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 2025-06-29 18:27:25	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 502 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-29 18:27:24	card stringlengths 11 1.01M
domini4/flan-t5-base-imdb-text-classification	domini4	2024-03-21T03:44:56Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-21T02:10:54Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - f1 model-index: - name: flan-t5-base-imdb-text-classification 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. --> # flan-t5-base-imdb-text-classification This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0821 - F1: 94.88 - Gen Len: 2.5030 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
Awaz-e-Sehat/whisper-large-v3	Awaz-e-Sehat	2024-03-21T03:43:33Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:adapter:openai/whisper-large-v3", "license:apache-2.0", "region:us" ]	null	2024-03-21T03:43:27Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openai/whisper-large-v3 model-index: - name: whisper-large-v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v3 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 3.8257 - eval_runtime: 1.4346 - eval_samples_per_second: 0.697 - eval_steps_per_second: 0.697 - epoch: 2.0 - step: 142 ## 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: 10 - 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: 50 - num_epochs: 50 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Lewdiculous/Irene-RP-v2-7B-GGUF-IQ-Imatrix	Lewdiculous	2024-03-21T03:37:26Z	55	5	null	[ "gguf", "experimental", "endpoints_compatible", "region:us" ]	null	2024-03-21T03:06:12Z	--- tags: - experimental --- This is experimental. Model information: [Virt-io/Irene-RP-v2-7B](https://huggingface.co/Virt-io/Irene-RP-v2-7B)
feizhe/vit-base-patch16-224-in21k-pheno	feizhe	2024-03-21T03:35:45Z	63	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-21T01:19:28Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: feizhe/vit-base-patch16-224-in21k-pheno 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. --> # feizhe/vit-base-patch16-224-in21k-pheno This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0384 - Train Accuracy: 1.0 - Train Top-3-accuracy: 1.0 - Validation Loss: 1.5644 - Validation Accuracy: 0.5848 - Validation Top-3-accuracy: 0.9064 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1615, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 0.7874 \| 0.8031 \| 0.9555 \| 1.2405 \| 0.5380 \| 0.9006 \| 0 \| \| 0.1372 \| 0.9893 \| 0.9999 \| 1.4714 \| 0.5380 \| 0.8947 \| 1 \| \| 0.0644 \| 0.9989 \| 1.0 \| 1.6014 \| 0.5673 \| 0.9064 \| 2 \| \| 0.0465 \| 0.9990 \| 1.0 \| 1.5618 \| 0.5906 \| 0.9064 \| 3 \| \| 0.0384 \| 1.0 \| 1.0 \| 1.5644 \| 0.5848 \| 0.9064 \| 4 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.10.0 - Datasets 2.18.0 - Tokenizers 0.13.3
