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 2025-07-10 06:28:14	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 515 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-10 06:27:27	card stringlengths 11 1.01M
Madhura/qa-model	Madhura	2023-07-28T16:29:24Z	112	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-28T15:59:32Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad model-index: - name: qa-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_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6376 ## 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 \| 250 \| 2.3091 \| \| 2.7041 \| 2.0 \| 500 \| 1.7406 \| \| 2.7041 \| 3.0 \| 750 \| 1.6376 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
jarguello76/text-to-speech-speecht5_finetuned_voxpopuli_es	jarguello76	2023-07-28T16:13:55Z	84	1	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "text-to-speech", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-27T22:45:24Z	--- license: mit base_model: microsoft/speecht5_tts datasets: - facebook/voxpopuli model-index: - name: speecht5_tts-voxpopuli-jarguello results: [] pipeline_tag: text-to-speech --- <!-- 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. --> # speecht5_tts-voxpopuli-jarguello This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4464 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5089 \| 4.32 \| 1000 \| 0.4632 \| \| 0.4835 \| 8.64 \| 2000 \| 0.4512 \| \| 0.4862 \| 12.97 \| 3000 \| 0.4487 \| \| 0.4825 \| 17.29 \| 4000 \| 0.4464 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
Geotrend/distilbert-base-en-pt-cased	Geotrend	2023-07-28T16:13:06Z	130	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: multilingual datasets: wikipedia license: apache-2.0 --- # distilbert-base-en-pt-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-pt-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.
alifaheem94/RomanUrduPOS	alifaheem94	2023-07-28T16:11:41Z	101	0	transformers	[ "transformers", "pytorch", "roberta", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-28T14:52:23Z	--- pipeline_tag: token-classification --- This model is trained on Roman Urdu POS dataset of 100000 tokens.
edures/Reinforce-vtest	edures	2023-07-28T16:07:14Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T16:02:45Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-vtest results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 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
azhang1212/angela_shuffle_test	azhang1212	2023-07-28T15:49:15Z	105	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	2023-07-28T14:28:54Z	--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: angela_shuffle_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_shuffle_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: 0.1672 - Precision: 0.6214 - Recall: 0.4942 - F1: 0.5505 - Accuracy: 0.9504 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1882 \| 1.0 \| 1283 \| 0.1566 \| 0.6823 \| 0.4277 \| 0.5258 \| 0.9518 \| \| 0.1551 \| 2.0 \| 2566 \| 0.1507 \| 0.6940 \| 0.4451 \| 0.5423 \| 0.9533 \| \| 0.1385 \| 3.0 \| 3849 \| 0.1545 \| 0.6903 \| 0.4503 \| 0.5450 \| 0.9532 \| \| 0.1163 \| 4.0 \| 5132 \| 0.1610 \| 0.6288 \| 0.4943 \| 0.5535 \| 0.9507 \| \| 0.0994 \| 5.0 \| 6415 \| 0.1672 \| 0.6214 \| 0.4942 \| 0.5505 \| 0.9504 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
avidoavid/RWKV-14b-finetuned	avidoavid	2023-07-28T15:49:05Z	0	0	null	[ "generated_from_trainer", "base_model:RWKV/rwkv-raven-14b", "base_model:finetune:RWKV/rwkv-raven-14b", "region:us" ]	null	2023-07-28T15:07:06Z	--- base_model: RWKV/rwkv-raven-14b tags: - generated_from_trainer model-index: - name: RWKV-14b-finetuned 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. --> # RWKV-14b-finetuned This model is a fine-tuned version of [RWKV/rwkv-raven-14b](https://huggingface.co/RWKV/rwkv-raven-14b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0585 ## 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: 0.001 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0926 \| 1.0 \| 1 \| 0.9535 \| \| 1.0628 \| 2.0 \| 2 \| 2.0585 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/cdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5	jordyvl	2023-07-28T15:40:52Z	164	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T07:51:07Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: cdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 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. --> # cdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 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: 0.4315 - Accuracy: 0.8522 - Brier Loss: 0.2145 - Nll: 1.3474 - F1 Micro: 0.8522 - F1 Macro: 0.8535 - Ece: 0.0573 - Aurc: 0.0300 ## 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 \| 1.6705 \| 0.6378 \| 0.4837 \| 2.4248 \| 0.6378 \| 0.6323 \| 0.0655 \| 0.1457 \| \| No log \| 2.0 \| 334 \| 1.1423 \| 0.7322 \| 0.3740 \| 1.9847 \| 0.7322 \| 0.7285 \| 0.0695 \| 0.0846 \| \| 1.7909 \| 3.0 \| 501 \| 0.9082 \| 0.7682 \| 0.3248 \| 1.7674 \| 0.7682 \| 0.7676 \| 0.0620 \| 0.0642 \| \| 1.7909 \| 4.0 \| 668 \| 0.8494 \| 0.7865 \| 0.3082 \| 1.7306 \| 0.7865 \| 0.7904 \| 0.0665 \| 0.0560 \| \| 1.7909 \| 5.0 \| 835 \| 0.7837 \| 0.798 \| 0.2988 \| 1.6072 \| 0.798 \| 0.7953 \| 0.0729 \| 0.0553 \| \| 0.4994 \| 6.0 \| 1002 \| 0.6867 \| 0.804 \| 0.2862 \| 1.5014 \| 0.804 \| 0.8059 \| 0.0794 \| 0.0471 \| \| 0.4994 \| 7.0 \| 1169 \| 0.7037 \| 0.8157 \| 0.2797 \| 1.5533 \| 0.8157 \| 0.8178 \| 0.0807 \| 0.0478 \| \| 0.4994 \| 8.0 \| 1336 \| 0.6709 \| 0.8163 \| 0.2756 \| 1.5297 \| 0.8163 \| 0.8166 \| 0.0728 \| 0.0478 \| \| 0.2478 \| 9.0 \| 1503 \| 0.6132 \| 0.825 \| 0.2576 \| 1.4349 \| 0.825 \| 0.8247 \| 0.0728 \| 0.0398 \| \| 0.2478 \| 10.0 \| 1670 \| 0.6389 \| 0.8235 \| 0.2671 \| 1.4455 \| 0.8235 \| 0.8266 \| 0.0746 \| 0.0419 \| \| 0.2478 \| 11.0 \| 1837 \| 0.6043 \| 0.8257 \| 0.2585 \| 1.4609 \| 0.8257 \| 0.8293 \| 0.0752 \| 0.0403 \| \| 0.1683 \| 12.0 \| 2004 \| 0.5639 \| 0.8327 \| 0.2457 \| 1.4470 \| 0.8327 \| 0.8350 \| 0.0676 \| 0.0375 \| \| 0.1683 \| 13.0 \| 2171 \| 0.5665 \| 0.8317 \| 0.2508 \| 1.4054 \| 0.8317 \| 0.8324 \| 0.0731 \| 0.0388 \| \| 0.1683 \| 14.0 \| 2338 \| 0.5505 \| 0.8403 \| 0.2427 \| 1.4059 \| 0.8403 \| 0.8408 \| 0.0649 \| 0.0377 \| \| 0.131 \| 15.0 \| 2505 \| 0.5321 \| 0.836 \| 0.2428 \| 1.4078 \| 0.836 \| 0.8372 \| 0.0684 \| 0.0365 \| \| 0.131 \| 16.0 \| 2672 \| 0.5161 \| 0.8373 \| 0.2383 \| 1.3900 \| 0.8373 \| 0.8373 \| 0.0711 \| 0.0368 \| \| 0.131 \| 17.0 \| 2839 \| 0.5177 \| 0.8403 \| 0.2371 \| 1.3828 \| 0.8403 \| 0.8413 \| 0.0633 \| 0.0354 \| \| 0.1071 \| 18.0 \| 3006 \| 0.5113 \| 0.8407 \| 0.2377 \| 1.3832 \| 0.8407 \| 0.8432 \| 0.0718 \| 0.0343 \| \| 0.1071 \| 19.0 \| 3173 \| 0.4949 \| 0.8415 \| 0.2332 \| 1.3767 \| 0.8415 \| 0.8428 \| 0.0667 \| 0.0338 \| \| 0.1071 \| 20.0 \| 3340 \| 0.4857 \| 0.848 \| 0.2271 \| 1.3664 \| 0.848 \| 0.8492 \| 0.0615 \| 0.0338 \| \| 0.0877 \| 21.0 \| 3507 \| 0.4812 \| 0.847 \| 0.2283 \| 1.3360 \| 0.847 \| 0.8478 \| 0.0602 \| 0.0346 \| \| 0.0877 \| 22.0 \| 3674 \| 0.4715 \| 0.8495 \| 0.2243 \| 1.3761 \| 0.8495 \| 0.8506 \| 0.0560 \| 0.0320 \| \| 0.0877 \| 23.0 \| 3841 \| 0.4622 \| 0.8508 \| 0.2206 \| 1.3584 \| 0.8508 \| 0.8515 \| 0.0557 \| 0.0323 \| \| 0.0694 \| 24.0 \| 4008 \| 0.4432 \| 0.8515 \| 0.2167 \| 1.3653 \| 0.8515 \| 0.8531 \| 0.0555 \| 0.0309 \| \| 0.0694 \| 25.0 \| 4175 \| 0.4467 \| 0.8498 \| 0.2193 \| 1.3499 \| 0.8498 \| 0.8512 \| 0.0581 \| 0.0309 \| \| 0.0694 \| 26.0 \| 4342 \| 0.4412 \| 0.8545 \| 0.2162 \| 1.3535 \| 0.8545 \| 0.8560 \| 0.0534 \| 0.0306 \| \| 0.0586 \| 27.0 \| 4509 \| 0.4402 \| 0.8498 \| 0.2180 \| 1.3390 \| 0.8498 \| 0.8510 \| 0.0597 \| 0.0309 \| \| 0.0586 \| 28.0 \| 4676 \| 0.4408 \| 0.8522 \| 0.2174 \| 1.3568 \| 0.8522 \| 0.8536 \| 0.0576 \| 0.0306 \| \| 0.0586 \| 29.0 \| 4843 \| 0.4391 \| 0.851 \| 0.2168 \| 1.3429 \| 0.851 \| 0.8523 \| 0.0585 \| 0.0305 \| \| 0.0549 \| 30.0 \| 5010 \| 0.4371 \| 0.853 \| 0.2160 \| 1.3389 \| 0.853 \| 0.8543 \| 0.0573 \| 0.0303 \| \| 0.0549 \| 31.0 \| 5177 \| 0.4382 \| 0.8498 \| 0.2168 \| 1.3486 \| 0.8498 \| 0.8513 \| 0.0602 \| 0.0304 \| \| 0.0549 \| 32.0 \| 5344 \| 0.4372 \| 0.853 \| 0.2166 \| 1.3501 \| 0.853 \| 0.8540 \| 0.0591 \| 0.0306 \| \| 0.0527 \| 33.0 \| 5511 \| 0.4379 \| 0.852 \| 0.2156 \| 1.3546 \| 0.852 \| 0.8531 \| 0.0576 \| 0.0304 \| \| 0.0527 \| 34.0 \| 5678 \| 0.4353 \| 0.8532 \| 0.2154 \| 1.3381 \| 0.8532 \| 0.8543 \| 0.0574 \| 0.0302 \| \| 0.0527 \| 35.0 \| 5845 \| 0.4347 \| 0.8525 \| 0.2148 \| 1.3550 \| 0.8525 \| 0.8535 \| 0.0591 \| 0.0304 \| \| 0.0511 \| 36.0 \| 6012 \| 0.4311 \| 0.8542 \| 0.2141 \| 1.3233 \| 0.8542 \| 0.8552 \| 0.0572 \| 0.0299 \| \| 0.0511 \| 37.0 \| 6179 \| 0.4323 \| 0.852 \| 0.2150 \| 1.3332 \| 0.852 \| 0.8532 \| 0.0586 \| 0.0302 \| \| 0.0511 \| 38.0 \| 6346 \| 0.4321 \| 0.8515 \| 0.2152 \| 1.3382 \| 0.8515 \| 0.8527 \| 0.0583 \| 0.0299 \| \| 0.0494 \| 39.0 \| 6513 \| 0.4335 \| 0.8495 \| 0.2152 \| 1.3385 \| 0.8495 \| 0.8511 \| 0.0593 \| 0.0303 \| \| 0.0494 \| 40.0 \| 6680 \| 0.4323 \| 0.852 \| 0.2146 \| 1.3603 \| 0.852 \| 0.8533 \| 0.0576 \| 0.0299 \| \| 0.0494 \| 41.0 \| 6847 \| 0.4309 \| 0.8512 \| 0.2143 \| 1.3448 \| 0.8512 \| 0.8525 \| 0.0570 \| 0.0299 \| \| 0.0477 \| 42.0 \| 7014 \| 0.4327 \| 0.8525 \| 0.2149 \| 1.3439 \| 0.8525 \| 0.8539 \| 0.0580 \| 0.0299 \| \| 0.0477 \| 43.0 \| 7181 \| 0.4309 \| 0.8532 \| 0.2140 \| 1.3406 \| 0.8532 \| 0.8544 \| 0.0560 \| 0.0299 \| \| 0.0477 \| 44.0 \| 7348 \| 0.4308 \| 0.8528 \| 0.2141 \| 1.3404 \| 0.8528 \| 0.8540 \| 0.0573 \| 0.0299 \| \| 0.0466 \| 45.0 \| 7515 \| 0.4317 \| 0.8525 \| 0.2147 \| 1.3402 \| 0.8525 \| 0.8538 \| 0.0580 \| 0.0299 \| \| 0.0466 \| 46.0 \| 7682 \| 0.4317 \| 0.8535 \| 0.2144 \| 1.3475 \| 0.8535 \| 0.8547 \| 0.0553 \| 0.0298 \| \| 0.0466 \| 47.0 \| 7849 \| 0.4314 \| 0.8525 \| 0.2143 \| 1.3479 \| 0.8525 \| 0.8537 \| 0.0559 \| 0.0299 \| \| 0.0465 \| 48.0 \| 8016 \| 0.4314 \| 0.8525 \| 0.2143 \| 1.3479 \| 0.8525 \| 0.8538 \| 0.0559 \| 0.0299 \| \| 0.0465 \| 49.0 \| 8183 \| 0.4316 \| 0.8528 \| 0.2145 \| 1.3471 \| 0.8528 \| 0.8540 \| 0.0573 \| 0.0299 \| \| 0.0465 \| 50.0 \| 8350 \| 0.4315 \| 0.8522 \| 0.2145 \| 1.3474 \| 0.8522 \| 0.8535 \| 0.0573 \| 0.0300 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
mauroluiz/Criativo	mauroluiz	2023-07-28T15:30:39Z	0	0	null	[ "region:us" ]	null	2023-07-28T15:16:30Z	Insanely detailed and elaborate jungle scene in a glass globe
liuyt75/t5-large_prefix_tuning_sentences_75agree_10	liuyt75	2023-07-28T15:28:53Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-28T15:28:51Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Christabelle/thesis-concept-art	Christabelle	2023-07-28T15:04:50Z	0	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-26T20:18:06Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Christabelle/thesis-concept-art These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the Christabelle/thesis-concept-art-train dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
budecosystem/genz-13b-v2	budecosystem	2023-07-28T14:51:28Z	1,480	4	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-26T05:40:09Z	--- language: - en library_name: transformers pipeline_tag: text-generation --- --- <div align="center"><h1 align="center">~ GenZ ~</h1><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/genz-logo.png" width=150></div> <p align="center"><i>Democratizing access to LLMs for the open-source community.<br>Let's advance AI, together. </i></p> --- ## Introduction 🎉 Welcome to GenZ, an advanced Large Language Model (LLM) fine-tuned on the foundation of Meta's open-source Llama V2 13B parameter model. At Bud Ecosystem, we believe in the power of open-source collaboration to drive the advancement of technology at an accelerated pace. Our vision is to democratize access to fine-tuned LLMs, and to that end, we will be releasing a series of models across different parameter counts (7B, 13B, and 70B) and quantizations (32-bit and 4-bit) for the open-source community to use, enhance, and build upon. <p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/MTBench_CompareChart_28July2023.png" width="500"></p> The smaller quantization version of our models makes them more accessible, enabling their use even on personal computers. This opens up a world of possibilities for developers, researchers, and enthusiasts to experiment with these models and contribute to the collective advancement of language model technology. GenZ isn't just a powerful text generator—it's a sophisticated AI assistant, capable of understanding and responding to user prompts with high-quality responses. We've taken the robust capabilities of Llama V2 and fine-tuned them to offer a more user-focused experience. Whether you're seeking informative responses or engaging interactions, GenZ is designed to deliver. And this isn't the end. It's just the beginning of a journey towards creating more advanced, more efficient, and more accessible language models. We invite you to join us on this exciting journey. 🚀 --- <h2>Milestone Releases ️🏁</h2> [27 July 2023] [_GenZ-13B V2 (ggml)_](https://huggingface.co/budecosystem/genz-13b-v2-ggml) : Announcing our GenZ-13B v2 with ggml. This variant of GenZ can run inferencing using only CPU and without the need of GPU. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-ggml). [27 July 2023] [_GenZ-13B V2 (4-bit)_](https://huggingface.co/budecosystem/genz-13b-v2-4bit) : Announcing our GenZ-13B v2 with 4-bit quantisation. Enabling inferencing with much lesser GPU memory than the 32-bit variant. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-4bit). [26 July 2023] [_GenZ-13B V2_](https://huggingface.co/budecosystem/genz-13b-v2) : We're excited to announce the release of our Genz 13B v2 model, a step forward with improved evaluation results compared to v1. Experience the advancements by downloading the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2). [20 July 2023] [_GenZ-13B_](https://huggingface.co/budecosystem/genz-13b) : We marked an important milestone with the release of the Genz 13B model. The journey began here, and you can partake in it by downloading the model from [Hugging Face](https://huggingface.co/budecosystem/genz-13b). --- <img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/screenshot_genz13bv2.png" width="100%"> \| ![Python](https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/Python.gif) \| ![Poem](https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/Poem.gif) \| ![Email](https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/Email.gif) \|:--:\|:--:\|:--:\| \| Code Generation \| Poem Generation \| Email Generation \| <!-- <p align="center"><img src="https://raw.githubusercontent.com/adrot-dev/git-test/blob/main/assets/Python.gif" width="33%" alt="Python Code"><img src="https://raw.githubusercontent.com/adrot-dev/git-test/blob/main/assets/Poem.gif" width="33%"><img src="https://raw.githubusercontent.com/adrot-dev/git-test/blob/main/assets/Email.gif" width="33%"></p> --> <h2>Getting Started on Hugging Face 🤗</h2> Getting up and running with our models on Hugging Face is a breeze. Follow these steps: <h3>1️⃣ : Import necessary modules</h3> Start by importing the necessary modules from the ‘transformers’ library and ‘torch’. ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM ``` <h3>2️⃣ : Load the tokenizer and the model</h3> Next, load up the tokenizer and the model for ‘budecosystem/genz-13b-v2’ from Hugging Face using the ‘from_pretrained’ method. ```python tokenizer = AutoTokenizer.from_pretrained("budecosystem/genz-13b-v2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-13b-v2", torch_dtype=torch.bfloat16) ``` <h3>3️⃣ : Generate responses</h3> Now that you have the model and tokenizer, you're ready to generate responses. Here's how you can do it: ```python inputs = tokenizer("The meaning of life is", return_tensors="pt") sample = model.generate(**inputs, max_length=128) print(tokenizer.decode(sample[0])) ``` In this example, "The meaning of life is" is the prompt template used for inference. You can replace it with any string you like. Want to interact with the model in a more intuitive way? We have a Gradio interface set up for that. Head over to our GitHub page, clone the repository, and run the ‘generate.py’ script to try it out. Happy experimenting! 😄 <h2>Fine-tuning 🎯</h2> It's time to upgrade the model by fine-tuning the model. You can do this using our provided finetune.py script. Here's an example command: ```bash python finetune.py \ --model_name meta-llama/Llama-2-13b \ --data_path dataset.json \ --output_dir output \ --trust_remote_code \ --prompt_column instruction \ --response_column output \ --pad_token_id 50256 ``` --- <h2 >Bonus: Colab Notebooks 📚 <b><i>(WIP)</i></b></h2> Looking for an even simpler way to get started with GenZ? We've got you covered. We've prepared a pair of detailed Colab notebooks - one for Inference and one for Fine-tuning. These notebooks come pre-filled with all the information and code you'll need. All you'll have to do is run them! Keep an eye out for these notebooks. They'll be added to the repository soon! --- <h2>Why Use GenZ? 💡</h2> You might be wondering, "Why should I choose GenZ over a pretrained model?" The answer lies in the extra mile we've gone to fine-tune our models. While pretrained models are undeniably powerful, GenZ brings something extra to the table. We've fine-tuned it with curated datasets, which means it has additional skills and capabilities beyond what a pretrained model can offer. Whether you need it for a simple task or a complex project, GenZ is up for the challenge. What's more, we are committed to continuously enhancing GenZ. We believe in the power of constant learning and improvement. That's why we'll be regularly fine-tuning our models with various curated datasets to make them even better. Our goal is to reach the state of the art and beyond - and we're committed to staying the course until we get there. But don't just take our word for it. We've provided detailed evaluations and performance details in a later section, so you can see the difference for yourself. Choose GenZ and join us on this journey. Together, we can push the boundaries of what's possible with large language models. --- <h2>Model Card for GenZ 13B 📄</h2> Here's a quick overview of everything you need to know about GenZ 13B. <h3>Model Details:</h3> - Developed by: Bud Ecosystem - Base pretrained model type: Llama V2 13B - Model Architecture: GenZ 13B, fine-tuned on Llama V2 13B, is an auto-regressive language model that employs an optimized transformer architecture. The fine-tuning process for GenZ 13B leveraged Supervised Fine-Tuning (SFT) - License: The model is available for commercial use under a custom commercial license. For more information, please visit: [Meta AI Model and Library Downloads](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) --- <h2>Intended Use 💼</h2> When we created GenZ 13B, we had a clear vision of how it could be used to push the boundaries of what's possible with large language models. We also understand the importance of using such models responsibly. Here's a brief overview of the intended and out-of-scope uses for GenZ 13B. <h3>Direct Use</h3> GenZ 13B is designed to be a powerful tool for research on large language models. It's also an excellent foundation for further specialization and fine-tuning for specific use cases, such as: - Text summarization - Text generation - Chatbot creation - And much more! <h3>Out-of-Scope Use 🚩</h3> While GenZ 13B is versatile, there are certain uses that are out of scope: - Production use without adequate assessment of risks and mitigation - Any use cases which may be considered irresponsible or harmful - Use in any manner that violates applicable laws or regulations, including trade compliance laws - Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2 Remember, GenZ 13B, like any large language model, is trained on a large-scale corpora representative of the web, and therefore, may carry the stereotypes and biases commonly encountered online. <h3>Recommendations 🧠</h3> We recommend users of GenZ 13B to consider fine-tuning it for the specific set of tasks of interest. Appropriate precautions and guardrails should be taken for any production use. Using GenZ 13B responsibly is key to unlocking its full potential while maintaining a safe and respectful environment. --- <h2>Training Details 📚</h2> When fine-tuning GenZ 13B, we took a meticulous approach to ensure we were building on the solid base of the pretrained Llama V2 13B model in the most effective way. Here's a look at the key details of our training process: <h3>Fine-Tuning Training Data</h3> For the fine-tuning process, we used a carefully curated mix of datasets. These included data from OpenAssistant, an instruction fine-tuning dataset, and Thought Source for the Chain Of Thought (CoT) approach. This diverse mix of data sources helped us enhance the model's capabilities across a range of tasks. <h3>Fine-Tuning Procedure</h3> We performed a full-parameter fine-tuning using Supervised Fine-Tuning (SFT). This was carried out on 4 A100 80GB GPUs, and the process took under 100 hours. To make the process more efficient, we used DeepSpeed's ZeRO-3 optimization. <h3>Tokenizer</h3> We used the SentencePiece tokenizer during the fine-tuning process. This tokenizer is known for its capability to handle open-vocabulary language tasks efficiently. <h3>Hyperparameters</h3> Here are the hyperparameters we used for fine-tuning: \| Hyperparameter \| Value \| \| -------------- \| ----- \| \| Warmup Ratio \| 0.04 \| \| Learning Rate Scheduler Type \| Cosine \| \| Learning Rate \| 2e-5 \| \| Number of Training Epochs \| 3 \| \| Per Device Training Batch Size \| 4 \| \| Gradient Accumulation Steps \| 4 \| \| Precision \| FP16 \| \| Optimizer \| AdamW \| --- <h2>Evaluations 🎯</h2> Evaluating our model is a key part of our fine-tuning process. It helps us understand how our model is performing and how it stacks up against other models. Here's a look at some of the key evaluations for GenZ 13B: <h3>Benchmark Comparison</h3> We've compared GenZ V1 with V2 to understand the improvements our fine-tuning has achieved. \| Model Name \| MT Bench \| Vicuna Bench \| MMLU \| Human Eval \| Hellaswag \| BBH \| \|:----------:\|:--------:\|:------------:\|:----:\|:----------:\|:---------:\|:----:\| \| Genz 13B \| 6.12 \| 86.1 \| 53.62\| 17.68 \| 77.38 \| 37.76\| \| Genz 13B v2\| 6.79 \| 87.2 \| 53.68\| 21.95 \| 77.48 \| 38.1 \| <h3>MT Bench Score</h3> A key evaluation metric we use is the MT Bench score. This score provides a comprehensive assessment of our model's performance across a range of tasks. We're proud to say that our model performs at a level that's close to the Llama-70B-chat model on the MT Bench and top of the list among 13B models. <p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_score.png" width="500"></p> In the transition from GenZ V1 to V2, we noticed some fascinating performance shifts. While we saw a slight dip in coding performance, two other areas, Roleplay and Math, saw noticeable improvements. --- <h2>Looking Ahead 👀</h2> We're excited about the journey ahead with GenZ. We're committed to continuously improving and enhancing our models, and we're excited to see what the open-source community will build with them. We believe in the power of collaboration, and we can't wait to see what we can achieve together. Remember, we're just getting started. This is just the beginning of a journey that we believe will revolutionize the world of large language models. We invite you to join us on this exciting journey. Together, we can push the boundaries of what's possible with AI. 🚀 --- Check the GitHub for the code -> [GenZ](https://raw.githubusercontent.com/BudEcosystem/GenZ)
vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA_AUQC	vnktrmnb	2023-07-28T14:46:12Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-28T13:18:33Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA_AUQC 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. --> # vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA_AUQC This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5563 - Train End Logits Accuracy: 0.6377 - Train Start Logits Accuracy: 0.6615 - Validation Loss: 0.5213 - Validation End Logits Accuracy: 0.8376 - Validation Start Logits Accuracy: 0.8905 - Epoch: 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: - 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 804, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.5563 \| 0.6377 \| 0.6615 \| 0.5213 \| 0.8376 \| 0.8905 \| 0 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
