2	0	peft	[ "peft", "dataset:HachiML/databricks-dolly-15k-ja-for-peft", "region:us" ]	null	2023-07-27T13:03:12Z	--- library_name: peft datasets: - HachiML/databricks-dolly-15k-ja-for-peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
PeterBrendan/llama-2-7b-Ads	PeterBrendan	2023-07-30T01:12:00Z	21	6	transformers	[ "transformers", "pytorch", "llama", "text-generation", "Advertising", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T17:59:15Z	--- license: mit language: - en tags: - Advertising widget: - text: Write me an online ad for Old Spice for a 300x250 creative --- ## llama-2-7b-Ads ### Model Overview The "llama-2-7b-Ads" model is a fine-tuned version of the "meta-llama/Llama-2-7b-chat-hf" language model. The base model, "meta-llama/Llama-2-7b-chat-hf," was trained on a vast corpus of text, enabling it to generate coherent and contextually relevant responses for various chat-based applications. The "PeterBrendan/llama-2-7b-Ads" model was fine-tuned using the "PeterBrendan/Ads_Creative_Ad_Copy_Programmatic" dataset. ### Dataset Overview The "PeterBrendan/Ads_Creative_Ad_Copy_Programmatic" dataset used for fine-tuning contains 7097 samples of online programmatic ad creatives, along with their respective ad sizes. The dataset includes 8 unique ad sizes, namely: 1. (300, 250) 2. (728, 90) 3. (970, 250) 4. (300, 600) 5. (160, 600) 6. (970, 90) 7. (336, 280) 8. (320, 50) This dataset is a random sample from Project300x250.com's complete creative data set. The primary application of this dataset is to train and evaluate natural language processing models specifically for advertising creatives. ### Use Cases The "llama-2-7b-Ads" model can be used in various natural language processing tasks related to advertising creatives. Some potential use cases include: 1. Ad Creative Generation: The model can generate ad copy text given different prompts, enabling advertisers to create compelling ad creatives automatically. 2. Personalization: By inputting user-specific data into a prompt, such as demographics or preferences, the model can generate personalized ad copy tailored to different ad sizes for targeted advertising. #### Example Prompts: Example Prompt 1: Write me an online ad for Old Spice for a 300x250 creative Output: OLD SPICE The Smell of a Man GET YOUR SMELL ON SHOP NOW Example Prompt 2: Write me an online ad for Nike Basketball Shoes for a 300x250 creative Output: Nike Basketball Shoes Shop Now Nike ### Performance and Limitations As this model is a fine-tuned version of "meta-llama/Llama-2-7b-chat-hf," it inherits its base model's performance characteristics and limitations. The quality of generated responses depends on the complexity and diversity of the input data during fine-tuning. Performance: The model generally performs well in generating coherent ad copy text based on the input ad sizes. However, the actual performance may vary depending on the complexity and creativity required for the given task. Limitations: 1. Domain-Specific Bias: The model's responses might be biased towards the content found in the "PeterBrendan/Ads_Creative_Ad_Copy_Programmatic" dataset, which primarily focuses on advertising creatives. 2. Out-of-Domain Queries: The model may not perform optimally when faced with queries or inputs unrelated to advertising creatives or the specified ad sizes. 3. Limited Generalization: Although fine-tuned, the model's generalization capabilities are still bounded by the data it was trained on. Extreme or out-of-distribution inputs may lead to inaccurate or nonsensical outputs. ### How to Use ~~~python # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PeterBrendan/llama-2-7b-Ads") # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PeterBrendan/llama-2-7b-Ads") model = AutoModelForCausalLM.from_pretrained("PeterBrendan/llama-2-7b-Ads") ~~~ ### Acknowledgments The "PeterBrendan/llama-2-7b-Ads" model was fine-tuned using the Hugging Face Transformers library and relies on the "meta-llama/Llama-2-7b-chat-hf" base model. We extend our gratitude to the creators of the base model for their contributions. ### Disclaimer The "PeterBrendan/llama-2-7b-Ads" model card provides an overview of the model and its use cases. However, it is essential to exercise caution and human review when deploying any AI model for critical applications like advertising. As with any AI system, the model's outputs should be thoroughly analyzed, especially in real-world scenarios, to ensure alignment with business objectives and ethical considerations.
Leaked/Latto	Leaked	2023-07-30T01:09:06Z	0	0	null	[ "arxiv:1910.09700", "license:openrail", "region:us" ]	null	2023-07-30T00:25:18Z	--- license: openrail --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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timjwhite/Reinforce-CartPole-v1	timjwhite	2023-07-30T00:48:16Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T23:39:54Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
NasimB/switchboard-rarity-seed	NasimB	2023-07-30T00:46:51Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-29T21:29:44Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: switchboard-rarity-seed 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. --> # switchboard-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0985 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3581 \| 0.29 \| 500 \| 5.3466 \| \| 5.0332 \| 0.58 \| 1000 \| 4.9336 \| \| 4.7065 \| 0.87 \| 1500 \| 4.6924 \| \| 4.4439 \| 1.17 \| 2000 \| 4.5465 \| \| 4.2929 \| 1.46 \| 2500 \| 4.4328 \| \| 4.1869 \| 1.75 \| 3000 \| 4.3248 \| \| 4.0802 \| 2.04 \| 3500 \| 4.2481 \| \| 3.8877 \| 2.33 \| 4000 \| 4.2060 \| \| 3.8547 \| 2.62 \| 4500 \| 4.1542 \| \| 3.83 \| 2.92 \| 5000 \| 4.0982 \| \| 3.6375 \| 3.21 \| 5500 \| 4.0946 \| \| 3.5896 \| 3.5 \| 6000 \| 4.0648 \| \| 3.5596 \| 3.79 \| 6500 \| 4.0309 \| \| 3.474 \| 4.08 \| 7000 \| 4.0282 \| \| 3.3101 \| 4.37 \| 7500 \| 4.0247 \| \| 3.3055 \| 4.66 \| 8000 \| 4.0122 \| \| 3.2891 \| 4.96 \| 8500 \| 3.9981 \| \| 3.1562 \| 5.25 \| 9000 \| 4.0102 \| \| 3.1289 \| 5.54 \| 9500 \| 4.0093 \| \| 3.1216 \| 5.83 \| 10000 \| 4.0085 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
anas-awadalla/mpt-1b-redpajama-200b-dolly	anas-awadalla	2023-07-30T00:23:55Z	3,301	0	transformers	[ "transformers", "pytorch", "mosaic_gpt", "text-generation", "custom_code", "dataset:togethercomputer/RedPajama-Data-1T", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2108.12409", "license:cc-by-sa-3.0", "autotrain_compatible", "region:us" ]	text-generation	2023-06-03T19:00:49Z	--- license: cc-by-sa-3.0 datasets: - togethercomputer/RedPajama-Data-1T --- # MPT-1b-RedPajama-200b-dolly MPT-1b-RedPajama-200b-dolly is a 1.3 billion parameter decoder-only transformer pre-trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and subsequently fine-tuned on the [Databricks Dolly](https://github.com/databrickslabs/dolly/tree/master/data) instruction dataset. The model was pre-trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971). This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. This model is an instruction fine-tuned version of [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). In other words, the pre-trained version of this model is [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). ## Model Date April 20, 2023 ## How to Use Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture `MosaicGPT` that is not yet part of the `transformers` package. `MosaicGPT` includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALIBI](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True) ``` To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so: ```python model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True, attn_impl='triton') model.to(device='cuda:0', dtype=torch.bfloat16) ``` ## Model Description This model uses the MosaicML LLM codebase, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm). The architecture is a modification of a standard decoder-only transformer. The transformer has 24 layers, 16 attention heads, and width 2048. The model has been modified from a standard transformer in the following ways: * It uses ALiBi and does not use positional embeddings. * It uses QK LayerNorm. * It does not use biases. ## Training Data ### Pre-Training The model was pre-trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix: * 67% RedPajama Common Crawl * 15% [C4](https://huggingface.co/datasets/c4) * 4.5% RedPajama GitHub * 4.5% RedPajama Wikipedia * 4.5% RedPajama Books * 2.5% RedPajama Arxiv * 2% RedPajama StackExchange This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971). Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above. The examples were shuffled within each dataset. Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ### Fine-Tuning We fine tuned this model on the [databricks-dolly-15k dataset](https://github.com/databrickslabs/dolly/tree/master/data) released by Databricks, following the same hyperparameters found in their [train_dolly.py](https://github.com/databrickslabs/dolly/blob/master/train_dolly.py) script. ## Training Configuration This model was pre-trained on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was pre-trained with sharded data parallelism using FSDP. ## Acknowledgements This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models. We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work. This model also builds on the work of [Databricks](https://www.databricks.com/), which created the Dolly instruction fine-tuning dataset. We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.
NasimB/bnc_spoken_aochildes_rarity-seed	NasimB	2023-07-30T00:05:01Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-29T20:10:25Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc_spoken_aochildes_rarity-seed 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. --> # bnc_spoken_aochildes_rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1487 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3667 \| 0.29 \| 500 \| 5.3506 \| \| 5.0646 \| 0.59 \| 1000 \| 4.9379 \| \| 4.7383 \| 0.88 \| 1500 \| 4.7025 \| \| 4.478 \| 1.18 \| 2000 \| 4.5619 \| \| 4.3265 \| 1.47 \| 2500 \| 4.4462 \| \| 4.2316 \| 1.77 \| 3000 \| 4.3544 \| \| 4.1042 \| 2.06 \| 3500 \| 4.2884 \| \| 3.936 \| 2.36 \| 4000 \| 4.2414 \| \| 3.9057 \| 2.65 \| 4500 \| 4.1840 \| \| 3.8624 \| 2.95 \| 5000 \| 4.1340 \| \| 3.6568 \| 3.24 \| 5500 \| 4.1357 \| \| 3.622 \| 3.54 \| 6000 \| 4.1047 \| \| 3.6008 \| 3.83 \| 6500 \| 4.0736 \| \| 3.486 \| 4.12 \| 7000 \| 4.0760 \| \| 3.3511 \| 4.42 \| 7500 \| 4.0744 \| \| 3.3423 \| 4.71 \| 8000 \| 4.0601 \| \| 3.332 \| 5.01 \| 8500 \| 4.0531 \| \| 3.1709 \| 5.3 \| 9000 \| 4.0641 \| \| 3.1645 \| 5.6 \| 9500 \| 4.0631 \| \| 3.1651 \| 5.89 \| 10000 \| 4.0630 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
RyanDA/MNIST-Model	RyanDA	2023-07-29T23:26:53Z	5	0	keras	[ "keras", "license:bigscience-openrail-m", "region:us" ]	null	2023-07-29T23:14:03Z	--- license: bigscience-openrail-m --- # MNIST Model This model was created using the full MNIST model dataset with no transforms applied (it's very easy to trick)
ld76/whisper-tiny-en	ld76	2023-07-29T23:25:17Z	86	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-29T21:27:26Z	--- language: - en license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: Whisper Tiny en - ld76 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.3616212792906903 --- <!-- 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 Tiny en - ld76 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.8795 - Wer Ortho: 0.3577 - Wer: 0.3616 ## 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: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0006 \| 17.54 \| 500 \| 0.7812 \| 0.3419 \| 0.3477 \| \| 0.0 \| 35.09 \| 1000 \| 0.8306 \| 0.3267 \| 0.3319 \| \| 0.0 \| 52.63 \| 1500 \| 0.8573 \| 0.3570 \| 0.3610 \| \| 0.0 \| 70.18 \| 2000 \| 0.8795 \| 0.3577 \| 0.3616 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
sshalini6/medium-5e4-r16-a32-d0.1	sshalini6	2023-07-29T23:19:59Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-29T19:20:36Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
YaTharThShaRma999/Llama2-chat-kimiko-Sharded-2gb	YaTharThShaRma999	2023-07-29T23:17:10Z	6	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-29T19:32:00Z	--- license: other language: - en library_name: transformers tags: - text-generation-inference --- This is a model that uses kimiko lora but merges it with llama-2-chat 7b instead of the base llama-2 model. It performs pretty well and could be thought of as a uncensored llama-2-chat model. The prompt template is similar to the normal kimiko. I haven't tested out all possible prompts but this one works best for me. The system prompt should just describe a character, not say something like act like a character. An example system prompt is John is a buisnessman in paris. He is tired and in his home right now. ``` <<SYSTEM>> ##system prompt for the ai(where you would put personas as well) <<HUMAN>> ##Chat with the bot <<Character Name>> ``` To use with huggingface, just check out llama docs and would work since its the same architecture.
vladfatu/lunar-lander	vladfatu	2023-07-29T22:10:41Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T22:10:20Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.88 +/- 23.18 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Ahsankhan123/q-FrozenLake-v1-4x4-noSlippery	Ahsankhan123	2023-07-29T22:08:25Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T22:08:23Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Ahsankhan123/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
mgmeskill/ppo-SnowballTarget	mgmeskill	2023-07-29T22:04:40Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-29T22:04:37Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: mgmeskill/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
0x05a4/DeepRL-DQN-SIv4	0x05a4	2023-07-29T21:52:36Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T21:52:01Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 650.00 +/- 136.11 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga 0x05a4 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga 0x05a4 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga 0x05a4 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 16), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 2000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Jinmane/anyloracleanlinearmix_v10	Jinmane	2023-07-29T21:49:58Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-29T21:08:25Z	--- license: creativeml-openrail-m ---
VinEuro/a2c-AntBulletEnv-v0	VinEuro	2023-07-29T21:49:27Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T21:48:06Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 833.14 +/- 76.31 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Huggingfly/rl_course_vizdoom_health_gathering_supreme	Huggingfly	2023-07-29T21:40:58Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T20:34:00Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.38 +/- 4.93 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Huggingfly/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Jinmane/calicomix_v60	Jinmane	2023-07-29T21:29:24Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-29T21:24:25Z	--- license: creativeml-openrail-m ---
froggoboom/xgen-7b-dolly-15k-4bit-final-2	froggoboom	2023-07-29T21:27:25Z	0	0	peft	[ "peft", "license:other", "region:us" ]	null	2023-07-29T21:27:04Z	--- license: other library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
apple/coreml-stable-diffusion-2-base-palettized	apple	2023-07-29T21:25:22Z	17	3	null	[ "coreml", "stable-diffusion", "text-to-image", "core-ml", "arxiv:2112.10752", "arxiv:2202.00512", "arxiv:1910.09700", "license:other", "region:us" ]	text-to-image	2023-06-14T09:41:50Z	--- license: other tags: - stable-diffusion - text-to-image - core-ml --- # Stable Diffusion v2 Model Card This model was generated by Hugging Face using [Apple’s repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md). This version contains 6-bit palettized Core ML weights for iOS 17 or macOS 14. To use weights without quantization, please visit [this model instead](https://huggingface.co/apple/coreml-stable-diffusion-2-base). This model card focuses on the model associated with the Stable Diffusion v2 model, available [here](https://github.com/Stability-AI/stablediffusion). The model is trained from scratch 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. Then it is further trained for 850k steps at resolution `512x512` on the same dataset on images with resolution `>= 512x512`. ![image](https://github.com/Stability-AI/stablediffusion/blob/main/assets/stable-samples/txt2img/merged-0003.png?raw=true) These weights here have been converted to Core ML for use on Apple Silicon hardware. There are 4 variants of the Core ML weights: ``` coreml-stable-diffusion-2-base ├── original │ ├── compiled # Swift inference, "original" attention │ └── packages # Python inference, "original" attention └── split_einsum ├── compiled # Swift inference, "split_einsum" attention └── packages # Python inference, "split_einsum" attention ``` There are also two zip archives suitable for use in the [Hugging Face demo app](https://github.com/huggingface/swift-coreml-diffusers) and other third party tools: - `coreml-stable-diffusion-2-base-palettized_original_compiled.zip` contains the compiled, 6-bit model with `ORIGINAL` attention implementation. - `coreml-stable-diffusion-2-base-palettized_split_einsum_v2_compiled.zip` contains the compiled, 6-bit model with `SPLIT_EINSUM_V2` attention implementation. Please, refer to https://huggingface.co/blog/diffusers-coreml for details. - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base#examples) - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt). ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - Resources for more information: [GitHub Repository](https://github.com/Stability-AI/). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training Training Data The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. Training Procedure Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - Hardware: 32 x 8 x A100 GPUs - Optimizer: AdamW - Gradient Accumulations: 1 - Batch: 32 x 8 x 2 x 4 = 2048 - Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints: ![pareto](model-variants.jpg) Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 200000 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).
azhang1212/angela_untranslated_entities_test	azhang1212	2023-07-29T21:23:13Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-29T20:03:18Z	--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: angela_untranslated_entities_test 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. --> # angela_untranslated_entities_test This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1681 - Precision: 0.4056 - Recall: 0.2198 - F1: 0.2851 - Accuracy: 0.9528 ## 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-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1724 \| 1.0 \| 1283 \| 0.1654 \| 0.4156 \| 0.1148 \| 0.1800 \| 0.9550 \| \| 0.1583 \| 2.0 \| 2566 \| 0.1551 \| 0.4532 \| 0.1307 \| 0.2029 \| 0.9558 \| \| 0.147 \| 3.0 \| 3849 \| 0.1576 \| 0.4250 \| 0.2063 \| 0.2778 \| 0.9542 \| \| 0.1367 \| 4.0 \| 5132 \| 0.1627 \| 0.4105 \| 0.2133 \| 0.2807 \| 0.9534 \| \| 0.1215 \| 5.0 \| 6415 \| 0.1681 \| 0.4056 \| 0.2198 \| 0.2851 \| 0.9528 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
VinEuro/PyramidsTraining	VinEuro	2023-07-29T21:08:36Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-29T20:56:57Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: VinEuro/PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
NasimB/simple_wikipedia-rarity-seed	NasimB	2023-07-29T21:05:57Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-29T17:12:01Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: simple_wikipedia-rarity-seed 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. --> # simple_wikipedia-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1526 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3353 \| 0.29 \| 500 \| 5.3432 \| \| 5.0331 \| 0.58 \| 1000 \| 4.9248 \| \| 4.7045 \| 0.87 \| 1500 \| 4.6995 \| \| 4.4613 \| 1.17 \| 2000 \| 4.5660 \| \| 4.3127 \| 1.46 \| 2500 \| 4.4566 \| \| 4.2095 \| 1.75 \| 3000 \| 4.3611 \| \| 4.1096 \| 2.04 \| 3500 \| 4.3006 \| \| 3.9248 \| 2.33 \| 4000 \| 4.2579 \| \| 3.8938 \| 2.62 \| 4500 \| 4.2018 \| \| 3.8576 \| 2.91 \| 5000 \| 4.1488 \| \| 3.6767 \| 3.21 \| 5500 \| 4.1456 \| \| 3.6134 \| 3.5 \| 6000 \| 4.1125 \| \| 3.5925 \| 3.79 \| 6500 \| 4.0849 \| \| 3.5138 \| 4.08 \| 7000 \| 4.0796 \| \| 3.3429 \| 4.37 \| 7500 \| 4.0750 \| \| 3.3382 \| 4.66 \| 8000 \| 4.0624 \| \| 3.328 \| 4.95 \| 8500 \| 4.0507 \| \| 3.1885 \| 5.24 \| 9000 \| 4.0623 \| \| 3.1615 \| 5.54 \| 9500 \| 4.0621 \| \| 3.1534 \| 5.83 \| 10000 \| 4.0617 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
athrado/bert-finetuned-nli	athrado	2023-07-29T20:43:39Z	8	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T14:30:23Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: athrado/bert-finetuned-nli results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # athrado/bert-finetuned-nli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0641 - Train Accuracy: 0.9797 - Validation Loss: 0.4812 - Validation Accuracy: 0.8586 - Epoch: 4 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2775, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.5572 \| 0.7599 \| 0.4802 \| 0.8020 \| 0 \| \| 0.3324 \| 0.8795 \| 0.3869 \| 0.8444 \| 1 \| \| 0.2057 \| 0.9272 \| 0.3933 \| 0.8646 \| 2 \| \| 0.1212 \| 0.9597 \| 0.4413 \| 0.8747 \| 3 \| \| 0.0641 \| 0.9797 \| 0.4812 \| 0.8586 \| 4 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.0 - Datasets 2.14.1 - Tokenizers 0.13.3
VinEuro/ppo-SnowballTarget	VinEuro	2023-07-29T20:41:42Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-29T20:41:39Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: VinEuro/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Pula23/Hggjg	Pula23	2023-07-29T20:36:46Z	0	0	adapter-transformers	[ "adapter-transformers", "conversational", "aa", "dataset:Open-Orca/OpenOrca", "license:openrail", "region:us" ]	text-generation	2023-07-29T20:32:45Z	--- license: openrail datasets: - Open-Orca/OpenOrca language: - aa metrics: - accuracy - bertscore library_name: adapter-transformers pipeline_tag: conversational ---
etetet/my_awesome_eli5_mlm_model	etetet	2023-07-29T20:34:10Z	178	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-29T20:07:20Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_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. --> # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9854 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2578 \| 1.0 \| 1145 \| 2.0618 \| \| 2.1775 \| 2.0 \| 2290 \| 2.0267 \| \| 2.1086 \| 3.0 \| 3435 \| 2.0174 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
PhilSad/sdxl_phil_lora_attempts	PhilSad	2023-07-29T20:32:21Z	0	1	null	[ "region:us" ]	null	2023-07-29T03:01:31Z	This is a repo to store my attemps to find a quick training working for photorealist and stylised lora dreambooth currently ~15 min & 17 GB vram use "shs man" to trigger
gArthur98/Greg-DistilBert-classifier	gArthur98	2023-07-29T20:30:50Z	113	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-29T19:42:30Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: Greg-DistilBert-classifier 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. --> # Greg-DistilBert-classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7835 - F1: 0.7175 ## 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-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8486 \| 0.5 \| 500 \| 0.9540 \| 0.6137 \| \| 0.7857 \| 1.0 \| 1000 \| 0.7877 \| 0.6671 \| \| 0.7039 \| 1.5 \| 1500 \| 0.7438 \| 0.7035 \| \| 0.6526 \| 2.01 \| 2000 \| 0.7396 \| 0.7107 \| \| 0.5131 \| 2.51 \| 2500 \| 0.7835 \| 0.7175 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_2_beta_1	asenella	2023-07-29T20:28:26Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:28:07Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_2_beta_2.5	asenella	2023-07-29T20:27:39Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:27:21Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_3_beta_1	asenella	2023-07-29T20:27:10Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:26:22Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_3_beta_2.5	asenella	2023-07-29T20:27:02Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:26:35Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_0_beta_2.5	asenella	2023-07-29T20:26:56Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:26:24Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_0_beta_1	asenella	2023-07-29T20:22:36Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:22:17Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_3_beta_0.5	asenella	2023-07-29T20:22:24Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:22:05Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/whole_mmnist_JNFDccaconfig_resnet_seed_1_beta_1	asenella	2023-07-29T20:21:05Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:20:47Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
Sookeyy/q-Taxi-v3	Sookeyy	2023-07-29T20:18:49Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T20:18:48Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.42 +/- 2.75 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Sookeyy/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
asenella/whole_mmnist_JNFconfig_resnet_seed_2_beta_1	asenella	2023-07-29T20:06:20Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-29T20:06:00Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
rzambrano/rnd-PyramidsTraining	rzambrano	2023-07-29T20:01:55Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-29T20:01:49Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: rzambrano/rnd-PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
RohaanKhanCentric/llama2-qlora-finetunined-Arabic	RohaanKhanCentric	2023-07-29T19:54:47Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-29T19:54:40Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
darthPanda/Reinforce-CartPole-v1	darthPanda	2023-07-29T19:52:54Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T19:08:40Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 269.29 +/- 125.27 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
JMullings/ts-xor-demo	JMullings	2023-07-29T19:43:43Z	0	0	null	[ "tabular-classification", "en", "license:mit", "region:us" ]	tabular-classification	2023-07-29T14:53:17Z	--- license: mit language: - en pipeline_tag: tabular-classification --- # XOR Training Exercise using TensorFlow.js In this example, we will use TensorFlow.js to train a neural network for the XOR (exclusive OR) problem. The XOR problem is a classic problem in machine learning where the model needs to learn a non-linear decision boundary to correctly classify inputs. ## Model Description The neural network used for this XOR problem consists of an input layer with two neurons, a hidden layer with two neurons, and an output layer with one neuron. The activation function used in the hidden layer is ReLU (Rectified Linear Unit), and the output layer uses the sigmoid activation function to produce values between 0 and 1. ## Dataset The XOR dataset consists of four samples, each with two features (inputs) and one label (output). The dataset is as follows: \| Input 1 \| Input 2 \| Output \| \|:-------:\|:-------:\|:------:\| \| 0 \| 0 \| 0 \| \| 0 \| 1 \| 1 \| \| 1 \| 0 \| 1 \| \| 1 \| 1 \| 0 \| ## Training Procedure The training is performed using TensorFlow.js with the following hyperparameters: - Learning Rate: 0.1 - Number of Epochs: 10000 - Loss Function: Mean Squared Error (MSE) - Optimizer: Stochastic Gradient Descent (SGD) ## Code Implementation Below is the code implementation for training the XOR neural network using TensorFlow.js: ```javascript // Import TensorFlow.js library import * as tf from '@tensorflow/tfjs-node-gpu'; this.model = await tf.loadLayersModel(`file://${this.model_path}/model.json`); this.model.compile({ optimizer: tf.train.sgd(0.1), loss: 'binaryCrossentropy', // Binary classification loss metrics: ['accuracy'], }); this.model.summary(); const x = tf.tensor2d([[1,1]]); const prediction = this.model.predict(x) as tf.Tensor; ``` ## Resulting Prediction The resulting predictions for the XOR dataset after training the model are as follows: ``` Tensor [[0.0020679], [0.9994502], [0.9994048], [0.0002599]] ``` ## Intended Uses & Limitations - The trained XOR neural network is suitable for solving the XOR problem and may not be suitable for more complex tasks without modifications. - TensorFlow.js allows running machine learning models directly in the browser, making it useful for web applications with client-side inference. ## Framework Versions Used - TensorFlow.js Please note that this example assumes you have set up your project with the necessary dependencies and have a basic understanding of JavaScript and TensorFlow.js.