Pot-l/llama-7b-lawbot-true	Pot-l	2024-03-21T03:26:49Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-03-21T02:26:34Z	--- library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: llama-7b-lawbot-true 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. --> # llama-7b-lawbot-true This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
captainrobotfly/demo3	captainrobotfly	2024-03-21T03:23:46Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "id", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-20T22:03:58Z	--- license: mit language: - id pipeline_tag: text-generation ---
shubov/omop_bert	shubov	2024-03-21T03:12:50Z	125	1	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-02-01T14:15:58Z	--- tags: - generated_from_trainer model-index: - name: omop_bert 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. --> # omop_bert This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0120 - Num Input Tokens Seen: 8192000000 ## 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: 128 - 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: 1000000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:-----:\|:-------:\|:---------------:\|:-----------------:\| \| 6.663 \| 0.25 \| 5000 \| 6.6552 \| 40960000 \| \| 5.8854 \| 0.49 \| 10000 \| 5.8635 \| 81920000 \| \| 5.5478 \| 0.74 \| 15000 \| 5.4674 \| 122880000 \| \| 5.1915 \| 0.98 \| 20000 \| 5.1096 \| 163840000 \| \| 4.4684 \| 1.23 \| 25000 \| 4.4560 \| 204800000 \| \| 3.7495 \| 1.47 \| 30000 \| 3.6515 \| 245760000 \| \| 3.3969 \| 1.72 \| 35000 \| 3.3215 \| 286720000 \| \| 3.2418 \| 1.97 \| 40000 \| 3.0943 \| 327680000 \| \| 2.7464 \| 2.21 \| 45000 \| 2.5451 \| 368640000 \| \| 2.2447 \| 2.46 \| 50000 \| 2.1026 \| 409600000 \| \| 1.8854 \| 2.7 \| 55000 \| 1.8477 \| 450560000 \| \| 1.7938 \| 2.95 \| 60000 \| 1.6798 \| 491520000 \| \| 1.6738 \| 3.2 \| 65000 \| 1.5679 \| 532480000 \| \| 1.6701 \| 3.44 \| 70000 \| 1.5054 \| 573440000 \| \| 1.485 \| 3.69 \| 75000 \| 1.4480 \| 614400000 \| \| 1.5258 \| 3.93 \| 80000 \| 1.4007 \| 655360000 \| \| 1.4916 \| 4.18 \| 85000 \| 1.3906 \| 696320000 \| \| 1.4113 \| 4.42 \| 90000 \| 1.3684 \| 737280000 \| \| 1.4387 \| 4.67 \| 95000 \| 1.3493 \| 778240000 \| \| 1.388 \| 4.92 \| 100000 \| 1.3386 \| 819200000 \| \| 1.346 \| 5.16 \| 105000 \| 1.3352 \| 