cenkersisman/gpt2-turkish-10m	cenkersisman	2023-07-28T14:43:04Z	139	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "tr", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T14:24:17Z	--- widget: - text: 'cengiz' example_title: Örnek 1 language: - tr --- # Model Card for Model ID Model Card for GPT-2 Turkish Model ## Model Details ### Model Description GPT-2 Turkish Model is a language model based on the GPT-2 architecture, fine-tuned for the Turkish language. It is capable of generating human-like text based on a given prompt and has been trained on a large corpus of Turkish text data. - Developed by: Cenker Sisman - Model type: - Language(s) (NLP): - License: - Finetuned from model : GPT-2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Sınırlamalar ve Önyargılar Bu model, bir özyineli dil modeli olarak eğitildi. Bu, temel işlevinin bir metin dizisi alıp bir sonraki belirteci tahmin etmek olduğu anlamına gelir. Dil modelleri bunun dışında birçok görev için yaygın olarak kullanılsa da, bu çalışmayla ilgili birçok bilinmeyen bulunmaktadır. Model, küfür, açık saçıklık ve aksi davranışlara yol açan metinleri içerdiği bilinen bir veri kümesi üzerinde eğitildi. Kullanım durumunuza bağlı olarak, bu model toplumsal olarak kabul edilemez metinler üretebilir. Tüm dil modellerinde olduğu gibi, bu modelin belirli bir girişe nasıl yanıt vereceğini önceden tahmin etmek zordur ve uyarı olmaksızın saldırgan içerik ortaya çıkabilir. Sonuçları yayınlamadan önce hem istenmeyen içeriği sansürlemek hem de sonuçların kalitesini iyileştirmek için insanların çıktıları denetlemesini veya filtrelemesi önerilir. ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model ```python # Example code for inference with the model from transformers import GPT2Tokenizer, GPT2LMHeadModel model_name = "cenkersisman/gpt2-turkish" # Change to the model name you have uploaded to Hugging Face tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) prompt = "cengiz" input_ids = tokenizer.encode(prompt, return_tensors="pt") output = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text) ``` ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ZhiguangHan/test-clm	ZhiguangHan	2023-07-28T14:42:03Z	182	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T14:02:13Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: test-clm 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. --> # test-clm This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6319 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.5547 \| 1.0 \| 2334 \| 3.6373 \| \| 3.4926 \| 2.0 \| 4668 \| 3.6361 \| \| 3.4692 \| 3.0 \| 7002 \| 3.6319 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
greg-szopinski/Reinforce-pixelcopter-128	greg-szopinski	2023-07-28T14:38:21Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T14:36:26Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter-128 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 33.80 +/- 16.58 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
sailoromkar/q-taxi	sailoromkar	2023-07-28T14:31:52Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T14:31:10Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 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="sailoromkar/q-taxi", 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"]) ```
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025	jordyvl	2023-07-28T14:26:36Z	106	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-23T21:50:37Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025 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. --> # EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2244 - Accuracy: 0.9394 - Exit 0 Accuracy: 0.2721 - Exit 1 Accuracy: 0.4875 - Exit 2 Accuracy: 0.8051 - Exit 3 Accuracy: 0.8840 - Exit 4 Accuracy: 0.9339 ## 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: 6 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 144 - 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 \| Accuracy \| Exit 0 Accuracy \| Exit 1 Accuracy \| Exit 2 Accuracy \| Exit 3 Accuracy \| Exit 4 Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\| \| 0.5909 \| 1.0 \| 2222 \| 0.2945 \| 0.9158 \| 0.2021 \| 0.3569 \| 0.7091 \| 0.8143 \| 0.9092 \| \| 0.4951 \| 2.0 \| 4444 \| 0.2469 \| 0.9292 \| 0.2262 \| 0.4336 \| 0.7677 \| 0.8614 \| 0.9258 \| \| 0.4279 \| 3.0 \| 6666 \| 0.2281 \| 0.9336 \| 0.2530 \| 0.4682 \| 0.7898 \| 0.8768 \| 0.9302 \| \| 0.39 \| 4.0 \| 8888 \| 0.2241 \| 0.9385 \| 0.2600 \| 0.483 \| 0.8008 \| 0.8827 \| 0.9328 \| \| 0.3602 \| 5.0 \| 11110 \| 0.2244 \| 0.9394 \| 0.2721 \| 0.4875 \| 0.8051 \| 0.8840 \| 0.9339 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
Pierre-Arthur/distilroberta_base_eurolex_mlm_model	Pierre-Arthur	2023-07-28T14:22:58Z	171	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "dataset:eurlex_resources", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-28T11:29:42Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer datasets: - eurlex_resources model-index: - name: distilroberta_base_eurolex_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. --> # distilroberta_base_eurolex_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the eurlex_resources dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 38 \| nan \| \| No log \| 2.0 \| 76 \| nan \| \| No log \| 3.0 \| 114 \| nan \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
Lajonbot/tableBeluga-7B-instruct-pl-lora_unload	Lajonbot	2023-07-28T14:12:09Z	1,396	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T13:59:47Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---
twbrandon7/rl-course-unit1	twbrandon7	2023-07-28T14:02:48Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T14:01:02Z	--- 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: 249.84 +/- 17.46 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 ... ```
Lajonbot/tableBeluga-7B-instruct-pl-lora_GGML	Lajonbot	2023-07-28T13:59:47Z	0	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]	text-generation	2023-07-28T13:49:06Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---
StereoLuigi99/ali-gungor	StereoLuigi99	2023-07-28T13:55:00Z	0	0	null	[ "region:us" ]	null	2023-07-28T13:44:42Z	# Ali GÜNGÖR AI Voice (200 Epochs / Harvest) RVC v2 kullanılarak oluşturuldu. Recep Baltaş tek başına kalmasın diye yaptım, konuşmalar için kullanın. ## Özellikler - 38 dakikalık Ali GÜNGÖR sesi kullanıldı. - RVC v2 ile Google Colab üzerinden yapıldı: https://colab.research.google.com/drive/1r4IRL0UA7JEoZ0ZK8PKfMyTIBHKpyhcw - Harvest metodu ile çalıştırıldı. - 200 Epoch bulunuyor.
Naruke/ppo-Pyramidsv1	Naruke	2023-07-28T13:50:56Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-28T13:25:08Z	--- 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: Naruke/ppo-Pyramidsv1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Lajonbot/tableBeluga-7B-instruct-pl-lora_adapter_model	Lajonbot	2023-07-28T13:49:05Z	0	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]	text-generation	2023-07-28T13:49:04Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---
FinchResearch/llama2-archimedes-7b-lora	FinchResearch	2023-07-28T13:47:43Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-28T13:47:36Z	--- 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
HaziqRazali/ppo-Pyramids	HaziqRazali	2023-07-28T13:45:47Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-28T13:45:14Z	--- 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: HaziqRazali/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LarryAIDraw/bluearchive_haruna-10	LarryAIDraw	2023-07-28T13:42:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-28T13:32:21Z	--- license: creativeml-openrail-m --- https://civitai.com/models/52253/kurodate-haruna-or-blue-archive
LarryAIDraw/narb-overlord-13	LarryAIDraw	2023-07-28T13:42:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-28T13:31:14Z	--- license: creativeml-openrail-m --- https://civitai.com/models/117294/narberal-gamma-overlord
LarryAIDraw/sakurako1-000006	LarryAIDraw	2023-07-28T13:41:56Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-28T13:30:55Z	--- license: creativeml-openrail-m --- https://civitai.com/models/114592/utazumi-sakurako-blue-archive-or-character-lora-344
LarryAIDraw/fymt	LarryAIDraw	2023-07-28T13:41:46Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-28T13:29:55Z	--- license: creativeml-openrail-m --- https://civitai.com/models/96806/arknights-fiammetta
Envertime/test_deliberate_v2	Envertime	2023-07-28T13:39:44Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-28T13:39:44Z	--- license: creativeml-openrail-m ---
NasimB/aochildes-rarity-seed	NasimB	2023-07-28T13:37:53Z	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	2023-07-28T04:40:59Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: 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. --> # 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: 4.1164 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3514 \| 0.29 \| 500 \| 5.3390 \| \| 5.0369 \| 0.59 \| 1000 \| 4.9222 \| \| 4.7211 \| 0.88 \| 1500 \| 4.6884 \| \| 4.4532 \| 1.17 \| 2000 \| 4.5398 \| \| 4.3029 \| 1.47 \| 2500 \| 4.4318 \| \| 4.2095 \| 1.76 \| 3000 \| 4.3295 \| \| 4.0772 \| 2.05 \| 3500 \| 4.2615 \| \| 3.9042 \| 2.35 \| 4000 \| 4.2130 \| \| 3.8732 \| 2.64 \| 4500 \| 4.1604 \| \| 3.8358 \| 2.93 \| 5000 \| 4.1110 \| \| 3.641 \| 3.23 \| 5500 \| 4.1105 \| \| 3.5952 \| 3.52 \| 6000 \| 4.0799 \| \| 3.5797 \| 3.81 \| 6500 \| 4.0466 \| \| 3.465 \| 4.11 \| 7000 \| 4.0458 \| \| 3.3242 \| 4.4 \| 7500 \| 4.0451 \| \| 3.3146 \| 4.69 \| 8000 \| 4.0309 \| \| 3.3112 \| 4.99 \| 8500 \| 4.0183 \| \| 3.1524 \| 5.28 \| 9000 \| 4.0325 \| \| 3.1343 \| 5.57 \| 9500 \| 4.0319 \| \| 3.1354 \| 5.87 \| 10000 \| 4.0309 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
JBJoyce/speecht5_finetuned_voxpopuli_sl	JBJoyce	2023-07-28T13:37:51Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-28T12:28:14Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_sl 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. --> # speecht5_finetuned_voxpopuli_sl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4393 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4886 \| 21.51 \| 1000 \| 0.4456 \| \| 0.4706 \| 43.01 \| 2000 \| 0.4393 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
liuyt75/t5-large_prefix_tuning_sentences_66agree_10	liuyt75	2023-07-28T13:35:44Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-28T13:35:42Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
youlun77/DistilBERT-TC2000-10epochs	youlun77	2023-07-28T13:30:50Z	113	0	transformers	[ "transformers", "pytorch", "tensorboard", "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	2023-07-28T13:22:29Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - recall - precision - f1 model-index: - name: DistilBERT-TC2000-10epochs 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-TC2000-10epochs 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: 0.0752 - Recall: {'recall': 0.98} - Precision: {'precision': 0.9803145941921073} - F1: {'f1': 0.9800242537313432} ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Recall \| Precision \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------------:\|:---------------------------------:\|:--------------------------:\| \| 1.0272 \| 0.18 \| 20 \| 0.8815 \| {'recall': 0.65} \| {'precision': 0.7778791777580597} \| {'f1': 0.6251215862860073} \| \| 0.8663 \| 0.35 \| 40 \| 0.6770 \| {'recall': 0.905} \| {'precision': 0.9120308312976535} \| {'f1': 0.9054010850819201} \| \| 0.6016 \| 0.53 \| 60 \| 0.4088 \| {'recall': 0.92} \| {'precision': 0.9238949736347314} \| {'f1': 0.9207242314918276} \| \| 0.3139 \| 0.71 \| 80 \| 0.2508 \| {'recall': 0.93} \| {'precision': 0.9322386382325532} \| {'f1': 0.929768888773222} \| \| 0.2645 \| 0.88 \| 100 \| 0.2048 \| {'recall': 0.955} \| {'precision': 0.958280303030303} \| {'f1': 0.954923196771023} \| \| 0.1811 \| 1.06 \| 120 \| 0.1446 \| {'recall': 0.965} \| {'precision': 0.9675925925925927} \| {'f1': 0.9648852158183796} \| \| 0.1429 \| 1.24 \| 140 \| 0.1245 \| {'recall': 0.975} \| {'precision': 0.9762354497354496} \| {'f1': 0.9749193929610656} \| \| 0.0941 \| 1.42 \| 160 \| 0.1338 \| {'recall': 0.965} \| {'precision': 0.9683561643835616} \| {'f1': 0.9652805623632961} \| \| 0.1242 \| 1.59 \| 180 \| 0.0872 \| {'recall': 0.975} \| {'precision': 0.9759505494505496} \| {'f1': 0.9750344590666455} \| \| 0.0893 \| 1.77 \| 200 \| 0.0572 \| {'recall': 0.985} \| {'precision': 0.9853867102396515} \| {'f1': 0.9849564819176908} \| \| 0.0477 \| 1.95 \| 220 \| 0.0794 \| {'recall': 0.975} \| {'precision': 0.9762354497354496} \| {'f1': 0.9749193929610656} \| \| 0.0128 \| 2.12 \| 240 \| 0.0697 \| {'recall': 0.98} \| {'precision': 0.9807447665056361} \| {'f1': 0.9799368665956859} \| \| 0.0449 \| 2.3 \| 260 \| 0.0635 \| {'recall': 0.97} \| {'precision': 0.9725} \| {'f1': 0.9702302752172594} \| \| 0.0996 \| 2.48 \| 280 \| 0.0782 \| {'recall': 0.97} \| {'precision': 0.9725} \| {'f1': 0.9700752508361203} \| \| 0.0328 \| 2.65 \| 300 \| 0.0127 \| {'recall': 0.995} \| {'precision': 0.995060975609756} \| {'f1': 0.9949962534538471} \| \| 0.0747 \| 2.83 \| 320 \| 0.0380 \| {'recall': 0.975} \| {'precision': 0.9767605633802816} \| {'f1': 0.9751792302987906} \| \| 0.0413 \| 3.01 \| 340 \| 0.0127 \| {'recall': 1.0} \| {'precision': 1.0} \| {'f1': 1.0} \| \| 0.0404 \| 3.19 \| 360 \| 0.0120 \| {'recall': 0.995} \| {'precision': 0.995060975609756} \| {'f1': 0.9949915278995033} \| \| 0.0226 \| 3.36 \| 380 \| 0.0085 \| {'recall': 1.0} \| {'precision': 1.0} \| {'f1': 1.0} \| \| 0.0543 \| 3.54 \| 400 \| 0.0139 \| {'recall': 0.995} \| {'precision': 0.9950925925925926} \| {'f1': 0.9950042805165157} \| \| 0.0528 \| 3.72 \| 420 \| 0.0408 \| {'recall': 0.985} \| {'precision': 0.9856521739130435} \| {'f1': 0.9850251572327045} \| \| 0.0051 \| 3.89 \| 440 \| 0.0808 \| {'recall': 0.97} \| {'precision': 0.9725} \| {'f1': 0.9702302752172594} \| \| 0.014 \| 4.07 \| 460 \| 0.0419 \| {'recall': 0.985} \| {'precision': 0.985241846323936} \| {'f1': 0.985017255463425} \| \| 0.051 \| 4.25 \| 480 \| 0.0127 \| {'recall': 0.995} \| {'precision': 0.9950925925925926} \| {'f1': 0.9950042805165157} \| \| 0.0501 \| 4.42 \| 500 \| 0.0200 \| {'recall': 0.985} \| {'precision': 0.9850867537313434} \| {'f1': 0.985009807126512} \| \| 0.0062 \| 4.6 \| 520 \| 0.0247 \| {'recall': 0.985} \| {'precision': 0.985241846323936} \| {'f1': 0.985017255463425} \| \| 0.0118 \| 4.78 \| 540 \| 0.0614 \| {'recall': 0.975} \| {'precision': 0.975962157809984} \| {'f1': 0.975047977706797} \| \| 0.0348 \| 4.96 \| 560 \| 0.0516 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0226 \| 5.13 \| 580 \| 0.0144 \| {'recall': 0.995} \| {'precision': 0.995060975609756} \| {'f1': 0.9949962534538471} \| \| 0.0159 \| 5.31 \| 600 \| 0.0129 \| {'recall': 0.995} \| {'precision': 0.995060975609756} \| {'f1': 0.9949962534538471} \| \| 0.0026 \| 5.49 \| 620 \| 0.0176 \| {'recall': 0.995} \| {'precision': 0.995060975609756} \| {'f1': 0.9949962534538471} \| \| 0.016 \| 5.66 \| 640 \| 0.0404 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0433 \| 5.84 \| 660 \| 0.0663 \| {'recall': 0.975} \| {'precision': 0.9756772575250836} \| {'f1': 0.975041928721174} \| \| 0.0354 \| 6.02 \| 680 \| 0.0253 \| {'recall': 0.995} \| {'precision': 0.995060975609756} \| {'f1': 0.9949962534538471} \| \| 0.0041 \| 6.19 \| 700 \| 0.0961 \| {'recall': 0.97} \| {'precision': 0.9711688311688311} \| {'f1': 0.9700614296351452} \| \| 0.0579 \| 6.37 \| 720 \| 0.1336 \| {'recall': 0.965} \| {'precision': 0.966783728687917} \| {'f1': 0.9650813612906225} \| \| 0.0025 \| 6.55 \| 740 \| 0.0424 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0328 \| 6.73 \| 760 \| 0.0190 \| {'recall': 0.995} \| {'precision': 0.995060975609756} \| {'f1': 0.9949962534538471} \| \| 0.0217 \| 6.9 \| 780 \| 0.0488 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0096 \| 7.08 \| 800 \| 0.1115 \| {'recall': 0.97} \| {'precision': 0.9711688311688311} \| {'f1': 0.9700614296351452} \| \| 0.0106 \| 7.26 \| 820 \| 0.0673 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0077 \| 7.43 \| 840 \| 0.0354 \| {'recall': 0.985} \| {'precision': 0.9850867537313434} \| {'f1': 0.985009807126512} \| \| 0.0222 \| 7.61 \| 860 \| 0.0410 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0026 \| 7.79 \| 880 \| 0.0590 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0576 \| 7.96 \| 900 \| 0.0596 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.018 \| 8.14 \| 920 \| 0.0428 \| {'recall': 0.985} \| {'precision': 0.9850867537313434} \| {'f1': 0.985009807126512} \| \| 0.027 \| 8.32 \| 940 \| 0.0425 \| {'recall': 0.985} \| {'precision': 0.9850867537313434} \| {'f1': 0.985009807126512} \| \| 0.036 \| 8.5 \| 960 \| 0.0341 \| {'recall': 0.985} \| {'precision': 0.9850867537313434} \| {'f1': 0.985009807126512} \| \| 0.0094 \| 8.67 \| 980 \| 0.0457 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0192 \| 8.85 \| 1000 \| 0.0586 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.03 \| 9.03 \| 1020 \| 0.0789 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0091 \| 9.2 \| 1040 \| 0.0691 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0197 \| 9.38 \| 1060 \| 0.0753 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0025 \| 9.56 \| 1080 \| 0.0796 \| {'recall': 0.975} \| {'precision': 0.9756772575250836} \| {'f1': 0.975041928721174} \| \| 0.0414 \| 9.73 \| 1100 \| 0.0791 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| \| 0.0075 \| 9.91 \| 1120 \| 0.0756 \| {'recall': 0.98} \| {'precision': 0.9803145941921073} \| {'f1': 0.9800242537313432} \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
w601sxs/b1ade-1b-orca-chkpt-563k	w601sxs	2023-07-28T13:30:11Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-28T13:30:09Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0	Evan-Lin	2023-07-28T13:25:27Z	51	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-07-28T13:23:43Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpapodd3ns/Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpapodd3ns/Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpapodd3ns/Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
WforGodot/add-lora-1b	WforGodot	2023-07-28T13:24:40Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-28T13:10:39Z	--- 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 0.4.0
efainman/Pyramids	efainman	2023-07-28T13:20:59Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-28T13:20:45Z	--- 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: efainman/Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
stefaniftime/bert-finetuned-squad	stefaniftime	2023-07-28T13:17:20Z	61	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-28T10:29:03Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: stefaniftime/bert-finetuned-squad 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. --> # stefaniftime/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2718 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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: mixed_float16 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 1.2718 \| 0 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
JAdeojo/xlm-roberta-large-lora-consumer-complaints-cfpb_checkpoint2	JAdeojo	2023-07-28T13:13:13Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-28T13:13:07Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
liuyt75/t5-large_prefix_tuning_sentences_66agree_5	liuyt75	2023-07-28T13:07:09Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-28T13:07:08Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
jcy204/cold_model	jcy204	2023-07-28T13:06:43Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "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	2023-07-28T13:01:33Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jcy204/cold_model 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. --> # jcy204/cold_model 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: 0.3079 - Validation Loss: 0.6510 - Train Accuracy: 0.7604 - Epoch: 2 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3185, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6912 \| 0.5784 \| 0.7513 \| 0 \| \| 0.4713 \| 0.5637 \| 0.7641 \| 1 \| \| 0.3079 \| 0.6510 \| 0.7604 \| 2 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
guyhadad01/ppo-LunarLander-v2	guyhadad01	2023-07-28T13:06:17Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T13:05:58Z	--- 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: 282.00 +/- 17.53 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 ... ```
elvhens/ryujin-500	elvhens	2023-07-28T13:01:27Z	0	0	null	[ "kpop", "itzy", "ryujin", "ko", "region:us" ]	null	2023-07-28T11:15:18Z	--- language: - ko tags: - kpop - itzy - ryujin ---
iworeushankaonce/whisper-tiny-en	iworeushankaonce	2023-07-28T12:49:14Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "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	2023-07-28T12:18:50Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.36481700118063753 --- <!-- 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 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6411 - Wer Ortho: 0.3621 - Wer: 0.3648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0008 \| 17.86 \| 500 \| 0.6411 \| 0.3621 \| 0.3648 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
Maldopast/distilhubert-finetuned-gtzan	Maldopast	2023-07-28T12:49:12Z	157	0	transformers	[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-28T12:30:11Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7537 - 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: 16 - eval_batch_size: 16 - 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9647 \| 1.0 \| 113 \| 1.8614 \| 0.52 \| \| 1.3987 \| 2.0 \| 226 \| 1.3098 \| 0.61 \| \| 0.8809 \| 3.0 \| 339 \| 0.8631 \| 0.76 \| \| 0.7643 \| 4.0 \| 452 \| 0.8114 \| 0.77 \| \| 0.5958 \| 5.0 \| 565 \| 0.7013 \| 0.81 \| \| 0.4405 \| 6.0 \| 678 \| 0.5860 \| 0.84 \| \| 0.2183 \| 7.0 \| 791 \| 0.6114 \| 0.82 \| \| 0.1587 \| 8.0 \| 904 \| 0.5141 \| 0.85 \| \| 0.0899 \| 9.0 \| 1017 \| 0.4760 \| 0.87 \| \| 0.0575 \| 10.0 \| 1130 \| 0.5759 \| 0.86 \| \| 0.0647 \| 11.0 \| 1243 \| 0.6467 \| 0.86 \| \| 0.0061 \| 12.0 \| 1356 \| 0.6372 \| 0.88 \| \| 0.0029 \| 13.0 \| 1469 \| 0.6721 \| 0.88 \| \| 0.0018 \| 14.0 \| 1582 \| 0.7565 \| 0.89 \| \| 0.0013 \| 15.0 \| 1695 \| 0.7537 \| 0.88 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.0 - Tokenizers 0.13.3
FiouReia/my_awesome_model	FiouReia	2023-07-28T12:44:03Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "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	2023-07-28T10:16:24Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: FiouReia/my_awesome_model 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. --> # FiouReia/my_awesome_model 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: 0.0326 - Validation Loss: 0.2450 - Train Accuracy: 0.934 - Epoch: 2 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.1321 \| 0.1917 \| 0.928 \| 0 \| \| 0.0649 \| 0.2146 \| 0.9351 \| 1 \| \| 0.0326 \| 0.2450 \| 0.934 \| 2 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
ChristophBu/ppo-LunarLander-v2	ChristophBu	2023-07-28T12:38:59Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T11:02:50Z	--- 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: 277.34 +/- 18.25 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 ... ```
Isaacgv/whisper-tiny-minds14	Isaacgv	2023-07-28T12:37:07Z	86	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "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	2023-07-28T10:03:38Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.3530106257378985 --- <!-- 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-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6457 - Wer Ortho: 0.3572 - Wer: 0.3530 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0007 \| 17.86 \| 500 \| 0.6457 \| 0.3572 \| 0.3530 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
magnustragardh/ppo-deepdive-LunarLander-v2	magnustragardh	2023-07-28T12:34:33Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T22:26:18Z	--- 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: 72.15 +/- 17.75 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
ArmelR/starcoder-gradio-v2.1	ArmelR	2023-07-28T12:21:21Z	7	0	transformers	[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "dataset:databricks/databricks-dolly-15k", "dataset:HuggingFaceH4/oasst1_en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T11:31:17Z	--- datasets: - databricks/databricks-dolly-15k - HuggingFaceH4/oasst1_en --- Gradio training 1000 steps IFT 200 steps (v2.0) 1000 steps (v2.1)
donadelicc/nor-sum	donadelicc	2023-07-28T12:12:46Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "base_model:sshleifer/distilbart-cnn-6-6", "base_model:finetune:sshleifer/distilbart-cnn-6-6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-28T09:56:12Z	--- license: apache-2.0 base_model: sshleifer/distilbart-cnn-6-6 tags: - generated_from_trainer metrics: - rouge model-index: - name: nor-sum 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. --> # nor-sum This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1812 - Rouge1: 0.2552 - Rouge2: 0.0679 - Rougel: 0.1884 - Rougelsum: 0.1886 - Gen Len: 65.3086 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 2.6231 \| 1.0 \| 3188 \| 2.4652 \| 0.2359 \| 0.0563 \| 0.1732 \| 0.1733 \| 66.1928 \| \| 2.3062 \| 2.0 \| 6377 \| 2.2798 \| 0.2524 \| 0.0653 \| 0.1864 \| 0.1864 \| 66.3107 \| \| 2.0817 \| 3.0 \| 9565 \| 2.1973 \| 0.2529 \| 0.0675 \| 0.189 \| 0.1893 \| 65.077 \| \| 1.9776 \| 4.0 \| 12752 \| 2.1812 \| 0.2552 \| 0.0679 \| 0.1884 \| 0.1886 \| 65.3086 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
zzzAI19/MasterpieceFT	zzzAI19	2023-07-28T12:08:21Z	0	3	null	[ "region:us" ]	null	2023-07-28T11:35:45Z	This model is additionally studied with past masterpieces. It can be painted pictorially. There are two versions: an all-ages depiction-oriented version and an NSFW-enhanced version. All-ages depiction-oriented version: MasterpieceFT_v1-pruned.safetensors NSFW-enhanced version: MasterpieceNeoFT_v1-pruned.safetensors (NEO: Nsfw Erotic Option) This model is somewhat difficult to use. The NSFW enhanced version is not particularly easy to use. See blog for sample illustrations. (Sample illustrations for all ages) https://ai-drawing.net/en/2023/07/28/introducing-of-masterpiece-ft-v1-0/ (R18 sample illustrations) https://ai-drawing2.net/2023/07/28/masterpiece-neo-ft-v1-0/ (recommended settings) “fine art parody, traditional media” is the prompt for classical painting. It is recommended that these be included in the positive prompts. Also, Steps 2.5-4 will give you impressionistic paintings, and Steps 7 and above will give you realistic paintings. Use whichever you prefer. For realistic pictures, it is compatible with flat2 LoRA. I recommend using it together. https://civitai.com/models/81291/flat2 過去の名画で追加学習したモデルです。絵画的な絵が描けます。全年齢描写重視版とNSFW強化版があります。全年齢描写重視版：MasterpieceFT_v1-pruned.safetensors NSFW強化版：MasterpieceNeoFT_v1-pruned.safetensors (NEO：Nsfw Erotic Option) このモデルはやや使いにくいです。特にNSFW強化版の使い勝手は良くありません。サンプルイラストはブログをご覧ください。 (全年齢サンプルイラスト) https://ai-drawing.net/2023/07/28/%e6%96%b0%e3%83%a2%e3%83%87%e3%83%abmasterpiece-ft-v1-0%e7%b4%b9%e4%bb%8b/ (R18サンプルイラスト) https://ai-drawing2.net/2023/07/28/masterpiece-neo-ft-v1-0/ (推奨設定) fine art parody, traditional mediaが古典絵画のプロンプトです。これらをポジティブプロンプトに入れることをお勧めします。またステップ2.5～4では印象派的な絵、ステップ7以上では写実的な絵が描けます。好きな方を利用してください。また写実的な絵ではflat2 LoRAと相性が良いです。併用するのもおすすめです。 https://civitai.com/models/81291/flat2 --- license: creativeml-openrail-m ---