froggoboom/results	froggoboom	2023-07-29T19:40:07Z	0	0	null	[ "generated_from_trainer", "region:us" ]	null	2023-07-29T19:36:30Z	--- tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3
aphi/a2c-AntBulletEnv-v0	aphi	2023-07-29T19:38:48Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T19:37:41Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2036.04 +/- 73.10 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Huggingfly/ppo-CartPole-v1	Huggingfly	2023-07-29T19:35:12Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T19:35:07Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -141.58 +/- 106.24 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Huggingfly/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
VicBeltran/ppo-LunarLander-v2	VicBeltran	2023-07-29T19:23:19Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T18:53:13Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 273.36 +/- 21.98 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
cgr28/rl_course_vizdoom_health_gathering_supreme	cgr28	2023-07-29T19:15:54Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T04:42:25Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.25 +/- 3.57 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r cgr28/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
digiplay/KawaiiRealisticAnimeMix_A0.3	digiplay	2023-07-29T19:14:25Z	328	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-29T18:46:32Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/104100?modelVersionId=128610 Sample image and prompt : 1girl, anime key visual, outdoor,vibrant color,very close-up,tiny smile ![b916088a-20fc-4c50-a7f0-e35defe59834.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/EPWfFfFVsFFlBIvIkVszU.jpeg)
rzambrano/ppo-SnowballTarget	rzambrano	2023-07-29T19:11:34Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-29T19:09:45Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: rzambrano/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ShekDass/donut-base-cord-smart-86	ShekDass	2023-07-29T19:10:42Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base-finetuned-cord-v2", "base_model:finetune:naver-clova-ix/donut-base-finetuned-cord-v2", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-07-29T17:37:11Z	--- license: mit base_model: naver-clova-ix/donut-base-finetuned-cord-v2 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-cord-smart-86 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. --> # donut-base-cord-smart-86 This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
maije/llama2-qlora-finetuned-cm-repo-create-7.17-loss	maije	2023-07-29T18:52:31Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-29T18:52:13Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
wilson-wei/wav2vec2-base-finetuned-gtzan	wilson-wei	2023-07-29T18:47:34Z	161	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-29T14:52:44Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: train split: train args: train metrics: - name: Accuracy type: accuracy value: 0.84 --- <!-- 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. --> # wav2vec2-base-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.8879 - Accuracy: 0.84 ## 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-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 17 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9838 \| 1.0 \| 113 \| 1.8627 \| 0.37 \| \| 1.6128 \| 2.0 \| 226 \| 1.5998 \| 0.48 \| \| 1.0259 \| 3.0 \| 339 \| 1.3821 \| 0.57 \| \| 1.2766 \| 4.0 \| 452 \| 1.1708 \| 0.66 \| \| 0.6014 \| 5.0 \| 565 \| 0.7257 \| 0.77 \| \| 0.5815 \| 6.0 \| 678 \| 1.0738 \| 0.68 \| \| 0.7664 \| 7.0 \| 791 \| 0.7244 \| 0.8 \| \| 0.2303 \| 8.0 \| 904 \| 0.5838 \| 0.84 \| \| 0.4829 \| 9.0 \| 1017 \| 0.5741 \| 0.87 \| \| 0.0859 \| 10.0 \| 1130 \| 0.6199 \| 0.83 \| \| 0.2983 \| 11.0 \| 1243 \| 0.8117 \| 0.84 \| \| 0.0642 \| 12.0 \| 1356 \| 0.5938 \| 0.88 \| \| 0.0688 \| 13.0 \| 1469 \| 0.9978 \| 0.84 \| \| 0.1542 \| 14.0 \| 1582 \| 0.7437 \| 0.85 \| \| 0.0117 \| 15.0 \| 1695 \| 0.9100 \| 0.84 \| \| 0.039 \| 16.0 \| 1808 \| 0.7757 \| 0.85 \| \| 0.0661 \| 17.0 \| 1921 \| 0.8879 \| 0.84 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.0 - Tokenizers 0.13.3
mohamedemam/bert_sentaces_similarty	mohamedemam	2023-07-29T18:35:28Z	105	0	transformers	[ "transformers", "pytorch", "resnet", "sentence-similarity", "en", "dataset:AlekseyKorshuk/quora-question-pairs", "license:mit", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-07-29T17:47:12Z	--- license: mit datasets: - AlekseyKorshuk/quora-question-pairs language: - en metrics: - accuracy pipeline_tag: sentence-similarity ---
chaimag/ko-llama-7B	chaimag	2023-07-29T18:33:06Z	6	0	peft	[ "peft", "region:us" ]	null	2023-07-29T15:49:00Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
atari713/taxi-v3	atari713	2023-07-29T18:29:07Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T18:24:38Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="atari713/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
minatosnow/swinv2-tiny-patch4-window8-256-mineral	minatosnow	2023-07-29T18:25:36Z	147	0	transformers	[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-tiny-patch4-window8-256", "base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-29T18:24:58Z	--- license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window8-256 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-tiny-patch4-window8-256-mineral results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.24666666666666667 --- <!-- 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. --> # swinv2-tiny-patch4-window8-256-mineral This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 5.2045 - Accuracy: 0.2467 ## 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-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 5.692 \| 0.96 \| 18 \| 5.6904 \| 0.0017 \| \| 5.6759 \| 1.97 \| 37 \| 5.6829 \| 0.0017 \| \| 5.6699 \| 2.99 \| 56 \| 5.6722 \| 0.0033 \| \| 5.6484 \| 4.0 \| 75 \| 5.6601 \| 0.0033 \| \| 5.6355 \| 4.96 \| 93 \| 5.6486 \| 0.005 \| \| 5.6088 \| 5.97 \| 112 \| 5.6375 \| 0.0067 \| \| 5.5941 \| 6.99 \| 131 \| 5.6268 \| 0.0083 \| \| 5.5636 \| 8.0 \| 150 \| 5.6131 \| 0.0117 \| \| 5.5499 \| 8.96 \| 168 \| 5.5987 \| 0.0117 \| \| 5.5038 \| 9.97 \| 187 \| 5.5798 \| 0.0083 \| \| 5.437 \| 10.99 \| 206 \| 5.5757 \| 0.0133 \| \| 5.3642 \| 12.0 \| 225 \| 5.5482 \| 0.0133 \| \| 5.2877 \| 12.96 \| 243 \| 5.5107 \| 0.0183 \| \| 5.1992 \| 13.97 \| 262 \| 5.4744 \| 0.025 \| \| 5.0956 \| 14.99 \| 281 \| 5.4006 \| 0.0333 \| \| 4.9566 \| 16.0 \| 300 \| 5.3490 \| 0.0433 \| \| 4.8827 \| 16.96 \| 318 \| 5.2856 \| 0.0517 \| \| 4.7089 \| 17.97 \| 337 \| 5.1565 \| 0.075 \| \| 4.5187 \| 18.99 \| 356 \| 5.0459 \| 0.0817 \| \| 4.4164 \| 20.0 \| 375 \| 4.9614 \| 0.0983 \| \| 4.2402 \| 20.96 \| 393 \| 4.8814 \| 0.0983 \| \| 4.0046 \| 21.97 \| 412 \| 4.7763 \| 0.1167 \| \| 3.8336 \| 22.99 \| 431 \| 4.6775 \| 0.125 \| \| 3.6379 \| 24.0 \| 450 \| 4.6089 \| 0.14 \| \| 3.4647 \| 24.96 \| 468 \| 4.5512 \| 0.1433 \| \| 3.3663 \| 25.97 \| 487 \| 4.4674 \| 0.1533 \| \| 3.1445 \| 26.99 \| 506 \| 4.4247 \| 0.15 \| \| 3.0248 \| 28.0 \| 525 \| 4.3519 \| 0.1633 \| \| 2.8415 \| 28.96 \| 543 \| 4.3256 \| 0.1633 \| \| 2.6664 \| 29.97 \| 562 \| 4.3015 \| 0.18 \| \| 2.5225 \| 30.99 \| 581 \| 4.2571 \| 0.1867 \| \| 2.4433 \| 32.0 \| 600 \| 4.2507 \| 0.1833 \| \| 2.3169 \| 32.96 \| 618 \| 4.2097 \| 0.1933 \| \| 2.145 \| 33.97 \| 637 \| 4.1741 \| 0.1883 \| \| 2.0045 \| 34.99 \| 656 \| 4.1681 \| 0.1967 \| \| 1.9272 \| 36.0 \| 675 \| 4.1376 \| 0.2083 \| \| 1.7761 \| 36.96 \| 693 \| 4.1391 \| 0.1933 \| \| 1.7038 \| 37.97 \| 712 \| 4.0957 \| 0.205 \| \| 1.5902 \| 38.99 \| 731 \| 4.1309 \| 0.2067 \| \| 1.4921 \| 40.0 \| 750 \| 4.1324 \| 0.2033 \| \| 1.3552 \| 40.96 \| 768 \| 4.1510 \| 0.2067 \| \| 1.2597 \| 41.97 \| 787 \| 4.1636 \| 0.205 \| \| 1.2909 \| 42.99 \| 806 \| 4.1422 \| 0.2267 \| \| 1.1661 \| 44.0 \| 825 \| 4.1553 \| 0.21 \| \| 1.1031 \| 44.96 \| 843 \| 4.1976 \| 0.2167 \| \| 1.0203 \| 45.97 \| 862 \| 4.1673 \| 0.2183 \| \| 0.9883 \| 46.99 \| 881 \| 4.1628 \| 0.2167 \| \| 0.976 \| 48.0 \| 900 \| 4.1852 \| 0.2267 \| \| 0.8848 \| 48.96 \| 918 \| 4.2108 \| 0.2167 \| \| 0.8616 \| 49.97 \| 937 \| 4.2583 \| 0.2133 \| \| 0.8079 \| 50.99 \| 956 \| 4.1821 \| 0.23 \| \| 0.8256 \| 52.0 \| 975 \| 4.2021 \| 0.2217 \| \| 0.7439 \| 52.96 \| 993 \| 4.2577 \| 0.2167 \| \| 0.6863 \| 53.97 \| 1012 \| 4.2398 \| 0.225 \| \| 0.715 \| 54.99 \| 1031 \| 4.2934 \| 0.21 \| \| 0.6924 \| 56.0 \| 1050 \| 4.2675 \| 0.225 \| \| 0.6454 \| 56.96 \| 1068 \| 4.2916 \| 0.2117 \| \| 0.5688 \| 57.97 \| 1087 \| 4.3216 \| 0.2217 \| \| 0.5958 \| 58.99 \| 1106 \| 4.3202 \| 0.2133 \| \| 0.5834 \| 60.0 \| 1125 \| 4.3022 \| 0.2267 \| \| 0.5583 \| 60.96 \| 1143 \| 4.3746 \| 0.225 \| \| 0.565 \| 61.97 \| 1162 \| 4.3334 \| 0.215 \| \| 0.55 \| 62.99 \| 1181 \| 4.3330 \| 0.2217 \| \| 0.4972 \| 64.0 \| 1200 \| 4.3866 \| 0.2183 \| \| 0.4895 \| 64.96 \| 1218 \| 4.3797 \| 0.2267 \| \| 0.4537 \| 65.97 \| 1237 \| 4.3761 \| 0.2267 \| \| 0.4941 \| 66.99 \| 1256 \| 4.4122 \| 0.2317 \| \| 0.4422 \| 68.0 \| 1275 \| 4.4636 \| 0.2217 \| \| 0.3731 \| 68.96 \| 1293 \| 4.4118 \| 0.2217 \| \| 0.4338 \| 69.97 \| 1312 \| 4.4002 \| 0.2217 \| \| 0.4482 \| 70.99 \| 1331 \| 4.4558 \| 0.2233 \| \| 0.4206 \| 72.0 \| 1350 \| 4.4238 \| 0.23 \| \| 0.4009 \| 72.96 \| 1368 \| 4.4717 \| 0.2267 \| \| 0.4297 \| 73.97 \| 1387 \| 4.5047 \| 0.23 \| \| 0.4031 \| 74.99 \| 1406 \| 4.4637 \| 0.2233 \| \| 0.3489 \| 76.0 \| 1425 \| 4.4878 \| 0.2183 \| \| 0.4202 \| 76.96 \| 1443 \| 4.4785 \| 0.23 \| \| 0.3307 \| 77.97 \| 1462 \| 4.4647 \| 0.2267 \| \| 0.3919 \| 78.99 \| 1481 \| 4.5011 \| 0.2233 \| \| 0.3511 \| 80.0 \| 1500 \| 4.4895 \| 0.2367 \| \| 0.334 \| 80.96 \| 1518 \| 4.4878 \| 0.2283 \| \| 0.3958 \| 81.97 \| 1537 \| 4.5025 \| 0.2367 \| \| 0.316 \| 82.99 \| 1556 \| 4.5792 \| 0.2317 \| \| 0.3135 \| 84.0 \| 1575 \| 4.4894 \| 0.2333 \| \| 0.3292 \| 84.96 \| 1593 \| 4.5495 \| 0.235 \| \| 0.3116 \| 85.97 \| 1612 \| 4.5069 \| 0.2317 \| \| 0.3162 \| 86.99 \| 1631 \| 4.5407 \| 0.23 \| \| 0.3173 \| 88.0 \| 1650 \| 4.5997 \| 0.2217 \| \| 0.2857 \| 88.96 \| 1668 \| 4.5476 \| 0.2283 \| \| 0.2869 \| 89.97 \| 1687 \| 4.5655 \| 0.2217 \| \| 0.299 \| 90.99 \| 1706 \| 4.5743 \| 0.24 \| \| 0.3142 \| 92.0 \| 1725 \| 4.5478 \| 0.23 \| \| 0.3066 \| 92.96 \| 1743 \| 4.5509 \| 0.2367 \| \| 0.2542 \| 93.97 \| 1762 \| 4.5452 \| 0.225 \| \| 0.2707 \| 94.99 \| 1781 \| 4.5017 \| 0.225 \| \| 0.2781 \| 96.0 \| 1800 \| 4.5425 \| 0.23 \| \| 0.2611 \| 96.96 \| 1818 \| 4.6214 \| 0.2233 \| \| 0.2816 \| 97.97 \| 1837 \| 4.5780 \| 0.2233 \| \| 0.2698 \| 98.99 \| 1856 \| 4.5237 \| 0.225 \| \| 0.2568 \| 100.0 \| 1875 \| 4.6262 \| 0.2167 \| \| 0.2925 \| 100.96 \| 1893 \| 4.6138 \| 0.2133 \| \| 0.2542 \| 101.97 \| 1912 \| 4.6329 \| 0.22 \| \| 0.2276 \| 102.99 \| 1931 \| 4.5854 \| 0.23 \| \| 0.2701 \| 104.0 \| 1950 \| 4.5662 \| 0.2167 \| \| 0.2492 \| 104.96 \| 1968 \| 4.6098 \| 0.225 \| \| 0.2394 \| 105.97 \| 1987 \| 4.6215 \| 0.2333 \| \| 0.2409 \| 106.99 \| 2006 \| 4.6277 \| 0.2317 \| \| 0.2578 \| 108.0 \| 2025 \| 4.6173 \| 0.2267 \| \| 0.229 \| 108.96 \| 2043 \| 4.6405 \| 0.2283 \| \| 0.2438 \| 109.97 \| 2062 \| 4.6041 \| 0.2233 \| \| 0.2441 \| 110.99 \| 2081 \| 4.6367 \| 0.23 \| \| 0.2353 \| 112.0 \| 2100 \| 4.5877 \| 0.2467 \| \| 0.1876 \| 112.96 \| 2118 \| 4.6518 \| 0.2333 \| \| 0.1996 \| 113.97 \| 2137 \| 4.6810 \| 0.2367 \| \| 0.1908 \| 114.99 \| 2156 \| 4.6483 \| 0.245 \| \| 0.1905 \| 116.0 \| 2175 \| 4.6117 \| 0.22 \| \| 0.1995 \| 116.96 \| 2193 \| 4.6283 \| 0.2183 \| \| 0.2044 \| 117.97 \| 2212 \| 4.6130 \| 0.2283 \| \| 0.2156 \| 118.99 \| 2231 \| 4.6729 \| 0.2167 \| \| 0.2003 \| 120.0 \| 2250 \| 4.6124 \| 0.24 \| \| 0.1861 \| 120.96 \| 2268 \| 4.6839 \| 0.23 \| \| 0.2072 \| 121.97 \| 2287 \| 4.7217 \| 0.2267 \| \| 0.1973 \| 122.99 \| 2306 \| 4.7596 \| 0.23 \| \| 0.2191 \| 124.0 \| 2325 \| 4.7394 \| 0.2283 \| \| 0.1738 \| 124.96 \| 2343 \| 4.7356 \| 0.23 \| \| 0.1669 \| 125.97 \| 2362 \| 4.7210 \| 0.235 \| \| 0.1971 \| 126.99 \| 2381 \| 4.6826 \| 0.23 \| \| 0.1972 \| 128.0 \| 2400 \| 4.7256 \| 0.2233 \| \| 0.1794 \| 128.96 \| 2418 \| 4.6589 \| 0.235 \| \| 0.1894 \| 129.97 \| 2437 \| 4.7391 \| 0.2317 \| \| 0.1854 \| 130.99 \| 2456 \| 4.7441 \| 0.23 \| \| 0.1551 \| 132.0 \| 2475 \| 4.7559 \| 0.2233 \| \| 0.179 \| 132.96 \| 2493 \| 4.7555 \| 0.235 \| \| 0.2235 \| 133.97 \| 2512 \| 4.7686 \| 0.2183 \| \| 0.179 \| 134.99 \| 2531 \| 4.7334 \| 0.2283 \| \| 0.1595 \| 136.0 \| 2550 \| 4.7324 \| 0.2267 \| \| 0.1598 \| 136.96 \| 2568 \| 4.7099 \| 0.245 \| \| 0.178 \| 137.97 \| 2587 \| 4.7363 \| 0.2317 \| \| 0.1976 \| 138.99 \| 2606 \| 4.7806 \| 0.2217 \| \| 0.153 \| 140.0 \| 2625 \| 4.8128 \| 0.2267 \| \| 0.1946 \| 140.96 \| 2643 \| 4.7925 \| 0.2233 \| \| 0.1597 \| 141.97 \| 2662 \| 4.8279 \| 0.2117 \| \| 0.1515 \| 142.99 \| 2681 \| 4.8203 \| 0.2233 \| \| 0.2013 \| 144.0 \| 2700 \| 4.7393 \| 0.23 \| \| 0.1431 \| 144.96 \| 2718 \| 4.7866 \| 0.2367 \| \| 0.1864 \| 145.97 \| 2737 \| 4.7730 \| 0.2317 \| \| 0.1676 \| 146.99 \| 2756 \| 4.8112 \| 0.2333 \| \| 0.1502 \| 148.0 \| 2775 \| 4.8374 \| 0.215 \| \| 0.1584 \| 148.96 \| 2793 \| 4.8600 \| 0.2317 \| \| 0.1901 \| 149.97 \| 2812 \| 4.8647 \| 0.2167 \| \| 0.1347 \| 150.99 \| 2831 \| 4.8073 \| 0.215 \| \| 0.1859 \| 152.0 \| 2850 \| 4.8766 \| 0.2283 \| \| 0.1483 \| 152.96 \| 2868 \| 4.8392 \| 0.23 \| \| 0.149 \| 153.97 \| 2887 \| 4.8734 \| 0.215 \| \| 0.1411 \| 154.99 \| 2906 \| 4.9380 \| 0.2083 \| \| 0.1589 \| 156.0 \| 2925 \| 4.8365 \| 0.2367 \| \| 0.1608 \| 156.96 \| 2943 \| 4.8053 \| 0.23 \| \| 0.1759 \| 157.97 \| 2962 \| 4.8690 \| 0.2383 \| \| 0.142 \| 158.99 \| 2981 \| 4.8490 \| 0.23 \| \| 0.1433 \| 160.0 \| 3000 \| 4.8658 \| 0.2267 \| \| 0.1407 \| 160.96 \| 3018 \| 4.8936 \| 0.2267 \| \| 0.1442 \| 161.97 \| 3037 \| 4.8534 \| 0.235 \| \| 0.1669 \| 162.99 \| 3056 \| 4.9050 \| 0.2317 \| \| 0.1384 \| 164.0 \| 3075 \| 4.8717 \| 0.235 \| \| 0.1366 \| 164.96 \| 3093 \| 4.8951 \| 0.2317 \| \| 0.1428 \| 165.97 \| 3112 \| 4.9253 \| 0.225 \| \| 0.1376 \| 166.99 \| 3131 \| 4.9108 \| 0.22 \| \| 0.1238 \| 168.0 \| 3150 \| 4.8965 \| 0.2267 \| \| 0.1129 \| 168.96 \| 3168 \| 4.8446 \| 0.2317 \| \| 0.1296 \| 169.97 \| 3187 \| 4.9100 \| 0.2217 \| \| 0.1383 \| 170.99 \| 3206 \| 4.8886 \| 0.2333 \| \| 0.1511 \| 172.0 \| 3225 \| 4.8883 \| 0.235 \| \| 0.1426 \| 172.96 \| 3243 \| 4.8783 \| 0.2333 \| \| 0.1112 \| 173.97 \| 3262 \| 4.9305 \| 0.225 \| \| 0.1456 \| 174.99 \| 3281 \| 4.8354 \| 0.2417 \| \| 0.1343 \| 176.0 \| 3300 \| 4.8553 \| 0.225 \| \| 0.1133 \| 176.96 \| 3318 \| 4.8739 \| 0.2317 \| \| 0.1213 \| 177.97 \| 3337 \| 4.8865 \| 0.2333 \| \| 0.1309 \| 178.99 \| 3356 \| 4.9231 \| 0.22 \| \| 0.1197 \| 180.0 \| 3375 \| 4.8976 \| 0.2383 \| \| 0.1619 \| 180.96 \| 3393 \| 4.8812 \| 0.2383 \| \| 0.1254 \| 181.97 \| 3412 \| 4.8260 \| 0.225 \| \| 0.0934 \| 182.99 \| 3431 \| 4.8645 \| 0.2283 \| \| 0.1156 \| 184.0 \| 3450 \| 4.8253 \| 0.24 \| \| 0.1008 \| 184.96 \| 3468 \| 4.8692 \| 0.2467 \| \| 0.1273 \| 185.97 \| 3487 \| 4.9049 \| 0.24 \| \| 0.1352 \| 186.99 \| 3506 \| 4.8660 \| 0.2333 \| \| 0.1411 \| 188.0 \| 3525 \| 4.8252 \| 0.2333 \| \| 0.124 \| 188.96 \| 3543 \| 4.8715 \| 0.2317 \| \| 0.1121 \| 189.97 \| 3562 \| 4.8768 \| 0.24 \| \| 0.1337 \| 190.99 \| 3581 \| 4.9004 \| 0.2433 \| \| 0.106 \| 192.0 \| 3600 \| 4.8300 \| 0.2333 \| \| 0.1044 \| 192.96 \| 3618 \| 4.7903 \| 0.25 \| \| 0.1259 \| 193.97 \| 3637 \| 4.8227 \| 0.2267 \| \| 0.1182 \| 194.99 \| 3656 \| 4.8286 \| 0.2283 \| \| 0.1269 \| 196.0 \| 3675 \| 4.8475 \| 0.2467 \| \| 0.1304 \| 196.96 \| 3693 \| 4.8759 \| 0.2367 \| \| 0.1086 \| 197.97 \| 3712 \| 4.8773 \| 0.2283 \| \| 0.1184 \| 198.99 \| 3731 \| 4.8875 \| 0.2317 \| \| 0.114 \| 200.0 \| 3750 \| 4.9083 \| 0.2283 \| \| 0.1028 \| 200.96 \| 3768 \| 4.8888 \| 0.2283 \| \| 0.1355 \| 201.97 \| 3787 \| 4.8358 \| 0.23 \| \| 0.1038 \| 202.99 \| 3806 \| 4.8567 \| 0.23 \| \| 0.1122 \| 204.0 \| 3825 \| 4.8578 \| 0.2317 \| \| 0.0864 \| 204.96 \| 3843 \| 4.9288 \| 0.2333 \| \| 0.0969 \| 205.97 \| 3862 \| 4.8940 \| 0.2567 \| \| 0.0986 \| 206.99 \| 3881 \| 4.8632 \| 0.2483 \| \| 0.1291 \| 208.0 \| 3900 \| 4.9114 \| 0.2433 \| \| 0.1244 \| 208.96 \| 3918 \| 4.9076 \| 0.2483 \| \| 0.1112 \| 209.97 \| 3937 \| 4.8954 \| 0.245 \| \| 0.1032 \| 210.99 \| 3956 \| 4.8885 \| 0.2433 \| \| 0.0919 \| 212.0 \| 3975 \| 4.9197 \| 0.2433 \| \| 0.1153 \| 212.96 \| 3993 \| 4.9020 \| 0.25 \| \| 0.0978 \| 213.97 \| 4012 \| 4.9312 \| 0.2433 \| \| 0.1316 \| 214.99 \| 4031 \| 4.9504 \| 0.2417 \| \| 0.1222 \| 216.0 \| 4050 \| 4.9403 \| 0.2367 \| \| 0.1108 \| 216.96 \| 4068 \| 4.8719 \| 0.2483 \| \| 0.0996 \| 217.97 \| 4087 \| 4.8812 \| 0.24 \| \| 0.0907 \| 218.99 \| 4106 \| 4.9203 \| 0.25 \| \| 0.0974 \| 220.0 \| 4125 \| 4.9728 \| 0.2417 \| \| 0.1127 \| 220.96 \| 4143 \| 4.9708 \| 0.2467 \| \| 0.0891 \| 221.97 \| 4162 \| 5.0154 \| 0.2517 \| \| 0.0973 \| 222.99 \| 4181 \| 4.9533 \| 0.24 \| \| 0.0912 \| 224.0 \| 4200 \| 4.9306 \| 0.2333 \| \| 0.0971 \| 224.96 \| 4218 \| 4.9873 \| 0.2283 \| \| 0.1069 \| 225.97 \| 4237 \| 4.9154 \| 0.2433 \| \| 0.1163 \| 226.99 \| 4256 \| 4.9178 \| 0.2367 \| \| 0.1139 \| 228.0 \| 4275 \| 4.9395 \| 0.2383 \| \| 0.0816 \| 228.96 \| 4293 \| 4.8986 \| 0.2333 \| \| 0.0899 \| 229.97 \| 4312 \| 4.9542 \| 0.2517 \| \| 0.0984 \| 230.99 \| 4331 \| 4.9305 \| 0.2433 \| \| 0.0732 \| 232.0 \| 4350 \| 4.9264 \| 0.2417 \| \| 0.0947 \| 232.96 \| 4368 \| 4.9676 \| 0.235 \| \| 0.0826 \| 233.97 \| 4387 \| 5.0179 \| 0.235 \| \| 0.086 \| 234.99 \| 4406 \| 4.9719 \| 0.2283 \| \| 0.1055 \| 236.0 \| 4425 \| 4.9457 \| 0.235 \| \| 0.0872 \| 236.96 \| 4443 \| 4.9355 \| 0.2533 \| \| 0.0826 \| 237.97 \| 4462 \| 4.9467 \| 0.245 \| \| 0.081 \| 238.99 \| 4481 \| 5.0036 \| 0.245 \| \| 0.0746 \| 240.0 \| 4500 \| 5.0125 \| 0.245 \| \| 0.0725 \| 240.96 \| 4518 \| 5.0061 \| 0.2367 \| \| 0.1104 \| 241.97 \| 4537 \| 5.0150 \| 0.2483 \| \| 0.0885 \| 242.99 \| 4556 \| 5.0063 \| 0.2333 \| \| 0.0847 \| 244.0 \| 4575 \| 4.9919 \| 0.2383 \| \| 0.0751 \| 244.96 \| 4593 \| 4.9881 \| 0.2367 \| \| 0.0872 \| 245.97 \| 4612 \| 5.0013 \| 0.2383 \| \| 0.0771 \| 246.99 \| 4631 \| 5.0273 \| 0.235 \| \| 0.0941 \| 248.0 \| 4650 \| 5.0507 \| 0.2317 \| \| 0.116 \| 248.96 \| 4668 \| 5.0491 \| 0.245 \| \| 0.0733 \| 249.97 \| 4687 \| 5.0391 \| 0.24 \| \| 0.0821 \| 250.99 \| 4706 \| 5.0231 \| 0.235 \| \| 0.075 \| 252.0 \| 4725 \| 5.0388 \| 0.2317 \| \| 0.0885 \| 252.96 \| 4743 \| 4.9838 \| 0.2383 \| \| 0.0759 \| 253.97 \| 4762 \| 4.9536 \| 0.2433 \| \| 0.0773 \| 254.99 \| 4781 \| 5.0145 \| 0.2383 \| \| 0.0707 \| 256.0 \| 4800 \| 5.0352 \| 0.2217 \| \| 0.0861 \| 256.96 \| 4818 \| 5.0295 \| 0.2483 \| \| 0.0739 \| 257.97 \| 4837 \| 5.0354 \| 0.2433 \| \| 0.0822 \| 258.99 \| 4856 \| 5.0563 \| 0.245 \| \| 0.0681 \| 260.0 \| 4875 \| 5.0407 \| 0.245 \| \| 0.0872 \| 260.96 \| 4893 \| 5.0511 \| 0.2367 \| \| 0.079 \| 261.97 \| 4912 \| 5.1087 \| 0.23 \| \| 0.0733 \| 262.99 \| 4931 \| 5.0523 \| 0.225 \| \| 0.097 \| 264.0 \| 4950 \| 5.0368 \| 0.2317 \| \| 0.0669 \| 264.96 \| 4968 \| 5.0501 \| 0.2283 \| \| 0.0801 \| 265.97 \| 4987 \| 5.0515 \| 0.235 \| \| 0.0894 \| 266.99 \| 5006 \| 5.0182 \| 0.245 \| \| 0.0815 \| 268.0 \| 5025 \| 5.0713 \| 0.23 \| \| 0.0934 \| 268.96 \| 5043 \| 5.0082 \| 0.2417 \| \| 0.0728 \| 269.97 \| 5062 \| 5.0473 \| 0.23 \| \| 0.088 \| 270.99 \| 5081 \| 5.0689 \| 0.2267 \| \| 0.0706 \| 272.0 \| 5100 \| 5.0403 \| 0.2383 \| \| 0.0931 \| 272.96 \| 5118 \| 5.0298 \| 0.235 \| \| 0.0784 \| 273.97 \| 5137 \| 5.0141 \| 0.2367 \| \| 0.0831 \| 274.99 \| 5156 \| 5.0314 \| 0.2417 \| \| 0.0624 \| 276.0 \| 5175 \| 5.0445 \| 0.2383 \| \| 0.0819 \| 276.96 \| 5193 \| 5.0632 \| 0.2517 \| \| 0.0714 \| 277.97 \| 5212 \| 5.0520 \| 0.255 \| \| 0.0893 \| 278.99 \| 5231 \| 5.0075 \| 0.2533 \| \| 0.0777 \| 280.0 \| 5250 \| 5.0122 \| 0.24 \| \| 0.0686 \| 280.96 \| 5268 \| 5.0477 \| 0.2333 \| \| 0.0849 \| 281.97 \| 5287 \| 5.0238 \| 0.2433 \| \| 0.0969 \| 282.99 \| 5306 \| 5.0061 \| 0.2383 \| \| 0.0906 \| 284.0 \| 5325 \| 5.0771 \| 0.2517 \| \| 0.0843 \| 284.96 \| 5343 \| 5.0882 \| 0.2417 \| \| 0.0538 \| 285.97 \| 5362 \| 5.0800 \| 0.2467 \| \| 0.0678 \| 286.99 \| 5381 \| 5.0976 \| 0.2367 \| \| 0.0729 \| 288.0 \| 5400 \| 5.0817 \| 0.2333 \| \| 0.0922 \| 288.96 \| 5418 \| 5.0955 \| 0.2433 \| \| 0.0684 \| 289.97 \| 5437 \| 5.0873 \| 0.2467 \| \| 0.082 \| 290.99 \| 5456 \| 5.1424 \| 0.2383 \| \| 0.0798 \| 292.0 \| 5475 \| 5.0631 \| 0.2433 \| \| 0.0781 \| 292.96 \| 5493 \| 5.0474 \| 0.2517 \| \| 0.0857 \| 293.97 \| 5512 \| 5.0502 \| 0.2417 \| \| 0.0733 \| 294.99 \| 5531 \| 5.0599 \| 0.235 \| \| 0.072 \| 296.0 \| 5550 \| 5.0549 \| 0.2433 \| \| 0.101 \| 296.96 \| 5568 \| 5.0783 \| 0.24 \| \| 0.0982 \| 297.97 \| 5587 \| 5.1048 \| 0.24 \| \| 0.0782 \| 298.99 \| 5606 \| 5.1096 \| 0.235 \| \| 0.0654 \| 300.0 \| 5625 \| 5.1158 \| 0.2333 \| \| 0.0761 \| 300.96 \| 5643 \| 5.0416 \| 0.2383 \| \| 0.079 \| 301.97 \| 5662 \| 5.1202 \| 0.235 \| \| 0.0845 \| 302.99 \| 5681 \| 5.1711 \| 0.2333 \| \| 0.0718 \| 304.0 \| 5700 \| 5.1572 \| 0.2383 \| \| 0.0675 \| 304.96 \| 5718 \| 5.1230 \| 0.235 \| \| 0.0683 \| 305.97 \| 5737 \| 5.1464 \| 0.23 \| \| 0.0899 \| 306.99 \| 5756 \| 5.0815 \| 0.24 \| \| 0.0692 \| 308.0 \| 5775 \| 5.0867 \| 0.2367 \| \| 0.0843 \| 308.96 \| 5793 \| 5.1006 \| 0.2283 \| \| 0.0742 \| 309.97 \| 5812 \| 5.0545 \| 0.2317 \| \| 0.0689 \| 310.99 \| 5831 \| 5.0509 \| 0.2317 \| \| 0.0714 \| 312.0 \| 5850 \| 5.0536 \| 0.2367 \| \| 0.0669 \| 312.96 \| 5868 \| 5.0488 \| 0.2433 \| \| 0.0726 \| 313.97 \| 5887 \| 5.0903 \| 0.2433 \| \| 0.0509 \| 314.99 \| 5906 \| 5.1158 \| 0.2433 \| \| 0.062 \| 316.0 \| 5925 \| 5.0651 \| 0.2417 \| \| 0.0528 \| 316.96 \| 5943 \| 5.0545 \| 0.2467 \| \| 0.0627 \| 317.97 \| 5962 \| 5.0730 \| 0.2417 \| \| 0.0678 \| 318.99 \| 5981 \| 5.0354 \| 0.24 \| \| 0.0603 \| 320.0 \| 6000 \| 5.0496 \| 0.2483 \| \| 0.0703 \| 320.96 \| 6018 \| 5.0788 \| 0.2367 \| \| 0.0797 \| 321.97 \| 6037 \| 5.1071 \| 0.24 \| \| 0.0914 \| 322.99 \| 6056 \| 5.0996 \| 0.24 \| \| 0.0688 \| 324.0 \| 6075 \| 5.0954 \| 0.24 \| \| 0.0591 \| 324.96 \| 6093 \| 5.1014 \| 0.245 \| \| 0.0622 \| 325.97 \| 6112 \| 5.0859 \| 0.24 \| \| 0.0715 \| 326.99 \| 6131 \| 5.0557 \| 0.2533 \| \| 0.0483 \| 328.0 \| 6150 \| 5.1148 \| 0.2383 \| \| 0.0922 \| 328.96 \| 6168 \| 5.1338 \| 0.24 \| \| 0.0588 \| 329.97 \| 6187 \| 5.1553 \| 0.245 \| \| 0.0615 \| 330.99 \| 6206 \| 5.1083 \| 0.2483 \| \| 0.0508 \| 332.0 \| 6225 \| 5.1167 \| 0.245 \| \| 0.0691 \| 332.96 \| 6243 \| 5.1116 \| 0.2433 \| \| 0.0684 \| 333.97 \| 6262 \| 5.1211 \| 0.2417 \| \| 0.0519 \| 334.99 \| 6281 \| 5.1481 \| 0.2433 \| \| 0.0571 \| 336.0 \| 6300 \| 5.1662 \| 0.245 \| \| 0.038 \| 336.96 \| 6318 \| 5.1616 \| 0.245 \| \| 0.0589 \| 337.97 \| 6337 \| 5.1507 \| 0.2483 \| \| 0.0609 \| 338.99 \| 6356 \| 5.1083 \| 0.245 \| \| 0.0484 \| 340.0 \| 6375 \| 5.1392 \| 0.2417 \| \| 0.0842 \| 340.96 \| 6393 \| 5.1476 \| 0.2483 \| \| 0.0569 \| 341.97 \| 6412 \| 5.1547 \| 0.245 \| \| 0.0626 \| 342.99 \| 6431 \| 5.1824 \| 0.2383 \| \| 0.0399 \| 344.0 \| 6450 \| 5.1972 \| 0.2417 \| \| 0.0803 \| 344.96 \| 6468 \| 5.1678 \| 0.2433 \| \| 0.0533 \| 345.97 \| 6487 \| 5.1815 \| 0.2417 \| \| 0.0542 \| 346.99 \| 6506 \| 5.1768 \| 0.2383 \| \| 0.0624 \| 348.0 \| 6525 \| 5.1759 \| 0.2333 \| \| 0.055 \| 348.96 \| 6543 \| 5.1954 \| 0.2433 \| \| 0.0678 \| 349.97 \| 6562 \| 5.1478 \| 0.2417 \| \| 0.0557 \| 350.99 \| 6581 \| 5.1236 \| 0.24 \| \| 0.0581 \| 352.0 \| 6600 \| 5.1462 \| 0.245 \| \| 0.0509 \| 352.96 \| 6618 \| 5.1428 \| 0.235 \| \| 0.0636 \| 353.97 \| 6637 \| 5.1659 \| 0.24 \| \| 0.0671 \| 354.99 \| 6656 \| 5.1583 \| 0.24 \| \| 0.0768 \| 356.0 \| 6675 \| 5.1143 \| 0.2283 \| \| 0.0719 \| 356.96 \| 6693 \| 5.1047 \| 0.235 \| \| 0.0653 \| 357.97 \| 6712 \| 5.1262 \| 0.2467 \| \| 0.0646 \| 358.99 \| 6731 \| 5.1456 \| 0.245 \| \| 0.0466 \| 360.0 \| 6750 \| 5.1514 \| 0.2517 \| \| 0.0447 \| 360.96 \| 6768 \| 5.1479 \| 0.24 \| \| 0.0671 \| 361.97 \| 6787 \| 5.1508 \| 0.2383 \| \| 0.0618 \| 362.99 \| 6806 \| 5.1520 \| 0.2367 \| \| 0.0559 \| 364.0 \| 6825 \| 5.1495 \| 0.2367 \| \| 0.0745 \| 364.96 \| 6843 \| 5.1606 \| 0.235 \| \| 0.0542 \| 365.97 \| 6862 \| 5.1888 \| 0.235 \| \| 0.0737 \| 366.99 \| 6881 \| 5.2100 \| 0.2417 \| \| 0.0524 \| 368.0 \| 6900 \| 5.2309 \| 0.2317 \| \| 0.0548 \| 368.96 \| 6918 \| 5.2278 \| 0.2283 \| \| 0.0742 \| 369.97 \| 6937 \| 5.1995 \| 0.2383 \| \| 0.0512 \| 370.99 \| 6956 \| 5.2024 \| 0.24 \| \| 0.0529 \| 372.0 \| 6975 \| 5.1839 \| 0.24 \| \| 0.0647 \| 372.96 \| 6993 \| 5.2155 \| 0.2433 \| \| 0.055 \| 373.97 \| 7012 \| 5.1983 \| 0.2417 \| \| 0.0586 \| 374.99 \| 7031 \| 5.1605 \| 0.2467 \| \| 0.0619 \| 376.0 \| 7050 \| 5.1739 \| 0.24 \| \| 0.0462 \| 376.96 \| 7068 \| 5.1929 \| 0.2467 \| \| 0.0406 \| 377.97 \| 7087 \| 5.2178 \| 0.2467 \| \| 0.0578 \| 378.99 \| 7106 \| 5.2028 \| 0.2433 \| \| 0.0643 \| 380.0 \| 7125 \| 5.1912 \| 0.245 \| \| 0.0442 \| 380.96 \| 7143 \| 5.2069 \| 0.2417 \| \| 0.0642 \| 381.97 \| 7162 \| 5.1740 \| 0.2417 \| \| 0.0423 \| 382.99 \| 7181 \| 5.1597 \| 0.2417 \| \| 0.0663 \| 384.0 \| 7200 \| 5.1497 \| 0.2467 \| \| 0.0539 \| 384.96 \| 7218 \| 5.1274 \| 0.2467 \| \| 0.0426 \| 385.97 \| 7237 \| 5.1322 \| 0.24 \| \| 0.0489 \| 386.99 \| 7256 \| 5.1582 \| 0.2433 \| \| 0.0591 \| 388.0 \| 7275 \| 5.1627 \| 0.245 \| \| 0.0528 \| 388.96 \| 7293 \| 5.1561 \| 0.2483 \| \| 0.0666 \| 389.97 \| 7312 \| 5.1548 \| 0.2433 \| \| 0.053 \| 390.99 \| 7331 \| 5.1537 \| 0.2433 \| \| 0.0546 \| 392.0 \| 7350 \| 5.1460 \| 0.25 \| \| 0.0546 \| 392.96 \| 7368 \| 5.1467 \| 0.2467 \| \| 0.0334 \| 393.97 \| 7387 \| 5.1394 \| 0.25 \| \| 0.0548 \| 394.99 \| 7406 \| 5.1488 \| 0.2417 \| \| 0.0441 \| 396.0 \| 7425 \| 5.1643 \| 0.2467 \| \| 0.0476 \| 396.96 \| 7443 \| 5.1578 \| 0.255 \| \| 0.0602 \| 397.97 \| 7462 \| 5.1754 \| 0.2517 \| \| 0.0455 \| 398.99 \| 7481 \| 5.1758 \| 0.2533 \| \| 0.0364 \| 400.0 \| 7500 \| 5.1471 \| 0.255 \| \| 0.0479 \| 400.96 \| 7518 \| 5.1424 \| 0.2517 \| \| 0.052 \| 401.97 \| 7537 \| 5.1626 \| 0.245 \| \| 0.0521 \| 402.99 \| 7556 \| 5.1680 \| 0.2533 \| \| 0.0527 \| 404.0 \| 7575 \| 5.1557 \| 0.2517 \| \| 0.0534 \| 404.96 \| 7593 \| 5.1781 \| 0.2567 \| \| 0.0431 \| 405.97 \| 7612 \| 5.1900 \| 0.2533 \| \| 0.0506 \| 406.99 \| 7631 \| 5.1651 \| 0.2533 \| \| 0.043 \| 408.0 \| 7650 \| 5.1529 \| 0.2567 \| \| 0.0419 \| 408.96 \| 7668 \| 5.1589 \| 0.255 \| \| 0.0388 \| 409.97 \| 7687 \| 5.1641 \| 0.2567 \| \| 0.0601 \| 410.99 \| 7706 \| 5.1679 \| 0.2567 \| \| 0.0521 \| 412.0 \| 7725 \| 5.1805 \| 0.255 \| \| 0.0564 \| 412.96 \| 7743 \| 5.1811 \| 0.2517 \| \| 0.0395 \| 413.97 \| 7762 \| 5.1670 \| 0.2483 \| \| 0.0542 \| 414.99 \| 7781 \| 5.1530 \| 0.2517 \| \| 0.0482 \| 416.0 \| 7800 \| 5.1599 \| 0.2483 \| \| 0.0597 \| 416.96 \| 7818 \| 5.1715 \| 0.2517 \| \| 0.0579 \| 417.97 \| 7837 \| 5.1748 \| 0.2517 \| \| 0.0331 \| 418.99 \| 7856 \| 5.1811 \| 0.25 \| \| 0.045 \| 420.0 \| 7875 \| 5.1903 \| 0.25 \| \| 0.0424 \| 420.96 \| 7893 \| 5.1830 \| 0.2583 \| \| 0.0474 \| 421.97 \| 7912 \| 5.1867 \| 0.2567 \| \| 0.051 \| 422.99 \| 7931 \| 5.1793 \| 0.255 \| \| 0.0563 \| 424.0 \| 7950 \| 5.1588 \| 0.2517 \| \| 0.039 \| 424.96 \| 7968 \| 5.1612 \| 0.2483 \| \| 0.0722 \| 425.97 \| 7987 \| 5.1692 \| 0.2433 \| \| 0.0492 \| 426.99 \| 8006 \| 5.1873 \| 0.245 \| \| 0.0594 \| 428.0 \| 8025 \| 5.1983 \| 0.245 \| \| 0.0646 \| 428.96 \| 8043 \| 5.2049 \| 0.2483 \| \| 0.0412 \| 429.97 \| 8062 \| 5.1985 \| 0.25 \| \| 0.0493 \| 430.99 \| 8081 \| 5.2013 \| 0.24 \| \| 0.0582 \| 432.0 \| 8100 \| 5.1916 \| 0.2467 \| \| 0.0528 \| 432.96 \| 8118 \| 5.1852 \| 0.2483 \| \| 0.0432 \| 433.97 \| 8137 \| 5.1820 \| 0.25 \| \| 0.0313 \| 434.99 \| 8156 \| 5.1844 \| 0.2483 \| \| 0.048 \| 436.0 \| 8175 \| 5.1884 \| 0.2467 \| \| 0.0591 \| 436.96 \| 8193 \| 5.1952 \| 0.2467 \| \| 0.0379 \| 437.97 \| 8212 \| 5.2027 \| 0.25 \| \| 0.0355 \| 438.99 \| 8231 \| 5.2032 \| 0.2517 \| \| 0.0499 \| 440.0 \| 8250 \| 5.1997 \| 0.2533 \| \| 0.0464 \| 440.96 \| 8268 \| 5.1866 \| 0.255 \| \| 0.0455 \| 441.97 \| 8287 \| 5.1744 \| 0.255 \| \| 0.0429 \| 442.99 \| 8306 \| 5.1745 \| 0.2533 \| \| 0.0532 \| 444.0 \| 8325 \| 5.1885 \| 0.2483 \| \| 0.053 \| 444.96 \| 8343 \| 5.1893 \| 0.2517 \| \| 0.0453 \| 445.97 \| 8362 \| 5.1899 \| 0.2467 \| \| 0.0414 \| 446.99 \| 8381 \| 5.1957 \| 0.2433 \| \| 0.0449 \| 448.0 \| 8400 \| 5.2016 \| 0.2433 \| \| 0.0424 \| 448.96 \| 8418 \| 5.2080 \| 0.245 \| \| 0.0377 \| 449.97 \| 8437 \| 5.2015 \| 0.2417 \| \| 0.0448 \| 450.99 \| 8456 \| 5.1974 \| 0.2433 \| \| 0.0647 \| 452.0 \| 8475 \| 5.1928 \| 0.24 \| \| 0.0549 \| 452.96 \| 8493 \| 5.1991 \| 0.24 \| \| 0.0455 \| 453.97 \| 8512 \| 5.1975 \| 0.245 \| \| 0.0531 \| 454.99 \| 8531 \| 5.1890 \| 0.2417 \| \| 0.0476 \| 456.0 \| 8550 \| 5.1756 \| 0.245 \| \| 0.0366 \| 456.96 \| 8568 \| 5.1726 \| 0.2467 \| \| 0.0534 \| 457.97 \| 8587 \| 5.1726 \| 0.2467 \| \| 0.0521 \| 458.99 \| 8606 \| 5.1757 \| 0.2467 \| \| 0.042 \| 460.0 \| 8625 \| 5.1777 \| 0.2467 \| \| 0.0513 \| 460.96 \| 8643 \| 5.1781 \| 0.2483 \| \| 0.0463 \| 461.97 \| 8662 \| 5.1803 \| 0.25 \| \| 0.0563 \| 462.99 \| 8681 \| 5.1841 \| 0.2483 \| \| 0.0366 \| 464.0 \| 8700 \| 5.1888 \| 0.245 \| \| 0.0439 \| 464.96 \| 8718 \| 5.1896 \| 0.2467 \| \| 0.0645 \| 465.97 \| 8737 \| 5.1916 \| 0.2483 \| \| 0.0358 \| 466.99 \| 8756 \| 5.1958 \| 0.25 \| \| 0.0397 \| 468.0 \| 8775 \| 5.1982 \| 0.2467 \| \| 0.0498 \| 468.96 \| 8793 \| 5.2001 \| 0.245 \| \| 0.0597 \| 469.97 \| 8812 \| 5.2035 \| 0.245 \| \| 0.031 \| 470.99 \| 8831 \| 5.2055 \| 0.245 \| \| 0.0466 \| 472.0 \| 8850 \| 5.2071 \| 0.245 \| \| 0.0406 \| 472.96 \| 8868 \| 5.2064 \| 0.245 \| \| 0.047 \| 473.97 \| 8887 \| 5.2062 \| 0.2467 \| \| 0.0472 \| 474.99 \| 8906 \| 5.2057 \| 0.245 \| \| 0.0392 \| 476.0 \| 8925 \| 5.2060 \| 0.2467 \| \| 0.0314 \| 476.96 \| 8943 \| 5.2054 \| 0.2467 \| \| 0.0411 \| 477.97 \| 8962 \| 5.2049 \| 0.2467 \| \| 0.0427 \| 478.99 \| 8981 \| 5.2046 \| 0.2467 \| \| 0.0469 \| 480.0 \| 9000 \| 5.2045 \| 0.2467 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.13.1 - Datasets 2.14.0 - Tokenizers 0.13.3