860160000 \| \| 1.3504 \| 5.41 \| 110000 \| 1.3294 \| 901120000 \| \| 1.3432 \| 5.65 \| 115000 \| 1.3168 \| 942080000 \| \| 1.2821 \| 5.9 \| 120000 \| 1.3041 \| 983040000 \| \| 1.2748 \| 6.15 \| 125000 \| 1.2871 \| 1024000000 \| \| 1.3076 \| 6.39 \| 130000 \| 1.2783 \| 1064960000 \| \| 1.3397 \| 6.64 \| 135000 \| 1.2690 \| 1105920000 \| \| 1.301 \| 6.88 \| 140000 \| 1.2653 \| 1146880000 \| \| 1.2416 \| 7.13 \| 145000 \| 1.2584 \| 1187840000 \| \| 1.2513 \| 7.37 \| 150000 \| 1.2515 \| 1228800000 \| \| 1.2618 \| 7.62 \| 155000 \| 1.2415 \| 1269760000 \| \| 1.2366 \| 7.87 \| 160000 \| 1.2399 \| 1310720000 \| \| 1.2584 \| 8.11 \| 165000 \| 1.2245 \| 1351680000 \| \| 1.1951 \| 8.36 \| 170000 \| 1.2225 \| 1392640000 \| \| 1.2576 \| 8.6 \| 175000 \| 1.2286 \| 1433600000 \| \| 1.278 \| 8.85 \| 180000 \| 1.2140 \| 1474560000 \| \| 1.1975 \| 9.09 \| 185000 \| 1.2103 \| 1515520000 \| \| 1.1596 \| 9.34 \| 190000 \| 1.2052 \| 1556480000 \| \| 1.2061 \| 9.59 \| 195000 \| 1.2034 \| 1597440000 \| \| 1.1677 \| 9.83 \| 200000 \| 1.2079 \| 1638400000 \| \| 1.1977 \| 10.08 \| 205000 \| 1.1966 \| 1679360000 \| \| 1.1448 \| 10.32 \| 210000 \| 1.2031 \| 1720320000 \| \| 1.1119 \| 10.57 \| 215000 \| 1.1866 \| 1761280000 \| \| 1.1695 \| 10.82 \| 220000 \| 1.1823 \| 1802240000 \| \| 1.0998 \| 11.06 \| 225000 \| 1.1874 \| 1843200000 \| \| 1.1157 \| 11.31 \| 230000 \| 1.1791 \| 1884160000 \| \| 1.191 \| 11.55 \| 235000 \| 1.1802 \| 1925120000 \| \| 1.1884 \| 11.8 \| 240000 \| 1.1706 \| 1966080000 \| \| 1.1723 \| 12.04 \| 245000 \| 1.1750 \| 2007040000 \| \| 1.1576 \| 12.29 \| 250000 \| 1.1720 \| 2048000000 \| \| 1.1847 \| 12.54 \| 255000 \| 1.1596 \| 2088960000 \| \| 1.1229 \| 12.78 \| 260000 \| 1.1594 \| 2129920000 \| \| 1.1683 \| 13.03 \| 265000 \| 1.1550 \| 2170880000 \| \| 1.1718 \| 13.27 \| 270000 \| 1.1511 \| 2211840000 \| \| 1.1374 \| 13.52 \| 275000 \| 1.1531 \| 2252800000 \| \| 1.1199 \| 13.77 \| 280000 \| 1.1615 \| 2293760000 \| \| 1.1275 \| 14.01 \| 285000 \| 1.1555 \| 2334720000 \| \| 1.1267 \| 14.26 \| 290000 \| 1.1442 \| 2375680000 \| \| 1.1603 \| 14.5 \| 295000 \| 1.1426 \| 2416640000 \| \| 1.1739 \| 14.75 \| 300000 \| 1.1443 \| 2457600000 \| \| 1.1022 \| 14.99 \| 305000 \| 1.1438 \| 2498560000 \| \| 1.1225 \| 15.24 \| 310000 \| 1.1323 \| 2539520000 \| \| 1.1244 \| 15.49 \| 315000 \| 1.1389 \| 2580480000 \| \| 1.1358 \| 15.73 \| 320000 \| 1.1377 \| 2621440000 \| \| 1.1499 \| 15.98 \| 325000 \| 1.1318 \| 2662400000 \| \| 1.1266 \| 16.22 \| 330000 \| 1.1313 \| 2703360000 \| \| 1.1604 \| 16.47 \| 335000 \| 1.1264 \| 2744320000 \| \| 1.0391 \| 