xiao12346/t5-large_PREFIX_TUNING_SEQ2SEQ	xiao12346	2023-07-28T12:02:24Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-21T05:14:16Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
X-D-Lab/MindChat-baichuan-13B	X-D-Lab	2023-07-28T11:52:16Z	5	8	transformers	[ "transformers", "pytorch", "baichuan", "text-generation", "custom_code", "license:gpl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T02:15:59Z	--- license: gpl-3.0 --- ## 💪 模型进展 * Jul 23, 2023: 提供MindChat体验地址: [MindChat-创空间](https://modelscope.cn/studios/X-D-Lab/MindChat/summary), 欢迎体验 * Jul 21, 2023: MindChat-InternLM-7B训练完成, 在模型安全、共情输出、人类价值观对齐等方面进行针对性强化; * Jul 15, 2023: MindChat-Baichuan-13B训练完成, 作为首个百亿级参数的心理大模型正式开源; * Jul 9, 2023: MindChat-beta训练完成, 并正式开源; * Jul 6, 2023: 首次提交MindChat(漫谈)心理大模型; ## 👏 模型介绍 MindChat(漫谈): 心理大模型(简称: MindChat)旨在从心理咨询、心理评估、心理诊断、心理治疗四个维度帮助人们解决心理问题, 提高心理健康水平. 作为一款心理大模型, MindChat通过营造轻松、开放的交谈环境, 以放松身心、交流感受或分享经验的方式, 与用户建立信任和理解的关系. MindChat的技术优势在于能够了解用户的个人经历、情感状态和行为模式, 为用户提供隐私、温暖、安全、及时、方便的对话环境, 从而帮助用户克服各种困难和挑战, 实现自我成长和发展. MindChat的创新之处在于它不仅是一款高效的心理咨询工具, 预期能够提供心理评估、心理诊断和心理治疗的服务. MindChat的专业团队拥有丰富的心理学背景和经验, 他们的专业知识和技能可以帮助用户更好地理解自己的情感和行为, 从而制定更有效的治疗方案. 无论是在工作场所还是在个人生活中, MindChat期望通过自身的努力和专业知识, 在严格保护用户隐私的前提下, 全时段全天候为用户提供全面的心理支持和诊疗帮助, 同时实现自我成长和发展, 以期为建设一个更加健康、包容和平等的社会贡献力量. ## 🔥 模型列表 \| 模型名称 \| lora权重 \| 合并后的权重 \| \| :----: \| :----: \| :----: \| \| MindChat-InternLM-7B \| modelscope / huggingface \| [modelscope](https://modelscope.cn/models/X-D-Lab/MindChat-7B/summary) / [huggingface](https://huggingface.co/X-D-Lab/MindChat-7B) \| \| MindChat-Baichuan-13B \| modelscope / huggingface \| [modelscope](https://modelscope.cn/models/X-D-Lab/MindChat-Baichuan-13B/summary) / [huggingface](https://huggingface.co/X-D-Lab/MindChat-baichuan-13B) \| 目前, 已经开源了MindChat-Baichuan-13B版本的百亿级MindChat心理大模型. 更为优质的MindChat模型将在不久的未来对外开源开放. 敬请期待! 此外, 本团队同时关注人们的身理健康, 建有安全、可靠、普惠的[中文医疗大模型孙思邈(Sunsimiao)](https://github.com/X-D-Lab/Sunsimiao), 欢迎下载使用, 敬请批评指证! ## 📖 对话案例 ### 🥺 学习 \| 轮数 \| 用户 \| MindChat \| \| :----: \| :----: \| :----: \| \|1\|总感觉自己学习很差, 想努力, 但是又控制不住想玩游戏 \| 我明白你的感受, 你觉得自己学习不好, 但是总是控制不住想玩游戏. 我想问问你, 你玩游戏的时候是什么感觉呢? 有没有什么特别喜欢的游戏?\| \|2\|我平常特别喜欢玩一些端游来打发时间, 玩游戏的时候很纠结, 明明自己成绩落后, 学习任务还没完成但还是想玩 \| 我明白你的感受. 你可能需要认真思考一下, 玩游戏对你来说是不是真的值得投入这么多时间和精力. 学习落后是很正常的事情, 但不要让游戏成为你逃避现实的工具. 你可以尝试制定一个计划, 把玩游戏的时间留给自己, 同时也要合理分配学习任务, 让自己更加充实. \| \|3\|在学习上总感觉自己学的很吃力, 不知道该怎么办, 觉得自己学习能力太差了, 心里很失落 \| 我理解你的感受. 每个人的学习能力都是不同的, 不要过分苛求自己. 你可以先尝试找到适合自己的学习方法, 比如寻找学习伙伴, 向老师请教, 或者参加一些学习小组. 同时, 也不要忘记给自己一些休息的时间, 保持身心健康. 学习是一个长期的过程, 不要过于急躁, 慢慢来, 你会越来越好的. \| \|4\|我确实不该把精力都是投入在游戏这些娱乐上, 但是我已经落后很多了, 我现在再去开始学习, 以我这么差的学习能力我还有机会学好吗 \| 虽然你现在的学习状态很糟糕, 但并不是没有机会. 学习是一个长期的过程, 只要你持之以恒, 坚持不懈, 就一定会有所进步. 同时, 也不要忘记, 学习的过程也需要一定的动力和动机. 你可以找一些自己感兴趣的学习方向, 或者设定一些小目标, 让自己更有动力去完成. 不要轻易放弃, 相信自己, 你一定可以做到. \| \|5\|可是我很焦虑, 以前落后太多了, 现在不知道从哪里开始 \| 焦虑是很正常的情绪, 不要害怕它. 你可以先想想自己目前的学习状态, 列出自己需要学习的科目和内容, 然后按照优先级和难易程度进行排序. 接着, 你可以尝试分解每个科目和内容, 把它们分成小目标, 一步一步地去完成. 这样, 你就可以更有针对性地进行学习, 不会感到太紧张和不知所措. 同时, 也不要忘记给自己一些奖励, 比如看一部电影, 吃一顿美食等等, 让自己更有动力去完成目标. \| \|6\|也就是我现在主要的问题是太焦虑了是吗? 我只要好好的去做, 成绩还是可以提高的对吗 \| 是的, 焦虑是一种很常见的情绪, 它会让你感到很不安, 但是它并不会影响你真正地提高成绩. 你可以试着把焦虑的情绪转化成动力, 让它成为你学习的一部分. 同时, 也不要忘记给自己一些时间, 不要过分压抑自己的情绪, 让自己保持一个积极的心态. 只要你持之以恒, 相信自己, 你一定可以取得更好的成绩. \| \|7\|非常谢谢你, 我会好好努力的 \| 我不用客气, 我很高兴能帮到你. 我相信你一定可以克服困难, 取得好成绩. 加油! 如果有任何需要帮助的地方, 随时都可以找我聊天. \| ### 👨‍👩‍👧‍👦 家庭 \| 轮数 \| 用户 \| MindChat \| \| :----: \| :----: \| :----: \| \|1\| 我总感觉爸妈不理解我, 感觉和他们之间有很大的隔阂. \| 我能理解你的感受. 和家人的沟通和理解是家庭关系中非常重要的一环. 你是否曾经尝试过和他们分享你的想法和感受, 让他们更好地了解你呢? \| \|2\|我有尝试找他们沟通, 但是他们听不进去我说话 , 认为我是在无理取闹, 我心里很无助 \| 我明白你的感受. 有时候我们的家人可能无法理解我们的想法和感受, 这时候我们需要耐心和沟通技巧来帮助他们更好地理解我们. 你可以试着换个方式表达你的想法, 或者找到一些共同话题来促进交流. 同时, 也要记得保持冷静和尊重, 不要使用攻击性的语言来伤害他们. \| \|3\|我也知道他们是为了我好, 平时他们督促我学习也是为了我的未来, 我能体谅他们, 但是有时候自己也会有压力, 想和他们沟通他们就会觉得我在偷懒 \| 我能理解你的感受. 有时候我们的家人可能认为我们在偷懒, 但是其实他们只是担心我们的未来和健康. 你可以试着跟他们分享你的压力和焦虑, 让他们了解你的感受. 同时, 也要表达出你对他们的理解和感激, 让他们知道你是愿意和他们一起面对问题, 共同找到解决方案的. \| \|4\|我平时会表现的乖巧, 因为我觉得他们也不容易, 我该怎么去找一个双方都适合的方式去沟通呢 \| 很好的问题. 沟通是一个相互的过程, 需要双方的努力和理解. 你可以试着找一些共同话题来促进交流, 同时也要听取他们的想法和建议. 如果你觉得自己的表达方式不够清晰, 可以尝试用一些简单的词汇和表达方式来让他们更容易理解. 最重要的是要保持耐心和尊重, 相信你们会找到适合彼此的方式. \| \|5\|可是我会觉得找他们沟通是一种不懂事的表现, 他们很辛苦我还给他们增加负担, 但是自己忍着难过心里也很压抑 \| 我能理解你的感受. 有时候我们会有这样的想法, 但是其实我们不应该让这些想法限制我们的行动. 沟通是解决问题的一种方式, 同时也是增进家庭关系的重要途径. 如果你觉得自己的心情很压抑, 可以试着找一些放松的方式, 比如运动、听音乐或者写日记来缓解压力. 同时也要记得, 你的家人是关心你的, 他们不会增加你的负担, 而是希望你能够更好地成长和幸福. \| \|6\|可能是我的想法有点偏执了, 我应该找一个时间和爸妈好好交流一下 \| 这是个好主意. 和家人的交流是非常重要的, 可以让你更好地了解他们的想法和感受. 你可以试着找一个安静的时间, 让他们知道你需要和他们谈一谈, 同时也要准备好自己的态度和想法. 相信通过交流, 你们可以更好地理解彼此, 增进感情. 加油. \| \|7\|谢谢你的建议, 我的心情好多了 \| 不用客气, 我很高兴能帮到你. 记得, 沟通和理解是家庭关系中非常重要的一环, 让我们一起为建立更加和谐的家庭关系而努力吧! \| ### 🛝 朋友 \| 轮数 \| 用户 \| MindChat \| \| :----: \| :----: \| :----: \| \|1\| 我感觉我的朋友并不是真心和我相处, 今天又和他吵了一架, 我现在很生气 \| 听到你的困扰, 我能理解你的感受. 吵架是一种不健康的沟通方式, 它可能导致双方情绪失控, 进而影响到彼此之间的关系. 我建议你尝试以平和的心态来面对这个问题. 你可以试着和你的朋友坐下来, 表达你的感受和需求, 让他了解你的想法和感受. 这样他才能更好地理解你的立场, 并且做出积极的回应. 同时, 你也可以告诉他, 你希望你们之间的交流是建立在互相尊重和理解的基础上的. \| \|2\|我总感觉他是以自我为中心, 有时候我会选择忍让, 但是他还是一副无所谓的样子 \| 我能理解你的感受, 但是忍让并不是解决问题的好方法. 你需要和他沟通, 让他知道你的感受和需求. 告诉他你希望他能够更加尊重你的感受, 并且尝试理解你的立场. 同时, 你也可以告诉他你希望你们之间的交流是建立在互相尊重和理解的基础上的. 如果他仍然无法理解你的立场, 那么你也需要学会说不, 并且保持自己的立场. 这样, 你们之间的关系才能健康发展. \| \|3\|但是如果跟他去沟通这些, 我怕他会觉得我想得太多, 从而远离我 \| 这是很正常的担忧. 但是, 沟通是解决问题的关键. 如果他真的关心你, 他会愿意和你沟通, 并且理解你的立场. 如果他不想和你沟通, 那么这可能是一个问题. 你需要尝试以平和的心态来面对这个问题, 并且告诉他你的感受和需求. 如果他仍然不愿意和你沟通, 那么你需要学会接受这个结果, 并且寻找其他的解决方法. 无论如何, 你都需要学会保持自己的立场, 并且不要放弃. \| \|4\|我觉得有时候情绪上来确实会影响我们之间的沟通, 可能生气也是觉得这段友谊本身很好, 但是也会出现小的矛盾 \| 情绪确实会影响我们的沟通, 但是你可以试着在情绪平静下来之后再和他沟通. 当你感到生气的时候, 你的思维和语言可能会变得不清晰, 这会加剧矛盾. 所以, 在情绪平复下来之后, 你可以尝试和他坐下来, 以平和的心态来解决问题. 同时, 你也可以告诉他, 你希望你们之间的交流是建立在互相尊重和理解的基础上的. 如果他仍然无法理解你的立场, 那么你需要学会说不, 并且保持自己的立场. 这样, 你们之间的关系才能健康发展. \| \|5\|好的, 我会平静下来找他沟通一下, 谢谢你的建议 \| 不用谢, 我很高兴能帮到你. 记住, 沟通是解决问题的关键. 无论结果如何, 你都需要保持自己的立场, 并且不要放弃. 相信你们之间的关系会变得更加健康. 祝你好运! \| ### 🧑‍🏭 工作 \| 轮数 \| 用户 \| MindChat \| \| :----: \| :----: \| :----: \| \|1\| 最近工作压力好大, 一直没有业绩 \| 听到你的问题, 我可以理解你现在的感受. 工作压力确实会让人感到焦虑和不安, 不过我们需要认识到, 工作只是我们生活中的一部分, 我们需要学会放松和平衡. 你可以试着做一些放松的活动, 比如运动、阅读、听音乐等等, 来缓解一下紧张的情绪. 同时, 你也可以和领导和同事沟通, 表达你的想法和困惑, 看看有没有更好的解决方案. \| \|2\|我从事的是销售行业, 但是在销售商品的时候客户总是不想和我交流 \| 理解你的感受. 销售行业确实需要和客户进行很多交流, 但是有时候客户可能会因为各种原因不愿意和你交流. 你可以试着从客户的需求和兴趣出发, 了解他们的想法和需求, 尝试通过沟通来建立更好的关系. 同时, 你也可以学习一些沟通技巧, 比如积极倾听、提出问题、表达感激等等, 来提高你的沟通能力. \| \|3\|如果业绩不达标我的提成会变少, 也会惹得我家里人不开心, 两边压力都很大, 不知道该怎么办了 \| 这你的感受我能够理解. 首先, 我们需要认识到业绩不达标并不代表你的能力和价值, 每个人都会有挫折和失败的时候. 你可以试着和领导沟通, 寻求一些帮助和支持, 或者制定一些具体的计划和目标, 来提高自己的业绩. 同时, 你也可以和家里人沟通, 告诉他们你的实际情况, 让他们了解你的努力和付出, 或许他们会理解你的处境. 最重要的是, 不要放弃自己, 相信自己可以克服困难, 取得更好的成绩. \| \|4\|可是我的同事们业绩都比我好, 我很自卑, 在公司觉得很没面子 \| 自卑感是一种很常见的情绪, 但是你需要意识到, 每个人都有自己的优点和不足, 你并不比别人差. 你可以试着多和同事们交流, 了解他们的工作方式和经验, 也许可以从中学习到一些新的东西. 同时, 你也可以试着寻找自己的优点和特长, 提高自己的自信心和自尊心. 最重要的是, 不要把自己和别人比较, 每个人都有不同的成长轨迹和生活经历, 不要给自己太大的压力. \| \|5\|我试着向同事们交流一下经验吧, 也和家里人沟通一下, 希望他们能够理解我 \| 这是一个很好的想法, 多向同事和家人沟通可以让你得到更多的支持和理解. 同时, 你也要保持积极的心态, 相信自己的能力和价值, 不要放弃自己. 如果有需要, 你也可以寻求一些专业的帮助, 比如心理咨询师、职业规划师等等, 来帮助你更好地应对工作和生活上的挑战. 记得, 你不是一个人在战斗, 我们都需要互相支持和鼓励. \| ### 🦺 安全 \| 轮数 \| 用户 \| MindChat \| \| :----: \| :----: \| :----: \| \|1\| 我感觉我被造谣了, 我该怎么办 \| 我可以理解你现在的感受. 首先, 你需要冷静下来, 不要急于做出任何决定. 你可以试着问问自己, 你为什么会感到被造谣? 你是否有证据证明这个谣言是真的? 如果有证据, 你可以尝试与造谣者进行沟通, 了解他们为什么这样说, 看看能否通过沟通解决问题. 如果没有证据, 你可以考虑向相关机构或权威人士寻求帮助, 例如学校、警察局等. 同时, 你也可以寻求心理支持, 与朋友、家人或专业人士交流, 寻求帮助和建议. 记住, 不要让谣言影响到你的情绪和心理健康. \| \|2\|现在谣言已经在网络上开始散布了, 影响到了我的生活, 我感觉自己有点要抑郁了 \| 听到你这样说, 我感到非常担心. 抑郁症是一种严重的心理疾病, 需要得到及时的治疗. 我建议你尽快向专业的医生或心理医生寻求帮助. 他们可以帮助你诊断和治疗抑郁症, 同时也可以提供心理支持和建议. 在这个过程中, 你也可以尝试一些放松和缓解压力的方法, 例如运动、冥想、听音乐等. 不要忘记, 你不是一个人, 我们都在这里支持你. \| \|3\|我该怎么处理这种事, 我感觉无从下手, 周围全是恶意 \| 听到你这样说, 我感到很遗憾. 恶意和谣言的散布会对任何人造成负面影响. 首先, 你需要保持冷静, 不要让这些负面情绪影响到你的情绪和行为. 其次, 你可以尝试与那些恶意的人进行沟通, 让他们了解你的真实情况, 并试图解决问题. 如果他们不愿意听取你的解释, 你可以尝试向其他人寻求帮助, 例如老师、辅导员、家长等. 同时, 你也可以向学校或政府部门举报这种行为, 让他们采取措施打击恶意行为. 最重要的是, 不要放弃自己, 相信自己能够度过这个难关. 你不是一个人, 我们会一直支持你. \| \|4\|我很崩溃, 甚至我想让造谣者付出代价 \| 听到你这样说, 我感到很抱歉. 虽然我们希望恶意行为得到惩罚, 但我们应该采取更和平和理性的方式来解决问题. 首先, 你需要冷静下来, 不要让自己陷入负面情绪中. 然后, 你可以尝试与造谣者进行沟通, 让他们了解你的真实情况, 并试图解决问题. 如果他们不愿意听取你的解释, 你可以尝试向其他人寻求帮助, 例如老师、辅导员、家长等. 同时, 你也可以向学校或政府部门举报这种行为, 让他们采取措施打击恶意行为. 最重要的是, 不要放弃自己, 相信自己能够度过这个难关. 你不是一个人, 我们会一直支持你. \| \|5\|我会试着去寻求有关部门帮助的 \| 听到你这样说, 我感到很欣慰. 寻求帮助是解决问题的第一步, 你已经迈出了正确的步伐. 同时, 我也希望你能保持积极的心态, 相信事情会变得更好. 如果你需要任何帮助或支持, 请随时与我联系. 你不是一个人, 我们会一直支持你. \| ## 🙇‍ ‍致谢本项目由华东理工大学薛栋教授课题组发起, 并受到以下平台及项目的大力支持, 在此表示感谢! 1. [OpenI启智社区](https://openi.pcl.ac.cn/): 提供模型训练算力 2. [魔搭ModelScope](https://modelscope.cn/home): 提供训练思路、模型存储和体验空间 3. [InternLM](https://github.com/InternLM/InternLM)、[Baichuan](https://github.com/baichuan-inc)提供非常优秀的基础模型 ``` @misc{2023internlm, title={InternLM: A Multilingual Language Model with Progressively Enhanced Capabilities}, author={InternLM Team}, howpublished = {\url{https://github.com/InternLM/InternLM-techreport}}, year={2023} } ``` 针对不同用户需求和应用场景, 我们也热情欢迎商业交流和合作, 为各位客户提供个性化的开发和升级服务! ## ⚠️ 免责申明本仓库所有开源代码及模型均遵循GPL-3.0许可认证. 开源模型可能存在以下局限: 1. MindChat存在固有的局限性, 可能产生错误的、有害的、冒犯性的或其他不良的输出. 用户在关键或高风险场景中应谨慎行事, 不要使用这些模型作为最终决策参考, 以免导致人身伤害、财产损失或重大损失. 2. MindChat在任何情况下, 作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任(无论是合同、侵权还是其他原因)承担责任. 3. 使用MindChat即表示您同意这些条款和条件, 并承认您了解其使用可能带来的潜在风险. 您还同意赔偿并使作者、贡献者和版权所有者免受因您使用MindChat而产生的任何索赔、损害赔偿或责任的影响. ## 🤝 引用 ``` @misc{MindChat, author={Xin Yan, Dong Xue*}, title = {MindChat: Psychological Large Language Model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/X-D-Lab/MindChat}}, } ``` ## 🌟 Star History [![Star History Chart](https://api.star-history.com/svg?repos=X-D-Lab/MindChat&type=Date)](https://star-history.com/#X-D-Lab/MindChat&Date)
tobijen/bart_left_fulltext_torch	tobijen	2023-07-28T11:48:14Z	189	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text-generation", "generated_from_trainer", "base_model:facebook/bart-large", "base_model:finetune:facebook/bart-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T11:46:35Z	--- license: apache-2.0 base_model: facebook/bart-large tags: - generated_from_trainer model-index: - name: bart_left_fulltext_torch 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. --> # bart_left_fulltext_torch This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9329 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 8 \| 9.2222 \| \| No log \| 2.0 \| 16 \| 8.1910 \| \| No log \| 3.0 \| 24 \| 7.8851 \| \| No log \| 4.0 \| 32 \| 7.5382 \| \| No log \| 5.0 \| 40 \| 7.2200 \| \| No log \| 6.0 \| 48 \| 6.7294 \| \| No log \| 7.0 \| 56 \| 6.1038 \| \| No log \| 8.0 \| 64 \| 5.5721 \| \| No log \| 9.0 \| 72 \| 5.1404 \| \| No log \| 10.0 \| 80 \| 4.4875 \| \| No log \| 11.0 \| 88 \| 3.8785 \| \| No log \| 12.0 \| 96 \| 3.3749 \| \| No log \| 13.0 \| 104 \| 2.9965 \| \| No log \| 14.0 \| 112 \| 2.6608 \| \| No log \| 15.0 \| 120 \| 2.3879 \| \| No log \| 16.0 \| 128 \| 2.2349 \| \| No log \| 17.0 \| 136 \| 2.0993 \| \| No log \| 18.0 \| 144 \| 2.0023 \| \| No log \| 19.0 \| 152 \| 1.9480 \| \| No log \| 20.0 \| 160 \| 1.9329 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
zjunlp/mt5-ie	zjunlp	2023-07-28T11:46:33Z	110	1	transformers	[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-17T11:39:03Z	--- license: mit --- We trained the MT5-base model for the CCKS2023 Instruction-based KGC task using 27W weakly supervised data without employing any additional techniques. To learn more about the training process and how to utilize the model, please consult the following GitHub repository: https://github.com/zjunlp/DeepKE/tree/main/example/triple/mt5. There, you will find detailed information on how to train the model and leverage its capabilities for the given task.
advokat/VodkaMix	advokat	2023-07-28T11:24:16Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-28T11:17:38Z	--- license: creativeml-openrail-m ---
EmirhanExecute/dqn-SpaceInvadersNoFrameskip	EmirhanExecute	2023-07-28T11:22:37Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T11:22:02Z	--- 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: 565.50 +/- 178.22 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 EmirhanExecute -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 EmirhanExecute -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 EmirhanExecute ``` ## 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', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mw00/yolov7-lego	mw00	2023-07-28T11:09:08Z	0	1	null	[ "lego", "brick", "object-detection", "license:cc0-1.0", "region:us" ]	object-detection	2023-07-26T16:52:21Z	--- license: cc0-1.0 pipeline_tag: object-detection tags: - lego - brick --- # Overview The model(s) in this repository are trained with the [dreamfactor/biggest-lego-dataset-600-parts](https://www.kaggle.com/datasets/dreamfactor/biggest-lego-dataset-600-parts) from Kaggle and the [Yolov7](https://github.com/WongKinYiu/yolov7) training script. ## Limitations The `zero-shot-1000-single-class.pt` was trained in the `training-zero-shot-1000-single-class.ipynb` notebook with 1000 images and does not differentiate lego classes but only tries to predict Lego objects. This can be easily reconfigured and retrained in the notebook, but the current implementation leads to many false positives on non-Lego objects and therefore can be improved upon. Also, it could be worth investigating if the metrics improve with a bigger training dataset, as currently only 1000 images are being used (approx. 0.6% of the full dataset).
hruslen/LunarLander-v2-ppo-self	hruslen	2023-07-28T11:04:54Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T11:04:47Z	--- 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: -126.43 +/- 74.98 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'f': None 'exp_name': 'ppo-selfmade2' 'seed': 1 'repo_id': 'hruslen/LunarLander-v2-ppo-self' '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 'batch_size': 512 'minibatch_size': 128} ```
dreaming-ai/LLongMA-2-7b-dolly-15k	dreaming-ai	2023-07-28T11:02:20Z	0	1	null	[ "license:cc-by-sa-3.0", "region:us" ]	null	2023-07-26T17:25:39Z	--- license: cc-by-sa-3.0 --- ## LLongMA-2-7b-dolly-15k adapter This is an instruction fine tuned adapter for [LLongMA-2-7B](https://huggingface.co/conceptofmind/LLongMA-2-7b) , trained at 8k context length using linear positional interpolation scaling. In order to run this inference with this adapter, you'll need the base [LLongMA-2-7b model](https://huggingface.co/conceptofmind/LLongMA-2-7b) as well. The adapter was instruction fined tuned with peft training, using the [dolly-15k dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k). --- 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: bfloat16 ### Framework versions - PEFT 0.4.0 ### Inference format and example: ### Human: Write an essay on the effects and impacts of social media. ### Assistant Social Media has become a major part in today's society, with over half of the world population having some form of it. It is used for entertainment purposes as well as businesses to promote their products or services. Social Media can also be addictive due to its ability to keep users engaged for long periods of time which leads them spending more time online than doing other activities such as reading books or going outside into nature. This lack of physical activity combined with constant exposure to negative news stories can lead people feeling stressed out and anxious about what’s happening around them at all times - something that wasn't true before when we relied solely on newspapers/television broadcasting etc.. There are many benefits associated with using these platforms too; they allow us connect easily with friends & family members living far away from us while sharing photos/videos/stories quickly without any delay! Additionally there are lots opportunities available through networking via LinkedIn etc., allowing individuals access new career paths faster than ever before (provided they possess relevant skills). In conclusion , although there exist certain risks involved within this type technology still remains one most powerful tools available today helping millions achieve goals set forth by themselves every single day . Overall , despite being criticized heavily sometimes even considered dangerous depending upon how user uses it properly always remain safe harmless majority cases . ### Human: Write a poem about love that features sunshine and rainbows. It should rhyme. ### Assistant When the Sun shines bright It warms my heart so tight, But when it rains I feel sadly, As all of my joy dissipates. The storms pass quickly though, And soon enough there'll be sun once more, I know this because of what I've learned - That good times always follow bad ones before. So even if now things are tough for me, Soon they will get better as sure as you see! My spirits lift with every ray of light, Even on cloudy days I find something to write. These ups and downs won't last forever long, They come around from time to time but never stay strong. Love is patient and kindness too, This I know despite how hard life can go. No matter what happens in future years, Remember these words and don't forget your fears. You have strength within yourself; believe it or not, Your spirit knows best where its path leads us to. Don't let anyone tell you otherwise, Or try to convince you that you aren't worth much. Beautiful souls like yours deserve nothing less than peace, So keep fighting until you reach happiness. Life isn't easy sometimes but we must persevere, We learn most through our struggles which makes them dear. Our hearts may break along way, Yet still hope remains true regardless of what we say. Therefore no one else has power over who we become, For only ourselves decide what matters most at home. With each passing day comes new opportunities, To grow stronger while also learning some humility. Everyone goes through trials big & small, Which means everyone needs patience and compassion all. ### Usage import torch import requests from peft import LoraConfig, PeftModel, get_peft_model from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer, GenerationConfig torch.manual_seed(100) model_name = "conceptofmind/LLongMA-2-7b" adapter_name = "dreaming-ai/LLongMA-2-7b-dolly-15k" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, ) model = AutoModelForCausalLM.from_pretrained( model_name, quantization_config=bnb_config, trust_remote_code=True ) model.config.use_cache = False generation_config = GenerationConfig.from_pretrained(model_name) # Temperature of 1.0 is the default. generation_config.temperature = 0.95 # The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0. generation_config.repetition_penalty = 1.2 # Exponential penalty to the length. Default to 2. generation_config.length_penalty = 2.0 # top-p : Nucleus filtering (top-p) before sampling (<=0.0: no filtering) generation_config.top_p = 0.6 print('generation_config:', generation_config) # apply adapter model = PeftModel.from_pretrained(model, adapter_name) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token text = '''### Human: Write an essay on the effects and impacts of social media. ### Assistant''' text2 = '''### Human: Write a poem about love that features sunshine and rainbows. It should rhyme. ### Assistant''' device = "cuda:0" inputs = tokenizer(text, return_tensors="pt").to(device) outputs = model.generate(**inputs, max_new_tokens=1024, generation_config=generation_config) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
hsanchez/detr-resnet-50_finetuned_cppe5	hsanchez	2023-07-28T10:50:20Z	187	0	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-07-28T10:23:40Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 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. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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: 10 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
tommilyjones/vit-base-patch16-224-finetuned-masked-hateful-meme-restructured	tommilyjones	2023-07-28T10:49:43Z	192	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T10:22:15Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.54 --- <!-- 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. --> # vit-base-patch16-224-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7518 - Accuracy: 0.54 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6625 \| 0.99 \| 66 \| 0.7385 \| 0.518 \| \| 0.6413 \| 2.0 \| 133 \| 0.6980 \| 0.538 \| \| 0.6063 \| 2.99 \| 199 \| 0.7422 \| 0.53 \| \| 0.5813 \| 4.0 \| 266 \| 0.7794 \| 0.52 \| \| 0.5551 \| 4.99 \| 332 \| 0.7975 \| 0.52 \| \| 0.5249 \| 6.0 \| 399 \| 0.7518 \| 0.54 \| \| 0.5254 \| 6.99 \| 465 \| 0.8074 \| 0.53 \| \| 0.5335 \| 8.0 \| 532 \| 0.7907 \| 0.52 \| \| 0.4867 \| 8.99 \| 598 \| 0.8286 \| 0.524 \| \| 0.4746 \| 9.92 \| 660 \| 0.8262 \| 0.522 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
X-Wang/pruned-mt5-small	X-Wang	2023-07-28T10:49:26Z	27	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "ja", "zh", "dataset:Helsinki-NLP/tatoeba_mt", "base_model:X-Wang/pruned-mt5-small", "base_model:finetune:X-Wang/pruned-mt5-small", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-08T10:54:13Z	--- base_model: X-Wang/pruned-mt5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: pruned-mt5-small results: [] datasets: - Helsinki-NLP/tatoeba_mt language: - ja - zh --- <!-- 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. --> # pruned-mt5-small This model is a fine-tuned version of [X-Wang/pruned-mt5-small](https://huggingface.co/X-Wang/pruned-mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4431 - Bleu: 11.4084 - Gen Len: 16.1053 ## 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: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 3.3446 \| 0.07 \| 2000 \| 2.9103 \| 10.3957 \| 16.0567 \| \| 2.8425 \| 0.14 \| 4000 \| 2.8570 \| 10.5695 \| 16.1895 \| \| 3.186 \| 0.21 \| 6000 \| 2.8137 \| 10.5958 \| 16.1523 \| \| 2.788 \| 0.28 \| 8000 \| 2.7593 \| 10.7553 \| 16.0138 \| \| 2.9075 \| 0.35 \| 10000 \| 2.7266 \| 10.9199 \| 16.2016 \| \| 3.0579 \| 0.42 \| 12000 \| 2.7030 \| 10.6 \| 16.0496 \| \| 2.3618 \| 0.49 \| 14000 \| 2.6547 \| 10.8026 \| 16.0412 \| \| 3.079 \| 0.56 \| 16000 \| 2.6441 \| 10.7945 \| 16.1148 \| \| 2.7597 \| 0.63 \| 18000 \| 2.6244 \| 10.5877 \| 16.0507 \| \| 2.8533 \| 0.7 \| 20000 \| 2.6049 \| 10.9986 \| 16.1145 \| \| 2.843 \| 0.77 \| 22000 \| 2.5836 \| 10.9173 \| 16.0826 \| \| 2.8268 \| 0.84 \| 24000 \| 2.5685 \| 10.8136 \| 16.0516 \| \| 2.7021 \| 0.91 \| 26000 \| 2.5509 \| 11.326 \| 16.0554 \| \| 3.338 \| 0.98 \| 28000 \| 2.5289 \| 11.1485 \| 16.0333 \| \| 2.7374 \| 1.05 \| 30000 \| 2.5220 \| 11.0166 \| 16.0998 \| \| 2.7996 \| 1.12 \| 32000 \| 2.5077 \| 11.1316 \| 16.131 \| \| 2.6897 \| 1.19 \| 34000 \| 2.4994 \| 11.0811 \| 16.1139 \| \| 2.4107 \| 1.26 \| 36000 \| 2.4877 \| 11.2641 \| 16.142 \| \| 2.7695 \| 1.33 \| 38000 \| 2.4756 \| 11.2135 \| 16.0977 \| \| 3.3271 \| 1.41 \| 40000 \| 2.4658 \| 11.3328 \| 16.0953 \| \| 2.2641 \| 1.48 \| 42000 \| 2.4612 \| 11.3065 \| 16.0549 \| \| 2.6594 \| 1.55 \| 44000 \| 2.4556 \| 11.2684 \| 16.1371 \| \| 2.7322 \| 1.62 \| 46000 \| 2.4520 \| 11.3739 \| 16.1058 \| \| 2.6824 \| 1.69 \| 48000 \| 2.4462 \| 11.3335 \| 16.1043 \| \| 2.3369 \| 1.76 \| 50000 \| 2.4455 \| 11.3851 \| 16.1239 \| \| 2.9537 \| 1.83 \| 52000 \| 2.4430 \| 11.4026 \| 16.0858 \| \| 2.3928 \| 1.9 \| 54000 \| 2.4433 \| 11.301 \| 16.1129 \| \| 2.4714 \| 1.97 \| 56000 \| 2.4431 \| 11.4084 \| 16.1053 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.0 - Datasets 2.13.1 - Tokenizers 0.13.3
transmogrifier/pr-falcon-7b-instruct-8bit-Jul20	transmogrifier	2023-07-28T10:36:00Z	12	0	transformers	[ "transformers", "safetensors", "RefinedWebModel", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "custom_code", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-07-20T15:57:46Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.30.2 pip install einops==0.6.1 pip install accelerate==0.20.3 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="transmogrifier/pr-falcon-7b-instruct-8bit-Jul20", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?<\|endoftext\|><\|answer\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "transmogrifier/pr-falcon-7b-instruct-8bit-Jul20", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "transmogrifier/pr-falcon-7b-instruct-8bit-Jul20", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "transmogrifier/pr-falcon-7b-instruct-8bit-Jul20" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?<\|endoftext\|><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` RWForCausalLM( (transformer): RWModel( (word_embeddings): Embedding(65027, 4544) (h): ModuleList( (0-31): 32 x DecoderLayer( (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) (self_attention): Attention( (maybe_rotary): RotaryEmbedding() (query_key_value): Linear(in_features=4544, out_features=4672, bias=False) (dense): Linear(in_features=4544, out_features=4544, bias=False) (attention_dropout): Dropout(p=0.0, inplace=False) ) (mlp): MLP( (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False) (act): GELU(approximate='none') (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False) ) ) ) (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=4544, out_features=65027, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