squarelike/Gugugo-koen-1.3B-V1.0	squarelike	2023-07-29T18:18:11Z	129	6	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "translation", "en", "ko", "dataset:squarelike/sharegpt_deepl_ko_translation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2023-07-26T16:06:55Z	--- license: apache-2.0 datasets: - squarelike/sharegpt_deepl_ko_translation language: - en - ko pipeline_tag: translation --- [https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo) Prompt Template: ``` ### 한국어: {sentence}</끝> ### 영어: ``` ``` ### 영어: {sentence}</끝> ### 한국어: ```
s3nh/Baichuan-13B-Instruction-GGML	s3nh	2023-07-29T18:16:19Z	0	3	transformers	[ "transformers", "text-generation-inference", "text-generation", "en", "arxiv:2104.09864", "arxiv:2108.12409", "arxiv:2307.00360", "license:openrail", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T07:52:31Z	--- license: openrail language: - en tags: - text-generation-inference pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/AlpachinoNLP/Baichuan-13B-Instruction/). ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card ## 使用方式如下是一个使用Baichuan-13B-Chat进行对话的示例，正确输出为"乔戈里峰。世界第二高峰———乔戈里峰西方登山者称其为k2峰，海拔高度是8611米，位于喀喇昆仑山脉的中巴边境上" ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction") messages = [] messages.append({"role": "Human", "content": "世界上第二高的山峰是哪座"}) response = model.chat(tokenizer, messages) print(response) ``` ## 量化部署 Baichuan-13B 支持 int8 和 int4 量化，用户只需在推理代码中简单修改两行即可实现。请注意，如果是为了节省显存而进行量化，应加载原始精度模型到 CPU 后再开始量化；避免在 `from_pretrained` 时添加 `device_map='auto'` 或者其它会导致把原始精度模型直接加载到 GPU 的行为的参数。使用 int8 量化 (To use int8 quantization): ```python model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", torch_dtype=torch.float16, trust_remote_code=True) model = model.quantize(8).cuda() ``` 同样的，如需使用 int4 量化 (Similarly, to use int4 quantization): ```python model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", torch_dtype=torch.float16, trust_remote_code=True) model = model.quantize(4).cuda() ``` ## 模型详情 ### 模型结构 <!-- Provide the basic links for the model. --> 整体模型基于Baichuan-13B，为了获得更好的推理性能，Baichuan-13B 使用了 ALiBi 线性偏置技术，相对于 Rotary Embedding 计算量更小，对推理性能有显著提升；与标准的 LLaMA-13B 相比，生成 2000 个 tokens 的平均推理速度 (tokens/s)，实测提升 31.6%： \| Model \| tokens/s \| \| ------------ \| -------- \| \| LLaMA-13B \| 19.4 \| \| Baichuan-13B \| 25.4 \| 具体参数和见下表 \| 模型名称 \| 隐含层维度 \| 层数 \| 头数 \| 词表大小 \| 总参数量 \| 训练数据（tokens） \| 位置编码 \| 最大长度 \| \| ------------ \| ---------- \| ---- \| ---- \| -------- \| -------------- \| ------------------ \| ----------------------------------------- \| -------- \| \| Baichuan-7B \| 4,096 \| 32 \| 32 \| 64,000 \| 7,000,559,616 \| 1.2万亿 \| [RoPE](https://arxiv.org/abs/2104.09864) \| 4,096 \| \| Baichuan-13B \| 5,120 \| 40 \| 40 \| 64,000 \| 13,264,901,120 \| 1.4万亿 \| [ALiBi](https://arxiv.org/abs/2108.12409) \| 4,096 \| ## 训练详情数据集主要由三部分组成： * 在 [sharegpt_zh](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/ShareGPT) 数据集中筛选的出 13k 高质量数据。 * [lima](https://huggingface.co/datasets/GAIR/lima) * 按照任务类型挑选的 2.3k 高质量中文数据集，每个任务类型的数据量在 100 条左右。硬件：8A40 ## 测评结果 ## [CMMLU](https://github.com/haonan-li/CMMLU) \| Model 5-shot \| STEM \| Humanities \| Social Sciences \| Others \| China Specific \| Average \| \| ---------------------------------------------------------- \| :-------: \| :--------: \| :-------------: \| :------: \| :------------: \| :------: \| \| Baichuan-7B \| 34.4 \| 47.5 \| 47.6 \| 46.6 \| 44.3 \| 44.0 \| \| Vicuna-13B \| 31.8 \| 36.2 \| 37.6 \| 39.5 \| 34.3 \| 36.3 \| \| Chinese-Alpaca-Plus-13B \| 29.8 \| 33.4 \| 33.2 \| 37.9 \| 32.1 \| 33.4 \| \| Chinese-LLaMA-Plus-13B \| 28.1 \| 33.1 \| 35.4 \| 35.1 \| 33.5 \| 33.0 \| \| Ziya-LLaMA-13B-Pretrain \| 29.0 \| 30.7 \| 33.8 \| 34.4 \| 31.9 \| 32.1 \| \| LLaMA-13B \| 29.2 \| 30.8 \| 31.6 \| 33.0 \| 30.5 \| 31.2 \| \| moss-moon-003-base (16B) \| 27.2 \| 30.4 \| 28.8 \| 32.6 \| 28.7 \| 29.6 \| \| Baichuan-13B-Base \| 41.7 \| 61.1 \| 59.8 \| 59.0 \| 56.4 \| 55.3 \| \| Baichuan-13B-Chat \| 42.8 \| 62.6* \| 59.7 \| 59.0 \| 56.1 \| 55.8 \| \| Baichuan-13B-Instruction \| 44.50 \| 61.16 \| 59.07 \| 58.34 \| 55.55 \| 55.61 \| \| Model zero-shot \| STEM \| Humanities \| Social Sciences \| Others \| China Specific \| Average \| \| ------------------------------------------------------------ \| :-------: \| :--------: \| :-------------: \| :-------: \| :------------: \| :-------: \| \| [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) \| 41.28 \| 52.85 \| 53.37 \| 52.24 \| 50.58 \| 49.95 \| \| [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) \| 32.79 \| 44.43 \| 46.78 \| 44.79 \| 43.11 \| 42.33 \| \| [ChatGLM-6B](https://github.com/THUDM/GLM-130B) \| 32.22 \| 42.91 \| 44.81 \| 42.60 \| 41.93 \| 40.79 \| \| [BatGPT-15B](https://arxiv.org/abs/2307.00360) \| 33.72 \| 36.53 \| 38.07 \| 46.94 \| 38.32 \| 38.51 \| \| [Chinese-LLaMA-13B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) \| 26.76 \| 26.57 \| 27.42 \| 28.33 \| 26.73 \| 27.34 \| \| [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) \| 25.68 \| 26.35 \| 27.21 \| 27.92 \| 26.70 \| 26.88 \| \| [Chinese-GLM-10B](https://github.com/THUDM/GLM) \| 25.57 \| 25.01 \| 26.33 \| 25.94 \| 25.81 \| 25.80 \| \| [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) \| 42.04 \| 60.49 \| 59.55 \| 56.60 \| 55.72 \| 54.63 \| \| [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-13B) \| 37.32 \| 56.24 \| 54.79 \| 54.07 \| 52.23 \| 50.48 \| \| Baichuan-13B-Instruction \| 42.56 \| 62.09 \| 60.41 \| 58.97 \| 56.95 \| 55.88 \| > 说明：CMMLU 是一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力。我们直接使用其官方的[评测脚本](https://github.com/haonan-li/CMMLU)对模型进行评测。Model zero-shot 表格中 [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-13B) 的得分来自我们直接运行 CMMLU 官方的评测脚本得到，其他模型的的得分来自于 [CMMLU](https://github.com/haonan-li/CMMLU/tree/master) 官方的评测结果，Model 5-shot 中其他模型的得分来自于[Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) 官方的评测结果。
s3nh/StableBeluga-7B-GGML	s3nh	2023-07-29T18:15:45Z	0	2	transformers	[ "transformers", "text-generation-inference", "text-generation", "en", "arxiv:2307.09288", "arxiv:2306.02707", "license:openrail", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T06:48:02Z	--- license: openrail language: - en tags: - text-generation-inference pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/stabilityai/StableBeluga-7B). It consist of k_quant quantization in every format: -4_0 -4_1 -5_0 -5_1 -8_0 ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card ## Usage Start chatting with `Stable Beluga 7B` using the following code snippet: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("stabilityai/StableBeluga-7B", use_fast=False) model = AutoModelForCausalLM.from_pretrained("stabilityai/StableBeluga-7B", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto") system_prompt = "### System:\nYou are StableBeluga, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal.\n\n" message = "Write me a poem please" prompt = f"{system_prompt}### User: {message}\n\n### Assistant:\n" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") output = model.generate(*inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=256) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` Stable Beluga 7B should be used with this prompt format: ``` ### System: This is a system prompt, please behave and help the user. ### User: Your prompt here ### Assistant: The output of Stable Beluga 7B ``` ## Model Details Developed by: [Stability AI](https://stability.ai/) * Model type: Stable Beluga 7B is an auto-regressive language model fine-tuned on Llama2 7B. * Language(s): English * Library: [HuggingFace Transformers](https://github.com/huggingface/transformers) * License: Fine-tuned checkpoints (`Stable Beluga 7B`) is licensed under the [STABLE BELUGA NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT](https://huggingface.co/stabilityai/StableBeluga-7B/blob/main/LICENSE.txt) * Contact: For questions and comments about the model, please email `[email protected]` ### Training Dataset ` Stable Beluga 7B` is trained on our internal Orca-style dataset ### Training Procedure Models are learned via supervised fine-tuning on the aforementioned datasets, trained in mixed-precision (BF16), and optimized with AdamW. We outline the following hyperparameters: \| Dataset \| Batch Size \| Learning Rate \|Learning Rate Decay\| Warm-up \| Weight Decay \| Betas \| \|-------------------\|------------\|---------------\|-------------------\|---------\|--------------\|-------------\| \| Orca pt1 packed \| 256 \| 3e-5 \| Cosine to 3e-6 \| 100 \| 1e-6 \| (0.9, 0.95) \| \| Orca pt2 unpacked \| 512 \| 3e-5 \| Cosine to 3e-6 \| 100 \| 1e-6 \| (0.9, 0.95) \| ## Ethical Considerations and Limitations Beluga is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Beluga's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Beluga, developers should perform safety testing and tuning tailored to their specific applications of the model. ## Citations ```bibtext @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtext @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
s3nh/starcoderbase-3b-GPTQ	s3nh	2023-07-29T18:13:43Z	9	1	transformers	[ "transformers", "gpt_bigcode", "text-generation", "en", "arxiv:2305.06161", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T11:25:26Z	--- license: openrail language: - en pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/bigcode/starcoderbase-3b). ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card ## Use ### Intended use The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant. Feel free to share your generations in the Community tab! ### Generation ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoderbase-3b" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Fill-in-the-middle Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: ```python input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>" inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code from 80+ programming languages. The predominant natural language in source code is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) for an in-depth discussion of the model limitations. # Training ## Model - Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective - Pretraining steps: 500k - Pretraining tokens: 1 trillion - Precision: bfloat16 ## Hardware - GPUs: 256 Tesla A100 - Training time: 12 days ## Software - Orchestration: [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - Neural networks: [PyTorch](https://github.com/pytorch/pytorch) - BP16 if applicable: [apex](https://github.com/NVIDIA/apex) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). # Citation ``` @article{li2023starcoder, title={StarCoder: may the source be with you!}, author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2023}, eprint={2305.06161}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
s3nh/LLongMA-2-7b-16k-GGML	s3nh	2023-07-29T18:13:18Z	0	1	transformers	[ "transformers", "text-generation", "en", "license:openrail", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T19:32:42Z	--- license: openrail language: - en pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/conceptofmind/LLongMA-2-7b-16k). ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card
VinEuro/Pixelcopter-PLE-v0	VinEuro	2023-07-29T18:00:25Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T17:56:01Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 16.70 +/- 10.16 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
vishnun/peft-codevsnl	vishnun	2023-07-29T17:55:10Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-29T17:14:43Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
hadiqa123/xls-r_cv_ur	hadiqa123	2023-07-29T17:41:57Z	21	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-22T04:28:06Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: xls-r_cv_ur 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. --> # xls-r_cv_ur This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4888 - Wer: 1.0 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 8.4361 \| 1.14 \| 100 \| 2.3733 \| 1.0 \| \| 1.9609 \| 2.27 \| 200 \| 1.8280 \| 1.0 \| \| 1.8191 \| 3.41 \| 300 \| 1.8259 \| 1.0 \| \| 1.8359 \| 4.55 \| 400 \| 1.8069 \| 1.0 \| \| 1.7667 \| 5.68 \| 500 \| 1.8038 \| 1.0 \| \| 1.7751 \| 6.82 \| 600 \| 1.7473 \| 1.0 \| \| 1.7428 \| 7.95 \| 700 \| 1.6996 \| 1.0 \| \| 1.697 \| 9.09 \| 800 \| 1.6364 \| 1.0 \| \| 1.6532 \| 10.23 \| 900 \| 1.4985 \| 1.0 \| \| 1.5217 \| 11.36 \| 1000 \| 1.2836 \| 1.0 \| \| 1.3385 \| 12.5 \| 1100 \| 1.0293 \| 1.0 \| \| 1.1596 \| 13.64 \| 1200 \| 0.8294 \| 1.0 \| \| 1.0655 \| 14.77 \| 1300 \| 0.7150 \| 1.0 \| \| 0.9951 \| 15.91 \| 1400 \| 0.6364 \| 1.0 \| \| 0.9013 \| 17.05 \| 1500 \| 0.5548 \| 1.0 \| \| 0.8276 \| 18.18 \| 1600 \| 0.5200 \| 1.0 \| \| 0.8129 \| 19.32 \| 1700 \| 0.4888 \| 1.0 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
sm136599/chatfoodie-koalpaca-polyglot-5_8b-5150step-8batch_5epoch	sm136599	2023-07-29T17:36:47Z	4	0	peft	[ "peft", "region:us" ]	null	2023-07-29T17:36:46Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
lesliepzimmermann/dqn-SpaceInvadersNoFrameskip-v4	lesliepzimmermann	2023-07-29T17:28:15Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T17:27:38Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 667.50 +/- 132.75 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lesliepzimmermann -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lesliepzimmermann -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga lesliepzimmermann ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
VinEuro/CartPole-v1	VinEuro	2023-07-29T17:27:43Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T17:27:33Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Gerti/distilbert-base-multilingual-cased-finetuned-sentiment-albanian	Gerti	2023-07-29T17:02:43Z	106	1	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-26T07:59:47Z	--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: distilbert-base-multilingual-cased-finetuned-sentiment-albanian 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. --> # distilbert-base-multilingual-cased-finetuned-sentiment-albanian This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3126 - F1-score: : 0.9393 - Accuracy score: : 0.8902 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1-score: \| Accuracy score: \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------:\|:----------------:\| \| 0.3698 \| 1.0 \| 149 \| 0.3424 \| 0.9290 \| 0.8675 \| \| 0.2995 \| 2.0 \| 298 \| 0.3174 \| 0.9362 \| 0.8822 \| \| 0.251 \| 3.0 \| 447 \| 0.3126 \| 0.9393 \| 0.8902 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
jakezou/miaolora	jakezou	2023-07-29T16:59:16Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-29T06:25:34Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
digitaljungle/rl_course_vizdoom_health_gathering_supreme	digitaljungle	2023-07-29T16:57:25Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T16:57:07Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 9.78 +/- 4.91 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r digitaljungle/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Controlyourself/ppo-LunarLander-v2	Controlyourself	2023-07-29T16:55:47Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T16:55:28Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.96 +/- 25.25 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
NasimB/qed-rarity-seed	NasimB	2023-07-29T16:48:04Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-29T13:09:50Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: qed-rarity-seed 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. --> # qed-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1171 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3592 \| 0.29 \| 500 \| 5.3361 \| \| 5.0454 \| 0.58 \| 1000 \| 4.9330 \| \| 4.7118 \| 0.88 \| 1500 \| 4.6958 \| \| 4.4583 \| 1.17 \| 2000 \| 4.5571 \| \| 4.3049 \| 1.46 \| 2500 \| 4.4421 \| \| 4.2187 \| 1.75 \| 3000 \| 4.3410 \| \| 4.099 \| 2.05 \| 3500 \| 4.2645 \| \| 3.9157 \| 2.34 \| 4000 \| 4.2163 \| \| 3.8896 \| 2.63 \| 4500 \| 4.1655 \| \| 3.8487 \| 2.92 \| 5000 \| 4.1078 \| \| 3.6548 \| 3.21 \| 5500 \| 4.1099 \| \| 3.6051 \| 3.51 \| 6000 \| 4.0753 \| \| 3.5947 \| 3.8 \| 6500 \| 4.0445 \| \| 3.4956 \| 4.09 \| 7000 \| 4.0427 \| \| 3.3396 \| 4.38 \| 7500 \| 4.0386 \| \| 3.336 \| 4.68 \| 8000 \| 4.0234 \| \| 3.3241 \| 4.97 \| 8500 \| 4.0114 \| \| 3.1753 \| 5.26 \| 9000 \| 4.0252 \| \| 3.1537 \| 5.55 \| 9500 \| 4.0240 \| \| 3.1587 \| 5.84 \| 10000 \| 4.0228 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
darthPanda/dqn-SpaceInvadersNoFrameskip-v4_1	darthPanda	2023-07-29T16:33:58Z	11	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T16:33:27Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 457.00 +/- 67.39 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga darthPanda -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga darthPanda -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga darthPanda ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
2Nisavi2/Cifrar_10_Deployment	2Nisavi2	2023-07-29T16:31:15Z	2	0	tf-keras	[ "tf-keras", "region:us" ]	null	2023-07-29T05:10:05Z	<div align="center"> <img src="https://radcolombia.org/web/sites/default/files/archivos/instituciones/universidad-bosque/logo-ub.png" width="500"/> Facultad de Ciencias Maestría en Estadística Aplicada y Ciencia de Datos *"Despliegue Modelo VGG16 para Cifrar10 Desde HuggingFace"* Proyecto Final GCP </div> En este repositório encontrara el dataset Cifrar10 entrenado sobre VGG16. Para la prueba de la efectividad del modelo, ingrese a este [link](https://3174c43e92e93f5122.gradio.live)
HongyangLi/basedbert-finetuned-squad	HongyangLi	2023-07-29T16:30:13Z	116	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-07-29T16:09:49Z	--- tags: - generated_from_trainer model-index: - name: basedbert-finetuned-squad 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. --> # basedbert-finetuned-squad This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9130 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 29 \| 3.1855 \| \| No log \| 2.0 \| 58 \| 2.0997 \| \| No log \| 3.0 \| 87 \| 1.9130 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.14.1 - Tokenizers 0.13.3
VinEuro/dqn-SpaceInvadersNoFrameskip-v4	VinEuro	2023-07-29T16:25:41Z	7	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T16:25:06Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 561.00 +/- 94.55 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VinEuro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VinEuro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga VinEuro ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 750000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ichacon/ppo-LunarLander-v2	ichacon	2023-07-29T16:24:31Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T16:24:12Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.39 +/- 19.64 name: mean_reward verified: false --- # ppo Agent playing LunarLander-v2 This is a trained model of a ppo agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ClearRabbitt/Clear	ClearRabbitt	2023-07-29T16:04:12Z	0	0	null	[ "DMMD", "Dramatical Murder", "Dramatical Murder Re:Connect", "Yaoi", "BL", "ja", "license:openrail", "region:us" ]	null	2023-07-26T10:29:21Z	--- language: - ja tags: - DMMD - Dramatical Murder - Dramatical Murder Re:Connect - Yaoi - BL license: openrail --- ![576993.jpg](https://cdn-uploads.huggingface.co/production/uploads/64b93f4925e5f8a3e000371b/VGfvARYz1TYvnQqukVwiQ.jpeg) Clear from Dramatical Murder! Trained with Dramatical Murder, Dramatical Murder Re:Connect, Birthday CD, All of his drama CDs, His song クラゲの歌 I posted finished 500 epochs and a lower epoch where I think he might've just begun to overtrain. ![Board.PNG](https://cdn-uploads.huggingface.co/production/uploads/64b93f4925e5f8a3e000371b/FkQo_ApXpiRO0IZke-5QY.png) I might continue to train him if he hasn't overtrained but I truly think he has so I won't touch this, instead I'll just train my future Aoba model
chunwoolee0/distilbert-base-uncased-finetuned-emotion	chunwoolee0	2023-07-29T15:55:44Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-19T11:33:23Z	--- tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion 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. --> # distilbert-base-uncased-finetuned-emotion This model was trained from scratch on the emotion 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: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
user10211125/doodle_style_model	user10211125	2023-07-29T15:54:49Z	43	0	diffusers	[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-29T15:36:28Z	doodle(風格)+teary+lookingDisgusted+sadMadFace
Krainez/q-Taxi-v3	Krainez	2023-07-29T15:49:17Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T15:49:15Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Krainez/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Iam/llama2-qlora-finetunined-french	Iam	2023-07-29T15:45:02Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-29T15:40:08Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
ParthNakum21/GenzTranscribe-en-gu	ParthNakum21	2023-07-29T15:43:44Z	102	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus100", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-29T09:27:29Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: GenzTranscribe-en-gu results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 config: en-gu split: train args: en-gu metrics: - name: Bleu type: bleu value: 59.9227 --- <!-- 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. --> # GenzTranscribe-en-gu This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.3075 - Bleu: 59.9227 - Gen Len: 9.6443 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.3593 \| 1.0 \| 31831 \| 0.3253 \| 58.1921 \| 9.7108 \| \| 0.3421 \| 2.0 \| 63662 \| 0.3075 \| 59.9227 \| 9.6443 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
c72599/hubert-base-ls960-finetuned-gtzan	c72599	2023-07-29T15:42:18Z	3	0	transformers	[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/hubert-base-ls960", "base_model:finetune:facebook/hubert-base-ls960", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-29T07:10:24Z	--- license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: hubert-base-ls960-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.88 --- <!-- 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. --> # hubert-base-ls960-finetuned-gtzan This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6645 - Accuracy: 0.88 ## 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-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2685 \| 1.0 \| 56 \| 2.2069 \| 0.44 \| \| 2.0208 \| 1.99 \| 112 \| 1.8352 \| 0.46 \| \| 1.7603 \| 2.99 \| 168 \| 1.5275 \| 0.49 \| \| 1.4843 \| 4.0 \| 225 \| 1.4296 \| 0.52 \| \| 1.347 \| 5.0 \| 281 \| 1.2222 \| 0.52 \| \| 1.2364 \| 5.99 \| 337 \| 1.1477 \| 0.62 \| \| 1.2082 \| 6.99 \| 393 \| 1.0181 \| 0.67 \| \| 0.9861 \| 8.0 \| 450 \| 0.9598 \| 0.71 \| \| 0.752 \| 9.0 \| 506 \| 0.7499 \| 0.77 \| \| 1.006 \| 9.99 \| 562 \| 0.8190 \| 0.79 \| \| 0.6725 \| 10.99 \| 618 \| 0.8798 \| 0.75 \| \| 0.7457 \| 12.0 \| 675 \| 0.6276 \| 0.81 \| \| 0.4605 \| 13.0 \| 731 \| 0.6086 \| 0.85 \| \| 0.5751 \| 13.99 \| 787 \| 0.6894 \| 0.75 \| \| 0.4886 \| 14.99 \| 843 \| 0.6109 \| 0.83 \| \| 0.2429 \| 16.0 \| 900 \| 0.6076 \| 0.85 \| \| 0.3084 \| 17.0 \| 956 \| 0.4646 \| 0.86 \| \| 0.3762 \| 17.99 \| 1012 \| 0.8349 \| 0.81 \| \| 0.2897 \| 18.99 \| 1068 \| 0.4509 \| 0.89 \| \| 0.1296 \| 20.0 \| 1125 \| 0.6791 \| 0.86 \| \| 0.1291 \| 21.0 \| 1181 \| 0.6466 \| 0.85 \| \| 0.3784 \| 21.99 \| 1237 \| 0.6272 \| 0.88 \| \| 0.1156 \| 22.99 \| 1293 \| 0.7916 \| 0.85 \| \| 0.2093 \| 24.0 \| 1350 \| 0.6536 \| 0.85 \| \| 0.2167 \| 25.0 \| 1406 \| 0.7050 \| 0.87 \| \| 0.1095 \| 25.99 \| 1462 \| 0.6128 \| 0.88 \| \| 0.1004 \| 26.99 \| 1518 \| 0.6092 \| 0.89 \| \| 0.0897 \| 28.0 \| 1575 \| 0.6730 \| 0.88 \| \| 0.083 \| 29.0 \| 1631 \| 0.6396 \| 0.89 \| \| 0.0343 \| 29.87 \| 1680 \| 0.6645 \| 0.88 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.14.1 - Tokenizers 0.13.3
lesliepzimmermann/q-Taxi-v3	lesliepzimmermann	2023-07-29T15:41:01Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T15:15:14Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="lesliepzimmermann/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
nikbhi/LunarLander_ppo_v3	nikbhi	2023-07-29T15:35:57Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-29T15:35:37Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.69 +/- 9.19 name: mean_reward verified: false --- # ppo Agent playing LunarLander-v2 This is a trained model of a ppo agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
yzheng-1/FinBERT_Chinese	yzheng-1	2023-07-29T15:35:39Z	45	1	transformers	[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]	null	2023-07-29T15:31:55Z	FinBERT Model from https://github.com/valuesimplex/FinBERT