16.72 \| 340000 \| 1.1364 \| 2785280000 \| \| 1.1526 \| 16.96 \| 345000 \| 1.1289 \| 2826240000 \| \| 1.1299 \| 17.21 \| 350000 \| 1.1259 \| 2867200000 \| \| 1.1118 \| 17.45 \| 355000 \| 1.1238 \| 2908160000 \| \| 1.1049 \| 17.7 \| 360000 \| 1.1193 \| 2949120000 \| \| 1.1336 \| 17.94 \| 365000 \| 1.1211 \| 2990080000 \| \| 1.0504 \| 18.19 \| 370000 \| 1.1218 \| 3031040000 \| \| 1.1003 \| 18.44 \| 375000 \| 1.1174 \| 3072000000 \| \| 1.1284 \| 18.68 \| 380000 \| 1.1164 \| 3112960000 \| \| 1.1408 \| 18.93 \| 385000 \| 1.1115 \| 3153920000 \| \| 1.0548 \| 19.17 \| 390000 \| 1.1112 \| 3194880000 \| \| 1.1045 \| 19.42 \| 395000 \| 1.1102 \| 3235840000 \| \| 1.0618 \| 19.66 \| 400000 \| 1.1075 \| 3276800000 \| \| 1.0953 \| 19.91 \| 405000 \| 1.1070 \| 3317760000 \| \| 1.1543 \| 20.16 \| 410000 \| 1.1071 \| 3358720000 \| \| 1.1212 \| 20.4 \| 415000 \| 1.1032 \| 3399680000 \| \| 1.0678 \| 20.65 \| 420000 \| 1.1007 \| 3440640000 \| \| 1.0646 \| 20.89 \| 425000 \| 1.0982 \| 3481600000 \| \| 1.1047 \| 21.14 \| 430000 \| 1.1022 \| 3522560000 \| \| 1.092 \| 21.39 \| 435000 \| 1.0978 \| 3563520000 \| \| 1.0619 \| 21.63 \| 440000 \| 1.1075 \| 3604480000 \| \| 1.0233 \| 21.88 \| 445000 \| 1.0954 \| 3645440000 \| \| 1.0962 \| 22.12 \| 450000 \| 1.0891 \| 3686400000 \| \| 1.0733 \| 22.37 \| 455000 \| 1.0932 \| 3727360000 \| \| 1.1267 \| 22.61 \| 460000 \| 1.0935 \| 3768320000 \| \| 1.053 \| 22.86 \| 465000 \| 1.0904 \| 3809280000 \| \| 1.0558 \| 23.11 \| 470000 \| 1.0901 \| 3850240000 \| \| 1.0324 \| 23.35 \| 475000 \| 1.0955 \| 3891200000 \| \| 1.0651 \| 23.6 \| 480000 \| 1.0891 \| 3932160000 \| \| 1.0774 \| 23.84 \| 485000 \| 1.0901 \| 3973120000 \| \| 1.0929 \| 24.09 \| 490000 \| 1.0833 \| 4014080000 \| \| 1.0516 \| 24.34 \| 495000 \| 1.0805 \| 4055040000 \| \| 1.0482 \| 24.58 \| 500000 \| 1.0846 \| 4096000000 \| \| 1.1004 \| 24.83 \| 505000 \| 1.0802 \| 4136960000 \| \| 1.1119 \| 25.07 \| 510000 \| 1.0765 \| 4177920000 \| \| 1.0799 \| 25.32 \| 515000 \| 1.0843 \| 4218880000 \| \| 1.0794 \| 25.56 \| 520000 \| 1.0801 \| 4259840000 \| \| 1.0681 \| 25.81 \| 525000 \| 1.0785 \| 4300800000 \| \| 1.0183 \| 26.06 \| 530000 \| 1.0760 \| 4341760000 \| \| 1.0791 \| 26.3 \| 535000 \| 1.0722 \| 4382720000 \| \| 1.0285 \| 26.55 \| 540000 \| 1.0754 \| 4423680000 \| \| 1.0474 \| 26.79 \| 545000 \| 1.0688 \| 4464640000 \| \| 1.0258 \| 27.04 \| 550000 \| 1.0755 \| 4505600000 \| \| 1.0374 \| 27.28 \| 555000 \| 1.0677 \| 4546560000 \| \| 1.0385 \| 27.53 \| 560000 \| 1.0698 \| 4587520000 \| \| 1.1287 \| 27.78 \| 565000 \| 1.0692 \| 4628480000 \| \| 1.0774 \| 