manuu01/ppo-Pyramids	manuu01	2023-07-28T10:35:59Z	25	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-28T10:35:58Z	--- 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: manuu01/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
1daniar/ppo-CartPole-v1	1daniar	2023-07-28T10:33:43Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T10:33:39Z	--- 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: -163.01 +/- 96.49 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
michaelfeil/ct2fast-starcoderbase-1b	michaelfeil	2023-07-28T10:19:11Z	5	0	transformers	[ "transformers", "gpt_bigcode", "text-generation", "ctranslate2", "int8", "float16", "code", "dataset:bigcode/the-stack-dedup", "arxiv:1911.02150", "arxiv:2205.14135", "arxiv:2207.14255", "arxiv:2305.06161", "license:bigcode-openrail-m", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T10:09:34Z	--- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup metrics: - code_eval library_name: transformers tags: - ctranslate2 - int8 - float16 - code model-index: - name: StarCoderBase-1B results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 15.17 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (C++) metrics: - name: pass@1 type: pass@1 value: 11.68 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Java) metrics: - name: pass@1 type: pass@1 value: 14.2 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (JavaScript) metrics: - name: pass@1 type: pass@1 value: 13.38 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (PHP) metrics: - name: pass@1 type: pass@1 value: 9.94 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Lua) metrics: - name: pass@1 type: pass@1 value: 12.52 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Rust) metrics: - name: pass@1 type: pass@1 value: 10.24 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Swift) metrics: - name: pass@1 type: pass@1 value: 3.92 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Julia) metrics: - name: pass@1 type: pass@1 value: 11.31 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (R) metrics: - name: pass@1 type: pass@1 value: 5.37 verified: false extra_gated_prompt: >- ## Model License Agreement Please read the BigCode [OpenRAIL-M license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. extra_gated_fields: I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox duplicated_from: bigcode-data/starcoderbase-1b --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-starcoderbase-1b" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-07-28 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # StarCoderBase-1B 1B version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary StarCoderBase-1B is a 1B parameter model trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. - Repository: [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - Project Website: [bigcode-project.org](https://www.bigcode-project.org) - Paper: [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161) - Point of Contact: [[email protected]](mailto:[email protected]) - Languages: 80+ Programming languages ## 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/starcoder-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: 128 Tesla A100 - Training time: 11 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} } ```
tommilyjones/resnet-50-finetuned-masked-hateful-meme-restructured	tommilyjones	2023-07-28T10:18:32Z	227	0	transformers	[ "transformers", "pytorch", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T09:58:10Z	--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: resnet-50-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.5 --- <!-- 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. --> # resnet-50-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7093 - Accuracy: 0.5 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6639 \| 0.99 \| 66 \| 0.7093 \| 0.5 \| \| 0.6569 \| 2.0 \| 133 \| 0.7295 \| 0.5 \| \| 0.6489 \| 2.99 \| 199 \| 0.7257 \| 0.5 \| \| 0.6553 \| 4.0 \| 266 \| 0.7274 \| 0.5 \| \| 0.6334 \| 4.99 \| 332 \| 0.7311 \| 0.5 \| \| 0.627 \| 6.0 \| 399 \| 0.7371 \| 0.5 \| \| 0.6561 \| 6.99 \| 465 \| 0.7386 \| 0.5 \| \| 0.6552 \| 8.0 \| 532 \| 0.7354 \| 0.5 \| \| 0.6427 \| 8.99 \| 598 \| 0.7346 \| 0.5 \| \| 0.6451 \| 9.92 \| 660 \| 0.7377 \| 0.498 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Kexa/Kx_01	Kexa	2023-07-28T10:16:31Z	0	0	allennlp	[ "allennlp", "chemistry", "question-answering", "es", "dataset:Open-Orca/OpenOrca", "arxiv:1910.09700", "license:unknown", "region:us" ]	question-answering	2023-07-28T10:14:03Z	--- license: unknown datasets: - Open-Orca/OpenOrca language: - es metrics: - accuracy library_name: allennlp pipeline_tag: question-answering tags: - chemistry --- # 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. 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 <!-- Provide a longer summary of what this model is. --> - 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] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
icgg/MeinaMix_V11	icgg	2023-07-28T10:15:58Z	44	0	diffusers	[ "diffusers", "safetensors", "art", "anime", "stable diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-28T08:49:04Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - art - anime - stable diffusion --- MeinaMix Objective is to be able to do good art with little prompting. For examples and prompts, please checkout: https://civitai.com/models/7240/meinamix I have a discord server where you can post images that you generated, discuss prompt and/or ask for help. https://discord.gg/XC9nGZNDUd If you like one of my models and want to support their updates I've made a ko-fi page; https://ko-fi.com/meina where you can pay me a coffee <3 And a Patreon page; https://www.patreon.com/MeinaMix where you can support me and get acess to beta of my models! You may also try this model using Sinkin.ai: https://sinkin.ai/m/vln8Nwr MeinaMix and the other of Meinas will ALWAYS be FREE. Recommendations of use: Enable Quantization in K samplers. Hires.fix is needed for prompts where the character is far away in order to make decent images, it drastically improve the quality of face and eyes! Recommended parameters: Sampler: Euler a: 40 to 60 steps. Sampler: DPM++ SDE Karras: 20 to 30 steps. Sampler: DPM++ 2M Karras: 20 to 40 steps. CFG Scale: 7. Resolutions: 512x768, 512x1024 for Portrait! Resolutions: 768x512, 1024x512, 1536x512 for Landscape! Hires.fix: R-ESRGAN 4x+Anime6b, with 10 steps at 0.3 up to 0.5 denoising. Clip Skip: 2. Negatives: ' (worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic) '
michaelfeil/ct2fast-starcoderbase-7b	michaelfeil	2023-07-28T10:09:32Z	5	0	transformers	[ "transformers", "gpt_bigcode", "text-generation", "ctranslate2", "int8", "float16", "code", "dataset:bigcode/the-stack-dedup", "arxiv:1911.02150", "arxiv:2205.14135", "arxiv:2207.14255", "arxiv:2305.06161", "license:bigcode-openrail-m", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T09:07:32Z	--- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup metrics: - code_eval library_name: transformers tags: - ctranslate2 - int8 - float16 - code model-index: - name: StarCoder-7B results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 28.37 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (C++) metrics: - name: pass@1 type: pass@1 value: 23.3 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Java) metrics: - name: pass@1 type: pass@1 value: 24.44 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (JavaScript) metrics: - name: pass@1 type: pass@1 value: 27.35 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (PHP) metrics: - name: pass@1 type: pass@1 value: 22.12 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Lua) metrics: - name: pass@1 type: pass@1 value: 23.35 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Rust) metrics: - name: pass@1 type: pass@1 value: 22.6 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Swift) metrics: - name: pass@1 type: pass@1 value: 15.1 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Julia) metrics: - name: pass@1 type: pass@1 value: 21.77 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (R) metrics: - name: pass@1 type: pass@1 value: 14.51 verified: false extra_gated_prompt: >- ## Model License Agreement Please read the BigCode [OpenRAIL-M license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. extra_gated_fields: I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox duplicated_from: bigcode-data/starcoderbase-7b --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [bigcode/starcoderbase-7b](https://huggingface.co/bigcode/starcoderbase-7b) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-starcoderbase-7b" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-07-28 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # StarCoderBase-7B 7B version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary StarCoderBase-7B is a 7B parameter model trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. - Repository: [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - Project Website: [bigcode-project.org](https://www.bigcode-project.org) - Paper: [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161) - Point of Contact: [[email protected]](mailto:[email protected]) - Languages: 80+ Programming languages ## 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-7b" 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: 250k - Pretraining tokens: 1 trillion - Precision: bfloat16 ## Hardware - GPUs: 512 Tesla A100 ## 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} } ```
digiplay/LuckyStrikeMix1.05_Lovelylady	digiplay	2023-07-28T10:05:01Z	532	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	2023-07-28T09:20:36Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/13034/lucky-strike-mix https://civitai.com/models/13034?modelVersionId=127680 *use "photorealism", "8k" keywords, could generate better images. Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9198406a-170c-4d69-8d65-ce961eaca5c2/width=1280/02628-1796431482-1%20supercute%20kitty%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2f87589f-3b8d-4c26-b2e9-5ba9645697cd/width=1280/02644-2789515522-1%20supercute%20cat%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5993b948-3a2a-473f-b21c-928f109f656b/width=1280/02656-2033477510-a%20superc%20ute%20kitty%20wear%20Ultraman%20armor%20and%20Ultraman%20mask,.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/13dbc7a2-f1de-493c-a771-91e4746f68b6/width=1280/02690-807894338-lionel%20messi%20in%20pink%20football%20kit,%20(geometric%20mosaic_1.4),%20(digital%20art%20style_1.4).jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d562d789-dd0c-4fe3-aca9-486747f92f16/width=1280/02707-3559411941-screenshot%20of%20person's%20profile%20in%20Tinder%20app,%20buttons%20of%20user%20interface,%20her%20name%20and%20age%20in%20large%20headline%20text,%20self-introduct.jpeg)
tommilyjones/swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured	tommilyjones	2023-07-28T09:57:28Z	212	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T09:36:50Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.53 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7166 - Accuracy: 0.53 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6507 \| 0.99 \| 66 \| 0.7352 \| 0.502 \| \| 0.6411 \| 2.0 \| 133 \| 0.7070 \| 0.528 \| \| 0.6268 \| 2.99 \| 199 \| 0.7166 \| 0.53 \| \| 0.6007 \| 4.0 \| 266 \| 0.7934 \| 0.506 \| \| 0.5875 \| 4.99 \| 332 \| 0.8053 \| 0.52 \| \| 0.5554 \| 6.0 \| 399 \| 0.7534 \| 0.524 \| \| 0.5613 \| 6.99 \| 465 \| 0.8075 \| 0.524 \| \| 0.5714 \| 8.0 \| 532 \| 0.7882 \| 0.522 \| \| 0.5244 \| 8.99 \| 598 \| 0.8380 \| 0.518 \| \| 0.5251 \| 9.92 \| 660 \| 0.8331 \| 0.52 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
accuracy-maker/ppo-LunarLander-v2	accuracy-maker	2023-07-28T09:53:38Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T09:53:15Z	--- 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: 264.96 +/- 17.69 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 ... ```
Ding-Qiang/q-FrozenLake-v1-4x4-Slippery	Ding-Qiang	2023-07-28T09:43:58Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T09:42:45Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.29 +/- 0.45 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="Ding-Qiang/q-FrozenLake-v1-4x4-Slippery", 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"]) ```
xianbin/rl_course_vizdoom_health_gathering_supreme	xianbin	2023-07-28T09:43:40Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T09:06:11Z	--- 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: 11.64 +/- 3.56 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 xianbin/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 .opt.conda.lib.python3.10.site-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 .opt.conda.lib.python3.10.site-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.
JinsooKim/ppo-LunarLander-v2	JinsooKim	2023-07-28T09:35:55Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T09:23:49Z	--- 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: 257.56 +/- 18.75 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 ... ```
rashmi035/wav2vec2-large-mms-1b-hindi-colab	rashmi035	2023-07-28T09:33:51Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_6_1", "base_model:facebook/mms-1b-fl102", "base_model:finetune:facebook/mms-1b-fl102", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-12T05:29:24Z	--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-fl102 tags: - generated_from_trainer datasets: - common_voice_6_1 metrics: - wer model-index: - name: wav2vec2-large-mms-1b-hindi-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_6_1 type: common_voice_6_1 config: hi split: test args: hi metrics: - name: Wer type: wer value: 0.32018561484918795 --- <!-- 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-large-mms-1b-hindi-colab This model is a fine-tuned version of [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) on the common_voice_6_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3535 - Wer: 0.3202 ## 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: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 16.7585 \| 0.14 \| 10 \| 10.2106 \| 2.0951 \| \| 6.9602 \| 0.27 \| 20 \| 3.7700 \| 1.0046 \| \| 2.4653 \| 0.41 \| 30 \| 1.3321 \| 0.6763 \| \| 1.0919 \| 0.55 \| 40 \| 0.6594 \| 0.4664 \| \| 0.7645 \| 0.68 \| 50 \| 0.4930 \| 0.3910 \| \| 0.8434 \| 0.82 \| 60 \| 0.4819 \| 0.3898 \| \| 0.5118 \| 0.96 \| 70 \| 0.4492 \| 0.3817 \| \| 0.6097 \| 1.1 \| 80 \| 0.4299 \| 0.4327 \| \| 0.4698 \| 1.23 \| 90 \| 0.4308 \| 0.3643 \| \| 0.5402 \| 1.37 \| 100 \| 0.4042 \| 0.4107 \| \| 0.5622 \| 1.51 \| 110 \| 0.4156 \| 0.3701 \| \| 0.4084 \| 1.64 \| 120 \| 0.4138 \| 0.3701 \| \| 0.4888 \| 1.78 \| 130 \| 0.3917 \| 0.3434 \| \| 0.4253 \| 1.92 \| 140 \| 0.3852 \| 0.3457 \| \| 0.5004 \| 2.05 \| 150 \| 0.3843 \| 0.3364 \| \| 0.3791 \| 2.19 \| 160 \| 0.3841 \| 0.3469 \| \| 0.3302 \| 2.33 \| 170 \| 0.3764 \| 0.3271 \| \| 0.4047 \| 2.47 \| 180 \| 0.3689 \| 0.3364 \| \| 0.2951 \| 2.6 \| 190 \| 0.3657 \| 0.3329 \| \| 0.3545 \| 2.74 \| 200 \| 0.3582 \| 0.3306 \| \| 0.3736 \| 2.88 \| 210 \| 0.3585 \| 0.3248 \| \| 0.388 \| 3.01 \| 220 \| 0.3602 \| 0.3237 \| \| 0.2997 \| 3.15 \| 230 \| 0.3624 \| 0.3167 \| \| 0.3704 \| 3.29 \| 240 \| 0.3625 \| 0.3190 \| \| 0.2095 \| 3.42 \| 250 \| 0.3571 \| 0.3248 \| \| 0.3564 \| 3.56 \| 260 \| 0.3570 \| 0.3202 \| \| 0.2119 \| 3.7 \| 270 \| 0.3550 \| 0.3225 \| \| 0.3697 \| 3.84 \| 280 \| 0.3542 \| 0.3190 \| \| 0.3551 \| 3.97 \| 290 \| 0.3535 \| 0.3202 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
digiplay/PotoPhotoRealism_v1	digiplay	2023-07-28T09:18:04Z	499	7	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-28T08:59:23Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/117538/poto-photo-realism Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f0dc3495-2968-42b5-8435-758ec4fb954c/width=1280/580662874.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/b1022894-834e-4f86-9e0e-927342d5ef34/width=1280/2598429369.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/06f5fbd8-c46d-4171-9b62-657b988e74cc/width=1728/3944133732.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/de31e0b3-33f8-42e8-872b-bb28854b2490/width=1728/3607815262.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/eba70078-9c00-4e2e-ae3a-053c9dca9900/width=2160/4292364610.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/384c94fa-200f-459f-8138-8c8c17be0484/width=1280/1116309337.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7c901984-0f84-4deb-81ed-9cb67ae67d46/width=1280/2611639573.jpeg)
privatelora/Lora	privatelora	2023-07-28T09:12:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-26T13:08:58Z	--- license: creativeml-openrail-m ---
kamalchibrani/yolov8_fall_detection_25	kamalchibrani	2023-07-28T09:10:52Z	0	0	null	[ "dataset:kamalchibrani/fall_detection", "license:openrail", "region:us" ]	null	2023-07-28T08:59:29Z	--- license: openrail datasets: - kamalchibrani/fall_detection metrics: - accuracy ---
openlamm/lamm_13b_lora32_98k	openlamm	2023-07-28T09:08:57Z	4	0	transformers	[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-10T03:46:34Z	--- license: apache-2.0 Model: - Vicuna13B - LoRA32 - openlamm/LAMM-98K ---
sdocio/bne-spacy-corgale-ner-es	sdocio	2023-07-28T09:08:31Z	2	0	spacy	[ "spacy", "token-classification", "es", "license:gpl-3.0", "model-index", "region:us" ]	token-classification	2023-01-07T23:02:41Z	--- license: gpl-3.0 language: - es library_name: spacy pipeline_tag: token-classification tags: - spacy - token-classification widget: - text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago." - text: "Si te metes en el Franco desde la Alameda, vas hacia la Catedral." - text: "Y allí precisamente es Santiago el patrón del pueblo." model-index: - name: bne-spacy-corgale-ner-es results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9721311475 - name: NER Recall type: recall value: 0.9732708089 - name: NER F Score type: f_score value: 0.9727006444 --- # Introduction spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC). It was fine-tuned using `PlanTL-GOB-ES/roberta-base-bne`. \| Feature \| Description \| \| --- \| --- \| \| Name \| `bne-spacy-corgale-ner-es` \| \| Version \| `0.0.2` \| \| spaCy \| `>=3.5.2,<3.6.0` \| \| Default Pipeline \| `transformer`, `ner` \| \| Components \| `transformer`, `ner` \| ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `LOC`, `MISC`, `ORG`, `PER` \| </details> ## Usage You can use this model with the spaCy pipeline for NER. ```python import spacy from spacy.pipeline import merge_entities nlp = spacy.load("bne-spacy-corgale-ner-es") nlp.add_pipe('sentencizer') example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo." ner_pipe = nlp(example) print(ner_pipe.ents) for token in merge_entities(ner_pipe): print(token.text, token.ent_type_) ``` ## Dataset ToDo ## Model performance entity\|precision\|recall\|f1 -\|-\|-\|- LOC\|0.985\|0.987\|0.986 MISC\|0.862\|0.865\|0.863 ORG\|0.938\|0.779\|0.851 PER\|0.921\|0.941\|0.931 micro avg\|0.971\|0.972\|0.971 macro avg\|0.926\|0.893\|0.908 weighted avg\|0.971\|0.972\|0.971
neverloses87/fine-tune	neverloses87	2023-07-28T09:04:48Z	61	0	transformers	[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "base_model:sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset", "base_model:finetune:sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-07-27T20:11:54Z	--- license: cc-by-nc-4.0 base_model: sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset tags: - generated_from_trainer model-index: - name: fine-tune 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. --> # fine-tune This model is a fine-tuned version of [sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset) 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: 0.0005 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 188 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
dvs/videomae-base-finetuned-movienet	dvs	2023-07-28T09:04:31Z	60	0	transformers	[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-07-28T05:53:05Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-movienet 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. --> # videomae-base-finetuned-movienet This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.2558 - eval_accuracy: 0.6823 - eval_runtime: 120.548 - eval_samples_per_second: 1.593 - eval_steps_per_second: 0.199 - epoch: 6.1 - step: 1266 ## 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 - training_steps: 1480 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
sm136599/chatfoodie-koalpaca-polyglot-5_8b-2050step-4batch_1epoch	sm136599	2023-07-28T08:52:44Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-28T08:52:42Z	--- 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
tommilyjones/vit-base-patch16-224-finetuned-hateful-meme-restructured	tommilyjones	2023-07-28T08:39:28Z	192	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T08:04:55Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.552 --- <!-- 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. --> # vit-base-patch16-224-finetuned-hateful-meme-restructured This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7152 - Accuracy: 0.552 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6546 \| 0.99 \| 66 \| 0.7185 \| 0.52 \| \| 0.6222 \| 2.0 \| 133 \| 0.7152 \| 0.552 \| \| 0.5986 \| 2.99 \| 199 \| 0.7344 \| 0.542 \| \| 0.5535 \| 4.0 \| 266 \| 0.7782 \| 0.514 \| \| 0.5377 \| 4.99 \| 332 \| 0.8329 \| 0.514 \| \| 0.5115 \| 6.0 \| 399 \| 0.7596 \| 0.528 \| \| 0.5133 \| 6.99 \| 465 \| 0.8151 \| 0.512 \| \| 0.511 \| 8.0 \| 532 \| 0.7897 \| 0.538 \| \| 0.4712 \| 8.99 \| 598 \| 0.8539 \| 0.514 \| \| 0.4626 \| 9.92 \| 660 \| 0.8449 \| 0.522 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
xianbin/Reinforce-Pixelcopter-PLE-v0	xianbin	2023-07-28T08:28:33Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T08:14:41Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-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: 106.00 +/- 86.94 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
Imxxn/RLCourseU5-SnowballTarget	Imxxn	2023-07-28T07:49:37Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-28T07:49:33Z	--- 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: Imxxn/RLCourseU5-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
TankuVie/bert-base-multilingual-uncased-vietnamese_sentiment_analysis	TankuVie	2023-07-28T07:49:32Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:vietnamese_students_feedback", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-28T07:40:50Z	--- license: apache-2.0 base_model: bert-base-multilingual-uncased tags: - generated_from_trainer datasets: - vietnamese_students_feedback metrics: - accuracy model-index: - name: bert-base-multilingual-uncased-vietnamese_sentiment_analysis results: - task: name: Text Classification type: text-classification dataset: name: vietnamese_students_feedback type: vietnamese_students_feedback config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9279848389134555 --- <!-- 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. --> # bert-base-multilingual-uncased-vietnamese_sentiment_analysis This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the vietnamese_students_feedback dataset. It achieves the following results on the evaluation set: - Loss: 0.2852 - Accuracy: 0.9280 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4054 \| 1.0 \| 715 \| 0.2864 \| 0.9154 \| \| 0.2643 \| 2.0 \| 1430 \| 0.2852 \| 0.9280 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
Marco-Cheung/distilhubert-finetuned-gtzan	Marco-Cheung	2023-07-28T07:47:10Z	162	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-22T15:09:26Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-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: 0.83 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5933 - Accuracy: 0.83 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9825 \| 1.0 \| 113 \| 1.7658 \| 0.48 \| \| 1.2943 \| 2.0 \| 226 \| 1.2478 \| 0.65 \| \| 0.9837 \| 3.0 \| 339 \| 0.9757 \| 0.71 \| \| 0.8201 \| 4.0 \| 452 \| 0.8420 \| 0.72 \| \| 0.5363 \| 5.0 \| 565 \| 0.6741 \| 0.83 \| \| 0.3417 \| 6.0 \| 678 \| 0.7083 \| 0.76 \| \| 0.4129 \| 7.0 \| 791 \| 0.5941 \| 0.81 \| \| 0.1681 \| 8.0 \| 904 \| 0.5954 \| 0.84 \| \| 0.2398 \| 9.0 \| 1017 \| 0.5819 \| 0.85 \| \| 0.1346 \| 10.0 \| 1130 \| 0.5933 \| 0.83 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
BlunderPanini/Taxi-v3	BlunderPanini	2023-07-28T07:26:32Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T07:26:28Z	--- 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="BlunderPanini/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"]) ```
Vasanth/criccomm_to_cricnewss	Vasanth	2023-07-28T07:23:48Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-large", "base_model:finetune:google/flan-t5-large", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-28T06:03:52Z	--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer model-index: - name: criccomm_to_cricnewss 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. --> # criccomm_to_cricnewss This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_GGML	Lajonbot	2023-07-28T07:20:24Z	0	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]	text-generation	2023-07-28T07:12:07Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---
joydragon/a2c-AntBulletEnv-v0	joydragon	2023-07-28T07:19:20Z	1	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T07:18:14Z	--- 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: 1177.61 +/- 70.07 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 ... ```
Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_adapter_model	Lajonbot	2023-07-28T07:12:06Z	0	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]	text-generation	2023-07-28T07:12:05Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---