jordyvl/dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5

jordyvl

2023-07-30T04:43:54Z

163

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-29T22:45:24Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 results: [] ---  # dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.3085 - Accuracy: 0.828 - Brier Loss: 0.3005 - Nll: 1.3339 - F1 Micro: 0.828 - F1 Macro: 0.8285 - Ece: 0.1391 - Aurc: 0.0474 ## 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 167 | 5.3750 | 0.61 | 0.5591 | 2.2520 | 0.61 | 0.5922 | 0.1827 | 0.1664 | | No log | 2.0 | 334 | 5.0343 | 0.7117 | 0.4389 | 1.9483 | 0.7117 | 0.7096 | 0.1691 | 0.0962 | | 5.4927 | 3.0 | 501 | 4.8554 | 0.7472 | 0.3777 | 1.6689 | 0.7472 | 0.7474 | 0.1221 | 0.0780 | | 5.4927 | 4.0 | 668 | 4.7917 | 0.76 | 0.3524 | 1.5715 | 0.76 | 0.7644 | 0.0915 | 0.0699 | | 5.4927 | 5.0 | 835 | 4.7792 | 0.765 | 0.3461 | 1.5348 | 0.765 | 0.7590 | 0.0737 | 0.0704 | | 4.6216 | 6.0 | 1002 | 4.6378 | 0.7993 | 0.2954 | 1.3769 | 0.7993 | 0.7995 | 0.0546 | 0.0559 | | 4.6216 | 7.0 | 1169 | 4.8666 | 0.771 | 0.3359 | 1.5727 | 0.771 | 0.7728 | 0.0670 | 0.0666 | | 4.6216 | 8.0 | 1336 | 4.6834 | 0.7897 | 0.3047 | 1.3537 | 0.7897 | 0.7914 | 0.0531 | 0.0564 | | 4.2978 | 9.0 | 1503 | 4.6558 | 0.7997 | 0.2912 | 1.3758 | 0.7997 | 0.7988 | 0.0521 | 0.0508 | | 4.2978 | 10.0 | 1670 | 4.8214 | 0.7923 | 0.3144 | 1.5316 | 0.7923 | 0.7928 | 0.0815 | 0.0561 | | 4.2978 | 11.0 | 1837 | 4.8908 | 0.7923 | 0.3201 | 1.4158 | 0.7923 | 0.7931 | 0.0988 | 0.0573 | | 4.1375 | 12.0 | 2004 | 4.7703 | 0.8093 | 0.2971 | 1.3642 | 0.8093 | 0.8097 | 0.0812 | 0.0514 | | 4.1375 | 13.0 | 2171 | 4.8126 | 0.806 | 0.3039 | 1.3759 | 0.806 | 0.8053 | 0.0916 | 0.0491 | | 4.1375 | 14.0 | 2338 | 4.7875 | 0.8063 | 0.2990 | 1.3712 | 0.8062 | 0.8065 | 0.0904 | 0.0481 | | 4.0665 | 15.0 | 2505 | 4.7995 | 0.805 | 0.3016 | 1.4133 | 0.805 | 0.8049 | 0.0909 | 0.0503 | | 4.0665 | 16.0 | 2672 | 4.7712 | 0.8075 | 0.2957 | 1.4018 | 0.8075 | 0.8082 | 0.0880 | 0.0484 | | 4.0665 | 17.0 | 2839 | 4.7245 | 0.812 | 0.2886 | 1.2816 | 0.8120 | 0.8139 | 0.0831 | 0.0464 | | 4.0204 | 18.0 | 3006 | 4.8990 | 0.8055 | 0.3079 | 1.3884 | 0.8055 | 0.8046 | 0.1117 | 0.0504 | | 4.0204 | 19.0 | 3173 | 4.9286 | 0.802 | 0.3147 | 1.3977 | 0.802 | 0.7995 | 0.1078 | 0.0522 | | 4.0204 | 20.0 | 3340 | 4.9510 | 0.8055 | 0.3121 | 1.4482 | 0.8055 | 0.8062 | 0.1125 | 0.0521 | | 3.9854 | 21.0 | 3507 | 4.8837 | 0.8033 | 0.3082 | 1.4528 | 0.8033 | 0.8022 | 0.1052 | 0.0502 | | 3.9854 | 22.0 | 3674 | 5.0103 | 0.813 | 0.3069 | 1.4217 | 0.813 | 0.8169 | 0.1207 | 0.0500 | | 3.9854 | 23.0 | 3841 | 4.9602 | 0.8093 | 0.3091 | 1.4672 | 0.8093 | 0.8103 | 0.1187 | 0.0494 | | 3.9599 | 24.0 | 4008 | 4.8980 | 0.8177 | 0.2953 | 1.3589 | 0.8178 | 0.8203 | 0.1083 | 0.0451 | | 3.9599 | 25.0 | 4175 | 4.8753 | 0.8145 | 0.2932 | 1.3219 | 0.8145 | 0.8140 | 0.1054 | 0.0460 | | 3.9599 | 26.0 | 4342 | 4.9644 | 0.8193 | 0.3000 | 1.4336 | 0.8193 | 0.8200 | 0.1173 | 0.0458 | | 3.9358 | 27.0 | 4509 | 4.9648 | 0.8203 | 0.2985 | 1.4117 | 0.8203 | 0.8197 | 0.1132 | 0.0471 | | 3.9358 | 28.0 | 4676 | 5.0130 | 0.8195 | 0.3014 | 1.4618 | 0.8195 | 0.8201 | 0.1205 | 0.0456 | | 3.9358 | 29.0 | 4843 | 4.8974 | 0.8255 | 0.2874 | 1.3041 | 0.8255 | 0.8258 | 0.1097 | 0.0421 | | 3.9151 | 30.0 | 5010 | 4.9045 | 0.8255 | 0.2878 | 1.2801 | 0.8255 | 0.8250 | 0.1119 | 0.0426 | | 3.9151 | 31.0 | 5177 | 4.9720 | 0.823 | 0.2945 | 1.3551 | 0.823 | 0.8240 | 0.1212 | 0.0439 | | 3.9151 | 32.0 | 5344 | 4.9508 | 0.826 | 0.2913 | 1.2669 | 0.826 | 0.8268 | 0.1201 | 0.0422 | | 3.9003 | 33.0 | 5511 | 5.0336 | 0.8237 | 0.2991 | 1.3443 | 0.8237 | 0.8240 | 0.1243 | 0.0433 | | 3.9003 | 34.0 | 5678 | 4.9828 | 0.8237 | 0.2901 | 1.2843 | 0.8237 | 0.8239 | 0.1214 | 0.0440 | | 3.9003 | 35.0 | 5845 | 5.0256 | 0.8287 | 0.2920 | 1.2961 | 0.8287 | 0.8291 | 0.1232 | 0.0422 | | 3.89 | 36.0 | 6012 | 5.0229 | 0.8283 | 0.2922 | 1.2471 | 0.8283 | 0.8283 | 0.1236 | 0.0432 | | 3.89 | 37.0 | 6179 | 5.0835 | 0.8285 | 0.2936 | 1.2892 | 0.8285 | 0.8289 | 0.1254 | 0.0442 | | 3.89 | 38.0 | 6346 | 5.1148 | 0.8287 | 0.2946 | 1.3106 | 0.8287 | 0.8282 | 0.1287 | 0.0427 | | 3.8846 | 39.0 | 6513 | 5.1238 | 0.827 | 0.2954 | 1.2964 | 0.827 | 0.8275 | 0.1298 | 0.0441 | | 3.8846 | 40.0 | 6680 | 5.1481 | 0.8307 | 0.2950 | 1.3136 | 0.8308 | 0.8311 | 0.1277 | 0.0453 | | 3.8846 | 41.0 | 6847 | 5.1491 | 0.8293 | 0.2943 | 1.2841 | 0.8293 | 0.8294 | 0.1298 | 0.0451 | | 3.881 | 42.0 | 7014 | 5.1982 | 0.829 | 0.2969 | 1.3111 | 0.8290 | 0.8292 | 0.1331 | 0.0459 | | 3.881 | 43.0 | 7181 | 5.2041 | 0.8283 | 0.2970 | 1.3427 | 0.8283 | 0.8283 | 0.1327 | 0.0465 | | 3.881 | 44.0 | 7348 | 5.2310 | 0.8297 | 0.2985 | 1.3351 | 0.8297 | 0.8303 | 0.1346 | 0.0471 | | 3.8796 | 45.0 | 7515 | 5.2394 | 0.83 | 0.2999 | 1.3308 | 0.83 | 0.8305 | 0.1348 | 0.0467 | | 3.8796 | 46.0 | 7682 | 5.2632 | 0.83 | 0.2990 | 1.3350 | 0.83 | 0.8304 | 0.1355 | 0.0471 | | 3.8796 | 47.0 | 7849 | 5.2821 | 0.828 | 0.2998 | 1.3354 | 0.828 | 0.8286 | 0.1383 | 0.0470 | | 3.8753 | 48.0 | 8016 | 5.2949 | 0.829 | 0.2998 | 1.3341 | 0.8290 | 0.8294 | 0.1374 | 0.0472 | | 3.8753 | 49.0 | 8183 | 5.3026 | 0.8287 | 0.3004 | 1.3281 | 0.8287 | 0.8293 | 0.1382 | 0.0474 | | 3.8753 | 50.0 | 8350 | 5.3085 | 0.828 | 0.3005 | 1.3339 | 0.828 | 0.8285 | 0.1391 | 0.0474 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