28.02 \| 570000 \| 1.0671 \| 4669440000 \| \| 1.0264 \| 28.27 \| 575000 \| 1.0692 \| 4710400000 \| \| 1.0452 \| 28.51 \| 580000 \| 1.0676 \| 4751360000 \| \| 1.1144 \| 28.76 \| 585000 \| 1.0663 \| 4792320000 \| \| 1.0485 \| 29.01 \| 590000 \| 1.0658 \| 4833280000 \| \| 1.0556 \| 29.25 \| 595000 \| 1.0651 \| 4874240000 \| \| 0.996 \| 29.5 \| 600000 \| 1.0616 \| 4915200000 \| \| 1.0448 \| 29.74 \| 605000 \| 1.0665 \| 4956160000 \| \| 1.0094 \| 29.99 \| 610000 \| 1.0624 \| 4997120000 \| \| 1.0799 \| 30.23 \| 615000 \| 1.0605 \| 5038080000 \| \| 0.9995 \| 30.48 \| 620000 \| 1.0609 \| 5079040000 \| \| 1.0429 \| 30.73 \| 625000 \| 1.0616 \| 5120000000 \| \| 0.9966 \| 30.97 \| 630000 \| 1.0600 \| 5160960000 \| \| 1.0508 \| 31.22 \| 635000 \| 1.0576 \| 5201920000 \| \| 0.9879 \| 31.46 \| 640000 \| 1.0554 \| 5242880000 \| \| 1.0473 \| 31.71 \| 645000 \| 1.0581 \| 5283840000 \| \| 1.0364 \| 31.96 \| 650000 \| 1.0529 \| 5324800000 \| \| 1.0667 \| 32.2 \| 655000 \| 1.0567 \| 5365760000 \| \| 1.0108 \| 32.45 \| 660000 \| 1.0517 \| 5406720000 \| \| 0.9932 \| 32.69 \| 665000 \| 1.0550 \| 5447680000 \| \| 0.9917 \| 32.94 \| 670000 \| 1.0482 \| 5488640000 \| \| 1.0368 \| 33.18 \| 675000 \| 1.0519 \| 5529600000 \| \| 1.0942 \| 33.43 \| 680000 \| 1.0448 \| 5570560000 \| \| 1.0851 \| 33.68 \| 685000 \| 1.0484 \| 5611520000 \| \| 1.0568 \| 33.92 \| 690000 \| 1.0460 \| 5652480000 \| \| 1.0175 \| 34.17 \| 695000 \| 1.0484 \| 5693440000 \| \| 1.0051 \| 34.41 \| 700000 \| 1.0480 \| 5734400000 \| \| 1.0143 \| 34.66 \| 705000 \| 1.0443 \| 5775360000 \| \| 1.043 \| 34.9 \| 710000 \| 1.0429 \| 5816320000 \| \| 1.0354 \| 35.15 \| 715000 \| 1.0425 \| 5857280000 \| \| 1.0394 \| 35.4 \| 720000 \| 1.0442 \| 5898240000 \| \| 1.0074 \| 35.64 \| 725000 \| 1.0417 \| 5939200000 \| \| 1.0632 \| 35.89 \| 730000 \| 1.0446 \| 5980160000 \| \| 1.0117 \| 36.13 \| 735000 \| 1.0428 \| 6021120000 \| \| 1.0202 \| 36.38 \| 740000 \| 1.0403 \| 6062080000 \| \| 1.0315 \| 36.63 \| 745000 \| 1.0385 \| 6103040000 \| \| 0.9871 \| 36.87 \| 750000 \| 1.0380 \| 6144000000 \| \| 0.9502 \| 37.12 \| 755000 \| 1.0351 \| 6184960000 \| \| 1.0433 \| 37.36 \| 760000 \| 1.0398 \| 6225920000 \| \| 1.0148 \| 37.61 \| 765000 \| 1.0364 \| 6266880000 \| \| 0.9534 \| 37.85 \| 770000 \| 1.0380 \| 6307840000 \| \| 0.9569 \| 38.1 \| 775000 \| 1.0334 \| 6348800000 \| \| 1.0426 \| 38.35 \| 780000 \| 1.0338 \| 6389760000 \| \| 0.9923 \| 38.59 \| 785000 \| 1.0335 \| 6430720000 \| \| 1.0107 \| 38.84 \| 790000 \| 1.0325 \| 6471680000 \| \| 1.0252 \| 39.08 \| 795000 \| 1.0362 \| 6512640000 \| \| 1.0201 \| 39.33 \| 800000 \| 1.0332 \| 6553600000 \| \| 1.0066 \| 39.58 \| 805000 \| 1.0295 \| 6594560000 \| \| 0.9832 \| 39.82 \| 810000 \| 1.0325 \| 6635520000 \| \| 0.9948 \| 40.07 \| 815000 \| 1.0338 \| 6676480000 \| \| 1.0046 \| 40.31 \| 820000 \| 1.0299 \| 6717440000 \| \| 1.0472 \| 40.56 \| 825000 \| 1.0308 \| 6758400000 \| \| 1.0781 \| 40.8 \| 830000 \| 1.0276 \| 6799360000 \| \| 0.9824 \| 41.05 \| 835000 \| 1.0230 \| 6840320000 \| \| 0.9976 \| 41.3 \| 840000 \| 1.0262 \| 6881280000 \| \| 0.9951 \| 41.54 \| 845000 \| 1.0228 \| 6922240000 \| \| 1.0125 \| 41.79 \| 850000 \| 1.0277 \| 6963200000 \| \| 0.973 \| 42.03 \| 855000 \| 1.0245 \| 7004160000 \| \| 0.9853 \| 42.28 \| 860000 \| 1.0284 \| 7045120000 \| \| 1.0991 \| 42.52 \| 865000 \| 1.0244 \| 7086080000 \| \| 1.0388 \| 42.77 \| 870000 \| 1.0249 \| 7127040000 \| \| 0.9513 \| 43.02 \| 875000 \| 1.0256 \| 7168000000 \| \| 0.9948 \| 43.26 \| 880000 \| 1.0250 \| 7208960000 \| \| 1.0032 \| 43.51 \| 885000 \| 1.0180 \| 7249920000 \| \| 0.9846 \| 43.75 \| 890000 \| 1.0231 \| 7290880000 \| \| 0.9591 \| 44.0 \| 895000 \| 1.0202 \| 7331840000 \| \| 0.9872 \| 44.25 \| 900000 \| 1.0186 \| 7372800000 \| \| 0.9491 \| 44.49 \| 905000 \| 1.0202 \| 7413760000 \| \| 0.9904 \| 44.74 \| 910000 \| 1.0201 \| 7454720000 \| \| 1.0316 \| 44.98 \| 915000 \| 1.0207 \| 7495680000 \| \| 0.9535 \| 45.23 \| 920000 \| 1.0146 \| 7536640000 \| \| 0.9543 \| 45.47 \| 925000 \| 1.0189 \| 7577600000 \| \| 0.9583 \| 45.72 \| 930000 \| 1.0172 \| 7618560000 \| \| 1.0065 \| 45.97 \| 935000 \| 1.0179 \| 7659520000 \| \| 0.9711 \| 46.21 \| 940000 \| 1.0181 \| 7700480000 \| \| 0.9815 \| 46.46 \| 945000 \| 1.0152 \| 7741440000 \| \| 1.0238 \| 46.7 \| 950000 \| 1.0128 \| 7782400000 \| \| 0.9362 \| 46.95 \| 955000 \| 1.0136 \| 7823360000 \| \| 1.0079 \| 47.2 \| 960000 \| 1.0152 \| 7864320000 \| \| 0.9533 \| 47.44 \| 965000 \| 1.0155 \| 7905280000 \| \| 0.9806 \| 47.69 \| 970000 \| 1.0149 \| 7946240000 \| \| 0.9816 \| 47.93 \| 975000 \| 1.0132 \| 7987200000 \| \| 0.9743 \| 48.18 \| 980000 \| 1.0160 \| 8028160000 \| \| 0.9028 \| 48.42 \| 985000 \| 1.0148 \| 8069120000 \| \| 0.957 \| 48.67 \| 990000 \| 1.0147 \| 8110080000 \| \| 0.9769 \| 48.92 \| 995000 \| 1.0142 \| 8151040000 \| \| 1.0092 \| 49.16 \| 1000000 \| 1.0120 \| 8192000000 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0