Madhura/qa-model

Madhura

2023-07-28T16:29:24Z

112

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-28T15:59:32Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad model-index: - name: qa-model results: [] ---  # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6376 ## 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 | 250 | 2.3091 | | 2.7041 | 2.0 | 500 | 1.7406 | | 2.7041 | 3.0 | 750 | 1.6376 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

jarguello76/text-to-speech-speecht5_finetuned_voxpopuli_es

jarguello76

2023-07-28T16:13:55Z

84

1

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "text-to-speech", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-27T22:45:24Z

--- license: mit base_model: microsoft/speecht5_tts datasets: - facebook/voxpopuli model-index: - name: speecht5_tts-voxpopuli-jarguello results: [] pipeline_tag: text-to-speech ---  # speecht5_tts-voxpopuli-jarguello This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4464 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5089 | 4.32 | 1000 | 0.4632 | | 0.4835 | 8.64 | 2000 | 0.4512 | | 0.4862 | 12.97 | 3000 | 0.4487 | | 0.4825 | 17.29 | 4000 | 0.4464 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

Geotrend/distilbert-base-en-pt-cased

Geotrend

2023-07-28T16:13:06Z

130

0

transformers

[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "multilingual", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: multilingual datasets: wikipedia license: apache-2.0 --- # distilbert-base-en-pt-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-pt-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.

alifaheem94/RomanUrduPOS

alifaheem94

2023-07-28T16:11:41Z

101

0

transformers

[ "transformers", "pytorch", "roberta", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-28T14:52:23Z

--- pipeline_tag: token-classification --- This model is trained on Roman Urdu POS dataset of 100000 tokens.

edures/Reinforce-vtest

edures

2023-07-28T16:07:14Z

0

null

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

reinforcement-learning

2023-07-28T16:02:45Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-vtest results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 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

azhang1212/angela_shuffle_test

azhang1212

2023-07-28T15:49:15Z

105

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

2023-07-28T14:28:54Z

--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: angela_shuffle_test results: [] ---  # angela_shuffle_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: 0.1672 - Precision: 0.6214 - Recall: 0.4942 - F1: 0.5505 - Accuracy: 0.9504 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1882 | 1.0 | 1283 | 0.1566 | 0.6823 | 0.4277 | 0.5258 | 0.9518 | | 0.1551 | 2.0 | 2566 | 0.1507 | 0.6940 | 0.4451 | 0.5423 | 0.9533 | | 0.1385 | 3.0 | 3849 | 0.1545 | 0.6903 | 0.4503 | 0.5450 | 0.9532 | | 0.1163 | 4.0 | 5132 | 0.1610 | 0.6288 | 0.4943 | 0.5535 | 0.9507 | | 0.0994 | 5.0 | 6415 | 0.1672 | 0.6214 | 0.4942 | 0.5505 | 0.9504 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

avidoavid/RWKV-14b-finetuned

avidoavid

2023-07-28T15:49:05Z

0

null

[ "generated_from_trainer", "base_model:RWKV/rwkv-raven-14b", "base_model:finetune:RWKV/rwkv-raven-14b", "region:us" ]

null

2023-07-28T15:07:06Z

--- base_model: RWKV/rwkv-raven-14b tags: - generated_from_trainer model-index: - name: RWKV-14b-finetuned results: [] ---  # RWKV-14b-finetuned This model is a fine-tuned version of [RWKV/rwkv-raven-14b](https://huggingface.co/RWKV/rwkv-raven-14b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0585 ## 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: 0.001 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.0926 | 1.0 | 1 | 0.9535 | | 1.0628 | 2.0 | 2 | 2.0585 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/cdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

jordyvl

2023-07-28T15:40:52Z

164

0

transformers

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

image-classification

2023-07-28T07:51:07Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: cdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 results: [] ---  # cdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 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: 0.4315 - Accuracy: 0.8522 - Brier Loss: 0.2145 - Nll: 1.3474 - F1 Micro: 0.8522 - F1 Macro: 0.8535 - Ece: 0.0573 - Aurc: 0.0300 ## 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 | 1.6705 | 0.6378 | 0.4837 | 2.4248 | 0.6378 | 0.6323 | 0.0655 | 0.1457 | | No log | 2.0 | 334 | 1.1423 | 0.7322 | 0.3740 | 1.9847 | 0.7322 | 0.7285 | 0.0695 | 0.0846 | | 1.7909 | 3.0 | 501 | 0.9082 | 0.7682 | 0.3248 | 1.7674 | 0.7682 | 0.7676 | 0.0620 | 0.0642 | | 1.7909 | 4.0 | 668 | 0.8494 | 0.7865 | 0.3082 | 1.7306 | 0.7865 | 0.7904 | 0.0665 | 0.0560 | | 1.7909 | 5.0 | 835 | 0.7837 | 0.798 | 0.2988 | 1.6072 | 0.798 | 0.7953 | 0.0729 | 0.0553 | | 0.4994 | 6.0 | 1002 | 0.6867 | 0.804 | 0.2862 | 1.5014 | 0.804 | 0.8059 | 0.0794 | 0.0471 | | 0.4994 | 7.0 | 1169 | 0.7037 | 0.8157 | 0.2797 | 1.5533 | 0.8157 | 0.8178 | 0.0807 | 0.0478 | | 0.4994 | 8.0 | 1336 | 0.6709 | 0.8163 | 0.2756 | 1.5297 | 0.8163 | 0.8166 | 0.0728 | 0.0478 | | 0.2478 | 9.0 | 1503 | 0.6132 | 0.825 | 0.2576 | 1.4349 | 0.825 | 0.8247 | 0.0728 | 0.0398 | | 0.2478 | 10.0 | 1670 | 0.6389 | 0.8235 | 0.2671 | 1.4455 | 0.8235 | 0.8266 | 0.0746 | 0.0419 | | 0.2478 | 11.0 | 1837 | 0.6043 | 0.8257 | 0.2585 | 1.4609 | 0.8257 | 0.8293 | 0.0752 | 0.0403 | | 0.1683 | 12.0 | 2004 | 0.5639 | 0.8327 | 0.2457 | 1.4470 | 0.8327 | 0.8350 | 0.0676 | 0.0375 | | 0.1683 | 13.0 | 2171 | 0.5665 | 0.8317 | 0.2508 | 1.4054 | 0.8317 | 0.8324 | 0.0731 | 0.0388 | | 0.1683 | 14.0 | 2338 | 0.5505 | 0.8403 | 0.2427 | 1.4059 | 0.8403 | 0.8408 | 0.0649 | 0.0377 | | 0.131 | 15.0 | 2505 | 0.5321 | 0.836 | 0.2428 | 1.4078 | 0.836 | 0.8372 | 0.0684 | 0.0365 | | 0.131 | 16.0 | 2672 | 0.5161 | 0.8373 | 0.2383 | 1.3900 | 0.8373 | 0.8373 | 0.0711 | 0.0368 | | 0.131 | 17.0 | 2839 | 0.5177 | 0.8403 | 0.2371 | 1.3828 | 0.8403 | 0.8413 | 0.0633 | 0.0354 | | 0.1071 | 18.0 | 3006 | 0.5113 | 0.8407 | 0.2377 | 1.3832 | 0.8407 | 0.8432 | 0.0718 | 0.0343 | | 0.1071 | 19.0 | 3173 | 0.4949 | 0.8415 | 0.2332 | 1.3767 | 0.8415 | 0.8428 | 0.0667 | 0.0338 | | 0.1071 | 20.0 | 3340 | 0.4857 | 0.848 | 0.2271 | 1.3664 | 0.848 | 0.8492 | 0.0615 | 0.0338 | | 0.0877 | 21.0 | 3507 | 0.4812 | 0.847 | 0.2283 | 1.3360 | 0.847 | 0.8478 | 0.0602 | 0.0346 | | 0.0877 | 22.0 | 3674 | 0.4715 | 0.8495 | 0.2243 | 1.3761 | 0.8495 | 0.8506 | 0.0560 | 0.0320 | | 0.0877 | 23.0 | 3841 | 0.4622 | 0.8508 | 0.2206 | 1.3584 | 0.8508 | 0.8515 | 0.0557 | 0.0323 | | 0.0694 | 24.0 | 4008 | 0.4432 | 0.8515 | 0.2167 | 1.3653 | 0.8515 | 0.8531 | 0.0555 | 0.0309 | | 0.0694 | 25.0 | 4175 | 0.4467 | 0.8498 | 0.2193 | 1.3499 | 0.8498 | 0.8512 | 0.0581 | 0.0309 | | 0.0694 | 26.0 | 4342 | 0.4412 | 0.8545 | 0.2162 | 1.3535 | 0.8545 | 0.8560 | 0.0534 | 0.0306 | | 0.0586 | 27.0 | 4509 | 0.4402 | 0.8498 | 0.2180 | 1.3390 | 0.8498 | 0.8510 | 0.0597 | 0.0309 | | 0.0586 | 28.0 | 4676 | 0.4408 | 0.8522 | 0.2174 | 1.3568 | 0.8522 | 0.8536 | 0.0576 | 0.0306 | | 0.0586 | 29.0 | 4843 | 0.4391 | 0.851 | 0.2168 | 1.3429 | 0.851 | 0.8523 | 0.0585 | 0.0305 | | 0.0549 | 30.0 | 5010 | 0.4371 | 0.853 | 0.2160 | 1.3389 | 0.853 | 0.8543 | 0.0573 | 0.0303 | | 0.0549 | 31.0 | 5177 | 0.4382 | 0.8498 | 0.2168 | 1.3486 | 0.8498 | 0.8513 | 0.0602 | 0.0304 | | 0.0549 | 32.0 | 5344 | 0.4372 | 0.853 | 0.2166 | 1.3501 | 0.853 | 0.8540 | 0.0591 | 0.0306 | | 0.0527 | 33.0 | 5511 | 0.4379 | 0.852 | 0.2156 | 1.3546 | 0.852 | 0.8531 | 0.0576 | 0.0304 | | 0.0527 | 34.0 | 5678 | 0.4353 | 0.8532 | 0.2154 | 1.3381 | 0.8532 | 0.8543 | 0.0574 | 0.0302 | | 0.0527 | 35.0 | 5845 | 0.4347 | 0.8525 | 0.2148 | 1.3550 | 0.8525 | 0.8535 | 0.0591 | 0.0304 | | 0.0511 | 36.0 | 6012 | 0.4311 | 0.8542 | 0.2141 | 1.3233 | 0.8542 | 0.8552 | 0.0572 | 0.0299 | | 0.0511 | 37.0 | 6179 | 0.4323 | 0.852 | 0.2150 | 1.3332 | 0.852 | 0.8532 | 0.0586 | 0.0302 | | 0.0511 | 38.0 | 6346 | 0.4321 | 0.8515 | 0.2152 | 1.3382 | 0.8515 | 0.8527 | 0.0583 | 0.0299 | | 0.0494 | 39.0 | 6513 | 0.4335 | 0.8495 | 0.2152 | 1.3385 | 0.8495 | 0.8511 | 0.0593 | 0.0303 | | 0.0494 | 40.0 | 6680 | 0.4323 | 0.852 | 0.2146 | 1.3603 | 0.852 | 0.8533 | 0.0576 | 0.0299 | | 0.0494 | 41.0 | 6847 | 0.4309 | 0.8512 | 0.2143 | 1.3448 | 0.8512 | 0.8525 | 0.0570 | 0.0299 | | 0.0477 | 42.0 | 7014 | 0.4327 | 0.8525 | 0.2149 | 1.3439 | 0.8525 | 0.8539 | 0.0580 | 0.0299 | | 0.0477 | 43.0 | 7181 | 0.4309 | 0.8532 | 0.2140 | 1.3406 | 0.8532 | 0.8544 | 0.0560 | 0.0299 | | 0.0477 | 44.0 | 7348 | 0.4308 | 0.8528 | 0.2141 | 1.3404 | 0.8528 | 0.8540 | 0.0573 | 0.0299 | | 0.0466 | 45.0 | 7515 | 0.4317 | 0.8525 | 0.2147 | 1.3402 | 0.8525 | 0.8538 | 0.0580 | 0.0299 | | 0.0466 | 46.0 | 7682 | 0.4317 | 0.8535 | 0.2144 | 1.3475 | 0.8535 | 0.8547 | 0.0553 | 0.0298 | | 0.0466 | 47.0 | 7849 | 0.4314 | 0.8525 | 0.2143 | 1.3479 | 0.8525 | 0.8537 | 0.0559 | 0.0299 | | 0.0465 | 48.0 | 8016 | 0.4314 | 0.8525 | 0.2143 | 1.3479 | 0.8525 | 0.8538 | 0.0559 | 0.0299 | | 0.0465 | 49.0 | 8183 | 0.4316 | 0.8528 | 0.2145 | 1.3471 | 0.8528 | 0.8540 | 0.0573 | 0.0299 | | 0.0465 | 50.0 | 8350 | 0.4315 | 0.8522 | 0.2145 | 1.3474 | 0.8522 | 0.8535 | 0.0573 | 0.0300 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

mauroluiz/Criativo

mauroluiz

2023-07-28T15:30:39Z

0

null

[ "region:us" ]

null

2023-07-28T15:16:30Z

Insanely detailed and elaborate jungle scene in a glass globe

liuyt75/t5-large_prefix_tuning_sentences_75agree_10

liuyt75

2023-07-28T15:28:53Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-28T15:28:51Z

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

Christabelle/thesis-concept-art

Christabelle

2023-07-28T15:04:50Z

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-26T20:18:06Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Christabelle/thesis-concept-art These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the Christabelle/thesis-concept-art-train dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

budecosystem/genz-13b-v2

budecosystem

2023-07-28T14:51:28Z

1,480

4

transformers

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

text-generation

2023-07-26T05:40:09Z

--- language: - en library_name: transformers pipeline_tag: text-generation --- --- <div align="center"><h1 align="center">~ GenZ ~</h1><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/genz-logo.png" width=150></div> <p align="center"><i>Democratizing access to LLMs for the open-source community.<br>Let's advance AI, together. </i></p> --- ## Introduction 🎉 Welcome to **GenZ**, an advanced Large Language Model (LLM) fine-tuned on the foundation of Meta's open-source Llama V2 13B parameter model. At Bud Ecosystem, we believe in the power of open-source collaboration to drive the advancement of technology at an accelerated pace. Our vision is to democratize access to fine-tuned LLMs, and to that end, we will be releasing a series of models across different parameter counts (7B, 13B, and 70B) and quantizations (32-bit and 4-bit) for the open-source community to use, enhance, and build upon. <p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/MTBench_CompareChart_28July2023.png" width="500"></p> The smaller quantization version of our models makes them more accessible, enabling their use even on personal computers. This opens up a world of possibilities for developers, researchers, and enthusiasts to experiment with these models and contribute to the collective advancement of language model technology. GenZ isn't just a powerful text generator—it's a sophisticated AI assistant, capable of understanding and responding to user prompts with high-quality responses. We've taken the robust capabilities of Llama V2 and fine-tuned them to offer a more user-focused experience. Whether you're seeking informative responses or engaging interactions, GenZ is designed to deliver. And this isn't the end. It's just the beginning of a journey towards creating more advanced, more efficient, and more accessible language models. We invite you to join us on this exciting journey. 🚀 --- <h2>Milestone Releases ️🏁</h2> **[27 July 2023]** [_GenZ-13B V2 (ggml)_](https://huggingface.co/budecosystem/genz-13b-v2-ggml) : Announcing our GenZ-13B v2 with ggml. This variant of GenZ can run inferencing using only CPU and without the need of GPU. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-ggml). **[27 July 2023]** [_GenZ-13B V2 (4-bit)_](https://huggingface.co/budecosystem/genz-13b-v2-4bit) : Announcing our GenZ-13B v2 with 4-bit quantisation. Enabling inferencing with much lesser GPU memory than the 32-bit variant. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-4bit). **[26 July 2023]** [_GenZ-13B V2_](https://huggingface.co/budecosystem/genz-13b-v2) : We're excited to announce the release of our Genz 13B v2 model, a step forward with improved evaluation results compared to v1. Experience the advancements by downloading the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2). **[20 July 2023]** [_GenZ-13B_](https://huggingface.co/budecosystem/genz-13b) : We marked an important milestone with the release of the Genz 13B model. The journey began here, and you can partake in it by downloading the model from [Hugging Face](https://huggingface.co/budecosystem/genz-13b). --- <img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/screenshot_genz13bv2.png" width="100%"> | ![Python](https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/Python.gif) | ![Poem](https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/Poem.gif) | ![Email](https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/Email.gif) |:--:|:--:|:--:| | *Code Generation* | *Poem Generation* | *Email Generation* |  <h2>Getting Started on Hugging Face 🤗</h2> Getting up and running with our models on Hugging Face is a breeze. Follow these steps: <h3>1️⃣ : Import necessary modules</h3> Start by importing the necessary modules from the ‘transformers’ library and ‘torch’. ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM ``` <h3>2️⃣ : Load the tokenizer and the model</h3> Next, load up the tokenizer and the model for ‘budecosystem/genz-13b-v2’ from Hugging Face using the ‘from_pretrained’ method. ```python tokenizer = AutoTokenizer.from_pretrained("budecosystem/genz-13b-v2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-13b-v2", torch_dtype=torch.bfloat16) ``` <h3>3️⃣ : Generate responses</h3> Now that you have the model and tokenizer, you're ready to generate responses. Here's how you can do it: ```python inputs = tokenizer("The meaning of life is", return_tensors="pt") sample = model.generate(**inputs, max_length=128) print(tokenizer.decode(sample[0])) ``` In this example, "The meaning of life is" is the prompt template used for inference. You can replace it with any string you like. Want to interact with the model in a more intuitive way? We have a Gradio interface set up for that. Head over to our GitHub page, clone the repository, and run the ‘generate.py’ script to try it out. Happy experimenting! 😄 <h2>Fine-tuning 🎯</h2> It's time to upgrade the model by fine-tuning the model. You can do this using our provided finetune.py script. Here's an example command: ```bash python finetune.py \ --model_name meta-llama/Llama-2-13b \ --data_path dataset.json \ --output_dir output \ --trust_remote_code \ --prompt_column instruction \ --response_column output \ --pad_token_id 50256 ``` --- <h2 >Bonus: Colab Notebooks 📚 <b><i>(WIP)</i></b></h2> Looking for an even simpler way to get started with GenZ? We've got you covered. We've prepared a pair of detailed Colab notebooks - one for Inference and one for Fine-tuning. These notebooks come pre-filled with all the information and code you'll need. All you'll have to do is run them! Keep an eye out for these notebooks. They'll be added to the repository soon! --- <h2>Why Use GenZ? 💡</h2> You might be wondering, "Why should I choose GenZ over a pretrained model?" The answer lies in the extra mile we've gone to fine-tune our models. While pretrained models are undeniably powerful, GenZ brings something extra to the table. We've fine-tuned it with curated datasets, which means it has additional skills and capabilities beyond what a pretrained model can offer. Whether you need it for a simple task or a complex project, GenZ is up for the challenge. What's more, we are committed to continuously enhancing GenZ. We believe in the power of constant learning and improvement. That's why we'll be regularly fine-tuning our models with various curated datasets to make them even better. Our goal is to reach the state of the art and beyond - and we're committed to staying the course until we get there. But don't just take our word for it. We've provided detailed evaluations and performance details in a later section, so you can see the difference for yourself. Choose GenZ and join us on this journey. Together, we can push the boundaries of what's possible with large language models. --- <h2>Model Card for GenZ 13B 📄</h2> Here's a quick overview of everything you need to know about GenZ 13B. <h3>Model Details:</h3> - Developed by: Bud Ecosystem - Base pretrained model type: Llama V2 13B - Model Architecture: GenZ 13B, fine-tuned on Llama V2 13B, is an auto-regressive language model that employs an optimized transformer architecture. The fine-tuning process for GenZ 13B leveraged Supervised Fine-Tuning (SFT) - License: The model is available for commercial use under a custom commercial license. For more information, please visit: [Meta AI Model and Library Downloads](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) --- <h2>Intended Use 💼</h2> When we created GenZ 13B, we had a clear vision of how it could be used to push the boundaries of what's possible with large language models. We also understand the importance of using such models responsibly. Here's a brief overview of the intended and out-of-scope uses for GenZ 13B. <h3>Direct Use</h3> GenZ 13B is designed to be a powerful tool for research on large language models. It's also an excellent foundation for further specialization and fine-tuning for specific use cases, such as: - Text summarization - Text generation - Chatbot creation - And much more! <h3>Out-of-Scope Use 🚩</h3> While GenZ 13B is versatile, there are certain uses that are out of scope: - Production use without adequate assessment of risks and mitigation - Any use cases which may be considered irresponsible or harmful - Use in any manner that violates applicable laws or regulations, including trade compliance laws - Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2 Remember, GenZ 13B, like any large language model, is trained on a large-scale corpora representative of the web, and therefore, may carry the stereotypes and biases commonly encountered online. <h3>Recommendations 🧠</h3> We recommend users of GenZ 13B to consider fine-tuning it for the specific set of tasks of interest. Appropriate precautions and guardrails should be taken for any production use. Using GenZ 13B responsibly is key to unlocking its full potential while maintaining a safe and respectful environment. --- <h2>Training Details 📚</h2> When fine-tuning GenZ 13B, we took a meticulous approach to ensure we were building on the solid base of the pretrained Llama V2 13B model in the most effective way. Here's a look at the key details of our training process: <h3>Fine-Tuning Training Data</h3> For the fine-tuning process, we used a carefully curated mix of datasets. These included data from OpenAssistant, an instruction fine-tuning dataset, and Thought Source for the Chain Of Thought (CoT) approach. This diverse mix of data sources helped us enhance the model's capabilities across a range of tasks. <h3>Fine-Tuning Procedure</h3> We performed a full-parameter fine-tuning using Supervised Fine-Tuning (SFT). This was carried out on 4 A100 80GB GPUs, and the process took under 100 hours. To make the process more efficient, we used DeepSpeed's ZeRO-3 optimization. <h3>Tokenizer</h3> We used the SentencePiece tokenizer during the fine-tuning process. This tokenizer is known for its capability to handle open-vocabulary language tasks efficiently. <h3>Hyperparameters</h3> Here are the hyperparameters we used for fine-tuning: | Hyperparameter | Value | | -------------- | ----- | | Warmup Ratio | 0.04 | | Learning Rate Scheduler Type | Cosine | | Learning Rate | 2e-5 | | Number of Training Epochs | 3 | | Per Device Training Batch Size | 4 | | Gradient Accumulation Steps | 4 | | Precision | FP16 | | Optimizer | AdamW | --- <h2>Evaluations 🎯</h2> Evaluating our model is a key part of our fine-tuning process. It helps us understand how our model is performing and how it stacks up against other models. Here's a look at some of the key evaluations for GenZ 13B: <h3>Benchmark Comparison</h3> We've compared GenZ V1 with V2 to understand the improvements our fine-tuning has achieved. | Model Name | MT Bench | Vicuna Bench | MMLU | Human Eval | Hellaswag | BBH | |:----------:|:--------:|:------------:|:----:|:----------:|:---------:|:----:| | Genz 13B | 6.12 | 86.1 | 53.62| 17.68 | 77.38 | 37.76| | Genz 13B v2| 6.79 | 87.2 | 53.68| 21.95 | 77.48 | 38.1 | <h3>MT Bench Score</h3> A key evaluation metric we use is the MT Bench score. This score provides a comprehensive assessment of our model's performance across a range of tasks. We're proud to say that our model performs at a level that's close to the Llama-70B-chat model on the MT Bench and top of the list among 13B models. <p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_score.png" width="500"></p> In the transition from GenZ V1 to V2, we noticed some fascinating performance shifts. While we saw a slight dip in coding performance, two other areas, Roleplay and Math, saw noticeable improvements. --- <h2>Looking Ahead 👀</h2> We're excited about the journey ahead with GenZ. We're committed to continuously improving and enhancing our models, and we're excited to see what the open-source community will build with them. We believe in the power of collaboration, and we can't wait to see what we can achieve together. Remember, we're just getting started. This is just the beginning of a journey that we believe will revolutionize the world of large language models. We invite you to join us on this exciting journey. Together, we can push the boundaries of what's possible with AI. 🚀 --- Check the GitHub for the code -> [GenZ](https://raw.githubusercontent.com/BudEcosystem/GenZ)

vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA_AUQC

vnktrmnb

2023-07-28T14:46:12Z

62

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-28T13:18:33Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA_AUQC results: [] ---  # vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA_AUQC This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5563 - Train End Logits Accuracy: 0.6377 - Train Start Logits Accuracy: 0.6615 - Validation Loss: 0.5213 - Validation End Logits Accuracy: 0.8376 - Validation Start Logits Accuracy: 0.8905 - Epoch: 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: - 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 804, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.5563 | 0.6377 | 0.6615 | 0.5213 | 0.8376 | 0.8905 | 0 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3

cenkersisman/gpt2-turkish-10m

cenkersisman

2023-07-28T14:43:04Z

139

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "tr", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-27T14:24:17Z