NasimB/cbt-log-rarity-seed

NasimB

2023-07-30T04:27:02Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-30T01:22:05Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: cbt-log-rarity-seed results: [] ---  # cbt-log-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1049 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3551 | 0.29 | 500 | 5.3459 | | 5.0301 | 0.58 | 1000 | 4.9295 | | 4.7058 | 0.87 | 1500 | 4.6870 | | 4.4358 | 1.17 | 2000 | 4.5471 | | 4.2948 | 1.46 | 2500 | 4.4310 | | 4.1839 | 1.75 | 3000 | 4.3262 | | 4.0867 | 2.04 | 3500 | 4.2510 | | 3.8901 | 2.33 | 4000 | 4.2098 | | 3.8635 | 2.62 | 4500 | 4.1570 | | 3.8226 | 2.91 | 5000 | 4.1006 | | 3.6452 | 3.21 | 5500 | 4.0979 | | 3.5849 | 3.5 | 6000 | 4.0657 | | 3.5624 | 3.79 | 6500 | 4.0400 | | 3.475 | 4.08 | 7000 | 4.0368 | | 3.3171 | 4.37 | 7500 | 4.0306 | | 3.3088 | 4.66 | 8000 | 4.0165 | | 3.3004 | 4.95 | 8500 | 4.0034 | | 3.1584 | 5.24 | 9000 | 4.0145 | | 3.1334 | 5.54 | 9500 | 4.0146 | | 3.1257 | 5.83 | 10000 | 4.0136 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

Yntec/theallysMixIV-verisimilar

Yntec

2023-07-30T04:12:07Z

489

4

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "TheAlly", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-21T18:11:23Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image - TheAlly --- # TheAlly's Mix IV: Verisimilar Original page: https://civitai.com/models/40369/theallys-mix-iv-verisimilar

NasimB/bnc_spoken_cbt_rarity-seed

NasimB

2023-07-30T03:39:52Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-30T00:49:35Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc_spoken_cbt_rarity-seed results: [] ---  # bnc_spoken_cbt_rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1218 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.366 | 0.29 | 500 | 5.3305 | | 5.0478 | 0.59 | 1000 | 4.9325 | | 4.7159 | 0.88 | 1500 | 4.6882 | | 4.4541 | 1.17 | 2000 | 4.5478 | | 4.3142 | 1.46 | 2500 | 4.4363 | | 4.2068 | 1.76 | 3000 | 4.3334 | | 4.0948 | 2.05 | 3500 | 4.2611 | | 3.9065 | 2.34 | 4000 | 4.2156 | | 3.8793 | 2.63 | 4500 | 4.1613 | | 3.8461 | 2.93 | 5000 | 4.1146 | | 3.6533 | 3.22 | 5500 | 4.1112 | | 3.5945 | 3.51 | 6000 | 4.0788 | | 3.5863 | 3.81 | 6500 | 4.0459 | | 3.4902 | 4.1 | 7000 | 4.0491 | | 3.3324 | 4.39 | 7500 | 4.0429 | | 3.3243 | 4.68 | 8000 | 4.0290 | | 3.3156 | 4.98 | 8500 | 4.0193 | | 3.1646 | 5.27 | 9000 | 4.0316 | | 3.1471 | 5.56 | 9500 | 4.0313 | | 3.1498 | 5.85 | 10000 | 4.0303 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

zbulrush/controlnet-sd-xl-1.0-lineart

zbulrush

2023-07-30T03:35:56Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-30T03:35:56Z

--- license: creativeml-openrail-m ---

roman10/distilbert-base-uncased-finetuned-p1-en

roman10

2023-07-30T03:31:12Z

71

0

transformers

[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-30T03:27:40Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: roman10/distilbert-base-uncased-finetuned-p1-en results: [] ---  # roman10/distilbert-base-uncased-finetuned-p1-en This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.4677 - Validation Loss: 2.8371 - Epoch: 1 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -999, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.3261 | 3.5044 | 0 | | 3.4677 | 2.8371 | 1 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.1 - Datasets 2.13.1 - Tokenizers 0.13.3

bochen0909/a2c-PandaReachDense-v2

bochen0909

2023-07-30T02:52:16Z

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-30T02:50:09Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.60 +/- 0.85 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

RyanDA/QuickDraw-model

RyanDA

2023-07-30T02:29:06Z

4

0

keras

[ "keras", "license:bigscience-openrail-m", "region:us" ]

null

2023-07-30T01:50:55Z

--- license: bigscience-openrail-m --- # QuickDraw Model This model was trained on the quickdraw dataset from a selected list of doodles: ["Apple","Cell Phone","Chair","Hot Air Balloon","Jail","Ladder","Line","Spider","Windmill","Zigzag"]

bochen0909/a2c-AntBulletEnv-v0

bochen0909

2023-07-30T01:51:53Z

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-30T01:47:58Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1006.82 +/- 316.92 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Ahsankhan123/taxi-v3

Ahsankhan123

2023-07-30T01:46:43Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-30T01:46:42Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Ahsankhan123/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Lexic0n/segformer-b0-finetuned-human-parsing

Lexic0n

2023-07-30T01:39:16Z

170

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2023-07-30T01:38:09Z

--- license: other tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-human-parsing results: [] ---  # segformer-b0-finetuned-human-parsing This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9476 - Mean Iou: 0.0726 - Mean Accuracy: 0.1221 - Overall Accuracy: 0.3575 - Accuracy Background: nan - Accuracy Hat: 0.0048 - Accuracy Hair: 0.4813 - Accuracy Sunglasses: 0.0 - Accuracy Upper-clothes: 0.9405 - Accuracy Skirt: 0.0000 - Accuracy Pants: 0.0631 - Accuracy Dress: 0.1031 - Accuracy Belt: 0.0 - Accuracy Left-shoe: 0.0011 - Accuracy Right-shoe: 0.0010 - Accuracy Face: 0.4406 - Accuracy Left-leg: 0.0291 - Accuracy Right-leg: 0.0 - Accuracy Left-arm: 0.0 - Accuracy Right-arm: 0.0001 - Accuracy Bag: 0.0114 - Accuracy Scarf: 0.0 - Iou Background: 0.0 - Iou Hat: 0.0043 - Iou Hair: 0.4221 - Iou Sunglasses: 0.0 - Iou Upper-clothes: 0.3239 - Iou Skirt: 0.0000 - Iou Pants: 0.0559 - Iou Dress: 0.0728 - Iou Belt: 0.0 - Iou Left-shoe: 0.0011 - Iou Right-shoe: 0.0009 - Iou Face: 0.3872 - Iou Left-leg: 0.0271 - Iou Right-leg: 0.0 - Iou Left-arm: 0.0 - Iou Right-arm: 0.0001 - Iou Bag: 0.0106 - Iou Scarf: 0.0 ## 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Hat | Accuracy Hair | Accuracy Sunglasses | Accuracy Upper-clothes | Accuracy Skirt | Accuracy Pants | Accuracy Dress | Accuracy Belt | Accuracy Left-shoe | Accuracy Right-shoe | Accuracy Face | Accuracy Left-leg | Accuracy Right-leg | Accuracy Left-arm | Accuracy Right-arm | Accuracy Bag | Accuracy Scarf | Iou Background | Iou Hat | Iou Hair | Iou Sunglasses | Iou Upper-clothes | Iou Skirt | Iou Pants | Iou Dress | Iou Belt | Iou Left-shoe | Iou Right-shoe | Iou Face | Iou Left-leg | Iou Right-leg | Iou Left-arm | Iou Right-arm | Iou Bag | Iou Scarf | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:| | 2.5768 | 0.4 | 20 | 2.7812 | 0.0726 | 0.1332 | 0.2876 | nan | 0.0178 | 0.3204 | 0.0004 | 0.5548 | 0.0004 | 0.2555 | 0.2373 | 0.0 | 0.0103 | 0.0003 | 0.5637 | 0.0287 | 0.0302 | 0.0001 | 0.0008 | 0.2435 | 0.0 | 0.0 | 0.0166 | 0.2759 | 0.0001 | 0.2781 | 0.0004 | 0.1710 | 0.1295 | 0.0 | 0.0098 | 0.0003 | 0.3251 | 0.0260 | 0.0248 | 0.0001 | 0.0007 | 0.0491 | 0.0 | | 2.2093 | 0.8 | 40 | 2.5166 | 0.0563 | 0.1052 | 0.3288 | nan | 0.0 | 0.1994 | 0.0 | 0.9447 | 0.0015 | 0.0435 | 0.1164 | 0.0 | 0.0008 | 0.0000 | 0.4655 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0153 | 0.0 | 0.0 | 0.0 | 0.1946 | 0.0 | 0.3037 | 0.0015 | 0.0417 | 0.0842 | 0.0 | 0.0008 | 0.0000 | 0.3726 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0124 | 0.0 | | 1.8804 | 1.2 | 60 | 2.0209 | 0.0632 | 0.1110 | 0.3374 | nan | 0.0087 | 0.3724 | 0.0 | 0.9475 | 0.0014 | 0.0162 | 0.0528 | 0.0 | 0.0001 | 0.0008 | 0.4257 | 0.0561 | 0.0001 | 0.0 | 0.0 | 0.0055 | 0.0 | 0.0 | 0.0077 | 0.3472 | 0.0 | 0.3086 | 0.0014 | 0.0156 | 0.0403 | 0.0 | 0.0001 | 0.0008 | 0.3597 | 0.0515 | 0.0001 | 0.0 | 0.0 | 0.0052 | 0.0 | | 1.8776 | 1.6 | 80 | 2.0016 | 0.0665 | 0.1154 | 0.3454 | nan | 0.0056 | 0.4172 | 0.0 | 0.9412 | 0.0000 | 0.0490 | 0.0697 | 0.0 | 0.0002 | 0.0006 | 0.4349 | 0.0329 | 0.0000 | 0.0 | 0.0000 | 0.0100 | 0.0 | 0.0 | 0.0048 | 0.3791 | 0.0 | 0.3138 | 0.0000 | 0.0438 | 0.0542 | 0.0 | 0.0002 | 0.0006 | 0.3608 | 0.0304 | 0.0000 | 0.0 | 0.0000 | 0.0093 | 0.0 | | 1.8471 | 2.0 | 100 | 1.9476 | 0.0726 | 0.1221 | 0.3575 | nan | 0.0048 | 0.4813 | 0.0 | 0.9405 | 0.0000 | 0.0631 | 0.1031 | 0.0 | 0.0011 | 0.0010 | 0.4406 | 0.0291 | 0.0 | 0.0 | 0.0001 | 0.0114 | 0.0 | 0.0 | 0.0043 | 0.4221 | 0.0 | 0.3239 | 0.0000 | 0.0559 | 0.0728 | 0.0 | 0.0011 | 0.0009 | 0.3872 | 0.0271 | 0.0 | 0.0 | 0.0001 | 0.0106 | 0.0 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

HachiML/mpt-7b-instruct-ja-qlora

HachiML

2023-07-30T01:23:44Z

2

0

peft

[ "peft", "dataset:HachiML/databricks-dolly-15k-ja-for-peft", "region:us" ]

null

2023-07-27T13:03:12Z

--- library_name: peft datasets: - HachiML/databricks-dolly-15k-ja-for-peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

PeterBrendan/llama-2-7b-Ads

PeterBrendan

2023-07-30T01:12:00Z

21

6

transformers

[ "transformers", "pytorch", "llama", "text-generation", "Advertising", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T17:59:15Z

--- license: mit language: - en tags: - Advertising widget: - text: Write me an online ad for Old Spice for a 300x250 creative --- ## llama-2-7b-Ads ### Model Overview The "llama-2-7b-Ads" model is a fine-tuned version of the "meta-llama/Llama-2-7b-chat-hf" language model. The base model, "meta-llama/Llama-2-7b-chat-hf," was trained on a vast corpus of text, enabling it to generate coherent and contextually relevant responses for various chat-based applications. The "PeterBrendan/llama-2-7b-Ads" model was fine-tuned using the "PeterBrendan/Ads_Creative_Ad_Copy_Programmatic" dataset. ### Dataset Overview The "PeterBrendan/Ads_Creative_Ad_Copy_Programmatic" dataset used for fine-tuning contains 7097 samples of online programmatic ad creatives, along with their respective ad sizes. The dataset includes 8 unique ad sizes, namely: 1. (300, 250) 2. (728, 90) 3. (970, 250) 4. (300, 600) 5. (160, 600) 6. (970, 90) 7. (336, 280) 8. (320, 50) This dataset is a random sample from Project300x250.com's complete creative data set. The primary application of this dataset is to train and evaluate natural language processing models specifically for advertising creatives. ### Use Cases The "llama-2-7b-Ads" model can be used in various natural language processing tasks related to advertising creatives. Some potential use cases include: 1. **Ad Creative Generation**: The model can generate ad copy text given different prompts, enabling advertisers to create compelling ad creatives automatically. 2. **Personalization**: By inputting user-specific data into a prompt, such as demographics or preferences, the model can generate personalized ad copy tailored to different ad sizes for targeted advertising. #### Example Prompts: **Example Prompt 1:** Write me an online ad for Old Spice for a 300x250 creative **Output:** OLD SPICE The Smell of a Man GET YOUR SMELL ON SHOP NOW **Example Prompt 2:** Write me an online ad for Nike Basketball Shoes for a 300x250 creative **Output:** Nike Basketball Shoes Shop Now Nike ### Performance and Limitations As this model is a fine-tuned version of "meta-llama/Llama-2-7b-chat-hf," it inherits its base model's performance characteristics and limitations. The quality of generated responses depends on the complexity and diversity of the input data during fine-tuning. **Performance:** The model generally performs well in generating coherent ad copy text based on the input ad sizes. However, the actual performance may vary depending on the complexity and creativity required for the given task. **Limitations:** 1. **Domain-Specific Bias**: The model's responses might be biased towards the content found in the "PeterBrendan/Ads_Creative_Ad_Copy_Programmatic" dataset, which primarily focuses on advertising creatives. 2. **Out-of-Domain Queries**: The model may not perform optimally when faced with queries or inputs unrelated to advertising creatives or the specified ad sizes. 3. **Limited Generalization**: Although fine-tuned, the model's generalization capabilities are still bounded by the data it was trained on. Extreme or out-of-distribution inputs may lead to inaccurate or nonsensical outputs. ### How to Use ~~~python # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PeterBrendan/llama-2-7b-Ads") # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PeterBrendan/llama-2-7b-Ads") model = AutoModelForCausalLM.from_pretrained("PeterBrendan/llama-2-7b-Ads") ~~~ ### Acknowledgments The "PeterBrendan/llama-2-7b-Ads" model was fine-tuned using the Hugging Face Transformers library and relies on the "meta-llama/Llama-2-7b-chat-hf" base model. We extend our gratitude to the creators of the base model for their contributions. ### Disclaimer The "PeterBrendan/llama-2-7b-Ads" model card provides an overview of the model and its use cases. However, it is essential to exercise caution and human review when deploying any AI model for critical applications like advertising. As with any AI system, the model's outputs should be thoroughly analyzed, especially in real-world scenarios, to ensure alignment with business objectives and ethical considerations.

Leaked/Latto

Leaked

2023-07-30T01:09:06Z

0

null

[ "arxiv:1910.09700", "license:openrail", "region:us" ]

null

2023-07-30T00:25:18Z

--- license: openrail --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

timjwhite/Reinforce-CartPole-v1

timjwhite

2023-07-30T00:48:16Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T23:39:54Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

NasimB/switchboard-rarity-seed

NasimB

2023-07-30T00:46:51Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-29T21:29:44Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: switchboard-rarity-seed results: [] ---  # switchboard-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0985 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3581 | 0.29 | 500 | 5.3466 | | 5.0332 | 0.58 | 1000 | 4.9336 | | 4.7065 | 0.87 | 1500 | 4.6924 | | 4.4439 | 1.17 | 2000 | 4.5465 | | 4.2929 | 1.46 | 2500 | 4.4328 | | 4.1869 | 1.75 | 3000 | 4.3248 | | 4.0802 | 2.04 | 3500 | 4.2481 | | 3.8877 | 2.33 | 4000 | 4.2060 | | 3.8547 | 2.62 | 4500 | 4.1542 | | 3.83 | 2.92 | 5000 | 4.0982 | | 3.6375 | 3.21 | 5500 | 4.0946 | | 3.5896 | 3.5 | 6000 | 4.0648 | | 3.5596 | 3.79 | 6500 | 4.0309 | | 3.474 | 4.08 | 7000 | 4.0282 | | 3.3101 | 4.37 | 7500 | 4.0247 | | 3.3055 | 4.66 | 8000 | 4.0122 | | 3.2891 | 4.96 | 8500 | 3.9981 | | 3.1562 | 5.25 | 9000 | 4.0102 | | 3.1289 | 5.54 | 9500 | 4.0093 | | 3.1216 | 5.83 | 10000 | 4.0085 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

anas-awadalla/mpt-1b-redpajama-200b-dolly

anas-awadalla

2023-07-30T00:23:55Z

3,301

0

transformers

[ "transformers", "pytorch", "mosaic_gpt", "text-generation", "custom_code", "dataset:togethercomputer/RedPajama-Data-1T", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2108.12409", "license:cc-by-sa-3.0", "autotrain_compatible", "region:us" ]

text-generation

2023-06-03T19:00:49Z

--- license: cc-by-sa-3.0 datasets: - togethercomputer/RedPajama-Data-1T --- # MPT-1b-RedPajama-200b-dolly MPT-1b-RedPajama-200b-dolly is a 1.3 billion parameter decoder-only transformer pre-trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and subsequently fine-tuned on the [Databricks Dolly](https://github.com/databrickslabs/dolly/tree/master/data) instruction dataset. The model was pre-trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971). This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. This model is an instruction fine-tuned version of [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). In other words, the pre-trained version of this model is [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). ## Model Date April 20, 2023 ## How to Use Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture `MosaicGPT` that is not yet part of the `transformers` package. `MosaicGPT` includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALIBI](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True) ``` To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so: ```python model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True, attn_impl='triton') model.to(device='cuda:0', dtype=torch.bfloat16) ``` ## Model Description This model uses the MosaicML LLM codebase, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm). The architecture is a modification of a standard decoder-only transformer. The transformer has 24 layers, 16 attention heads, and width 2048. The model has been modified from a standard transformer in the following ways: * It uses ALiBi and does not use positional embeddings. * It uses QK LayerNorm. * It does not use biases. ## Training Data ### Pre-Training The model was pre-trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix: * 67% RedPajama Common Crawl * 15% [C4](https://huggingface.co/datasets/c4) * 4.5% RedPajama GitHub * 4.5% RedPajama Wikipedia * 4.5% RedPajama Books * 2.5% RedPajama Arxiv * 2% RedPajama StackExchange This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971). Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above. The examples were shuffled within each dataset. Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ### Fine-Tuning We fine tuned this model on the [databricks-dolly-15k dataset](https://github.com/databrickslabs/dolly/tree/master/data) released by Databricks, following the same hyperparameters found in their [train_dolly.py](https://github.com/databrickslabs/dolly/blob/master/train_dolly.py) script. ## Training Configuration This model was pre-trained on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was pre-trained with sharded data parallelism using FSDP. ## Acknowledgements This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models. We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work. This model also builds on the work of [Databricks](https://www.databricks.com/), which created the Dolly instruction fine-tuning dataset. We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.

NasimB/bnc_spoken_aochildes_rarity-seed

NasimB

2023-07-30T00:05:01Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-29T20:10:25Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc_spoken_aochildes_rarity-seed results: [] ---  # bnc_spoken_aochildes_rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1487 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3667 | 0.29 | 500 | 5.3506 | | 5.0646 | 0.59 | 1000 | 4.9379 | | 4.7383 | 0.88 | 1500 | 4.7025 | | 4.478 | 1.18 | 2000 | 4.5619 | | 4.3265 | 1.47 | 2500 | 4.4462 | | 4.2316 | 1.77 | 3000 | 4.3544 | | 4.1042 | 2.06 | 3500 | 4.2884 | | 3.936 | 2.36 | 4000 | 4.2414 | | 3.9057 | 2.65 | 4500 | 4.1840 | | 3.8624 | 2.95 | 5000 | 4.1340 | | 3.6568 | 3.24 | 5500 | 4.1357 | | 3.622 | 3.54 | 6000 | 4.1047 | | 3.6008 | 3.83 | 6500 | 4.0736 | | 3.486 | 4.12 | 7000 | 4.0760 | | 3.3511 | 4.42 | 7500 | 4.0744 | | 3.3423 | 4.71 | 8000 | 4.0601 | | 3.332 | 5.01 | 8500 | 4.0531 | | 3.1709 | 5.3 | 9000 | 4.0641 | | 3.1645 | 5.6 | 9500 | 4.0631 | | 3.1651 | 5.89 | 10000 | 4.0630 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

RyanDA/MNIST-Model

RyanDA

2023-07-29T23:26:53Z

5

0

keras

[ "keras", "license:bigscience-openrail-m", "region:us" ]

null

2023-07-29T23:14:03Z

--- license: bigscience-openrail-m --- # MNIST Model This model was created using the full MNIST model dataset with no transforms applied (it's very easy to trick)

ld76/whisper-tiny-en

ld76

2023-07-29T23:25:17Z

86

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-29T21:27:26Z

--- language: - en license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: Whisper Tiny en - ld76 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.3616212792906903 ---  # Whisper Tiny en - ld76 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.8795 - Wer Ortho: 0.3577 - Wer: 0.3616 ## 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: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0006 | 17.54 | 500 | 0.7812 | 0.3419 | 0.3477 | | 0.0 | 35.09 | 1000 | 0.8306 | 0.3267 | 0.3319 | | 0.0 | 52.63 | 1500 | 0.8573 | 0.3570 | 0.3610 | | 0.0 | 70.18 | 2000 | 0.8795 | 0.3577 | 0.3616 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

sshalini6/medium-5e4-r16-a32-d0.1

sshalini6

2023-07-29T23:19:59Z

0

peft

[ "peft", "region:us" ]

null

2023-07-29T19:20:36Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

YaTharThShaRma999/Llama2-chat-kimiko-Sharded-2gb

YaTharThShaRma999

2023-07-29T23:17:10Z

6

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-29T19:32:00Z

--- license: other language: - en library_name: transformers tags: - text-generation-inference --- This is a model that uses kimiko lora but merges it with llama-2-chat 7b instead of the base llama-2 model. It performs pretty well and could be thought of as a uncensored llama-2-chat model. The prompt template is similar to the normal kimiko. I haven't tested out all possible prompts but this one works best for me. **The system prompt should just describe a character, not say something like act like a character.** An example system prompt is John is a buisnessman in paris. He is tired and in his home right now. ``` <<SYSTEM>> ##system prompt for the ai(where you would put personas as well) <<HUMAN>> ##Chat with the bot <<Character Name>> ``` To use with huggingface, just check out llama docs and would work since its the same architecture.

vladfatu/lunar-lander

vladfatu

2023-07-29T22:10:41Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T22:10:20Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.88 +/- 23.18 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Ahsankhan123/q-FrozenLake-v1-4x4-noSlippery

Ahsankhan123

2023-07-29T22:08:25Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T22:08:23Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Ahsankhan123/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

mgmeskill/ppo-SnowballTarget

mgmeskill

2023-07-29T22:04:40Z

2

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-07-29T22:04:37Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** 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: mgmeskill/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

0x05a4/DeepRL-DQN-SIv4

0x05a4

2023-07-29T21:52:36Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T21:52:01Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 650.00 +/- 136.11 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga 0x05a4 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga 0x05a4 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga 0x05a4 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 16), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 2000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

Jinmane/anyloracleanlinearmix_v10

Jinmane

2023-07-29T21:49:58Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-29T21:08:25Z

--- license: creativeml-openrail-m ---

VinEuro/a2c-AntBulletEnv-v0

VinEuro

2023-07-29T21:49:27Z

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T21:48:06Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 833.14 +/- 76.31 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Huggingfly/rl_course_vizdoom_health_gathering_supreme

Huggingfly

2023-07-29T21:40:58Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T20:34:00Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.38 +/- 4.93 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Huggingfly/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

Jinmane/calicomix_v60

Jinmane

2023-07-29T21:29:24Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-29T21:24:25Z

--- license: creativeml-openrail-m ---

froggoboom/xgen-7b-dolly-15k-4bit-final-2

froggoboom

2023-07-29T21:27:25Z

0

peft

[ "peft", "license:other", "region:us" ]

null

2023-07-29T21:27:04Z

--- license: other library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

apple/coreml-stable-diffusion-2-base-palettized

apple

2023-07-29T21:25:22Z

17

3

null

[ "coreml", "stable-diffusion", "text-to-image", "core-ml", "arxiv:2112.10752", "arxiv:2202.00512", "arxiv:1910.09700", "license:other", "region:us" ]

text-to-image

2023-06-14T09:41:50Z

--- license: other tags: - stable-diffusion - text-to-image - core-ml --- # Stable Diffusion v2 Model Card This model was generated by Hugging Face using [Apple’s repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md). This version contains 6-bit palettized Core ML weights for iOS 17 or macOS 14. To use weights without quantization, please visit [this model instead](https://huggingface.co/apple/coreml-stable-diffusion-2-base). This model card focuses on the model associated with the Stable Diffusion v2 model, available [here](https://github.com/Stability-AI/stablediffusion). The model is trained from scratch 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. Then it is further trained for 850k steps at resolution `512x512` on the same dataset on images with resolution `>= 512x512`. ![image](https://github.com/Stability-AI/stablediffusion/blob/main/assets/stable-samples/txt2img/merged-0003.png?raw=true) These weights here have been converted to Core ML for use on Apple Silicon hardware. There are 4 variants of the Core ML weights: ``` coreml-stable-diffusion-2-base ├── original │ ├── compiled # Swift inference, "original" attention │ └── packages # Python inference, "original" attention └── split_einsum ├── compiled # Swift inference, "split_einsum" attention └── packages # Python inference, "split_einsum" attention ``` There are also two zip archives suitable for use in the [Hugging Face demo app](https://github.com/huggingface/swift-coreml-diffusers) and other third party tools: - `coreml-stable-diffusion-2-base-palettized_original_compiled.zip` contains the compiled, 6-bit model with `ORIGINAL` attention implementation. - `coreml-stable-diffusion-2-base-palettized_split_einsum_v2_compiled.zip` contains the compiled, 6-bit model with `SPLIT_EINSUM_V2` attention implementation. Please, refer to https://huggingface.co/blog/diffusers-coreml for details. - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base#examples) - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints: ![pareto](model-variants.jpg) Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

azhang1212/angela_untranslated_entities_test

azhang1212

2023-07-29T21:23:13Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-29T20:03:18Z

--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: angela_untranslated_entities_test results: [] ---  # angela_untranslated_entities_test This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1681 - Precision: 0.4056 - Recall: 0.2198 - F1: 0.2851 - Accuracy: 0.9528 ## 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-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1724 | 1.0 | 1283 | 0.1654 | 0.4156 | 0.1148 | 0.1800 | 0.9550 | | 0.1583 | 2.0 | 2566 | 0.1551 | 0.4532 | 0.1307 | 0.2029 | 0.9558 | | 0.147 | 3.0 | 3849 | 0.1576 | 0.4250 | 0.2063 | 0.2778 | 0.9542 | | 0.1367 | 4.0 | 5132 | 0.1627 | 0.4105 | 0.2133 | 0.2807 | 0.9534 | | 0.1215 | 5.0 | 6415 | 0.1681 | 0.4056 | 0.2198 | 0.2851 | 0.9528 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

VinEuro/PyramidsTraining

VinEuro

2023-07-29T21:08:36Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-07-29T20:56:57Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** 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: VinEuro/PyramidsTraining 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

NasimB/simple_wikipedia-rarity-seed

NasimB

2023-07-29T21:05:57Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-29T17:12:01Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: simple_wikipedia-rarity-seed results: [] ---  # simple_wikipedia-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1526 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3353 | 0.29 | 500 | 5.3432 | | 5.0331 | 0.58 | 1000 | 4.9248 | | 4.7045 | 0.87 | 1500 | 4.6995 | | 4.4613 | 1.17 | 2000 | 4.5660 | | 4.3127 | 1.46 | 2500 | 4.4566 | | 4.2095 | 1.75 | 3000 | 4.3611 | | 4.1096 | 2.04 | 3500 | 4.3006 | | 3.9248 | 2.33 | 4000 | 4.2579 | | 3.8938 | 2.62 | 4500 | 4.2018 | | 3.8576 | 2.91 | 5000 | 4.1488 | | 3.6767 | 3.21 | 5500 | 4.1456 | | 3.6134 | 3.5 | 6000 | 4.1125 | | 3.5925 | 3.79 | 6500 | 4.0849 | | 3.5138 | 4.08 | 7000 | 4.0796 | | 3.3429 | 4.37 | 7500 | 4.0750 | | 3.3382 | 4.66 | 8000 | 4.0624 | | 3.328 | 4.95 | 8500 | 4.0507 | | 3.1885 | 5.24 | 9000 | 4.0623 | | 3.1615 | 5.54 | 9500 | 4.0621 | | 3.1534 | 5.83 | 10000 | 4.0617 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

athrado/bert-finetuned-nli

athrado

2023-07-29T20:43:39Z

8

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-11T14:30:23Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: athrado/bert-finetuned-nli results: [] ---  # athrado/bert-finetuned-nli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0641 - Train Accuracy: 0.9797 - Validation Loss: 0.4812 - Validation Accuracy: 0.8586 - Epoch: 4 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2775, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.5572 | 0.7599 | 0.4802 | 0.8020 | 0 | | 0.3324 | 0.8795 | 0.3869 | 0.8444 | 1 | | 0.2057 | 0.9272 | 0.3933 | 0.8646 | 2 | | 0.1212 | 0.9597 | 0.4413 | 0.8747 | 3 | | 0.0641 | 0.9797 | 0.4812 | 0.8586 | 4 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.0 - Datasets 2.14.1 - Tokenizers 0.13.3

VinEuro/ppo-SnowballTarget

VinEuro

2023-07-29T20:41:42Z

8

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-07-29T20:41:39Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** 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: VinEuro/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Pula23/Hggjg

Pula23

2023-07-29T20:36:46Z

0

adapter-transformers

[ "adapter-transformers", "conversational", "aa", "dataset:Open-Orca/OpenOrca", "license:openrail", "region:us" ]

text-generation

2023-07-29T20:32:45Z

--- license: openrail datasets: - Open-Orca/OpenOrca language: - aa metrics: - accuracy - bertscore library_name: adapter-transformers pipeline_tag: conversational ---

etetet/my_awesome_eli5_mlm_model

etetet

2023-07-29T20:34:10Z

178

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-29T20:07:20Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_mlm_model results: [] ---  # my_awesome_eli5_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9854 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2578 | 1.0 | 1145 | 2.0618 | | 2.1775 | 2.0 | 2290 | 2.0267 | | 2.1086 | 3.0 | 3435 | 2.0174 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

PhilSad/sdxl_phil_lora_attempts

PhilSad

2023-07-29T20:32:21Z

0

1

null

[ "region:us" ]

null

2023-07-29T03:01:31Z

This is a repo to store my attemps to find a quick training working for photorealist and stylised lora dreambooth currently ~15 min & 17 GB vram use "shs man" to trigger

gArthur98/Greg-DistilBert-classifier

gArthur98

2023-07-29T20:30:50Z

113

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-29T19:42:30Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: Greg-DistilBert-classifier results: [] ---  # Greg-DistilBert-classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7835 - F1: 0.7175 ## 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-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8486 | 0.5 | 500 | 0.9540 | 0.6137 | | 0.7857 | 1.0 | 1000 | 0.7877 | 0.6671 | | 0.7039 | 1.5 | 1500 | 0.7438 | 0.7035 | | 0.6526 | 2.01 | 2000 | 0.7396 | 0.7107 | | 0.5131 | 2.51 | 2500 | 0.7835 | 0.7175 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_2_beta_1

asenella

2023-07-29T20:28:26Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:28:07Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_2_beta_2.5

asenella

2023-07-29T20:27:39Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:27:21Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_3_beta_1

asenella

2023-07-29T20:27:10Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:26:22Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_3_beta_2.5

asenella

2023-07-29T20:27:02Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:26:35Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_0_beta_2.5

asenella

2023-07-29T20:26:56Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:26:24Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_0_beta_1

asenella

2023-07-29T20:22:36Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:22:17Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_3_beta_0.5

asenella

2023-07-29T20:22:24Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:22:05Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/whole_mmnist_JNFDccaconfig_resnet_seed_1_beta_1

asenella

2023-07-29T20:21:05Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:20:47Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

Sookeyy/q-Taxi-v3

Sookeyy

2023-07-29T20:18:49Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T20:18:48Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.42 +/- 2.75 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Sookeyy/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

asenella/whole_mmnist_JNFconfig_resnet_seed_2_beta_1

asenella

2023-07-29T20:06:20Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-29T20:06:00Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

rzambrano/rnd-PyramidsTraining

rzambrano

2023-07-29T20:01:55Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-07-29T20:01:49Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** 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: rzambrano/rnd-PyramidsTraining 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

RohaanKhanCentric/llama2-qlora-finetunined-Arabic

RohaanKhanCentric

2023-07-29T19:54:47Z

0

peft

[ "peft", "region:us" ]

null

2023-07-29T19:54:40Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

darthPanda/Reinforce-CartPole-v1

darthPanda

2023-07-29T19:52:54Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T19:08:40Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 269.29 +/- 125.27 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

JMullings/ts-xor-demo

JMullings

2023-07-29T19:43:43Z

0

null

[ "tabular-classification", "en", "license:mit", "region:us" ]

tabular-classification

2023-07-29T14:53:17Z

--- license: mit language: - en pipeline_tag: tabular-classification --- # XOR Training Exercise using TensorFlow.js In this example, we will use TensorFlow.js to train a neural network for the XOR (exclusive OR) problem. The XOR problem is a classic problem in machine learning where the model needs to learn a non-linear decision boundary to correctly classify inputs. ## Model Description The neural network used for this XOR problem consists of an input layer with two neurons, a hidden layer with two neurons, and an output layer with one neuron. The activation function used in the hidden layer is ReLU (Rectified Linear Unit), and the output layer uses the sigmoid activation function to produce values between 0 and 1. ## Dataset The XOR dataset consists of four samples, each with two features (inputs) and one label (output). The dataset is as follows: | Input 1 | Input 2 | Output | |:-------:|:-------:|:------:| | 0 | 0 | 0 | | 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 0 | ## Training Procedure The training is performed using TensorFlow.js with the following hyperparameters: - Learning Rate: 0.1 - Number of Epochs: 10000 - Loss Function: Mean Squared Error (MSE) - Optimizer: Stochastic Gradient Descent (SGD) ## Code Implementation Below is the code implementation for training the XOR neural network using TensorFlow.js: ```javascript // Import TensorFlow.js library import * as tf from '@tensorflow/tfjs-node-gpu'; this.model = await tf.loadLayersModel(`file://${this.model_path}/model.json`); this.model.compile({ optimizer: tf.train.sgd(0.1), loss: 'binaryCrossentropy', // Binary classification loss metrics: ['accuracy'], }); this.model.summary(); const x = tf.tensor2d([[1,1]]); const prediction = this.model.predict(x) as tf.Tensor; ``` ## Resulting Prediction The resulting predictions for the XOR dataset after training the model are as follows: ``` Tensor [[0.0020679], [0.9994502], [0.9994048], [0.0002599]] ``` ## Intended Uses & Limitations - The trained XOR neural network is suitable for solving the XOR problem and may not be suitable for more complex tasks without modifications. - TensorFlow.js allows running machine learning models directly in the browser, making it useful for web applications with client-side inference. ## Framework Versions Used - TensorFlow.js Please note that this example assumes you have set up your project with the necessary dependencies and have a basic understanding of JavaScript and TensorFlow.js.

froggoboom/results

froggoboom

2023-07-29T19:40:07Z

0

null

[ "generated_from_trainer", "region:us" ]

null

2023-07-29T19:36:30Z

--- tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3

aphi/a2c-AntBulletEnv-v0

aphi

2023-07-29T19:38:48Z

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T19:37:41Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2036.04 +/- 73.10 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Huggingfly/ppo-CartPole-v1

Huggingfly

2023-07-29T19:35:12Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T19:35:07Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -141.58 +/- 106.24 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Huggingfly/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```

VicBeltran/ppo-LunarLander-v2

VicBeltran

2023-07-29T19:23:19Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T18:53:13Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 273.36 +/- 21.98 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

cgr28/rl_course_vizdoom_health_gathering_supreme

cgr28

2023-07-29T19:15:54Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-27T04:42:25Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.25 +/- 3.57 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r cgr28/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

digiplay/KawaiiRealisticAnimeMix_A0.3

digiplay

2023-07-29T19:14:25Z

328

3

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-29T18:46:32Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/104100?modelVersionId=128610 Sample image and prompt : 1girl, anime key visual, outdoor,vibrant color,very close-up,tiny smile ![b916088a-20fc-4c50-a7f0-e35defe59834.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/EPWfFfFVsFFlBIvIkVszU.jpeg)

rzambrano/ppo-SnowballTarget

rzambrano

2023-07-29T19:11:34Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-07-29T19:09:45Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** 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: rzambrano/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ShekDass/donut-base-cord-smart-86

ShekDass

2023-07-29T19:10:42Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base-finetuned-cord-v2", "base_model:finetune:naver-clova-ix/donut-base-finetuned-cord-v2", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-07-29T17:37:11Z

--- license: mit base_model: naver-clova-ix/donut-base-finetuned-cord-v2 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-cord-smart-86 results: [] ---  # donut-base-cord-smart-86 This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

maije/llama2-qlora-finetuned-cm-repo-create-7.17-loss

maije

2023-07-29T18:52:31Z

0

peft

[ "peft", "region:us" ]

null

2023-07-29T18:52:13Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

wilson-wei/wav2vec2-base-finetuned-gtzan

wilson-wei

2023-07-29T18:47:34Z

161

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-29T14:52:44Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: train split: train args: train metrics: - name: Accuracy type: accuracy value: 0.84 ---  # wav2vec2-base-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.8879 - Accuracy: 0.84 ## 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-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 17 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9838 | 1.0 | 113 | 1.8627 | 0.37 | | 1.6128 | 2.0 | 226 | 1.5998 | 0.48 | | 1.0259 | 3.0 | 339 | 1.3821 | 0.57 | | 1.2766 | 4.0 | 452 | 1.1708 | 0.66 | | 0.6014 | 5.0 | 565 | 0.7257 | 0.77 | | 0.5815 | 6.0 | 678 | 1.0738 | 0.68 | | 0.7664 | 7.0 | 791 | 0.7244 | 0.8 | | 0.2303 | 8.0 | 904 | 0.5838 | 0.84 | | 0.4829 | 9.0 | 1017 | 0.5741 | 0.87 | | 0.0859 | 10.0 | 1130 | 0.6199 | 0.83 | | 0.2983 | 11.0 | 1243 | 0.8117 | 0.84 | | 0.0642 | 12.0 | 1356 | 0.5938 | 0.88 | | 0.0688 | 13.0 | 1469 | 0.9978 | 0.84 | | 0.1542 | 14.0 | 1582 | 0.7437 | 0.85 | | 0.0117 | 15.0 | 1695 | 0.9100 | 0.84 | | 0.039 | 16.0 | 1808 | 0.7757 | 0.85 | | 0.0661 | 17.0 | 1921 | 0.8879 | 0.84 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.0 - Tokenizers 0.13.3

mohamedemam/bert_sentaces_similarty

mohamedemam

2023-07-29T18:35:28Z

105

0

transformers

[ "transformers", "pytorch", "resnet", "sentence-similarity", "en", "dataset:AlekseyKorshuk/quora-question-pairs", "license:mit", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-07-29T17:47:12Z

--- license: mit datasets: - AlekseyKorshuk/quora-question-pairs language: - en metrics: - accuracy pipeline_tag: sentence-similarity ---

chaimag/ko-llama-7B

chaimag

2023-07-29T18:33:06Z

6

0

peft

[ "peft", "region:us" ]

null

2023-07-29T15:49:00Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

atari713/taxi-v3

atari713

2023-07-29T18:29:07Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T18:24:38Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="atari713/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

minatosnow/swinv2-tiny-patch4-window8-256-mineral

minatosnow

2023-07-29T18:25:36Z

147

0

transformers

[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-tiny-patch4-window8-256", "base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-29T18:24:58Z