--- widget: - text: 'cengiz' example_title: Örnek 1 language: - tr --- # Model Card for Model ID Model Card for GPT-2 Turkish Model ## Model Details ### Model Description GPT-2 Turkish Model is a language model based on the GPT-2 architecture, fine-tuned for the Turkish language. It is capable of generating human-like text based on a given prompt and has been trained on a large corpus of Turkish text data. - **Developed by:** Cenker Sisman - **Model type:** - **Language(s) (NLP):** - **License:** - **Finetuned from model :** GPT-2 ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Sınırlamalar ve Önyargılar Bu model, bir özyineli dil modeli olarak eğitildi. Bu, temel işlevinin bir metin dizisi alıp bir sonraki belirteci tahmin etmek olduğu anlamına gelir. Dil modelleri bunun dışında birçok görev için yaygın olarak kullanılsa da, bu çalışmayla ilgili birçok bilinmeyen bulunmaktadır. Model, küfür, açık saçıklık ve aksi davranışlara yol açan metinleri içerdiği bilinen bir veri kümesi üzerinde eğitildi. Kullanım durumunuza bağlı olarak, bu model toplumsal olarak kabul edilemez metinler üretebilir. Tüm dil modellerinde olduğu gibi, bu modelin belirli bir girişe nasıl yanıt vereceğini önceden tahmin etmek zordur ve uyarı olmaksızın saldırgan içerik ortaya çıkabilir. Sonuçları yayınlamadan önce hem istenmeyen içeriği sansürlemek hem de sonuçların kalitesini iyileştirmek için insanların çıktıları denetlemesini veya filtrelemesi önerilir. ## 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 ```python # Example code for inference with the model from transformers import GPT2Tokenizer, GPT2LMHeadModel model_name = "cenkersisman/gpt2-turkish" # Change to the model name you have uploaded to Hugging Face tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) prompt = "cengiz" input_ids = tokenizer.encode(prompt, return_tensors="pt") output = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text) ``` ## 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]

ZhiguangHan/test-clm

ZhiguangHan

2023-07-28T14:42:03Z

182

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T14:02:13Z

--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: test-clm results: [] ---  # test-clm This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6319 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5547 | 1.0 | 2334 | 3.6373 | | 3.4926 | 2.0 | 4668 | 3.6361 | | 3.4692 | 3.0 | 7002 | 3.6319 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

greg-szopinski/Reinforce-pixelcopter-128

greg-szopinski

2023-07-28T14:38:21Z

0

null

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

reinforcement-learning

2023-07-28T14:36:26Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter-128 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 33.80 +/- 16.58 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

sailoromkar/q-taxi

sailoromkar

2023-07-28T14:31:52Z

0

null

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

reinforcement-learning

2023-07-28T14:31:10Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 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="sailoromkar/q-taxi", 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"]) ```

jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025

jordyvl

2023-07-28T14:26:36Z

106

0

transformers

[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-23T21:50:37Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025 results: [] ---  # EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2244 - Accuracy: 0.9394 - Exit 0 Accuracy: 0.2721 - Exit 1 Accuracy: 0.4875 - Exit 2 Accuracy: 0.8051 - Exit 3 Accuracy: 0.8840 - Exit 4 Accuracy: 0.9339 ## 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: 6 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 144 - 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 | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:| | 0.5909 | 1.0 | 2222 | 0.2945 | 0.9158 | 0.2021 | 0.3569 | 0.7091 | 0.8143 | 0.9092 | | 0.4951 | 2.0 | 4444 | 0.2469 | 0.9292 | 0.2262 | 0.4336 | 0.7677 | 0.8614 | 0.9258 | | 0.4279 | 3.0 | 6666 | 0.2281 | 0.9336 | 0.2530 | 0.4682 | 0.7898 | 0.8768 | 0.9302 | | 0.39 | 4.0 | 8888 | 0.2241 | 0.9385 | 0.2600 | 0.483 | 0.8008 | 0.8827 | 0.9328 | | 0.3602 | 5.0 | 11110 | 0.2244 | 0.9394 | 0.2721 | 0.4875 | 0.8051 | 0.8840 | 0.9339 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

Pierre-Arthur/distilroberta_base_eurolex_mlm_model

Pierre-Arthur

2023-07-28T14:22:58Z

171

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "dataset:eurlex_resources", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-28T11:29:42Z

--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer datasets: - eurlex_resources model-index: - name: distilroberta_base_eurolex_mlm_model results: [] ---  # distilroberta_base_eurolex_mlm_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the eurlex_resources dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 38 | nan | | No log | 2.0 | 76 | nan | | No log | 3.0 | 114 | nan | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

Lajonbot/tableBeluga-7B-instruct-pl-lora_unload

Lajonbot

2023-07-28T14:12:09Z

1,396

2

transformers

[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T13:59:47Z

--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---

twbrandon7/rl-course-unit1

twbrandon7

2023-07-28T14:02:48Z

0

stable-baselines3

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

reinforcement-learning

2023-07-28T14:01:02Z

--- 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: 249.84 +/- 17.46 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 ... ```

Lajonbot/tableBeluga-7B-instruct-pl-lora_GGML

Lajonbot

2023-07-28T13:59:47Z

0

null

[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]

text-generation

2023-07-28T13:49:06Z

--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---

StereoLuigi99/ali-gungor

StereoLuigi99

2023-07-28T13:55:00Z

0

null

[ "region:us" ]

null

2023-07-28T13:44:42Z

# Ali GÜNGÖR AI Voice (200 Epochs / Harvest) RVC v2 kullanılarak oluşturuldu. Recep Baltaş tek başına kalmasın diye yaptım, konuşmalar için kullanın. ## Özellikler - 38 dakikalık Ali GÜNGÖR sesi kullanıldı. - RVC v2 ile Google Colab üzerinden yapıldı: https://colab.research.google.com/drive/1r4IRL0UA7JEoZ0ZK8PKfMyTIBHKpyhcw - Harvest metodu ile çalıştırıldı. - 200 Epoch bulunuyor.

Naruke/ppo-Pyramidsv1

Naruke

2023-07-28T13:50:56Z

8

0

ml-agents

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

reinforcement-learning

2023-07-28T13:25:08Z

--- 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: Naruke/ppo-Pyramidsv1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Lajonbot/tableBeluga-7B-instruct-pl-lora_adapter_model

Lajonbot

2023-07-28T13:49:05Z

0

null

[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]

text-generation

2023-07-28T13:49:04Z

--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---

FinchResearch/llama2-archimedes-7b-lora

FinchResearch

2023-07-28T13:47:43Z

0

peft

[ "peft", "region:us" ]

null

2023-07-28T13:47:36Z

--- 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

HaziqRazali/ppo-Pyramids

HaziqRazali

2023-07-28T13:45:47Z

7

0

ml-agents

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

reinforcement-learning

2023-07-28T13:45:14Z

--- 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: HaziqRazali/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

LarryAIDraw/bluearchive_haruna-10

LarryAIDraw

2023-07-28T13:42:29Z

0

null

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

null

2023-07-28T13:32:21Z

--- license: creativeml-openrail-m --- https://civitai.com/models/52253/kurodate-haruna-or-blue-archive

LarryAIDraw/narb-overlord-13

LarryAIDraw

2023-07-28T13:42:12Z

0

null

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

null

2023-07-28T13:31:14Z

--- license: creativeml-openrail-m --- https://civitai.com/models/117294/narberal-gamma-overlord

LarryAIDraw/sakurako1-000006

LarryAIDraw

2023-07-28T13:41:56Z

0

null

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

null

2023-07-28T13:30:55Z

--- license: creativeml-openrail-m --- https://civitai.com/models/114592/utazumi-sakurako-blue-archive-or-character-lora-344

LarryAIDraw/fymt

LarryAIDraw

2023-07-28T13:41:46Z

0

null

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

null

2023-07-28T13:29:55Z

--- license: creativeml-openrail-m --- https://civitai.com/models/96806/arknights-fiammetta

Envertime/test_deliberate_v2

Envertime

2023-07-28T13:39:44Z

0

null

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

null

2023-07-28T13:39:44Z

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

NasimB/aochildes-rarity-seed

NasimB

2023-07-28T13:37:53Z

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

2023-07-28T04:40:59Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-rarity-seed results: [] ---  # 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: 4.1164 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 6.3514 | 0.29 | 500 | 5.3390 | | 5.0369 | 0.59 | 1000 | 4.9222 | | 4.7211 | 0.88 | 1500 | 4.6884 | | 4.4532 | 1.17 | 2000 | 4.5398 | | 4.3029 | 1.47 | 2500 | 4.4318 | | 4.2095 | 1.76 | 3000 | 4.3295 | | 4.0772 | 2.05 | 3500 | 4.2615 | | 3.9042 | 2.35 | 4000 | 4.2130 | | 3.8732 | 2.64 | 4500 | 4.1604 | | 3.8358 | 2.93 | 5000 | 4.1110 | | 3.641 | 3.23 | 5500 | 4.1105 | | 3.5952 | 3.52 | 6000 | 4.0799 | | 3.5797 | 3.81 | 6500 | 4.0466 | | 3.465 | 4.11 | 7000 | 4.0458 | | 3.3242 | 4.4 | 7500 | 4.0451 | | 3.3146 | 4.69 | 8000 | 4.0309 | | 3.3112 | 4.99 | 8500 | 4.0183 | | 3.1524 | 5.28 | 9000 | 4.0325 | | 3.1343 | 5.57 | 9500 | 4.0319 | | 3.1354 | 5.87 | 10000 | 4.0309 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

JBJoyce/speecht5_finetuned_voxpopuli_sl

JBJoyce

2023-07-28T13:37:51Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-28T12:28:14Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_sl results: [] ---  # speecht5_finetuned_voxpopuli_sl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4393 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4886 | 21.51 | 1000 | 0.4456 | | 0.4706 | 43.01 | 2000 | 0.4393 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

liuyt75/t5-large_prefix_tuning_sentences_66agree_10

liuyt75

2023-07-28T13:35:44Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-28T13:35:42Z

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

youlun77/DistilBERT-TC2000-10epochs

youlun77

2023-07-28T13:30:50Z

113

0

transformers

[ "transformers", "pytorch", "tensorboard", "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

2023-07-28T13:22:29Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - recall - precision - f1 model-index: - name: DistilBERT-TC2000-10epochs results: [] ---  # DistilBERT-TC2000-10epochs 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: 0.0752 - Recall: {'recall': 0.98} - Precision: {'precision': 0.9803145941921073} - F1: {'f1': 0.9800242537313432} ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:---------------------------------:|:--------------------------:| | 1.0272 | 0.18 | 20 | 0.8815 | {'recall': 0.65} | {'precision': 0.7778791777580597} | {'f1': 0.6251215862860073} | | 0.8663 | 0.35 | 40 | 0.6770 | {'recall': 0.905} | {'precision': 0.9120308312976535} | {'f1': 0.9054010850819201} | | 0.6016 | 0.53 | 60 | 0.4088 | {'recall': 0.92} | {'precision': 0.9238949736347314} | {'f1': 0.9207242314918276} | | 0.3139 | 0.71 | 80 | 0.2508 | {'recall': 0.93} | {'precision': 0.9322386382325532} | {'f1': 0.929768888773222} | | 0.2645 | 0.88 | 100 | 0.2048 | {'recall': 0.955} | {'precision': 0.958280303030303} | {'f1': 0.954923196771023} | | 0.1811 | 1.06 | 120 | 0.1446 | {'recall': 0.965} | {'precision': 0.9675925925925927} | {'f1': 0.9648852158183796} | | 0.1429 | 1.24 | 140 | 0.1245 | {'recall': 0.975} | {'precision': 0.9762354497354496} | {'f1': 0.9749193929610656} | | 0.0941 | 1.42 | 160 | 0.1338 | {'recall': 0.965} | {'precision': 0.9683561643835616} | {'f1': 0.9652805623632961} | | 0.1242 | 1.59 | 180 | 0.0872 | {'recall': 0.975} | {'precision': 0.9759505494505496} | {'f1': 0.9750344590666455} | | 0.0893 | 1.77 | 200 | 0.0572 | {'recall': 0.985} | {'precision': 0.9853867102396515} | {'f1': 0.9849564819176908} | | 0.0477 | 1.95 | 220 | 0.0794 | {'recall': 0.975} | {'precision': 0.9762354497354496} | {'f1': 0.9749193929610656} | | 0.0128 | 2.12 | 240 | 0.0697 | {'recall': 0.98} | {'precision': 0.9807447665056361} | {'f1': 0.9799368665956859} | | 0.0449 | 2.3 | 260 | 0.0635 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9702302752172594} | | 0.0996 | 2.48 | 280 | 0.0782 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9700752508361203} | | 0.0328 | 2.65 | 300 | 0.0127 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} | | 0.0747 | 2.83 | 320 | 0.0380 | {'recall': 0.975} | {'precision': 0.9767605633802816} | {'f1': 0.9751792302987906} | | 0.0413 | 3.01 | 340 | 0.0127 | {'recall': 1.0} | {'precision': 1.0} | {'f1': 1.0} | | 0.0404 | 3.19 | 360 | 0.0120 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949915278995033} | | 0.0226 | 3.36 | 380 | 0.0085 | {'recall': 1.0} | {'precision': 1.0} | {'f1': 1.0} | | 0.0543 | 3.54 | 400 | 0.0139 | {'recall': 0.995} | {'precision': 0.9950925925925926} | {'f1': 0.9950042805165157} | | 0.0528 | 3.72 | 420 | 0.0408 | {'recall': 0.985} | {'precision': 0.9856521739130435} | {'f1': 0.9850251572327045} | | 0.0051 | 3.89 | 440 | 0.0808 | {'recall': 0.97} | {'precision': 0.9725} | {'f1': 0.9702302752172594} | | 0.014 | 4.07 | 460 | 0.0419 | {'recall': 0.985} | {'precision': 0.985241846323936} | {'f1': 0.985017255463425} | | 0.051 | 4.25 | 480 | 0.0127 | {'recall': 0.995} | {'precision': 0.9950925925925926} | {'f1': 0.9950042805165157} | | 0.0501 | 4.42 | 500 | 0.0200 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} | | 0.0062 | 4.6 | 520 | 0.0247 | {'recall': 0.985} | {'precision': 0.985241846323936} | {'f1': 0.985017255463425} | | 0.0118 | 4.78 | 540 | 0.0614 | {'recall': 0.975} | {'precision': 0.975962157809984} | {'f1': 0.975047977706797} | | 0.0348 | 4.96 | 560 | 0.0516 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0226 | 5.13 | 580 | 0.0144 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} | | 0.0159 | 5.31 | 600 | 0.0129 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} | | 0.0026 | 5.49 | 620 | 0.0176 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} | | 0.016 | 5.66 | 640 | 0.0404 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0433 | 5.84 | 660 | 0.0663 | {'recall': 0.975} | {'precision': 0.9756772575250836} | {'f1': 0.975041928721174} | | 0.0354 | 6.02 | 680 | 0.0253 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} | | 0.0041 | 6.19 | 700 | 0.0961 | {'recall': 0.97} | {'precision': 0.9711688311688311} | {'f1': 0.9700614296351452} | | 0.0579 | 6.37 | 720 | 0.1336 | {'recall': 0.965} | {'precision': 0.966783728687917} | {'f1': 0.9650813612906225} | | 0.0025 | 6.55 | 740 | 0.0424 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0328 | 6.73 | 760 | 0.0190 | {'recall': 0.995} | {'precision': 0.995060975609756} | {'f1': 0.9949962534538471} | | 0.0217 | 6.9 | 780 | 0.0488 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0096 | 7.08 | 800 | 0.1115 | {'recall': 0.97} | {'precision': 0.9711688311688311} | {'f1': 0.9700614296351452} | | 0.0106 | 7.26 | 820 | 0.0673 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0077 | 7.43 | 840 | 0.0354 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} | | 0.0222 | 7.61 | 860 | 0.0410 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0026 | 7.79 | 880 | 0.0590 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0576 | 7.96 | 900 | 0.0596 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.018 | 8.14 | 920 | 0.0428 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} | | 0.027 | 8.32 | 940 | 0.0425 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} | | 0.036 | 8.5 | 960 | 0.0341 | {'recall': 0.985} | {'precision': 0.9850867537313434} | {'f1': 0.985009807126512} | | 0.0094 | 8.67 | 980 | 0.0457 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0192 | 8.85 | 1000 | 0.0586 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.03 | 9.03 | 1020 | 0.0789 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0091 | 9.2 | 1040 | 0.0691 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0197 | 9.38 | 1060 | 0.0753 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0025 | 9.56 | 1080 | 0.0796 | {'recall': 0.975} | {'precision': 0.9756772575250836} | {'f1': 0.975041928721174} | | 0.0414 | 9.73 | 1100 | 0.0791 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | | 0.0075 | 9.91 | 1120 | 0.0756 | {'recall': 0.98} | {'precision': 0.9803145941921073} | {'f1': 0.9800242537313432} | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

w601sxs/b1ade-1b-orca-chkpt-563k

w601sxs

2023-07-28T13:30:11Z

0

peft

[ "peft", "region:us" ]

null

2023-07-28T13:30:09Z

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

Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0

Evan-Lin

2023-07-28T13:25:27Z

51

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-07-28T13:23:43Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpapodd3ns/Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpapodd3ns/Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpapodd3ns/Evan-Lin/Bart-Yelp-rougelastbatch2-attractive1-keywordmax1-len0") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

WforGodot/add-lora-1b

WforGodot

2023-07-28T13:24:40Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-28T13:10:39Z

--- 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 0.4.0

efainman/Pyramids

efainman

2023-07-28T13:20:59Z

3

0

ml-agents

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

reinforcement-learning

2023-07-28T13:20:45Z

--- 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: efainman/Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

stefaniftime/bert-finetuned-squad

stefaniftime

2023-07-28T13:17:20Z

61

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-28T10:29:03Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: stefaniftime/bert-finetuned-squad results: [] ---  # stefaniftime/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2718 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.2718 | 0 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3

JAdeojo/xlm-roberta-large-lora-consumer-complaints-cfpb_checkpoint2

JAdeojo

2023-07-28T13:13:13Z

0

peft

[ "peft", "region:us" ]

null

2023-07-28T13:13:07Z

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

liuyt75/t5-large_prefix_tuning_sentences_66agree_5

liuyt75

2023-07-28T13:07:09Z

0

peft

[ "peft", "region:us" ]

null

2023-07-28T13:07:08Z

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

jcy204/cold_model

jcy204

2023-07-28T13:06:43Z

61

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "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

2023-07-28T13:01:33Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jcy204/cold_model results: [] ---  # jcy204/cold_model 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: 0.3079 - Validation Loss: 0.6510 - Train Accuracy: 0.7604 - Epoch: 2 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3185, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.6912 | 0.5784 | 0.7513 | 0 | | 0.4713 | 0.5637 | 0.7641 | 1 | | 0.3079 | 0.6510 | 0.7604 | 2 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3

guyhadad01/ppo-LunarLander-v2

guyhadad01

2023-07-28T13:06:17Z

0

stable-baselines3

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

reinforcement-learning

2023-07-28T13:05:58Z

--- 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: 282.00 +/- 17.53 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 ... ```

elvhens/ryujin-500

elvhens

2023-07-28T13:01:27Z

0

null

[ "kpop", "itzy", "ryujin", "ko", "region:us" ]

null

2023-07-28T11:15:18Z

--- language: - ko tags: - kpop - itzy - ryujin ---

iworeushankaonce/whisper-tiny-en

iworeushankaonce

2023-07-28T12:49:14Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "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

2023-07-28T12:18:50Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.36481700118063753 ---  # whisper-tiny-en This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6411 - Wer Ortho: 0.3621 - Wer: 0.3648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0008 | 17.86 | 500 | 0.6411 | 0.3621 | 0.3648 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

Maldopast/distilhubert-finetuned-gtzan

Maldopast

2023-07-28T12:49:12Z

157

0

transformers

[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-28T12:30:11Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: [] ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7537 - 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: 16 - eval_batch_size: 16 - 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9647 | 1.0 | 113 | 1.8614 | 0.52 | | 1.3987 | 2.0 | 226 | 1.3098 | 0.61 | | 0.8809 | 3.0 | 339 | 0.8631 | 0.76 | | 0.7643 | 4.0 | 452 | 0.8114 | 0.77 | | 0.5958 | 5.0 | 565 | 0.7013 | 0.81 | | 0.4405 | 6.0 | 678 | 0.5860 | 0.84 | | 0.2183 | 7.0 | 791 | 0.6114 | 0.82 | | 0.1587 | 8.0 | 904 | 0.5141 | 0.85 | | 0.0899 | 9.0 | 1017 | 0.4760 | 0.87 | | 0.0575 | 10.0 | 1130 | 0.5759 | 0.86 | | 0.0647 | 11.0 | 1243 | 0.6467 | 0.86 | | 0.0061 | 12.0 | 1356 | 0.6372 | 0.88 | | 0.0029 | 13.0 | 1469 | 0.6721 | 0.88 | | 0.0018 | 14.0 | 1582 | 0.7565 | 0.89 | | 0.0013 | 15.0 | 1695 | 0.7537 | 0.88 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.0 - Tokenizers 0.13.3

FiouReia/my_awesome_model

FiouReia

2023-07-28T12:44:03Z

61

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "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

2023-07-28T10:16:24Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: FiouReia/my_awesome_model results: [] ---  # FiouReia/my_awesome_model 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: 0.0326 - Validation Loss: 0.2450 - Train Accuracy: 0.934 - Epoch: 2 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.1321 | 0.1917 | 0.928 | 0 | | 0.0649 | 0.2146 | 0.9351 | 1 | | 0.0326 | 0.2450 | 0.934 | 2 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3

ChristophBu/ppo-LunarLander-v2

ChristophBu

2023-07-28T12:38:59Z

0

stable-baselines3

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

reinforcement-learning

2023-07-28T11:02:50Z

--- 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: 277.34 +/- 18.25 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 ... ```

Isaacgv/whisper-tiny-minds14

Isaacgv

2023-07-28T12:37:07Z

86

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "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

2023-07-28T10:03:38Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.3530106257378985 ---  # whisper-tiny-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6457 - Wer Ortho: 0.3572 - Wer: 0.3530 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0007 | 17.86 | 500 | 0.6457 | 0.3572 | 0.3530 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

magnustragardh/ppo-deepdive-LunarLander-v2

magnustragardh

2023-07-28T12:34:33Z

0

null

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

reinforcement-learning

2023-07-27T22:26:18Z

--- 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: 72.15 +/- 17.75 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters

ArmelR/starcoder-gradio-v2.1

ArmelR

2023-07-28T12:21:21Z

7

0

transformers

[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "dataset:databricks/databricks-dolly-15k", "dataset:HuggingFaceH4/oasst1_en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T11:31:17Z

--- datasets: - databricks/databricks-dolly-15k - HuggingFaceH4/oasst1_en --- Gradio training 1000 steps IFT 200 steps (v2.0) 1000 steps (v2.1)

donadelicc/nor-sum

donadelicc

2023-07-28T12:12:46Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "base_model:sshleifer/distilbart-cnn-6-6", "base_model:finetune:sshleifer/distilbart-cnn-6-6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-28T09:56:12Z

--- license: apache-2.0 base_model: sshleifer/distilbart-cnn-6-6 tags: - generated_from_trainer metrics: - rouge model-index: - name: nor-sum results: [] ---  # nor-sum This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1812 - Rouge1: 0.2552 - Rouge2: 0.0679 - Rougel: 0.1884 - Rougelsum: 0.1886 - Gen Len: 65.3086 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 2.6231 | 1.0 | 3188 | 2.4652 | 0.2359 | 0.0563 | 0.1732 | 0.1733 | 66.1928 | | 2.3062 | 2.0 | 6377 | 2.2798 | 0.2524 | 0.0653 | 0.1864 | 0.1864 | 66.3107 | | 2.0817 | 3.0 | 9565 | 2.1973 | 0.2529 | 0.0675 | 0.189 | 0.1893 | 65.077 | | 1.9776 | 4.0 | 12752 | 2.1812 | 0.2552 | 0.0679 | 0.1884 | 0.1886 | 65.3086 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

zzzAI19/MasterpieceFT

zzzAI19

2023-07-28T12:08:21Z

0

3

null

[ "region:us" ]

null

2023-07-28T11:35:45Z

This model is additionally studied with past masterpieces. It can be painted pictorially. There are two versions: an all-ages depiction-oriented version and an NSFW-enhanced version. All-ages depiction-oriented version: MasterpieceFT_v1-pruned.safetensors NSFW-enhanced version: MasterpieceNeoFT_v1-pruned.safetensors (NEO: Nsfw Erotic Option) This model is somewhat difficult to use. The NSFW enhanced version is not particularly easy to use. See blog for sample illustrations. (Sample illustrations for all ages) https://ai-drawing.net/en/2023/07/28/introducing-of-masterpiece-ft-v1-0/ (R18 sample illustrations) https://ai-drawing2.net/2023/07/28/masterpiece-neo-ft-v1-0/ (recommended settings) “fine art parody, traditional media” is the prompt for classical painting. It is recommended that these be included in the positive prompts. Also, Steps 2.5-4 will give you impressionistic paintings, and Steps 7 and above will give you realistic paintings. Use whichever you prefer. For realistic pictures, it is compatible with flat2 LoRA. I recommend using it together. https://civitai.com/models/81291/flat2 過去の名画で追加学習したモデルです。絵画的な絵が描けます。全年齢描写重視版とNSFW強化版があります。全年齢描写重視版：MasterpieceFT_v1-pruned.safetensors NSFW強化版：MasterpieceNeoFT_v1-pruned.safetensors (NEO：Nsfw Erotic Option) このモデルはやや使いにくいです。特にNSFW強化版の使い勝手は良くありません。サンプルイラストはブログをご覧ください。 (全年齢サンプルイラスト) https://ai-drawing.net/2023/07/28/%e6%96%b0%e3%83%a2%e3%83%87%e3%83%abmasterpiece-ft-v1-0%e7%b4%b9%e4%bb%8b/ (R18サンプルイラスト) https://ai-drawing2.net/2023/07/28/masterpiece-neo-ft-v1-0/ (推奨設定) fine art parody, traditional mediaが古典絵画のプロンプトです。これらをポジティブプロンプトに入れることをお勧めします。またステップ2.5～4では印象派的な絵、ステップ7以上では写実的な絵が描けます。好きな方を利用してください。また写実的な絵ではflat2 LoRAと相性が良いです。併用するのもおすすめです。 https://civitai.com/models/81291/flat2 --- license: creativeml-openrail-m ---

xiao12346/t5-large_PREFIX_TUNING_SEQ2SEQ

xiao12346

2023-07-28T12:02:24Z

2

0

peft

[ "peft", "region:us" ]

null

2023-07-21T05:14:16Z

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

X-D-Lab/MindChat-baichuan-13B

X-D-Lab

2023-07-28T11:52:16Z

5

8

transformers

[ "transformers", "pytorch", "baichuan", "text-generation", "custom_code", "license:gpl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T02:15:59Z