--- license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window8-256 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-tiny-patch4-window8-256-mineral results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.24666666666666667 ---  # swinv2-tiny-patch4-window8-256-mineral This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 5.2045 - Accuracy: 0.2467 ## 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-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 5.692 | 0.96 | 18 | 5.6904 | 0.0017 | | 5.6759 | 1.97 | 37 | 5.6829 | 0.0017 | | 5.6699 | 2.99 | 56 | 5.6722 | 0.0033 | | 5.6484 | 4.0 | 75 | 5.6601 | 0.0033 | | 5.6355 | 4.96 | 93 | 5.6486 | 0.005 | | 5.6088 | 5.97 | 112 | 5.6375 | 0.0067 | | 5.5941 | 6.99 | 131 | 5.6268 | 0.0083 | | 5.5636 | 8.0 | 150 | 5.6131 | 0.0117 | | 5.5499 | 8.96 | 168 | 5.5987 | 0.0117 | | 5.5038 | 9.97 | 187 | 5.5798 | 0.0083 | | 5.437 | 10.99 | 206 | 5.5757 | 0.0133 | | 5.3642 | 12.0 | 225 | 5.5482 | 0.0133 | | 5.2877 | 12.96 | 243 | 5.5107 | 0.0183 | | 5.1992 | 13.97 | 262 | 5.4744 | 0.025 | | 5.0956 | 14.99 | 281 | 5.4006 | 0.0333 | | 4.9566 | 16.0 | 300 | 5.3490 | 0.0433 | | 4.8827 | 16.96 | 318 | 5.2856 | 0.0517 | | 4.7089 | 17.97 | 337 | 5.1565 | 0.075 | | 4.5187 | 18.99 | 356 | 5.0459 | 0.0817 | | 4.4164 | 20.0 | 375 | 4.9614 | 0.0983 | | 4.2402 | 20.96 | 393 | 4.8814 | 0.0983 | | 4.0046 | 21.97 | 412 | 4.7763 | 0.1167 | | 3.8336 | 22.99 | 431 | 4.6775 | 0.125 | | 3.6379 | 24.0 | 450 | 4.6089 | 0.14 | | 3.4647 | 24.96 | 468 | 4.5512 | 0.1433 | | 3.3663 | 25.97 | 487 | 4.4674 | 0.1533 | | 3.1445 | 26.99 | 506 | 4.4247 | 0.15 | | 3.0248 | 28.0 | 525 | 4.3519 | 0.1633 | | 2.8415 | 28.96 | 543 | 4.3256 | 0.1633 | | 2.6664 | 29.97 | 562 | 4.3015 | 0.18 | | 2.5225 | 30.99 | 581 | 4.2571 | 0.1867 | | 2.4433 | 32.0 | 600 | 4.2507 | 0.1833 | | 2.3169 | 32.96 | 618 | 4.2097 | 0.1933 | | 2.145 | 33.97 | 637 | 4.1741 | 0.1883 | | 2.0045 | 34.99 | 656 | 4.1681 | 0.1967 | | 1.9272 | 36.0 | 675 | 4.1376 | 0.2083 | | 1.7761 | 36.96 | 693 | 4.1391 | 0.1933 | | 1.7038 | 37.97 | 712 | 4.0957 | 0.205 | | 1.5902 | 38.99 | 731 | 4.1309 | 0.2067 | | 1.4921 | 40.0 | 750 | 4.1324 | 0.2033 | | 1.3552 | 40.96 | 768 | 4.1510 | 0.2067 | | 1.2597 | 41.97 | 787 | 4.1636 | 0.205 | | 1.2909 | 42.99 | 806 | 4.1422 | 0.2267 | | 1.1661 | 44.0 | 825 | 4.1553 | 0.21 | | 1.1031 | 44.96 | 843 | 4.1976 | 0.2167 | | 1.0203 | 45.97 | 862 | 4.1673 | 0.2183 | | 0.9883 | 46.99 | 881 | 4.1628 | 0.2167 | | 0.976 | 48.0 | 900 | 4.1852 | 0.2267 | | 0.8848 | 48.96 | 918 | 4.2108 | 0.2167 | | 0.8616 | 49.97 | 937 | 4.2583 | 0.2133 | | 0.8079 | 50.99 | 956 | 4.1821 | 0.23 | | 0.8256 | 52.0 | 975 | 4.2021 | 0.2217 | | 0.7439 | 52.96 | 993 | 4.2577 | 0.2167 | | 0.6863 | 53.97 | 1012 | 4.2398 | 0.225 | | 0.715 | 54.99 | 1031 | 4.2934 | 0.21 | | 0.6924 | 56.0 | 1050 | 4.2675 | 0.225 | | 0.6454 | 56.96 | 1068 | 4.2916 | 0.2117 | | 0.5688 | 57.97 | 1087 | 4.3216 | 0.2217 | | 0.5958 | 58.99 | 1106 | 4.3202 | 0.2133 | | 0.5834 | 60.0 | 1125 | 4.3022 | 0.2267 | | 0.5583 | 60.96 | 1143 | 4.3746 | 0.225 | | 0.565 | 61.97 | 1162 | 4.3334 | 0.215 | | 0.55 | 62.99 | 1181 | 4.3330 | 0.2217 | | 0.4972 | 64.0 | 1200 | 4.3866 | 0.2183 | | 0.4895 | 64.96 | 1218 | 4.3797 | 0.2267 | | 0.4537 | 65.97 | 1237 | 4.3761 | 0.2267 | | 0.4941 | 66.99 | 1256 | 4.4122 | 0.2317 | | 0.4422 | 68.0 | 1275 | 4.4636 | 0.2217 | | 0.3731 | 68.96 | 1293 | 4.4118 | 0.2217 | | 0.4338 | 69.97 | 1312 | 4.4002 | 0.2217 | | 0.4482 | 70.99 | 1331 | 4.4558 | 0.2233 | | 0.4206 | 72.0 | 1350 | 4.4238 | 0.23 | | 0.4009 | 72.96 | 1368 | 4.4717 | 0.2267 | | 0.4297 | 73.97 | 1387 | 4.5047 | 0.23 | | 0.4031 | 74.99 | 1406 | 4.4637 | 0.2233 | | 0.3489 | 76.0 | 1425 | 4.4878 | 0.2183 | | 0.4202 | 76.96 | 1443 | 4.4785 | 0.23 | | 0.3307 | 77.97 | 1462 | 4.4647 | 0.2267 | | 0.3919 | 78.99 | 1481 | 4.5011 | 0.2233 | | 0.3511 | 80.0 | 1500 | 4.4895 | 0.2367 | | 0.334 | 80.96 | 1518 | 4.4878 | 0.2283 | | 0.3958 | 81.97 | 1537 | 4.5025 | 0.2367 | | 0.316 | 82.99 | 1556 | 4.5792 | 0.2317 | | 0.3135 | 84.0 | 1575 | 4.4894 | 0.2333 | | 0.3292 | 84.96 | 1593 | 4.5495 | 0.235 | | 0.3116 | 85.97 | 1612 | 4.5069 | 0.2317 | | 0.3162 | 86.99 | 1631 | 4.5407 | 0.23 | | 0.3173 | 88.0 | 1650 | 4.5997 | 0.2217 | | 0.2857 | 88.96 | 1668 | 4.5476 | 0.2283 | | 0.2869 | 89.97 | 1687 | 4.5655 | 0.2217 | | 0.299 | 90.99 | 1706 | 4.5743 | 0.24 | | 0.3142 | 92.0 | 1725 | 4.5478 | 0.23 | | 0.3066 | 92.96 | 1743 | 4.5509 | 0.2367 | | 0.2542 | 93.97 | 1762 | 4.5452 | 0.225 | | 0.2707 | 94.99 | 1781 | 4.5017 | 0.225 | | 0.2781 | 96.0 | 1800 | 4.5425 | 0.23 | | 0.2611 | 96.96 | 1818 | 4.6214 | 0.2233 | | 0.2816 | 97.97 | 1837 | 4.5780 | 0.2233 | | 0.2698 | 98.99 | 1856 | 4.5237 | 0.225 | | 0.2568 | 100.0 | 1875 | 4.6262 | 0.2167 | | 0.2925 | 100.96 | 1893 | 4.6138 | 0.2133 | | 0.2542 | 101.97 | 1912 | 4.6329 | 0.22 | | 0.2276 | 102.99 | 1931 | 4.5854 | 0.23 | | 0.2701 | 104.0 | 1950 | 4.5662 | 0.2167 | | 0.2492 | 104.96 | 1968 | 4.6098 | 0.225 | | 0.2394 | 105.97 | 1987 | 4.6215 | 0.2333 | | 0.2409 | 106.99 | 2006 | 4.6277 | 0.2317 | | 0.2578 | 108.0 | 2025 | 4.6173 | 0.2267 | | 0.229 | 108.96 | 2043 | 4.6405 | 0.2283 | | 0.2438 | 109.97 | 2062 | 4.6041 | 0.2233 | | 0.2441 | 110.99 | 2081 | 4.6367 | 0.23 | | 0.2353 | 112.0 | 2100 | 4.5877 | 0.2467 | | 0.1876 | 112.96 | 2118 | 4.6518 | 0.2333 | | 0.1996 | 113.97 | 2137 | 4.6810 | 0.2367 | | 0.1908 | 114.99 | 2156 | 4.6483 | 0.245 | | 0.1905 | 116.0 | 2175 | 4.6117 | 0.22 | | 0.1995 | 116.96 | 2193 | 4.6283 | 0.2183 | | 0.2044 | 117.97 | 2212 | 4.6130 | 0.2283 | | 0.2156 | 118.99 | 2231 | 4.6729 | 0.2167 | | 0.2003 | 120.0 | 2250 | 4.6124 | 0.24 | | 0.1861 | 120.96 | 2268 | 4.6839 | 0.23 | | 0.2072 | 121.97 | 2287 | 4.7217 | 0.2267 | | 0.1973 | 122.99 | 2306 | 4.7596 | 0.23 | | 0.2191 | 124.0 | 2325 | 4.7394 | 0.2283 | | 0.1738 | 124.96 | 2343 | 4.7356 | 0.23 | | 0.1669 | 125.97 | 2362 | 4.7210 | 0.235 | | 0.1971 | 126.99 | 2381 | 4.6826 | 0.23 | | 0.1972 | 128.0 | 2400 | 4.7256 | 0.2233 | | 0.1794 | 128.96 | 2418 | 4.6589 | 0.235 | | 0.1894 | 129.97 | 2437 | 4.7391 | 0.2317 | | 0.1854 | 130.99 | 2456 | 4.7441 | 0.23 | | 0.1551 | 132.0 | 2475 | 4.7559 | 0.2233 | | 0.179 | 132.96 | 2493 | 4.7555 | 0.235 | | 0.2235 | 133.97 | 2512 | 4.7686 | 0.2183 | | 0.179 | 134.99 | 2531 | 4.7334 | 0.2283 | | 0.1595 | 136.0 | 2550 | 4.7324 | 0.2267 | | 0.1598 | 136.96 | 2568 | 4.7099 | 0.245 | | 0.178 | 137.97 | 2587 | 4.7363 | 0.2317 | | 0.1976 | 138.99 | 2606 | 4.7806 | 0.2217 | | 0.153 | 140.0 | 2625 | 4.8128 | 0.2267 | | 0.1946 | 140.96 | 2643 | 4.7925 | 0.2233 | | 0.1597 | 141.97 | 2662 | 4.8279 | 0.2117 | | 0.1515 | 142.99 | 2681 | 4.8203 | 0.2233 | | 0.2013 | 144.0 | 2700 | 4.7393 | 0.23 | | 0.1431 | 144.96 | 2718 | 4.7866 | 0.2367 | | 0.1864 | 145.97 | 2737 | 4.7730 | 0.2317 | | 0.1676 | 146.99 | 2756 | 4.8112 | 0.2333 | | 0.1502 | 148.0 | 2775 | 4.8374 | 0.215 | | 0.1584 | 148.96 | 2793 | 4.8600 | 0.2317 | | 0.1901 | 149.97 | 2812 | 4.8647 | 0.2167 | | 0.1347 | 150.99 | 2831 | 4.8073 | 0.215 | | 0.1859 | 152.0 | 2850 | 4.8766 | 0.2283 | | 0.1483 | 152.96 | 2868 | 4.8392 | 0.23 | | 0.149 | 153.97 | 2887 | 4.8734 | 0.215 | | 0.1411 | 154.99 | 2906 | 4.9380 | 0.2083 | | 0.1589 | 156.0 | 2925 | 4.8365 | 0.2367 | | 0.1608 | 156.96 | 2943 | 4.8053 | 0.23 | | 0.1759 | 157.97 | 2962 | 4.8690 | 0.2383 | | 0.142 | 158.99 | 2981 | 4.8490 | 0.23 | | 0.1433 | 160.0 | 3000 | 4.8658 | 0.2267 | | 0.1407 | 160.96 | 3018 | 4.8936 | 0.2267 | | 0.1442 | 161.97 | 3037 | 4.8534 | 0.235 | | 0.1669 | 162.99 | 3056 | 4.9050 | 0.2317 | | 0.1384 | 164.0 | 3075 | 4.8717 | 0.235 | | 0.1366 | 164.96 | 3093 | 4.8951 | 0.2317 | | 0.1428 | 165.97 | 3112 | 4.9253 | 0.225 | | 0.1376 | 166.99 | 3131 | 4.9108 | 0.22 | | 0.1238 | 168.0 | 3150 | 4.8965 | 0.2267 | | 0.1129 | 168.96 | 3168 | 4.8446 | 0.2317 | | 0.1296 | 169.97 | 3187 | 4.9100 | 0.2217 | | 0.1383 | 170.99 | 3206 | 4.8886 | 0.2333 | | 0.1511 | 172.0 | 3225 | 4.8883 | 0.235 | | 0.1426 | 172.96 | 3243 | 4.8783 | 0.2333 | | 0.1112 | 173.97 | 3262 | 4.9305 | 0.225 | | 0.1456 | 174.99 | 3281 | 4.8354 | 0.2417 | | 0.1343 | 176.0 | 3300 | 4.8553 | 0.225 | | 0.1133 | 176.96 | 3318 | 4.8739 | 0.2317 | | 0.1213 | 177.97 | 3337 | 4.8865 | 0.2333 | | 0.1309 | 178.99 | 3356 | 4.9231 | 0.22 | | 0.1197 | 180.0 | 3375 | 4.8976 | 0.2383 | | 0.1619 | 180.96 | 3393 | 4.8812 | 0.2383 | | 0.1254 | 181.97 | 3412 | 4.8260 | 0.225 | | 0.0934 | 182.99 | 3431 | 4.8645 | 0.2283 | | 0.1156 | 184.0 | 3450 | 4.8253 | 0.24 | | 0.1008 | 184.96 | 3468 | 4.8692 | 0.2467 | | 0.1273 | 185.97 | 3487 | 4.9049 | 0.24 | | 0.1352 | 186.99 | 3506 | 4.8660 | 0.2333 | | 0.1411 | 188.0 | 3525 | 4.8252 | 0.2333 | | 0.124 | 188.96 | 3543 | 4.8715 | 0.2317 | | 0.1121 | 189.97 | 3562 | 4.8768 | 0.24 | | 0.1337 | 190.99 | 3581 | 4.9004 | 0.2433 | | 0.106 | 192.0 | 3600 | 4.8300 | 0.2333 | | 0.1044 | 192.96 | 3618 | 4.7903 | 0.25 | | 0.1259 | 193.97 | 3637 | 4.8227 | 0.2267 | | 0.1182 | 194.99 | 3656 | 4.8286 | 0.2283 | | 0.1269 | 196.0 | 3675 | 4.8475 | 0.2467 | | 0.1304 | 196.96 | 3693 | 4.8759 | 0.2367 | | 0.1086 | 197.97 | 3712 | 4.8773 | 0.2283 | | 0.1184 | 198.99 | 3731 | 4.8875 | 0.2317 | | 0.114 | 200.0 | 3750 | 4.9083 | 0.2283 | | 0.1028 | 200.96 | 3768 | 4.8888 | 0.2283 | | 0.1355 | 201.97 | 3787 | 4.8358 | 0.23 | | 0.1038 | 202.99 | 3806 | 4.8567 | 0.23 | | 0.1122 | 204.0 | 3825 | 4.8578 | 0.2317 | | 0.0864 | 204.96 | 3843 | 4.9288 | 0.2333 | | 0.0969 | 205.97 | 3862 | 4.8940 | 0.2567 | | 0.0986 | 206.99 | 3881 | 4.8632 | 0.2483 | | 0.1291 | 208.0 | 3900 | 4.9114 | 0.2433 | | 0.1244 | 208.96 | 3918 | 4.9076 | 0.2483 | | 0.1112 | 209.97 | 3937 | 4.8954 | 0.245 | | 0.1032 | 210.99 | 3956 | 4.8885 | 0.2433 | | 0.0919 | 212.0 | 3975 | 4.9197 | 0.2433 | | 0.1153 | 212.96 | 3993 | 4.9020 | 0.25 | | 0.0978 | 213.97 | 4012 | 4.9312 | 0.2433 | | 0.1316 | 214.99 | 4031 | 4.9504 | 0.2417 | | 0.1222 | 216.0 | 4050 | 4.9403 | 0.2367 | | 0.1108 | 216.96 | 4068 | 4.8719 | 0.2483 | | 0.0996 | 217.97 | 4087 | 4.8812 | 0.24 | | 0.0907 | 218.99 | 4106 | 4.9203 | 0.25 | | 0.0974 | 220.0 | 4125 | 4.9728 | 0.2417 | | 0.1127 | 220.96 | 4143 | 4.9708 | 0.2467 | | 0.0891 | 221.97 | 4162 | 5.0154 | 0.2517 | | 0.0973 | 222.99 | 4181 | 4.9533 | 0.24 | | 0.0912 | 224.0 | 4200 | 4.9306 | 0.2333 | | 0.0971 | 224.96 | 4218 | 4.9873 | 0.2283 | | 0.1069 | 225.97 | 4237 | 4.9154 | 0.2433 | | 0.1163 | 226.99 | 4256 | 4.9178 | 0.2367 | | 0.1139 | 228.0 | 4275 | 4.9395 | 0.2383 | | 0.0816 | 228.96 | 4293 | 4.8986 | 0.2333 | | 0.0899 | 229.97 | 4312 | 4.9542 | 0.2517 | | 0.0984 | 230.99 | 4331 | 4.9305 | 0.2433 | | 0.0732 | 232.0 | 4350 | 4.9264 | 0.2417 | | 0.0947 | 232.96 | 4368 | 4.9676 | 0.235 | | 0.0826 | 233.97 | 4387 | 5.0179 | 0.235 | | 0.086 | 234.99 | 4406 | 4.9719 | 0.2283 | | 0.1055 | 236.0 | 4425 | 4.9457 | 0.235 | | 0.0872 | 236.96 | 4443 | 4.9355 | 0.2533 | | 0.0826 | 237.97 | 4462 | 4.9467 | 0.245 | | 0.081 | 238.99 | 4481 | 5.0036 | 0.245 | | 0.0746 | 240.0 | 4500 | 5.0125 | 0.245 | | 0.0725 | 240.96 | 4518 | 5.0061 | 0.2367 | | 0.1104 | 241.97 | 4537 | 5.0150 | 0.2483 | | 0.0885 | 242.99 | 4556 | 5.0063 | 0.2333 | | 0.0847 | 244.0 | 4575 | 4.9919 | 0.2383 | | 0.0751 | 244.96 | 4593 | 4.9881 | 0.2367 | | 0.0872 | 245.97 | 4612 | 5.0013 | 0.2383 | | 0.0771 | 246.99 | 4631 | 5.0273 | 0.235 | | 0.0941 | 248.0 | 4650 | 5.0507 | 0.2317 | | 0.116 | 248.96 | 4668 | 5.0491 | 0.245 | | 0.0733 | 249.97 | 4687 | 5.0391 | 0.24 | | 0.0821 | 250.99 | 4706 | 5.0231 | 0.235 | | 0.075 | 252.0 | 4725 | 5.0388 | 0.2317 | | 0.0885 | 252.96 | 4743 | 4.9838 | 0.2383 | | 0.0759 | 253.97 | 4762 | 4.9536 | 0.2433 | | 0.0773 | 254.99 | 4781 | 5.0145 | 0.2383 | | 0.0707 | 256.0 | 4800 | 5.0352 | 0.2217 | | 0.0861 | 256.96 | 4818 | 5.0295 | 0.2483 | | 0.0739 | 257.97 | 4837 | 5.0354 | 0.2433 | | 0.0822 | 258.99 | 4856 | 5.0563 | 0.245 | | 0.0681 | 260.0 | 4875 | 5.0407 | 0.245 | | 0.0872 | 260.96 | 4893 | 5.0511 | 0.2367 | | 0.079 | 261.97 | 4912 | 5.1087 | 0.23 | | 0.0733 | 262.99 | 4931 | 5.0523 | 0.225 | | 0.097 | 264.0 | 4950 | 5.0368 | 0.2317 | | 0.0669 | 264.96 | 4968 | 5.0501 | 0.2283 | | 0.0801 | 265.97 | 4987 | 5.0515 | 0.235 | | 0.0894 | 266.99 | 5006 | 5.0182 | 0.245 | | 0.0815 | 268.0 | 5025 | 5.0713 | 0.23 | | 0.0934 | 268.96 | 5043 | 5.0082 | 0.2417 | | 0.0728 | 269.97 | 5062 | 5.0473 | 0.23 | | 0.088 | 270.99 | 5081 | 5.0689 | 0.2267 | | 0.0706 | 272.0 | 5100 | 5.0403 | 0.2383 | | 0.0931 | 272.96 | 5118 | 5.0298 | 0.235 | | 0.0784 | 273.97 | 5137 | 5.0141 | 0.2367 | | 0.0831 | 274.99 | 5156 | 5.0314 | 0.2417 | | 0.0624 | 276.0 | 5175 | 5.0445 | 0.2383 | | 0.0819 | 276.96 | 5193 | 5.0632 | 0.2517 | | 0.0714 | 277.97 | 5212 | 5.0520 | 0.255 | | 0.0893 | 278.99 | 5231 | 5.0075 | 0.2533 | | 0.0777 | 280.0 | 5250 | 5.0122 | 0.24 | | 0.0686 | 280.96 | 5268 | 5.0477 | 0.2333 | | 0.0849 | 281.97 | 5287 | 5.0238 | 0.2433 | | 0.0969 | 282.99 | 5306 | 5.0061 | 0.2383 | | 0.0906 | 284.0 | 5325 | 5.0771 | 0.2517 | | 0.0843 | 284.96 | 5343 | 5.0882 | 0.2417 | | 0.0538 | 285.97 | 5362 | 5.0800 | 0.2467 | | 0.0678 | 286.99 | 5381 | 5.0976 | 0.2367 | | 0.0729 | 288.0 | 5400 | 5.0817 | 0.2333 | | 0.0922 | 288.96 | 5418 | 5.0955 | 0.2433 | | 0.0684 | 289.97 | 5437 | 5.0873 | 0.2467 | | 0.082 | 290.99 | 5456 | 5.1424 | 0.2383 | | 0.0798 | 292.0 | 5475 | 5.0631 | 0.2433 | | 0.0781 | 292.96 | 5493 | 5.0474 | 0.2517 | | 0.0857 | 293.97 | 5512 | 5.0502 | 0.2417 | | 0.0733 | 294.99 | 5531 | 5.0599 | 0.235 | | 0.072 | 296.0 | 5550 | 5.0549 | 0.2433 | | 0.101 | 296.96 | 5568 | 5.0783 | 0.24 | | 0.0982 | 297.97 | 5587 | 5.1048 | 0.24 | | 0.0782 | 298.99 | 5606 | 5.1096 | 0.235 | | 0.0654 | 300.0 | 5625 | 5.1158 | 0.2333 | | 0.0761 | 300.96 | 5643 | 5.0416 | 0.2383 | | 0.079 | 301.97 | 5662 | 5.1202 | 0.235 | | 0.0845 | 302.99 | 5681 | 5.1711 | 0.2333 | | 0.0718 | 304.0 | 5700 | 5.1572 | 0.2383 | | 0.0675 | 304.96 | 5718 | 5.1230 | 0.235 | | 0.0683 | 305.97 | 5737 | 5.1464 | 0.23 | | 0.0899 | 306.99 | 5756 | 5.0815 | 0.24 | | 0.0692 | 308.0 | 5775 | 5.0867 | 0.2367 | | 0.0843 | 308.96 | 5793 | 5.1006 | 0.2283 | | 0.0742 | 309.97 | 5812 | 5.0545 | 0.2317 | | 0.0689 | 310.99 | 5831 | 5.0509 | 0.2317 | | 0.0714 | 312.0 | 5850 | 5.0536 | 0.2367 | | 0.0669 | 312.96 | 5868 | 5.0488 | 0.2433 | | 0.0726 | 313.97 | 5887 | 5.0903 | 0.2433 | | 0.0509 | 314.99 | 5906 | 5.1158 | 0.2433 | | 0.062 | 316.0 | 5925 | 5.0651 | 0.2417 | | 0.0528 | 316.96 | 5943 | 5.0545 | 0.2467 | | 0.0627 | 317.97 | 5962 | 5.0730 | 0.2417 | | 0.0678 | 318.99 | 5981 | 5.0354 | 0.24 | | 0.0603 | 320.0 | 6000 | 5.0496 | 0.2483 | | 0.0703 | 320.96 | 6018 | 5.0788 | 0.2367 | | 0.0797 | 321.97 | 6037 | 5.1071 | 0.24 | | 0.0914 | 322.99 | 6056 | 5.0996 | 0.24 | | 0.0688 | 324.0 | 6075 | 5.0954 | 0.24 | | 0.0591 | 324.96 | 6093 | 5.1014 | 0.245 | | 0.0622 | 325.97 | 6112 | 5.0859 | 0.24 | | 0.0715 | 326.99 | 6131 | 5.0557 | 0.2533 | | 0.0483 | 328.0 | 6150 | 5.1148 | 0.2383 | | 0.0922 | 328.96 | 6168 | 5.1338 | 0.24 | | 0.0588 | 329.97 | 6187 | 5.1553 | 0.245 | | 0.0615 | 330.99 | 6206 | 5.1083 | 0.2483 | | 0.0508 | 332.0 | 6225 | 5.1167 | 0.245 | | 0.0691 | 332.96 | 6243 | 5.1116 | 0.2433 | | 0.0684 | 333.97 | 6262 | 5.1211 | 0.2417 | | 0.0519 | 334.99 | 6281 | 5.1481 | 0.2433 | | 0.0571 | 336.0 | 6300 | 5.1662 | 0.245 | | 0.038 | 336.96 | 6318 | 5.1616 | 0.245 | | 0.0589 | 337.97 | 6337 | 5.1507 | 0.2483 | | 0.0609 | 338.99 | 6356 | 5.1083 | 0.245 | | 0.0484 | 340.0 | 6375 | 5.1392 | 0.2417 | | 0.0842 | 340.96 | 6393 | 5.1476 | 0.2483 | | 0.0569 | 341.97 | 6412 | 5.1547 | 0.245 | | 0.0626 | 342.99 | 6431 | 5.1824 | 0.2383 | | 0.0399 | 344.0 | 6450 | 5.1972 | 0.2417 | | 0.0803 | 344.96 | 6468 | 5.1678 | 0.2433 | | 0.0533 | 345.97 | 6487 | 5.1815 | 0.2417 | | 0.0542 | 346.99 | 6506 | 5.1768 | 0.2383 | | 0.0624 | 348.0 | 6525 | 5.1759 | 0.2333 | | 0.055 | 348.96 | 6543 | 5.1954 | 0.2433 | | 0.0678 | 349.97 | 6562 | 5.1478 | 0.2417 | | 0.0557 | 350.99 | 6581 | 5.1236 | 0.24 | | 0.0581 | 352.0 | 6600 | 5.1462 | 0.245 | | 0.0509 | 352.96 | 6618 | 5.1428 | 0.235 | | 0.0636 | 353.97 | 6637 | 5.1659 | 0.24 | | 0.0671 | 354.99 | 6656 | 5.1583 | 0.24 | | 0.0768 | 356.0 | 6675 | 5.1143 | 0.2283 | | 0.0719 | 356.96 | 6693 | 5.1047 | 0.235 | | 0.0653 | 357.97 | 6712 | 5.1262 | 0.2467 | | 0.0646 | 358.99 | 6731 | 5.1456 | 0.245 | | 0.0466 | 360.0 | 6750 | 5.1514 | 0.2517 | | 0.0447 | 360.96 | 6768 | 5.1479 | 0.24 | | 0.0671 | 361.97 | 6787 | 5.1508 | 0.2383 | | 0.0618 | 362.99 | 6806 | 5.1520 | 0.2367 | | 0.0559 | 364.0 | 6825 | 5.1495 | 0.2367 | | 0.0745 | 364.96 | 6843 | 5.1606 | 0.235 | | 0.0542 | 365.97 | 6862 | 5.1888 | 0.235 | | 0.0737 | 366.99 | 6881 | 5.2100 | 0.2417 | | 0.0524 | 368.0 | 6900 | 5.2309 | 0.2317 | | 0.0548 | 368.96 | 6918 | 5.2278 | 0.2283 | | 0.0742 | 369.97 | 6937 | 5.1995 | 0.2383 | | 0.0512 | 370.99 | 6956 | 5.2024 | 0.24 | | 0.0529 | 372.0 | 6975 | 5.1839 | 0.24 | | 0.0647 | 372.96 | 6993 | 5.2155 | 0.2433 | | 0.055 | 373.97 | 7012 | 5.1983 | 0.2417 | | 0.0586 | 374.99 | 7031 | 5.1605 | 0.2467 | | 0.0619 | 376.0 | 7050 | 5.1739 | 0.24 | | 0.0462 | 376.96 | 7068 | 5.1929 | 0.2467 | | 0.0406 | 377.97 | 7087 | 5.2178 | 0.2467 | | 0.0578 | 378.99 | 7106 | 5.2028 | 0.2433 | | 0.0643 | 380.0 | 7125 | 5.1912 | 0.245 | | 0.0442 | 380.96 | 7143 | 5.2069 | 0.2417 | | 0.0642 | 381.97 | 7162 | 5.1740 | 0.2417 | | 0.0423 | 382.99 | 7181 | 5.1597 | 0.2417 | | 0.0663 | 384.0 | 7200 | 5.1497 | 0.2467 | | 0.0539 | 384.96 | 7218 | 5.1274 | 0.2467 | | 0.0426 | 385.97 | 7237 | 5.1322 | 0.24 | | 0.0489 | 386.99 | 7256 | 5.1582 | 0.2433 | | 0.0591 | 388.0 | 7275 | 5.1627 | 0.245 | | 0.0528 | 388.96 | 7293 | 5.1561 | 0.2483 | | 0.0666 | 389.97 | 7312 | 5.1548 | 0.2433 | | 0.053 | 390.99 | 7331 | 5.1537 | 0.2433 | | 0.0546 | 392.0 | 7350 | 5.1460 | 0.25 | | 0.0546 | 392.96 | 7368 | 5.1467 | 0.2467 | | 0.0334 | 393.97 | 7387 | 5.1394 | 0.25 | | 0.0548 | 394.99 | 7406 | 5.1488 | 0.2417 | | 0.0441 | 396.0 | 7425 | 5.1643 | 0.2467 | | 0.0476 | 396.96 | 7443 | 5.1578 | 0.255 | | 0.0602 | 397.97 | 7462 | 5.1754 | 0.2517 | | 0.0455 | 398.99 | 7481 | 5.1758 | 0.2533 | | 0.0364 | 400.0 | 7500 | 5.1471 | 0.255 | | 0.0479 | 400.96 | 7518 | 5.1424 | 0.2517 | | 0.052 | 401.97 | 7537 | 5.1626 | 0.245 | | 0.0521 | 402.99 | 7556 | 5.1680 | 0.2533 | | 0.0527 | 404.0 | 7575 | 5.1557 | 0.2517 | | 0.0534 | 404.96 | 7593 | 5.1781 | 0.2567 | | 0.0431 | 405.97 | 7612 | 5.1900 | 0.2533 | | 0.0506 | 406.99 | 7631 | 5.1651 | 0.2533 | | 0.043 | 408.0 | 7650 | 5.1529 | 0.2567 | | 0.0419 | 408.96 | 7668 | 5.1589 | 0.255 | | 0.0388 | 409.97 | 7687 | 5.1641 | 0.2567 | | 0.0601 | 410.99 | 7706 | 5.1679 | 0.2567 | | 0.0521 | 412.0 | 7725 | 5.1805 | 0.255 | | 0.0564 | 412.96 | 7743 | 5.1811 | 0.2517 | | 0.0395 | 413.97 | 7762 | 5.1670 | 0.2483 | | 0.0542 | 414.99 | 7781 | 5.1530 | 0.2517 | | 0.0482 | 416.0 | 7800 | 5.1599 | 0.2483 | | 0.0597 | 416.96 | 7818 | 5.1715 | 0.2517 | | 0.0579 | 417.97 | 7837 | 5.1748 | 0.2517 | | 0.0331 | 418.99 | 7856 | 5.1811 | 0.25 | | 0.045 | 420.0 | 7875 | 5.1903 | 0.25 | | 0.0424 | 420.96 | 7893 | 5.1830 | 0.2583 | | 0.0474 | 421.97 | 7912 | 5.1867 | 0.2567 | | 0.051 | 422.99 | 7931 | 5.1793 | 0.255 | | 0.0563 | 424.0 | 7950 | 5.1588 | 0.2517 | | 0.039 | 424.96 | 7968 | 5.1612 | 0.2483 | | 0.0722 | 425.97 | 7987 | 5.1692 | 0.2433 | | 0.0492 | 426.99 | 8006 | 5.1873 | 0.245 | | 0.0594 | 428.0 | 8025 | 5.1983 | 0.245 | | 0.0646 | 428.96 | 8043 | 5.2049 | 0.2483 | | 0.0412 | 429.97 | 8062 | 5.1985 | 0.25 | | 0.0493 | 430.99 | 8081 | 5.2013 | 0.24 | | 0.0582 | 432.0 | 8100 | 5.1916 | 0.2467 | | 0.0528 | 432.96 | 8118 | 5.1852 | 0.2483 | | 0.0432 | 433.97 | 8137 | 5.1820 | 0.25 | | 0.0313 | 434.99 | 8156 | 5.1844 | 0.2483 | | 0.048 | 436.0 | 8175 | 5.1884 | 0.2467 | | 0.0591 | 436.96 | 8193 | 5.1952 | 0.2467 | | 0.0379 | 437.97 | 8212 | 5.2027 | 0.25 | | 0.0355 | 438.99 | 8231 | 5.2032 | 0.2517 | | 0.0499 | 440.0 | 8250 | 5.1997 | 0.2533 | | 0.0464 | 440.96 | 8268 | 5.1866 | 0.255 | | 0.0455 | 441.97 | 8287 | 5.1744 | 0.255 | | 0.0429 | 442.99 | 8306 | 5.1745 | 0.2533 | | 0.0532 | 444.0 | 8325 | 5.1885 | 0.2483 | | 0.053 | 444.96 | 8343 | 5.1893 | 0.2517 | | 0.0453 | 445.97 | 8362 | 5.1899 | 0.2467 | | 0.0414 | 446.99 | 8381 | 5.1957 | 0.2433 | | 0.0449 | 448.0 | 8400 | 5.2016 | 0.2433 | | 0.0424 | 448.96 | 8418 | 5.2080 | 0.245 | | 0.0377 | 449.97 | 8437 | 5.2015 | 0.2417 | | 0.0448 | 450.99 | 8456 | 5.1974 | 0.2433 | | 0.0647 | 452.0 | 8475 | 5.1928 | 0.24 | | 0.0549 | 452.96 | 8493 | 5.1991 | 0.24 | | 0.0455 | 453.97 | 8512 | 5.1975 | 0.245 | | 0.0531 | 454.99 | 8531 | 5.1890 | 0.2417 | | 0.0476 | 456.0 | 8550 | 5.1756 | 0.245 | | 0.0366 | 456.96 | 8568 | 5.1726 | 0.2467 | | 0.0534 | 457.97 | 8587 | 5.1726 | 0.2467 | | 0.0521 | 458.99 | 8606 | 5.1757 | 0.2467 | | 0.042 | 460.0 | 8625 | 5.1777 | 0.2467 | | 0.0513 | 460.96 | 8643 | 5.1781 | 0.2483 | | 0.0463 | 461.97 | 8662 | 5.1803 | 0.25 | | 0.0563 | 462.99 | 8681 | 5.1841 | 0.2483 | | 0.0366 | 464.0 | 8700 | 5.1888 | 0.245 | | 0.0439 | 464.96 | 8718 | 5.1896 | 0.2467 | | 0.0645 | 465.97 | 8737 | 5.1916 | 0.2483 | | 0.0358 | 466.99 | 8756 | 5.1958 | 0.25 | | 0.0397 | 468.0 | 8775 | 5.1982 | 0.2467 | | 0.0498 | 468.96 | 8793 | 5.2001 | 0.245 | | 0.0597 | 469.97 | 8812 | 5.2035 | 0.245 | | 0.031 | 470.99 | 8831 | 5.2055 | 0.245 | | 0.0466 | 472.0 | 8850 | 5.2071 | 0.245 | | 0.0406 | 472.96 | 8868 | 5.2064 | 0.245 | | 0.047 | 473.97 | 8887 | 5.2062 | 0.2467 | | 0.0472 | 474.99 | 8906 | 5.2057 | 0.245 | | 0.0392 | 476.0 | 8925 | 5.2060 | 0.2467 | | 0.0314 | 476.96 | 8943 | 5.2054 | 0.2467 | | 0.0411 | 477.97 | 8962 | 5.2049 | 0.2467 | | 0.0427 | 478.99 | 8981 | 5.2046 | 0.2467 | | 0.0469 | 480.0 | 9000 | 5.2045 | 0.2467 | ### Framework versions - Transformers 4.31.0 - Pytorch 1.13.1 - Datasets 2.14.0 - Tokenizers 0.13.3

squarelike/Gugugo-koen-1.3B-V1.0

squarelike

2023-07-29T18:18:11Z

129

6

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "translation", "en", "ko", "dataset:squarelike/sharegpt_deepl_ko_translation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

translation

2023-07-26T16:06:55Z

--- license: apache-2.0 datasets: - squarelike/sharegpt_deepl_ko_translation language: - en - ko pipeline_tag: translation --- [https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo) Prompt Template: ``` ### 한국어: {sentence}</끝> ### 영어: ``` ``` ### 영어: {sentence}</끝> ### 한국어: ```

s3nh/Baichuan-13B-Instruction-GGML

s3nh

2023-07-29T18:16:19Z

0

3

transformers

[ "transformers", "text-generation-inference", "text-generation", "en", "arxiv:2104.09864", "arxiv:2108.12409", "arxiv:2307.00360", "license:openrail", "endpoints_compatible", "region:us" ]

text-generation

2023-07-27T07:52:31Z

--- license: openrail language: - en tags: - text-generation-inference pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/AlpachinoNLP/Baichuan-13B-Instruction/). ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card ## 使用方式如下是一个使用Baichuan-13B-Chat进行对话的示例，正确输出为"乔戈里峰。世界第二高峰———乔戈里峰西方登山者称其为k2峰，海拔高度是8611米，位于喀喇昆仑山脉的中巴边境上" ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction") messages = [] messages.append({"role": "Human", "content": "世界上第二高的山峰是哪座"}) response = model.chat(tokenizer, messages) print(response) ``` ## 量化部署 Baichuan-13B 支持 int8 和 int4 量化，用户只需在推理代码中简单修改两行即可实现。请注意，如果是为了节省显存而进行量化，应加载原始精度模型到 CPU 后再开始量化；避免在 `from_pretrained` 时添加 `device_map='auto'` 或者其它会导致把原始精度模型直接加载到 GPU 的行为的参数。使用 int8 量化 (To use int8 quantization): ```python model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", torch_dtype=torch.float16, trust_remote_code=True) model = model.quantize(8).cuda() ``` 同样的，如需使用 int4 量化 (Similarly, to use int4 quantization): ```python model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-13B-Instruction", torch_dtype=torch.float16, trust_remote_code=True) model = model.quantize(4).cuda() ``` ## 模型详情 ### 模型结构  整体模型基于Baichuan-13B，为了获得更好的推理性能，Baichuan-13B 使用了 ALiBi 线性偏置技术，相对于 Rotary Embedding 计算量更小，对推理性能有显著提升；与标准的 LLaMA-13B 相比，生成 2000 个 tokens 的平均推理速度 (tokens/s)，实测提升 31.6%： | Model | tokens/s | | ------------ | -------- | | LLaMA-13B | 19.4 | | Baichuan-13B | 25.4 | 具体参数和见下表 | 模型名称 | 隐含层维度 | 层数 | 头数 | 词表大小 | 总参数量 | 训练数据（tokens） | 位置编码 | 最大长度 | | ------------ | ---------- | ---- | ---- | -------- | -------------- | ------------------ | ----------------------------------------- | -------- | | Baichuan-7B | 4,096 | 32 | 32 | 64,000 | 7,000,559,616 | 1.2万亿 | [RoPE](https://arxiv.org/abs/2104.09864) | 4,096 | | Baichuan-13B | 5,120 | 40 | 40 | 64,000 | 13,264,901,120 | 1.4万亿 | [ALiBi](https://arxiv.org/abs/2108.12409) | 4,096 | ## 训练详情数据集主要由三部分组成： * 在 [sharegpt_zh](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/ShareGPT) 数据集中筛选的出 13k 高质量数据。 * [lima](https://huggingface.co/datasets/GAIR/lima) * 按照任务类型挑选的 2.3k 高质量中文数据集，每个任务类型的数据量在 100 条左右。硬件：8*A40 ## 测评结果 ## [CMMLU](https://github.com/haonan-li/CMMLU) | Model 5-shot | STEM | Humanities | Social Sciences | Others | China Specific | Average | | ---------------------------------------------------------- | :-------: | :--------: | :-------------: | :------: | :------------: | :------: | | Baichuan-7B | 34.4 | 47.5 | 47.6 | 46.6 | 44.3 | 44.0 | | Vicuna-13B | 31.8 | 36.2 | 37.6 | 39.5 | 34.3 | 36.3 | | Chinese-Alpaca-Plus-13B | 29.8 | 33.4 | 33.2 | 37.9 | 32.1 | 33.4 | | Chinese-LLaMA-Plus-13B | 28.1 | 33.1 | 35.4 | 35.1 | 33.5 | 33.0 | | Ziya-LLaMA-13B-Pretrain | 29.0 | 30.7 | 33.8 | 34.4 | 31.9 | 32.1 | | LLaMA-13B | 29.2 | 30.8 | 31.6 | 33.0 | 30.5 | 31.2 | | moss-moon-003-base (16B) | 27.2 | 30.4 | 28.8 | 32.6 | 28.7 | 29.6 | | Baichuan-13B-Base | 41.7 | 61.1 | 59.8 | 59.0 | 56.4 | 55.3 | | Baichuan-13B-Chat | 42.8 | **62.6** | **59.7** | **59.0** | **56.1** | **55.8** | | **Baichuan-13B-Instruction** | **44.50** | 61.16 | 59.07 | 58.34 | 55.55 | 55.61 | | Model zero-shot | STEM | Humanities | Social Sciences | Others | China Specific | Average | | ------------------------------------------------------------ | :-------: | :--------: | :-------------: | :-------: | :------------: | :-------: | | [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) | 41.28 | 52.85 | 53.37 | 52.24 | 50.58 | 49.95 | | [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) | 32.79 | 44.43 | 46.78 | 44.79 | 43.11 | 42.33 | | [ChatGLM-6B](https://github.com/THUDM/GLM-130B) | 32.22 | 42.91 | 44.81 | 42.60 | 41.93 | 40.79 | | [BatGPT-15B](https://arxiv.org/abs/2307.00360) | 33.72 | 36.53 | 38.07 | 46.94 | 38.32 | 38.51 | | [Chinese-LLaMA-13B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 26.76 | 26.57 | 27.42 | 28.33 | 26.73 | 27.34 | | [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) | 25.68 | 26.35 | 27.21 | 27.92 | 26.70 | 26.88 | | [Chinese-GLM-10B](https://github.com/THUDM/GLM) | 25.57 | 25.01 | 26.33 | 25.94 | 25.81 | 25.80 | | [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) | 42.04 | 60.49 | 59.55 | 56.60 | 55.72 | 54.63 | | [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-13B) | 37.32 | 56.24 | 54.79 | 54.07 | 52.23 | 50.48 | | **Baichuan-13B-Instruction** | **42.56** | **62.09** | **60.41** | **58.97** | **56.95** | **55.88** | > 说明：CMMLU 是一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力。我们直接使用其官方的[评测脚本](https://github.com/haonan-li/CMMLU)对模型进行评测。Model zero-shot 表格中 [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-13B) 的得分来自我们直接运行 CMMLU 官方的评测脚本得到，其他模型的的得分来自于 [CMMLU](https://github.com/haonan-li/CMMLU/tree/master) 官方的评测结果，Model 5-shot 中其他模型的得分来自于[Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) 官方的评测结果。

s3nh/StableBeluga-7B-GGML

s3nh

2023-07-29T18:15:45Z

0

2

transformers

[ "transformers", "text-generation-inference", "text-generation", "en", "arxiv:2307.09288", "arxiv:2306.02707", "license:openrail", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T06:48:02Z

--- license: openrail language: - en tags: - text-generation-inference pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/stabilityai/StableBeluga-7B). It consist of k_quant quantization in every format: -4_0 -4_1 -5_0 -5_1 -8_0 ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card ## Usage Start chatting with `Stable Beluga 7B` using the following code snippet: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("stabilityai/StableBeluga-7B", use_fast=False) model = AutoModelForCausalLM.from_pretrained("stabilityai/StableBeluga-7B", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto") system_prompt = "### System:\nYou are StableBeluga, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal.\n\n" message = "Write me a poem please" prompt = f"{system_prompt}### User: {message}\n\n### Assistant:\n" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=256) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` Stable Beluga 7B should be used with this prompt format: ``` ### System: This is a system prompt, please behave and help the user. ### User: Your prompt here ### Assistant: The output of Stable Beluga 7B ``` ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: Stable Beluga 7B is an auto-regressive language model fine-tuned on Llama2 7B. * **Language(s)**: English * **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) * **License**: Fine-tuned checkpoints (`Stable Beluga 7B`) is licensed under the [STABLE BELUGA NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT](https://huggingface.co/stabilityai/StableBeluga-7B/blob/main/LICENSE.txt) * **Contact**: For questions and comments about the model, please email `[email protected]` ### Training Dataset ` Stable Beluga 7B` is trained on our internal Orca-style dataset ### Training Procedure Models are learned via supervised fine-tuning on the aforementioned datasets, trained in mixed-precision (BF16), and optimized with AdamW. We outline the following hyperparameters: | Dataset | Batch Size | Learning Rate |Learning Rate Decay| Warm-up | Weight Decay | Betas | |-------------------|------------|---------------|-------------------|---------|--------------|-------------| | Orca pt1 packed | 256 | 3e-5 | Cosine to 3e-6 | 100 | 1e-6 | (0.9, 0.95) | | Orca pt2 unpacked | 512 | 3e-5 | Cosine to 3e-6 | 100 | 1e-6 | (0.9, 0.95) | ## Ethical Considerations and Limitations Beluga is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Beluga's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Beluga, developers should perform safety testing and tuning tailored to their specific applications of the model. ## Citations ```bibtext @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtext @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

s3nh/starcoderbase-3b-GPTQ

s3nh

2023-07-29T18:13:43Z

9

1

transformers

[ "transformers", "gpt_bigcode", "text-generation", "en", "arxiv:2305.06161", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T11:25:26Z

--- license: openrail language: - en pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/bigcode/starcoderbase-3b). ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card ## Use ### Intended use The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant. **Feel free to share your generations in the Community tab!** ### Generation ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoderbase-3b" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Fill-in-the-middle Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: ```python input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>" inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code from 80+ programming languages. The predominant natural language in source code is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) for an in-depth discussion of the model limitations. # Training ## Model - **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective - **Pretraining steps:** 500k - **Pretraining tokens:** 1 trillion - **Precision:** bfloat16 ## Hardware - **GPUs:** 256 Tesla A100 - **Training time:** 12 days ## Software - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) - **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). # Citation ``` @article{li2023starcoder, title={StarCoder: may the source be with you!}, author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2023}, eprint={2305.06161}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

s3nh/LLongMA-2-7b-16k-GGML

s3nh

2023-07-29T18:13:18Z

0

1

transformers

[ "transformers", "text-generation", "en", "license:openrail", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T19:32:42Z

--- license: openrail language: - en pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/conceptofmind/LLongMA-2-7b-16k). ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Original model card

VinEuro/Pixelcopter-PLE-v0

VinEuro

2023-07-29T18:00:25Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T17:56:01Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 16.70 +/- 10.16 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

vishnun/peft-codevsnl

vishnun

2023-07-29T17:55:10Z

0

peft

[ "peft", "region:us" ]

null

2023-07-29T17:14:43Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0

hadiqa123/xls-r_cv_ur

hadiqa123

2023-07-29T17:41:57Z

21

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-09-22T04:28:06Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: xls-r_cv_ur results: [] ---  # xls-r_cv_ur This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4888 - Wer: 1.0 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 8.4361 | 1.14 | 100 | 2.3733 | 1.0 | | 1.9609 | 2.27 | 200 | 1.8280 | 1.0 | | 1.8191 | 3.41 | 300 | 1.8259 | 1.0 | | 1.8359 | 4.55 | 400 | 1.8069 | 1.0 | | 1.7667 | 5.68 | 500 | 1.8038 | 1.0 | | 1.7751 | 6.82 | 600 | 1.7473 | 1.0 | | 1.7428 | 7.95 | 700 | 1.6996 | 1.0 | | 1.697 | 9.09 | 800 | 1.6364 | 1.0 | | 1.6532 | 10.23 | 900 | 1.4985 | 1.0 | | 1.5217 | 11.36 | 1000 | 1.2836 | 1.0 | | 1.3385 | 12.5 | 1100 | 1.0293 | 1.0 | | 1.1596 | 13.64 | 1200 | 0.8294 | 1.0 | | 1.0655 | 14.77 | 1300 | 0.7150 | 1.0 | | 0.9951 | 15.91 | 1400 | 0.6364 | 1.0 | | 0.9013 | 17.05 | 1500 | 0.5548 | 1.0 | | 0.8276 | 18.18 | 1600 | 0.5200 | 1.0 | | 0.8129 | 19.32 | 1700 | 0.4888 | 1.0 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

sm136599/chatfoodie-koalpaca-polyglot-5_8b-5150step-8batch_5epoch

sm136599

2023-07-29T17:36:47Z

4

0

peft

[ "peft", "region:us" ]

null

2023-07-29T17:36:46Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

lesliepzimmermann/dqn-SpaceInvadersNoFrameskip-v4

lesliepzimmermann

2023-07-29T17:28:15Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T17:27:38Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 667.50 +/- 132.75 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lesliepzimmermann -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lesliepzimmermann -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga lesliepzimmermann ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

VinEuro/CartPole-v1

VinEuro

2023-07-29T17:27:43Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T17:27:33Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Gerti/distilbert-base-multilingual-cased-finetuned-sentiment-albanian

Gerti

2023-07-29T17:02:43Z

106

1

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-26T07:59:47Z

--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: distilbert-base-multilingual-cased-finetuned-sentiment-albanian results: [] ---  # distilbert-base-multilingual-cased-finetuned-sentiment-albanian This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3126 - F1-score: : 0.9393 - Accuracy score: : 0.8902 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1-score: | Accuracy score: | |:-------------:|:-----:|:----:|:---------------:|:----------:|:----------------:| | 0.3698 | 1.0 | 149 | 0.3424 | 0.9290 | 0.8675 | | 0.2995 | 2.0 | 298 | 0.3174 | 0.9362 | 0.8822 | | 0.251 | 3.0 | 447 | 0.3126 | 0.9393 | 0.8902 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

jakezou/miaolora

jakezou

2023-07-29T16:59:16Z

0

peft

[ "peft", "region:us" ]

null

2023-07-29T06:25:34Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0

digitaljungle/rl_course_vizdoom_health_gathering_supreme

digitaljungle

2023-07-29T16:57:25Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T16:57:07Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 9.78 +/- 4.91 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r digitaljungle/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

Controlyourself/ppo-LunarLander-v2

Controlyourself

2023-07-29T16:55:47Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T16:55:28Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.96 +/- 25.25 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

NasimB/qed-rarity-seed

NasimB

2023-07-29T16:48:04Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-29T13:09:50Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: qed-rarity-seed results: [] ---  # qed-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1171 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3592 | 0.29 | 500 | 5.3361 | | 5.0454 | 0.58 | 1000 | 4.9330 | | 4.7118 | 0.88 | 1500 | 4.6958 | | 4.4583 | 1.17 | 2000 | 4.5571 | | 4.3049 | 1.46 | 2500 | 4.4421 | | 4.2187 | 1.75 | 3000 | 4.3410 | | 4.099 | 2.05 | 3500 | 4.2645 | | 3.9157 | 2.34 | 4000 | 4.2163 | | 3.8896 | 2.63 | 4500 | 4.1655 | | 3.8487 | 2.92 | 5000 | 4.1078 | | 3.6548 | 3.21 | 5500 | 4.1099 | | 3.6051 | 3.51 | 6000 | 4.0753 | | 3.5947 | 3.8 | 6500 | 4.0445 | | 3.4956 | 4.09 | 7000 | 4.0427 | | 3.3396 | 4.38 | 7500 | 4.0386 | | 3.336 | 4.68 | 8000 | 4.0234 | | 3.3241 | 4.97 | 8500 | 4.0114 | | 3.1753 | 5.26 | 9000 | 4.0252 | | 3.1537 | 5.55 | 9500 | 4.0240 | | 3.1587 | 5.84 | 10000 | 4.0228 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

darthPanda/dqn-SpaceInvadersNoFrameskip-v4_1

darthPanda

2023-07-29T16:33:58Z

11

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T16:33:27Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 457.00 +/- 67.39 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga darthPanda -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga darthPanda -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga darthPanda ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

2Nisavi2/Cifrar_10_Deployment

2Nisavi2

2023-07-29T16:31:15Z

2

0

tf-keras

[ "tf-keras", "region:us" ]

null

2023-07-29T05:10:05Z

<div align="center"> <img src="https://radcolombia.org/web/sites/default/files/archivos/instituciones/universidad-bosque/logo-ub.png" width="500"/> Facultad de Ciencias Maestría en Estadística Aplicada y Ciencia de Datos ***"Despliegue Modelo VGG16 para Cifrar10 Desde HuggingFace"*** *Proyecto Final GCP* </div> En este repositório encontrara el dataset Cifrar10 entrenado sobre VGG16. Para la prueba de la efectividad del modelo, ingrese a este [link](https://3174c43e92e93f5122.gradio.live)

HongyangLi/basedbert-finetuned-squad

HongyangLi

2023-07-29T16:30:13Z

116

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2023-07-29T16:09:49Z

--- tags: - generated_from_trainer model-index: - name: basedbert-finetuned-squad results: [] ---  # basedbert-finetuned-squad This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9130 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 29 | 3.1855 | | No log | 2.0 | 58 | 2.0997 | | No log | 3.0 | 87 | 1.9130 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.14.1 - Tokenizers 0.13.3

VinEuro/dqn-SpaceInvadersNoFrameskip-v4

VinEuro

2023-07-29T16:25:41Z

7

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T16:25:06Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 561.00 +/- 94.55 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VinEuro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VinEuro -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga VinEuro ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 750000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ichacon/ppo-LunarLander-v2

ichacon

2023-07-29T16:24:31Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T16:24:12Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.39 +/- 19.64 name: mean_reward verified: false --- # **ppo** Agent playing **LunarLander-v2** This is a trained model of a **ppo** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

ClearRabbitt/Clear

ClearRabbitt

2023-07-29T16:04:12Z

0

null

[ "DMMD", "Dramatical Murder", "Dramatical Murder Re:Connect", "Yaoi", "BL", "ja", "license:openrail", "region:us" ]

null

2023-07-26T10:29:21Z

--- language: - ja tags: - DMMD - Dramatical Murder - Dramatical Murder Re:Connect - Yaoi - BL license: openrail --- ![576993.jpg](https://cdn-uploads.huggingface.co/production/uploads/64b93f4925e5f8a3e000371b/VGfvARYz1TYvnQqukVwiQ.jpeg) Clear from Dramatical Murder! Trained with Dramatical Murder, Dramatical Murder Re:Connect, Birthday CD, All of his drama CDs, His song クラゲの歌 I posted finished 500 epochs and a lower epoch where I think he might've just begun to overtrain. ![Board.PNG](https://cdn-uploads.huggingface.co/production/uploads/64b93f4925e5f8a3e000371b/FkQo_ApXpiRO0IZke-5QY.png) I might continue to train him if he hasn't overtrained but I truly think he has so I won't touch this, instead I'll just train my future Aoba model

chunwoolee0/distilbert-base-uncased-finetuned-emotion

chunwoolee0

2023-07-29T15:55:44Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-19T11:33:23Z

--- tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion This model was trained from scratch on the emotion 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: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

user10211125/doodle_style_model

user10211125

2023-07-29T15:54:49Z

43

0

diffusers

[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-29T15:36:28Z

doodle(風格)+teary+lookingDisgusted+sadMadFace

Krainez/q-Taxi-v3

Krainez

2023-07-29T15:49:17Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T15:49:15Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Krainez/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Iam/llama2-qlora-finetunined-french

Iam

2023-07-29T15:45:02Z

2

0

peft

[ "peft", "region:us" ]

null

2023-07-29T15:40:08Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

ParthNakum21/GenzTranscribe-en-gu

ParthNakum21

2023-07-29T15:43:44Z

102

1

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus100", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-29T09:27:29Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: GenzTranscribe-en-gu results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 config: en-gu split: train args: en-gu metrics: - name: Bleu type: bleu value: 59.9227 ---  # GenzTranscribe-en-gu This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.3075 - Bleu: 59.9227 - Gen Len: 9.6443 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.3593 | 1.0 | 31831 | 0.3253 | 58.1921 | 9.7108 | | 0.3421 | 2.0 | 63662 | 0.3075 | 59.9227 | 9.6443 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

c72599/hubert-base-ls960-finetuned-gtzan

c72599

2023-07-29T15:42:18Z

3

0

transformers

[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/hubert-base-ls960", "base_model:finetune:facebook/hubert-base-ls960", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-29T07:10:24Z

--- license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: hubert-base-ls960-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.88 ---  # hubert-base-ls960-finetuned-gtzan This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6645 - Accuracy: 0.88 ## 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-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2685 | 1.0 | 56 | 2.2069 | 0.44 | | 2.0208 | 1.99 | 112 | 1.8352 | 0.46 | | 1.7603 | 2.99 | 168 | 1.5275 | 0.49 | | 1.4843 | 4.0 | 225 | 1.4296 | 0.52 | | 1.347 | 5.0 | 281 | 1.2222 | 0.52 | | 1.2364 | 5.99 | 337 | 1.1477 | 0.62 | | 1.2082 | 6.99 | 393 | 1.0181 | 0.67 | | 0.9861 | 8.0 | 450 | 0.9598 | 0.71 | | 0.752 | 9.0 | 506 | 0.7499 | 0.77 | | 1.006 | 9.99 | 562 | 0.8190 | 0.79 | | 0.6725 | 10.99 | 618 | 0.8798 | 0.75 | | 0.7457 | 12.0 | 675 | 0.6276 | 0.81 | | 0.4605 | 13.0 | 731 | 0.6086 | 0.85 | | 0.5751 | 13.99 | 787 | 0.6894 | 0.75 | | 0.4886 | 14.99 | 843 | 0.6109 | 0.83 | | 0.2429 | 16.0 | 900 | 0.6076 | 0.85 | | 0.3084 | 17.0 | 956 | 0.4646 | 0.86 | | 0.3762 | 17.99 | 1012 | 0.8349 | 0.81 | | 0.2897 | 18.99 | 1068 | 0.4509 | 0.89 | | 0.1296 | 20.0 | 1125 | 0.6791 | 0.86 | | 0.1291 | 21.0 | 1181 | 0.6466 | 0.85 | | 0.3784 | 21.99 | 1237 | 0.6272 | 0.88 | | 0.1156 | 22.99 | 1293 | 0.7916 | 0.85 | | 0.2093 | 24.0 | 1350 | 0.6536 | 0.85 | | 0.2167 | 25.0 | 1406 | 0.7050 | 0.87 | | 0.1095 | 25.99 | 1462 | 0.6128 | 0.88 | | 0.1004 | 26.99 | 1518 | 0.6092 | 0.89 | | 0.0897 | 28.0 | 1575 | 0.6730 | 0.88 | | 0.083 | 29.0 | 1631 | 0.6396 | 0.89 | | 0.0343 | 29.87 | 1680 | 0.6645 | 0.88 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.14.1 - Tokenizers 0.13.3

lesliepzimmermann/q-Taxi-v3

lesliepzimmermann

2023-07-29T15:41:01Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T15:15:14Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="lesliepzimmermann/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

nikbhi/LunarLander_ppo_v3

nikbhi

2023-07-29T15:35:57Z

2

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-29T15:35:37Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.69 +/- 9.19 name: mean_reward verified: false --- # **ppo** Agent playing **LunarLander-v2** This is a trained model of a **ppo** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

yzheng-1/FinBERT_Chinese

yzheng-1

2023-07-29T15:35:39Z

45

1

transformers

[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]

null

2023-07-29T15:31:55Z

FinBERT Model from https://github.com/valuesimplex/FinBERT