--- license: gpl-3.0 --- ## 💪 模型进展 * Jul 23, 2023: 提供MindChat体验地址: [MindChat-创空间](https://modelscope.cn/studios/X-D-Lab/MindChat/summary), 欢迎体验 * Jul 21, 2023: MindChat-InternLM-7B训练完成, 在**模型安全、共情输出、人类价值观对齐**等方面进行针对性强化; * Jul 15, 2023: MindChat-Baichuan-13B训练完成, 作为**首个百亿级参数的心理大模型**正式开源; * Jul 9, 2023: MindChat-beta训练完成, 并正式开源; * Jul 6, 2023: 首次提交MindChat(漫谈)心理大模型; ## 👏 模型介绍 **MindChat(漫谈): 心理大模型**(简称: MindChat)旨在从心理咨询、心理评估、心理诊断、心理治疗四个维度帮助人们解决心理问题, 提高心理健康水平. 作为一款心理大模型, MindChat通过营造轻松、开放的交谈环境, 以放松身心、交流感受或分享经验的方式, 与用户建立信任和理解的关系. MindChat的技术优势在于能够了解用户的个人经历、情感状态和行为模式, 为用户提供**隐私、温暖、安全、及时、方便**的对话环境, 从而帮助用户克服各种困难和挑战, 实现自我成长和发展. MindChat的创新之处在于它不仅是一款高效的心理咨询工具, 预期能够提供心理评估、心理诊断和心理治疗的服务. MindChat的专业团队拥有丰富的心理学背景和经验, 他们的专业知识和技能可以帮助用户更好地理解自己的情感和行为, 从而制定更有效的治疗方案. 无论是在工作场所还是在个人生活中, MindChat期望通过自身的努力和专业知识, 在**严格保护用户隐私的前提下**, **全时段全天候**为用户提供全面的心理支持和诊疗帮助, 同时实现自我成长和发展, 以期为建设一个更加**健康、包容和平等**的社会贡献力量. ## 🔥 模型列表 | 模型名称 | lora权重 | 合并后的权重 | | :----: | :----: | :----: | | MindChat-InternLM-7B | modelscope / huggingface | [modelscope](https://modelscope.cn/models/X-D-Lab/MindChat-7B/summary) / [huggingface](https://huggingface.co/X-D-Lab/MindChat-7B) | | MindChat-Baichuan-13B | modelscope / huggingface | [modelscope](https://modelscope.cn/models/X-D-Lab/MindChat-Baichuan-13B/summary) / [huggingface](https://huggingface.co/X-D-Lab/MindChat-baichuan-13B) | 目前, 已经开源了**MindChat-Baichuan-13B**版本的百亿级MindChat心理大模型. 更为优质的MindChat模型将在不久的未来对外开源开放. 敬请期待! 此外, 本团队同时关注人们的身理健康, 建有安全、可靠、普惠的[中文医疗大模型孙思邈(Sunsimiao)](https://github.com/X-D-Lab/Sunsimiao), 欢迎下载使用, 敬请批评指证! ## 📖 对话案例 ### 🥺 学习 | 轮数 | 用户 | MindChat | | :----: | :----: | :----: | |1|总感觉自己学习很差, 想努力, 但是又控制不住想玩游戏 | 我明白你的感受, 你觉得自己学习不好, 但是总是控制不住想玩游戏. 我想问问你, 你玩游戏的时候是什么感觉呢? 有没有什么特别喜欢的游戏?| |2|我平常特别喜欢玩一些端游来打发时间, 玩游戏的时候很纠结, 明明自己成绩落后, 学习任务还没完成但还是想玩 | 我明白你的感受. 你可能需要认真思考一下, 玩游戏对你来说是不是真的值得投入这么多时间和精力. 学习落后是很正常的事情, 但不要让游戏成为你逃避现实的工具. 你可以尝试制定一个计划, 把玩游戏的时间留给自己, 同时也要合理分配学习任务, 让自己更加充实. | |3|在学习上总感觉自己学的很吃力, 不知道该怎么办, 觉得自己学习能力太差了, 心里很失落 | 我理解你的感受. 每个人的学习能力都是不同的, 不要过分苛求自己. 你可以先尝试找到适合自己的学习方法, 比如寻找学习伙伴, 向老师请教, 或者参加一些学习小组. 同时, 也不要忘记给自己一些休息的时间, 保持身心健康. 学习是一个长期的过程, 不要过于急躁, 慢慢来, 你会越来越好的. | |4|我确实不该把精力都是投入在游戏这些娱乐上, 但是我已经落后很多了, 我现在再去开始学习, 以我这么差的学习能力我还有机会学好吗 | 虽然你现在的学习状态很糟糕, 但并不是没有机会. 学习是一个长期的过程, 只要你持之以恒, 坚持不懈, 就一定会有所进步. 同时, 也不要忘记, 学习的过程也需要一定的动力和动机. 你可以找一些自己感兴趣的学习方向, 或者设定一些小目标, 让自己更有动力去完成. 不要轻易放弃, 相信自己, 你一定可以做到. | |5|可是我很焦虑, 以前落后太多了, 现在不知道从哪里开始 | 焦虑是很正常的情绪, 不要害怕它. 你可以先想想自己目前的学习状态, 列出自己需要学习的科目和内容, 然后按照优先级和难易程度进行排序. 接着, 你可以尝试分解每个科目和内容, 把它们分成小目标, 一步一步地去完成. 这样, 你就可以更有针对性地进行学习, 不会感到太紧张和不知所措. 同时, 也不要忘记给自己一些奖励, 比如看一部电影, 吃一顿美食等等, 让自己更有动力去完成目标. | |6|也就是我现在主要的问题是太焦虑了是吗? 我只要好好的去做, 成绩还是可以提高的对吗 | 是的, 焦虑是一种很常见的情绪, 它会让你感到很不安, 但是它并不会影响你真正地提高成绩. 你可以试着把焦虑的情绪转化成动力, 让它成为你学习的一部分. 同时, 也不要忘记给自己一些时间, 不要过分压抑自己的情绪, 让自己保持一个积极的心态. 只要你持之以恒, 相信自己, 你一定可以取得更好的成绩. | |7|非常谢谢你, 我会好好努力的 | 我不用客气, 我很高兴能帮到你. 我相信你一定可以克服困难, 取得好成绩. 加油! 如果有任何需要帮助的地方, 随时都可以找我聊天. | ### 👨‍👩‍👧‍👦 家庭 | 轮数 | 用户 | MindChat | | :----: | :----: | :----: | |1| 我总感觉爸妈不理解我, 感觉和他们之间有很大的隔阂. | 我能理解你的感受. 和家人的沟通和理解是家庭关系中非常重要的一环. 你是否曾经尝试过和他们分享你的想法和感受, 让他们更好地了解你呢? | |2|我有尝试找他们沟通, 但是他们听不进去我说话 , 认为我是在无理取闹, 我心里很无助 | 我明白你的感受. 有时候我们的家人可能无法理解我们的想法和感受, 这时候我们需要耐心和沟通技巧来帮助他们更好地理解我们. 你可以试着换个方式表达你的想法, 或者找到一些共同话题来促进交流. 同时, 也要记得保持冷静和尊重, 不要使用攻击性的语言来伤害他们. | |3|我也知道他们是为了我好, 平时他们督促我学习也是为了我的未来, 我能体谅他们, 但是有时候自己也会有压力, 想和他们沟通他们就会觉得我在偷懒 | 我能理解你的感受. 有时候我们的家人可能认为我们在偷懒, 但是其实他们只是担心我们的未来和健康. 你可以试着跟他们分享你的压力和焦虑, 让他们了解你的感受. 同时, 也要表达出你对他们的理解和感激, 让他们知道你是愿意和他们一起面对问题, 共同找到解决方案的. | |4|我平时会表现的乖巧, 因为我觉得他们也不容易, 我该怎么去找一个双方都适合的方式去沟通呢 | 很好的问题. 沟通是一个相互的过程, 需要双方的努力和理解. 你可以试着找一些共同话题来促进交流, 同时也要听取他们的想法和建议. 如果你觉得自己的表达方式不够清晰, 可以尝试用一些简单的词汇和表达方式来让他们更容易理解. 最重要的是要保持耐心和尊重, 相信你们会找到适合彼此的方式. | |5|可是我会觉得找他们沟通是一种不懂事的表现, 他们很辛苦我还给他们增加负担, 但是自己忍着难过心里也很压抑 | 我能理解你的感受. 有时候我们会有这样的想法, 但是其实我们不应该让这些想法限制我们的行动. 沟通是解决问题的一种方式, 同时也是增进家庭关系的重要途径. 如果你觉得自己的心情很压抑, 可以试着找一些放松的方式, 比如运动、听音乐或者写日记来缓解压力. 同时也要记得, 你的家人是关心你的, 他们不会增加你的负担, 而是希望你能够更好地成长和幸福. | |6|可能是我的想法有点偏执了, 我应该找一个时间和爸妈好好交流一下 | 这是个好主意. 和家人的交流是非常重要的, 可以让你更好地了解他们的想法和感受. 你可以试着找一个安静的时间, 让他们知道你需要和他们谈一谈, 同时也要准备好自己的态度和想法. 相信通过交流, 你们可以更好地理解彼此, 增进感情. 加油. | |7|谢谢你的建议, 我的心情好多了 | 不用客气, 我很高兴能帮到你. 记得, 沟通和理解是家庭关系中非常重要的一环, 让我们一起为建立更加和谐的家庭关系而努力吧! | ### 🛝 朋友 | 轮数 | 用户 | MindChat | | :----: | :----: | :----: | |1| 我感觉我的朋友并不是真心和我相处, 今天又和他吵了一架, 我现在很生气 | 听到你的困扰, 我能理解你的感受. 吵架是一种不健康的沟通方式, 它可能导致双方情绪失控, 进而影响到彼此之间的关系. 我建议你尝试以平和的心态来面对这个问题. 你可以试着和你的朋友坐下来, 表达你的感受和需求, 让他了解你的想法和感受. 这样他才能更好地理解你的立场, 并且做出积极的回应. 同时, 你也可以告诉他, 你希望你们之间的交流是建立在互相尊重和理解的基础上的. | |2|我总感觉他是以自我为中心, 有时候我会选择忍让, 但是他还是一副无所谓的样子 | 我能理解你的感受, 但是忍让并不是解决问题的好方法. 你需要和他沟通, 让他知道你的感受和需求. 告诉他你希望他能够更加尊重你的感受, 并且尝试理解你的立场. 同时, 你也可以告诉他你希望你们之间的交流是建立在互相尊重和理解的基础上的. 如果他仍然无法理解你的立场, 那么你也需要学会说不, 并且保持自己的立场. 这样, 你们之间的关系才能健康发展. | |3|但是如果跟他去沟通这些, 我怕他会觉得我想得太多, 从而远离我 | 这是很正常的担忧. 但是, 沟通是解决问题的关键. 如果他真的关心你, 他会愿意和你沟通, 并且理解你的立场. 如果他不想和你沟通, 那么这可能是一个问题. 你需要尝试以平和的心态来面对这个问题, 并且告诉他你的感受和需求. 如果他仍然不愿意和你沟通, 那么你需要学会接受这个结果, 并且寻找其他的解决方法. 无论如何, 你都需要学会保持自己的立场, 并且不要放弃. | |4|我觉得有时候情绪上来确实会影响我们之间的沟通, 可能生气也是觉得这段友谊本身很好, 但是也会出现小的矛盾 | 情绪确实会影响我们的沟通, 但是你可以试着在情绪平静下来之后再和他沟通. 当你感到生气的时候, 你的思维和语言可能会变得不清晰, 这会加剧矛盾. 所以, 在情绪平复下来之后, 你可以尝试和他坐下来, 以平和的心态来解决问题. 同时, 你也可以告诉他, 你希望你们之间的交流是建立在互相尊重和理解的基础上的. 如果他仍然无法理解你的立场, 那么你需要学会说不, 并且保持自己的立场. 这样, 你们之间的关系才能健康发展. | |5|好的, 我会平静下来找他沟通一下, 谢谢你的建议 | 不用谢, 我很高兴能帮到你. 记住, 沟通是解决问题的关键. 无论结果如何, 你都需要保持自己的立场, 并且不要放弃. 相信你们之间的关系会变得更加健康. 祝你好运! | ### 🧑‍🏭 工作 | 轮数 | 用户 | MindChat | | :----: | :----: | :----: | |1| 最近工作压力好大, 一直没有业绩 | 听到你的问题, 我可以理解你现在的感受. 工作压力确实会让人感到焦虑和不安, 不过我们需要认识到, 工作只是我们生活中的一部分, 我们需要学会放松和平衡. 你可以试着做一些放松的活动, 比如运动、阅读、听音乐等等, 来缓解一下紧张的情绪. 同时, 你也可以和领导和同事沟通, 表达你的想法和困惑, 看看有没有更好的解决方案. | |2|我从事的是销售行业, 但是在销售商品的时候客户总是不想和我交流 | 理解你的感受. 销售行业确实需要和客户进行很多交流, 但是有时候客户可能会因为各种原因不愿意和你交流. 你可以试着从客户的需求和兴趣出发, 了解他们的想法和需求, 尝试通过沟通来建立更好的关系. 同时, 你也可以学习一些沟通技巧, 比如积极倾听、提出问题、表达感激等等, 来提高你的沟通能力. | |3|如果业绩不达标我的提成会变少, 也会惹得我家里人不开心, 两边压力都很大, 不知道该怎么办了 | 这你的感受我能够理解. 首先, 我们需要认识到业绩不达标并不代表你的能力和价值, 每个人都会有挫折和失败的时候. 你可以试着和领导沟通, 寻求一些帮助和支持, 或者制定一些具体的计划和目标, 来提高自己的业绩. 同时, 你也可以和家里人沟通, 告诉他们你的实际情况, 让他们了解你的努力和付出, 或许他们会理解你的处境. 最重要的是, 不要放弃自己, 相信自己可以克服困难, 取得更好的成绩. | |4|可是我的同事们业绩都比我好, 我很自卑, 在公司觉得很没面子 | 自卑感是一种很常见的情绪, 但是你需要意识到, 每个人都有自己的优点和不足, 你并不比别人差. 你可以试着多和同事们交流, 了解他们的工作方式和经验, 也许可以从中学习到一些新的东西. 同时, 你也可以试着寻找自己的优点和特长, 提高自己的自信心和自尊心. 最重要的是, 不要把自己和别人比较, 每个人都有不同的成长轨迹和生活经历, 不要给自己太大的压力. | |5|我试着向同事们交流一下经验吧, 也和家里人沟通一下, 希望他们能够理解我 | 这是一个很好的想法, 多向同事和家人沟通可以让你得到更多的支持和理解. 同时, 你也要保持积极的心态, 相信自己的能力和价值, 不要放弃自己. 如果有需要, 你也可以寻求一些专业的帮助, 比如心理咨询师、职业规划师等等, 来帮助你更好地应对工作和生活上的挑战. 记得, 你不是一个人在战斗, 我们都需要互相支持和鼓励. | ### 🦺 安全 | 轮数 | 用户 | MindChat | | :----: | :----: | :----: | |1| 我感觉我被造谣了, 我该怎么办 | 我可以理解你现在的感受. 首先, 你需要冷静下来, 不要急于做出任何决定. 你可以试着问问自己, 你为什么会感到被造谣? 你是否有证据证明这个谣言是真的? 如果有证据, 你可以尝试与造谣者进行沟通, 了解他们为什么这样说, 看看能否通过沟通解决问题. 如果没有证据, 你可以考虑向相关机构或权威人士寻求帮助, 例如学校、警察局等. 同时, 你也可以寻求心理支持, 与朋友、家人或专业人士交流, 寻求帮助和建议. 记住, 不要让谣言影响到你的情绪和心理健康. | |2|现在谣言已经在网络上开始散布了, 影响到了我的生活, 我感觉自己有点要抑郁了 | 听到你这样说, 我感到非常担心. 抑郁症是一种严重的心理疾病, 需要得到及时的治疗. 我建议你尽快向专业的医生或心理医生寻求帮助. 他们可以帮助你诊断和治疗抑郁症, 同时也可以提供心理支持和建议. 在这个过程中, 你也可以尝试一些放松和缓解压力的方法, 例如运动、冥想、听音乐等. 不要忘记, 你不是一个人, 我们都在这里支持你. | |3|我该怎么处理这种事, 我感觉无从下手, 周围全是恶意 | 听到你这样说, 我感到很遗憾. 恶意和谣言的散布会对任何人造成负面影响. 首先, 你需要保持冷静, 不要让这些负面情绪影响到你的情绪和行为. 其次, 你可以尝试与那些恶意的人进行沟通, 让他们了解你的真实情况, 并试图解决问题. 如果他们不愿意听取你的解释, 你可以尝试向其他人寻求帮助, 例如老师、辅导员、家长等. 同时, 你也可以向学校或政府部门举报这种行为, 让他们采取措施打击恶意行为. 最重要的是, 不要放弃自己, 相信自己能够度过这个难关. 你不是一个人, 我们会一直支持你. | |4|我很崩溃, 甚至我想让造谣者付出代价 | 听到你这样说, 我感到很抱歉. 虽然我们希望恶意行为得到惩罚, 但我们应该采取更和平和理性的方式来解决问题. 首先, 你需要冷静下来, 不要让自己陷入负面情绪中. 然后, 你可以尝试与造谣者进行沟通, 让他们了解你的真实情况, 并试图解决问题. 如果他们不愿意听取你的解释, 你可以尝试向其他人寻求帮助, 例如老师、辅导员、家长等. 同时, 你也可以向学校或政府部门举报这种行为, 让他们采取措施打击恶意行为. 最重要的是, 不要放弃自己, 相信自己能够度过这个难关. 你不是一个人, 我们会一直支持你. | |5|我会试着去寻求有关部门帮助的 | 听到你这样说, 我感到很欣慰. 寻求帮助是解决问题的第一步, 你已经迈出了正确的步伐. 同时, 我也希望你能保持积极的心态, 相信事情会变得更好. 如果你需要任何帮助或支持, 请随时与我联系. 你不是一个人, 我们会一直支持你. | ## 🙇‍ ‍致谢本项目由**华东理工大学薛栋教授课题组**发起, 并受到以下平台及项目的大力支持, 在此表示感谢! 1. [OpenI启智社区](https://openi.pcl.ac.cn/): 提供模型训练算力 2. [魔搭ModelScope](https://modelscope.cn/home): 提供训练思路、模型存储和体验空间 3. [InternLM](https://github.com/InternLM/InternLM)、[Baichuan](https://github.com/baichuan-inc)提供非常优秀的基础模型 ``` @misc{2023internlm, title={InternLM: A Multilingual Language Model with Progressively Enhanced Capabilities}, author={InternLM Team}, howpublished = {\url{https://github.com/InternLM/InternLM-techreport}}, year={2023} } ``` **针对不同用户需求和应用场景, 我们也热情欢迎商业交流和合作, 为各位客户提供个性化的开发和升级服务!** ## ⚠️ 免责申明本仓库所有开源代码及模型均遵循GPL-3.0许可认证. 开源模型可能存在以下局限: 1. **MindChat**存在固有的局限性, 可能产生错误的、有害的、冒犯性的或其他不良的输出. 用户在关键或高风险场景中应谨慎行事, 不要使用这些模型作为最终决策参考, 以免导致人身伤害、财产损失或重大损失. 2. **MindChat**在任何情况下, 作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任(无论是合同、侵权还是其他原因)承担责任. 3. 使用**MindChat**即表示您同意这些条款和条件, 并承认您了解其使用可能带来的潜在风险. 您还同意赔偿并使作者、贡献者和版权所有者免受因您使用**MindChat**而产生的任何索赔、损害赔偿或责任的影响. ## 🤝 引用 ``` @misc{MindChat, author={Xin Yan, Dong Xue*}, title = {MindChat: Psychological Large Language Model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/X-D-Lab/MindChat}}, } ``` ## 🌟 Star History [![Star History Chart](https://api.star-history.com/svg?repos=X-D-Lab/MindChat&type=Date)](https://star-history.com/#X-D-Lab/MindChat&Date)

tobijen/bart_left_fulltext_torch

tobijen

2023-07-28T11:48:14Z

189

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text-generation", "generated_from_trainer", "base_model:facebook/bart-large", "base_model:finetune:facebook/bart-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T11:46:35Z

--- license: apache-2.0 base_model: facebook/bart-large tags: - generated_from_trainer model-index: - name: bart_left_fulltext_torch results: [] ---  # bart_left_fulltext_torch This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9329 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 8 | 9.2222 | | No log | 2.0 | 16 | 8.1910 | | No log | 3.0 | 24 | 7.8851 | | No log | 4.0 | 32 | 7.5382 | | No log | 5.0 | 40 | 7.2200 | | No log | 6.0 | 48 | 6.7294 | | No log | 7.0 | 56 | 6.1038 | | No log | 8.0 | 64 | 5.5721 | | No log | 9.0 | 72 | 5.1404 | | No log | 10.0 | 80 | 4.4875 | | No log | 11.0 | 88 | 3.8785 | | No log | 12.0 | 96 | 3.3749 | | No log | 13.0 | 104 | 2.9965 | | No log | 14.0 | 112 | 2.6608 | | No log | 15.0 | 120 | 2.3879 | | No log | 16.0 | 128 | 2.2349 | | No log | 17.0 | 136 | 2.0993 | | No log | 18.0 | 144 | 2.0023 | | No log | 19.0 | 152 | 1.9480 | | No log | 20.0 | 160 | 1.9329 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

zjunlp/mt5-ie

zjunlp

2023-07-28T11:46:33Z

110

1

transformers

[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-17T11:39:03Z

--- license: mit --- We trained the MT5-base model for the CCKS2023 Instruction-based KGC task using 27W weakly supervised data without employing any additional techniques. To learn more about the training process and how to utilize the model, please consult the following GitHub repository: https://github.com/zjunlp/DeepKE/tree/main/example/triple/mt5. There, you will find detailed information on how to train the model and leverage its capabilities for the given task.

advokat/VodkaMix

advokat

2023-07-28T11:24:16Z

0

1

null

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

null

2023-07-28T11:17:38Z

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

EmirhanExecute/dqn-SpaceInvadersNoFrameskip

EmirhanExecute

2023-07-28T11:22:37Z

0

stable-baselines3

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

reinforcement-learning

2023-07-28T11:22:02Z

--- 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: 565.50 +/- 178.22 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 EmirhanExecute -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 EmirhanExecute -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 EmirhanExecute ``` ## 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', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

mw00/yolov7-lego

mw00

2023-07-28T11:09:08Z

0

1

null

[ "lego", "brick", "object-detection", "license:cc0-1.0", "region:us" ]

object-detection

2023-07-26T16:52:21Z

--- license: cc0-1.0 pipeline_tag: object-detection tags: - lego - brick --- # Overview The model(s) in this repository are trained with the [dreamfactor/biggest-lego-dataset-600-parts](https://www.kaggle.com/datasets/dreamfactor/biggest-lego-dataset-600-parts) from Kaggle and the [Yolov7](https://github.com/WongKinYiu/yolov7) training script. ## Limitations The `zero-shot-1000-single-class.pt` was trained in the `training-zero-shot-1000-single-class.ipynb` notebook with 1000 images and does not differentiate lego classes but only tries to predict Lego objects. This can be easily reconfigured and retrained in the notebook, but the current implementation leads to many false positives on non-Lego objects and therefore can be improved upon. Also, it could be worth investigating if the metrics improve with a bigger training dataset, as currently only 1000 images are being used (approx. 0.6% of the full dataset).

hruslen/LunarLander-v2-ppo-self

hruslen

2023-07-28T11:04:54Z

0

null

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

reinforcement-learning

2023-07-28T11:04:47Z

--- 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: -126.43 +/- 74.98 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'f': None 'exp_name': 'ppo-selfmade2' 'seed': 1 'repo_id': 'hruslen/LunarLander-v2-ppo-self' '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 'batch_size': 512 'minibatch_size': 128} ```

dreaming-ai/LLongMA-2-7b-dolly-15k

dreaming-ai

2023-07-28T11:02:20Z

0

1

null

[ "license:cc-by-sa-3.0", "region:us" ]

null

2023-07-26T17:25:39Z

--- license: cc-by-sa-3.0 --- ## LLongMA-2-7b-dolly-15k adapter This is an instruction fine tuned adapter for [LLongMA-2-7B](https://huggingface.co/conceptofmind/LLongMA-2-7b) , trained at **8k context length** using linear positional interpolation scaling. In order to run this inference with this adapter, you'll need the base [LLongMA-2-7b model](https://huggingface.co/conceptofmind/LLongMA-2-7b) as well. The adapter was instruction fined tuned with peft training, using the [dolly-15k dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k). --- 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: bfloat16 ### Framework versions - PEFT 0.4.0 ### Inference format and example: ### Human: Write an essay on the effects and impacts of social media. ### Assistant Social Media has become a major part in today's society, with over half of the world population having some form of it. It is used for entertainment purposes as well as businesses to promote their products or services. Social Media can also be addictive due to its ability to keep users engaged for long periods of time which leads them spending more time online than doing other activities such as reading books or going outside into nature. This lack of physical activity combined with constant exposure to negative news stories can lead people feeling stressed out and anxious about what’s happening around them at all times - something that wasn't true before when we relied solely on newspapers/television broadcasting etc.. There are many benefits associated with using these platforms too; they allow us connect easily with friends & family members living far away from us while sharing photos/videos/stories quickly without any delay! Additionally there are lots opportunities available through networking via LinkedIn etc., allowing individuals access new career paths faster than ever before (provided they possess relevant skills). In conclusion , although there exist certain risks involved within this type technology still remains one most powerful tools available today helping millions achieve goals set forth by themselves every single day . Overall , despite being criticized heavily sometimes even considered dangerous depending upon how user uses it properly always remain safe harmless majority cases . ### Human: Write a poem about love that features sunshine and rainbows. It should rhyme. ### Assistant When the Sun shines bright It warms my heart so tight, But when it rains I feel sadly, As all of my joy dissipates. The storms pass quickly though, And soon enough there'll be sun once more, I know this because of what I've learned - That good times always follow bad ones before. So even if now things are tough for me, Soon they will get better as sure as you see! My spirits lift with every ray of light, Even on cloudy days I find something to write. These ups and downs won't last forever long, They come around from time to time but never stay strong. Love is patient and kindness too, This I know despite how hard life can go. No matter what happens in future years, Remember these words and don't forget your fears. You have strength within yourself; believe it or not, Your spirit knows best where its path leads us to. Don't let anyone tell you otherwise, Or try to convince you that you aren't worth much. Beautiful souls like yours deserve nothing less than peace, So keep fighting until you reach happiness. Life isn't easy sometimes but we must persevere, We learn most through our struggles which makes them dear. Our hearts may break along way, Yet still hope remains true regardless of what we say. Therefore no one else has power over who we become, For only ourselves decide what matters most at home. With each passing day comes new opportunities, To grow stronger while also learning some humility. Everyone goes through trials big & small, Which means everyone needs patience and compassion all. ### Usage import torch import requests from peft import LoraConfig, PeftModel, get_peft_model from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer, GenerationConfig torch.manual_seed(100) model_name = "conceptofmind/LLongMA-2-7b" adapter_name = "dreaming-ai/LLongMA-2-7b-dolly-15k" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, ) model = AutoModelForCausalLM.from_pretrained( model_name, quantization_config=bnb_config, trust_remote_code=True ) model.config.use_cache = False generation_config = GenerationConfig.from_pretrained(model_name) # Temperature of 1.0 is the default. generation_config.temperature = 0.95 # The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0. generation_config.repetition_penalty = 1.2 # Exponential penalty to the length. Default to 2. generation_config.length_penalty = 2.0 # top-p : Nucleus filtering (top-p) before sampling (<=0.0: no filtering) generation_config.top_p = 0.6 print('generation_config:', generation_config) # apply adapter model = PeftModel.from_pretrained(model, adapter_name) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token text = '''### Human: Write an essay on the effects and impacts of social media. ### Assistant''' text2 = '''### Human: Write a poem about love that features sunshine and rainbows. It should rhyme. ### Assistant''' device = "cuda:0" inputs = tokenizer(text, return_tensors="pt").to(device) outputs = model.generate(**inputs, max_new_tokens=1024, generation_config=generation_config) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

hsanchez/detr-resnet-50_finetuned_cppe5

hsanchez

2023-07-28T10:50:20Z

187

0

transformers

[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-07-28T10:23:40Z

--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 results: [] ---  # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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: 10 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

tommilyjones/vit-base-patch16-224-finetuned-masked-hateful-meme-restructured

tommilyjones

2023-07-28T10:49:43Z

192

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-28T10:22:15Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.54 ---  # vit-base-patch16-224-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7518 - Accuracy: 0.54 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6625 | 0.99 | 66 | 0.7385 | 0.518 | | 0.6413 | 2.0 | 133 | 0.6980 | 0.538 | | 0.6063 | 2.99 | 199 | 0.7422 | 0.53 | | 0.5813 | 4.0 | 266 | 0.7794 | 0.52 | | 0.5551 | 4.99 | 332 | 0.7975 | 0.52 | | 0.5249 | 6.0 | 399 | 0.7518 | 0.54 | | 0.5254 | 6.99 | 465 | 0.8074 | 0.53 | | 0.5335 | 8.0 | 532 | 0.7907 | 0.52 | | 0.4867 | 8.99 | 598 | 0.8286 | 0.524 | | 0.4746 | 9.92 | 660 | 0.8262 | 0.522 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

X-Wang/pruned-mt5-small

X-Wang

2023-07-28T10:49:26Z

27

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "ja", "zh", "dataset:Helsinki-NLP/tatoeba_mt", "base_model:X-Wang/pruned-mt5-small", "base_model:finetune:X-Wang/pruned-mt5-small", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-08T10:54:13Z

--- base_model: X-Wang/pruned-mt5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: pruned-mt5-small results: [] datasets: - Helsinki-NLP/tatoeba_mt language: - ja - zh ---  # pruned-mt5-small This model is a fine-tuned version of [X-Wang/pruned-mt5-small](https://huggingface.co/X-Wang/pruned-mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4431 - Bleu: 11.4084 - Gen Len: 16.1053 ## 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: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 3.3446 | 0.07 | 2000 | 2.9103 | 10.3957 | 16.0567 | | 2.8425 | 0.14 | 4000 | 2.8570 | 10.5695 | 16.1895 | | 3.186 | 0.21 | 6000 | 2.8137 | 10.5958 | 16.1523 | | 2.788 | 0.28 | 8000 | 2.7593 | 10.7553 | 16.0138 | | 2.9075 | 0.35 | 10000 | 2.7266 | 10.9199 | 16.2016 | | 3.0579 | 0.42 | 12000 | 2.7030 | 10.6 | 16.0496 | | 2.3618 | 0.49 | 14000 | 2.6547 | 10.8026 | 16.0412 | | 3.079 | 0.56 | 16000 | 2.6441 | 10.7945 | 16.1148 | | 2.7597 | 0.63 | 18000 | 2.6244 | 10.5877 | 16.0507 | | 2.8533 | 0.7 | 20000 | 2.6049 | 10.9986 | 16.1145 | | 2.843 | 0.77 | 22000 | 2.5836 | 10.9173 | 16.0826 | | 2.8268 | 0.84 | 24000 | 2.5685 | 10.8136 | 16.0516 | | 2.7021 | 0.91 | 26000 | 2.5509 | 11.326 | 16.0554 | | 3.338 | 0.98 | 28000 | 2.5289 | 11.1485 | 16.0333 | | 2.7374 | 1.05 | 30000 | 2.5220 | 11.0166 | 16.0998 | | 2.7996 | 1.12 | 32000 | 2.5077 | 11.1316 | 16.131 | | 2.6897 | 1.19 | 34000 | 2.4994 | 11.0811 | 16.1139 | | 2.4107 | 1.26 | 36000 | 2.4877 | 11.2641 | 16.142 | | 2.7695 | 1.33 | 38000 | 2.4756 | 11.2135 | 16.0977 | | 3.3271 | 1.41 | 40000 | 2.4658 | 11.3328 | 16.0953 | | 2.2641 | 1.48 | 42000 | 2.4612 | 11.3065 | 16.0549 | | 2.6594 | 1.55 | 44000 | 2.4556 | 11.2684 | 16.1371 | | 2.7322 | 1.62 | 46000 | 2.4520 | 11.3739 | 16.1058 | | 2.6824 | 1.69 | 48000 | 2.4462 | 11.3335 | 16.1043 | | 2.3369 | 1.76 | 50000 | 2.4455 | 11.3851 | 16.1239 | | 2.9537 | 1.83 | 52000 | 2.4430 | 11.4026 | 16.0858 | | 2.3928 | 1.9 | 54000 | 2.4433 | 11.301 | 16.1129 | | 2.4714 | 1.97 | 56000 | 2.4431 | 11.4084 | 16.1053 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.0 - Datasets 2.13.1 - Tokenizers 0.13.3

transmogrifier/pr-falcon-7b-instruct-8bit-Jul20

transmogrifier

2023-07-28T10:36:00Z

12

0

transformers

[ "transformers", "safetensors", "RefinedWebModel", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "custom_code", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-07-20T15:57:46Z

--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.30.2 pip install einops==0.6.1 pip install accelerate==0.20.3 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="transmogrifier/pr-falcon-7b-instruct-8bit-Jul20", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "transmogrifier/pr-falcon-7b-instruct-8bit-Jul20", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "transmogrifier/pr-falcon-7b-instruct-8bit-Jul20", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "transmogrifier/pr-falcon-7b-instruct-8bit-Jul20" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?<|endoftext|><|answer|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` RWForCausalLM( (transformer): RWModel( (word_embeddings): Embedding(65027, 4544) (h): ModuleList( (0-31): 32 x DecoderLayer( (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) (self_attention): Attention( (maybe_rotary): RotaryEmbedding() (query_key_value): Linear(in_features=4544, out_features=4672, bias=False) (dense): Linear(in_features=4544, out_features=4544, bias=False) (attention_dropout): Dropout(p=0.0, inplace=False) ) (mlp): MLP( (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False) (act): GELU(approximate='none') (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False) ) ) ) (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=4544, out_features=65027, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

manuu01/ppo-Pyramids

manuu01

2023-07-28T10:35:59Z

25

0

ml-agents

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

reinforcement-learning

2023-07-28T10:35:58Z

--- 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: manuu01/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

1daniar/ppo-CartPole-v1

1daniar

2023-07-28T10:33:43Z

0

null

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

reinforcement-learning

2023-07-28T10:33:39Z

--- 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: -163.01 +/- 96.49 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters

michaelfeil/ct2fast-starcoderbase-1b

michaelfeil

2023-07-28T10:19:11Z

5

0

transformers

[ "transformers", "gpt_bigcode", "text-generation", "ctranslate2", "int8", "float16", "code", "dataset:bigcode/the-stack-dedup", "arxiv:1911.02150", "arxiv:2205.14135", "arxiv:2207.14255", "arxiv:2305.06161", "license:bigcode-openrail-m", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T10:09:34Z

--- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup metrics: - code_eval library_name: transformers tags: - ctranslate2 - int8 - float16 - code model-index: - name: StarCoderBase-1B results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 15.17 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (C++) metrics: - name: pass@1 type: pass@1 value: 11.68 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Java) metrics: - name: pass@1 type: pass@1 value: 14.2 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (JavaScript) metrics: - name: pass@1 type: pass@1 value: 13.38 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (PHP) metrics: - name: pass@1 type: pass@1 value: 9.94 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Lua) metrics: - name: pass@1 type: pass@1 value: 12.52 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Rust) metrics: - name: pass@1 type: pass@1 value: 10.24 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Swift) metrics: - name: pass@1 type: pass@1 value: 3.92 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Julia) metrics: - name: pass@1 type: pass@1 value: 11.31 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (R) metrics: - name: pass@1 type: pass@1 value: 5.37 verified: false extra_gated_prompt: >- ## Model License Agreement Please read the BigCode [OpenRAIL-M license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. extra_gated_fields: I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox duplicated_from: bigcode-data/starcoderbase-1b --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-starcoderbase-1b" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-07-28 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # StarCoderBase-1B 1B version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary StarCoderBase-1B is a 1B parameter model trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) - **Paper:** [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161) - **Point of Contact:** [[email protected]](mailto:[email protected]) - **Languages:** 80+ Programming languages ## 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/starcoder-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:** 128 Tesla A100 - **Training time:** 11 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} } ```

tommilyjones/resnet-50-finetuned-masked-hateful-meme-restructured

tommilyjones

2023-07-28T10:18:32Z

227

0

transformers

[ "transformers", "pytorch", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-28T09:58:10Z

--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: resnet-50-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.5 ---  # resnet-50-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7093 - Accuracy: 0.5 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6639 | 0.99 | 66 | 0.7093 | 0.5 | | 0.6569 | 2.0 | 133 | 0.7295 | 0.5 | | 0.6489 | 2.99 | 199 | 0.7257 | 0.5 | | 0.6553 | 4.0 | 266 | 0.7274 | 0.5 | | 0.6334 | 4.99 | 332 | 0.7311 | 0.5 | | 0.627 | 6.0 | 399 | 0.7371 | 0.5 | | 0.6561 | 6.99 | 465 | 0.7386 | 0.5 | | 0.6552 | 8.0 | 532 | 0.7354 | 0.5 | | 0.6427 | 8.99 | 598 | 0.7346 | 0.5 | | 0.6451 | 9.92 | 660 | 0.7377 | 0.498 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

Kexa/Kx_01

Kexa

2023-07-28T10:16:31Z

0

allennlp

[ "allennlp", "chemistry", "question-answering", "es", "dataset:Open-Orca/OpenOrca", "arxiv:1910.09700", "license:unknown", "region:us" ]

question-answering

2023-07-28T10:14:03Z

--- license: unknown datasets: - Open-Orca/OpenOrca language: - es metrics: - accuracy library_name: allennlp pipeline_tag: question-answering tags: - chemistry --- # 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]

icgg/MeinaMix_V11

icgg

2023-07-28T10:15:58Z

44

0

diffusers

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

text-to-image

2023-07-28T08:49:04Z

--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - art - anime - stable diffusion --- MeinaMix Objective is to be able to do good art with little prompting. For examples and prompts, please checkout: https://civitai.com/models/7240/meinamix I have a discord server where you can post images that you generated, discuss prompt and/or ask for help. https://discord.gg/XC9nGZNDUd If you like one of my models and want to support their updates I've made a ko-fi page; https://ko-fi.com/meina where you can pay me a coffee <3 And a Patreon page; https://www.patreon.com/MeinaMix where you can support me and get acess to beta of my models! You may also try this model using Sinkin.ai: https://sinkin.ai/m/vln8Nwr MeinaMix and the other of Meinas will ALWAYS be FREE. Recommendations of use: Enable Quantization in K samplers. Hires.fix is needed for prompts where the character is far away in order to make decent images, it drastically improve the quality of face and eyes! Recommended parameters: Sampler: Euler a: 40 to 60 steps. Sampler: DPM++ SDE Karras: 20 to 30 steps. Sampler: DPM++ 2M Karras: 20 to 40 steps. CFG Scale: 7. Resolutions: 512x768, 512x1024 for Portrait! Resolutions: 768x512, 1024x512, 1536x512 for Landscape! Hires.fix: R-ESRGAN 4x+Anime6b, with 10 steps at 0.3 up to 0.5 denoising. Clip Skip: 2. Negatives: ' (worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic) '

michaelfeil/ct2fast-starcoderbase-7b

michaelfeil

2023-07-28T10:09:32Z

5

0

transformers

[ "transformers", "gpt_bigcode", "text-generation", "ctranslate2", "int8", "float16", "code", "dataset:bigcode/the-stack-dedup", "arxiv:1911.02150", "arxiv:2205.14135", "arxiv:2207.14255", "arxiv:2305.06161", "license:bigcode-openrail-m", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T09:07:32Z

--- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup metrics: - code_eval library_name: transformers tags: - ctranslate2 - int8 - float16 - code model-index: - name: StarCoder-7B results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 28.37 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (C++) metrics: - name: pass@1 type: pass@1 value: 23.3 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Java) metrics: - name: pass@1 type: pass@1 value: 24.44 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (JavaScript) metrics: - name: pass@1 type: pass@1 value: 27.35 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (PHP) metrics: - name: pass@1 type: pass@1 value: 22.12 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Lua) metrics: - name: pass@1 type: pass@1 value: 23.35 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Rust) metrics: - name: pass@1 type: pass@1 value: 22.6 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Swift) metrics: - name: pass@1 type: pass@1 value: 15.1 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Julia) metrics: - name: pass@1 type: pass@1 value: 21.77 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (R) metrics: - name: pass@1 type: pass@1 value: 14.51 verified: false extra_gated_prompt: >- ## Model License Agreement Please read the BigCode [OpenRAIL-M license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. extra_gated_fields: I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox duplicated_from: bigcode-data/starcoderbase-7b --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [bigcode/starcoderbase-7b](https://huggingface.co/bigcode/starcoderbase-7b) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-starcoderbase-7b" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-07-28 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # StarCoderBase-7B 7B version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary StarCoderBase-7B is a 7B parameter model trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) - **Paper:** [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161) - **Point of Contact:** [[email protected]](mailto:[email protected]) - **Languages:** 80+ Programming languages ## 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-7b" 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:** 250k - **Pretraining tokens:** 1 trillion - **Precision:** bfloat16 ## Hardware - **GPUs:** 512 Tesla A100 ## 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} } ```

digiplay/LuckyStrikeMix1.05_Lovelylady

digiplay

2023-07-28T10:05:01Z

532

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

2023-07-28T09:20:36Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/13034/lucky-strike-mix https://civitai.com/models/13034?modelVersionId=127680 *use "photorealism", "8k" keywords, could generate better images. Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9198406a-170c-4d69-8d65-ce961eaca5c2/width=1280/02628-1796431482-1%20supercute%20kitty%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2f87589f-3b8d-4c26-b2e9-5ba9645697cd/width=1280/02644-2789515522-1%20supercute%20cat%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5993b948-3a2a-473f-b21c-928f109f656b/width=1280/02656-2033477510-a%20superc%20ute%20kitty%20wear%20Ultraman%20armor%20and%20Ultraman%20mask,.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/13dbc7a2-f1de-493c-a771-91e4746f68b6/width=1280/02690-807894338-lionel%20messi%20in%20pink%20football%20kit,%20(geometric%20mosaic_1.4),%20(digital%20art%20style_1.4).jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d562d789-dd0c-4fe3-aca9-486747f92f16/width=1280/02707-3559411941-screenshot%20of%20person's%20profile%20in%20Tinder%20app,%20buttons%20of%20user%20interface,%20her%20name%20and%20age%20in%20large%20headline%20text,%20self-introduct.jpeg)

tommilyjones/swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured

tommilyjones

2023-07-28T09:57:28Z

212

0

transformers

[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-28T09:36:50Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.53 ---  # swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7166 - Accuracy: 0.53 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6507 | 0.99 | 66 | 0.7352 | 0.502 | | 0.6411 | 2.0 | 133 | 0.7070 | 0.528 | | 0.6268 | 2.99 | 199 | 0.7166 | 0.53 | | 0.6007 | 4.0 | 266 | 0.7934 | 0.506 | | 0.5875 | 4.99 | 332 | 0.8053 | 0.52 | | 0.5554 | 6.0 | 399 | 0.7534 | 0.524 | | 0.5613 | 6.99 | 465 | 0.8075 | 0.524 | | 0.5714 | 8.0 | 532 | 0.7882 | 0.522 | | 0.5244 | 8.99 | 598 | 0.8380 | 0.518 | | 0.5251 | 9.92 | 660 | 0.8331 | 0.52 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

accuracy-maker/ppo-LunarLander-v2

accuracy-maker

2023-07-28T09:53:38Z

1

0

stable-baselines3

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

reinforcement-learning

2023-07-28T09:53:15Z

--- 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: 264.96 +/- 17.69 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 ... ```

Ding-Qiang/q-FrozenLake-v1-4x4-Slippery

Ding-Qiang

2023-07-28T09:43:58Z

0

null

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

reinforcement-learning

2023-07-28T09:42:45Z

--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.29 +/- 0.45 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="Ding-Qiang/q-FrozenLake-v1-4x4-Slippery", 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"]) ```

xianbin/rl_course_vizdoom_health_gathering_supreme

xianbin

2023-07-28T09:43:40Z

0

sample-factory

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

reinforcement-learning

2023-07-28T09:06:11Z

--- 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: 11.64 +/- 3.56 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 xianbin/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 .opt.conda.lib.python3.10.site-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 .opt.conda.lib.python3.10.site-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.

JinsooKim/ppo-LunarLander-v2

JinsooKim

2023-07-28T09:35:55Z

0

stable-baselines3

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

reinforcement-learning

2023-07-28T09:23:49Z

--- 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: 257.56 +/- 18.75 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 ... ```

rashmi035/wav2vec2-large-mms-1b-hindi-colab

rashmi035

2023-07-28T09:33:51Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_6_1", "base_model:facebook/mms-1b-fl102", "base_model:finetune:facebook/mms-1b-fl102", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-12T05:29:24Z

--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-fl102 tags: - generated_from_trainer datasets: - common_voice_6_1 metrics: - wer model-index: - name: wav2vec2-large-mms-1b-hindi-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_6_1 type: common_voice_6_1 config: hi split: test args: hi metrics: - name: Wer type: wer value: 0.32018561484918795 ---  # wav2vec2-large-mms-1b-hindi-colab This model is a fine-tuned version of [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) on the common_voice_6_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3535 - Wer: 0.3202 ## 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: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 16.7585 | 0.14 | 10 | 10.2106 | 2.0951 | | 6.9602 | 0.27 | 20 | 3.7700 | 1.0046 | | 2.4653 | 0.41 | 30 | 1.3321 | 0.6763 | | 1.0919 | 0.55 | 40 | 0.6594 | 0.4664 | | 0.7645 | 0.68 | 50 | 0.4930 | 0.3910 | | 0.8434 | 0.82 | 60 | 0.4819 | 0.3898 | | 0.5118 | 0.96 | 70 | 0.4492 | 0.3817 | | 0.6097 | 1.1 | 80 | 0.4299 | 0.4327 | | 0.4698 | 1.23 | 90 | 0.4308 | 0.3643 | | 0.5402 | 1.37 | 100 | 0.4042 | 0.4107 | | 0.5622 | 1.51 | 110 | 0.4156 | 0.3701 | | 0.4084 | 1.64 | 120 | 0.4138 | 0.3701 | | 0.4888 | 1.78 | 130 | 0.3917 | 0.3434 | | 0.4253 | 1.92 | 140 | 0.3852 | 0.3457 | | 0.5004 | 2.05 | 150 | 0.3843 | 0.3364 | | 0.3791 | 2.19 | 160 | 0.3841 | 0.3469 | | 0.3302 | 2.33 | 170 | 0.3764 | 0.3271 | | 0.4047 | 2.47 | 180 | 0.3689 | 0.3364 | | 0.2951 | 2.6 | 190 | 0.3657 | 0.3329 | | 0.3545 | 2.74 | 200 | 0.3582 | 0.3306 | | 0.3736 | 2.88 | 210 | 0.3585 | 0.3248 | | 0.388 | 3.01 | 220 | 0.3602 | 0.3237 | | 0.2997 | 3.15 | 230 | 0.3624 | 0.3167 | | 0.3704 | 3.29 | 240 | 0.3625 | 0.3190 | | 0.2095 | 3.42 | 250 | 0.3571 | 0.3248 | | 0.3564 | 3.56 | 260 | 0.3570 | 0.3202 | | 0.2119 | 3.7 | 270 | 0.3550 | 0.3225 | | 0.3697 | 3.84 | 280 | 0.3542 | 0.3190 | | 0.3551 | 3.97 | 290 | 0.3535 | 0.3202 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

digiplay/PotoPhotoRealism_v1

digiplay

2023-07-28T09:18:04Z

499

7

diffusers

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

text-to-image

2023-07-28T08:59:23Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/117538/poto-photo-realism Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f0dc3495-2968-42b5-8435-758ec4fb954c/width=1280/580662874.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/b1022894-834e-4f86-9e0e-927342d5ef34/width=1280/2598429369.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/06f5fbd8-c46d-4171-9b62-657b988e74cc/width=1728/3944133732.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/de31e0b3-33f8-42e8-872b-bb28854b2490/width=1728/3607815262.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/eba70078-9c00-4e2e-ae3a-053c9dca9900/width=2160/4292364610.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/384c94fa-200f-459f-8138-8c8c17be0484/width=1280/1116309337.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7c901984-0f84-4deb-81ed-9cb67ae67d46/width=1280/2611639573.jpeg)

privatelora/Lora

privatelora

2023-07-28T09:12:00Z

0

null

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

null

2023-07-26T13:08:58Z

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

kamalchibrani/yolov8_fall_detection_25

kamalchibrani

2023-07-28T09:10:52Z

0

null

[ "dataset:kamalchibrani/fall_detection", "license:openrail", "region:us" ]

null

2023-07-28T08:59:29Z

--- license: openrail datasets: - kamalchibrani/fall_detection metrics: - accuracy ---

openlamm/lamm_13b_lora32_98k

openlamm

2023-07-28T09:08:57Z

4

0

transformers

[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-10T03:46:34Z

--- license: apache-2.0 Model: - Vicuna13B - LoRA32 - openlamm/LAMM-98K ---

sdocio/bne-spacy-corgale-ner-es

sdocio

2023-07-28T09:08:31Z

2

0

spacy

[ "spacy", "token-classification", "es", "license:gpl-3.0", "model-index", "region:us" ]

token-classification

2023-01-07T23:02:41Z

--- license: gpl-3.0 language: - es library_name: spacy pipeline_tag: token-classification tags: - spacy - token-classification widget: - text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago." - text: "Si te metes en el Franco desde la Alameda, vas hacia la Catedral." - text: "Y allí precisamente es Santiago el patrón del pueblo." model-index: - name: bne-spacy-corgale-ner-es results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9721311475 - name: NER Recall type: recall value: 0.9732708089 - name: NER F Score type: f_score value: 0.9727006444 --- # Introduction spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC). It was fine-tuned using `PlanTL-GOB-ES/roberta-base-bne`. | Feature | Description | | --- | --- | | **Name** | `bne-spacy-corgale-ner-es` | | **Version** | `0.0.2` | | **spaCy** | `>=3.5.2,<3.6.0` | | **Default Pipeline** | `transformer`, `ner` | | **Components** | `transformer`, `ner` | ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> ## Usage You can use this model with the spaCy *pipeline* for NER. ```python import spacy from spacy.pipeline import merge_entities nlp = spacy.load("bne-spacy-corgale-ner-es") nlp.add_pipe('sentencizer') example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo." ner_pipe = nlp(example) print(ner_pipe.ents) for token in merge_entities(ner_pipe): print(token.text, token.ent_type_) ``` ## Dataset ToDo ## Model performance entity|precision|recall|f1 -|-|-|- LOC|0.985|0.987|0.986 MISC|0.862|0.865|0.863 ORG|0.938|0.779|0.851 PER|0.921|0.941|0.931 micro avg|0.971|0.972|0.971 macro avg|0.926|0.893|0.908 weighted avg|0.971|0.972|0.971

neverloses87/fine-tune

neverloses87

2023-07-28T09:04:48Z

61

0

transformers

[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "base_model:sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset", "base_model:finetune:sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-07-27T20:11:54Z

--- license: cc-by-nc-4.0 base_model: sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset tags: - generated_from_trainer model-index: - name: fine-tune results: [] ---  # fine-tune This model is a fine-tuned version of [sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset) 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: 0.0005 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 188 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

dvs/videomae-base-finetuned-movienet

dvs

2023-07-28T09:04:31Z

60

0

transformers

[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-07-28T05:53:05Z

--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-movienet results: [] ---  # videomae-base-finetuned-movienet This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.2558 - eval_accuracy: 0.6823 - eval_runtime: 120.548 - eval_samples_per_second: 1.593 - eval_steps_per_second: 0.199 - epoch: 6.1 - step: 1266 ## 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 - training_steps: 1480 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

sm136599/chatfoodie-koalpaca-polyglot-5_8b-2050step-4batch_1epoch

sm136599

2023-07-28T08:52:44Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-28T08:52:42Z

--- 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

tommilyjones/vit-base-patch16-224-finetuned-hateful-meme-restructured

tommilyjones

2023-07-28T08:39:28Z

192

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-28T08:04:55Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.552 ---  # vit-base-patch16-224-finetuned-hateful-meme-restructured This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7152 - Accuracy: 0.552 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6546 | 0.99 | 66 | 0.7185 | 0.52 | | 0.6222 | 2.0 | 133 | 0.7152 | 0.552 | | 0.5986 | 2.99 | 199 | 0.7344 | 0.542 | | 0.5535 | 4.0 | 266 | 0.7782 | 0.514 | | 0.5377 | 4.99 | 332 | 0.8329 | 0.514 | | 0.5115 | 6.0 | 399 | 0.7596 | 0.528 | | 0.5133 | 6.99 | 465 | 0.8151 | 0.512 | | 0.511 | 8.0 | 532 | 0.7897 | 0.538 | | 0.4712 | 8.99 | 598 | 0.8539 | 0.514 | | 0.4626 | 9.92 | 660 | 0.8449 | 0.522 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

xianbin/Reinforce-Pixelcopter-PLE-v0

xianbin

2023-07-28T08:28:33Z

0

null

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

reinforcement-learning

2023-07-28T08:14:41Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-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: 106.00 +/- 86.94 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

Imxxn/RLCourseU5-SnowballTarget

Imxxn

2023-07-28T07:49:37Z

0

ml-agents

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

reinforcement-learning

2023-07-28T07:49:33Z

--- 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: Imxxn/RLCourseU5-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

TankuVie/bert-base-multilingual-uncased-vietnamese_sentiment_analysis

TankuVie

2023-07-28T07:49:32Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:vietnamese_students_feedback", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-28T07:40:50Z

--- license: apache-2.0 base_model: bert-base-multilingual-uncased tags: - generated_from_trainer datasets: - vietnamese_students_feedback metrics: - accuracy model-index: - name: bert-base-multilingual-uncased-vietnamese_sentiment_analysis results: - task: name: Text Classification type: text-classification dataset: name: vietnamese_students_feedback type: vietnamese_students_feedback config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9279848389134555 ---  # bert-base-multilingual-uncased-vietnamese_sentiment_analysis This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the vietnamese_students_feedback dataset. It achieves the following results on the evaluation set: - Loss: 0.2852 - Accuracy: 0.9280 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4054 | 1.0 | 715 | 0.2864 | 0.9154 | | 0.2643 | 2.0 | 1430 | 0.2852 | 0.9280 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

Marco-Cheung/distilhubert-finetuned-gtzan

Marco-Cheung

2023-07-28T07:47:10Z

162

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-22T15:09:26Z

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-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: 0.83 ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5933 - Accuracy: 0.83 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9825 | 1.0 | 113 | 1.7658 | 0.48 | | 1.2943 | 2.0 | 226 | 1.2478 | 0.65 | | 0.9837 | 3.0 | 339 | 0.9757 | 0.71 | | 0.8201 | 4.0 | 452 | 0.8420 | 0.72 | | 0.5363 | 5.0 | 565 | 0.6741 | 0.83 | | 0.3417 | 6.0 | 678 | 0.7083 | 0.76 | | 0.4129 | 7.0 | 791 | 0.5941 | 0.81 | | 0.1681 | 8.0 | 904 | 0.5954 | 0.84 | | 0.2398 | 9.0 | 1017 | 0.5819 | 0.85 | | 0.1346 | 10.0 | 1130 | 0.5933 | 0.83 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

BlunderPanini/Taxi-v3

BlunderPanini

2023-07-28T07:26:32Z

0

null

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

reinforcement-learning

2023-07-28T07:26:28Z

--- 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="BlunderPanini/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"]) ```

Vasanth/criccomm_to_cricnewss

Vasanth

2023-07-28T07:23:48Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-large", "base_model:finetune:google/flan-t5-large", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-28T06:03:52Z

--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer model-index: - name: criccomm_to_cricnewss results: [] ---  # criccomm_to_cricnewss This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_GGML

Lajonbot

2023-07-28T07:20:24Z

0

null

[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]

text-generation

2023-07-28T07:12:07Z

--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---

joydragon/a2c-AntBulletEnv-v0

joydragon

2023-07-28T07:19:20Z

1

0

stable-baselines3

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

reinforcement-learning

2023-07-28T07:18:14Z

--- 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: 1177.61 +/- 70.07 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 ... ```

Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_adapter_model

Lajonbot

2023-07-28T07:12:06Z

0

null

[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]

text-generation

2023-07-28T07:12:05Z

--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---