Adriatogi/segformer-b0-finetuned-segments-graffiti	Adriatogi	2024-03-21T03:04:55Z	188	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2024-03-19T22:25:50Z	--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-graffiti results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-graffiti This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Adriatogi/graffiti dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 - Mean Iou: 0.8048 - Mean Accuracy: 0.8943 - Overall Accuracy: 0.8929 - Accuracy Not Graf: 0.8830 - Accuracy Graf: 0.9056 - Iou Not Graf: 0.8227 - Iou Graf: 0.7870 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Not Graf \| Accuracy Graf \| Iou Not Graf \| Iou Graf \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------------:\|:-------------:\|:------------:\|:--------:\| \| 0.5235 \| 0.21 \| 20 \| 0.6135 \| 0.6499 \| 0.8016 \| 0.7879 \| 0.6926 \| 0.9105 \| 0.6476 \| 0.6523 \| \| 0.5744 \| 0.42 \| 40 \| 0.4091 \| 0.7237 \| 0.8496 \| 0.8398 \| 0.7714 \| 0.9279 \| 0.7305 \| 0.7169 \| \| 0.3705 \| 0.62 \| 60 \| 0.3959 \| 0.7389 \| 0.8592 \| 0.8500 \| 0.7864 \| 0.9320 \| 0.7469 \| 0.7309 \| \| 0.1897 \| 0.83 \| 80 \| 0.3006 \| 0.7748 \| 0.8666 \| 0.8774 \| 0.9525 \| 0.7807 \| 0.8139 \| 0.7357 \| \| 0.1662 \| 1.04 \| 100 \| 0.2900 \| 0.7817 \| 0.8723 \| 0.8809 \| 0.9407 \| 0.8040 \| 0.8164 \| 0.7469 \| \| 0.4537 \| 1.25 \| 120 \| 0.2751 \| 0.7956 \| 0.8830 \| 0.8886 \| 0.9276 \| 0.8384 \| 0.8242 \| 0.7669 \| \| 0.1249 \| 1.46 \| 140 \| 0.2719 \| 0.7944 \| 0.8841 \| 0.8873 \| 0.9094 \| 0.8588 \| 0.8196 \| 0.7691 \| \| 0.4985 \| 1.67 \| 160 \| 0.3441 \| 0.7463 \| 0.8630 \| 0.8550 \| 0.7995 \| 0.9264 \| 0.7563 \| 0.7363 \| \| 0.4279 \| 1.88 \| 180 \| 0.2911 \| 0.7819 \| 0.8764 \| 0.8796 \| 0.9016 \| 0.8512 \| 0.8082 \| 0.7555 \| \| 0.1776 \| 2.08 \| 200 \| 0.2808 \| 0.7928 \| 0.8831 \| 0.8864 \| 0.9093 \| 0.8569 \| 0.8184 \| 0.7673 \| \| 0.209 \| 2.29 \| 220 \| 0.2815 \| 0.7857 \| 0.8752 \| 0.8832 \| 0.9393 \| 0.8111 \| 0.8191 \| 0.7522 \| \| 0.152 \| 2.5 \| 240 \| 0.2833 \| 0.7921 \| 0.8846 \| 0.8854 \| 0.8916 \| 0.8775 \| 0.8142 \| 0.7700 \| \| 0.5696 \| 2.71 \| 260 \| 0.2698 \| 0.8035 \| 0.8921 \| 0.8923 \| 0.8941 \| 0.8901 \| 0.8238 \| 0.7832 \| \| 0.1003 \| 2.92 \| 280 \| 0.3147 \| 0.7739 \| 0.8796 \| 0.8729 \| 0.8263 \| 0.9329 \| 0.7854 \| 0.7624 \| \| 0.1349 \| 3.12 \| 300 \| 0.2961 \| 0.7980 \| 0.8906 \| 0.8886 \| 0.8747 \| 0.9064 \| 0.8154 \| 0.7805 \| \| 0.2552 \| 3.33 \| 320 \| 0.2701 \| 0.8001 \| 0.8914 \| 0.8900 \| 0.8800 \| 0.9028 \| 0.8183 \| 0.7820 \| \| 0.1138 \| 3.54 \| 340 \| 0.2808 \| 0.7890 \| 0.8854 \| 0.8830 \| 0.8664 \| 0.9044 \| 0.8065 \| 0.7716 \| \| 0.1602 \| 3.75 \| 360 \| 0.2815 \| 0.7956 \| 0.8875 \| 0.8875 \| 0.8874 \| 0.8875 \| 0.8161 \| 0.7751 \| \| 0.0823 \| 3.96 \| 380 \| 0.3195 \| 0.7753 \| 0.8799 \| 0.8739 \| 0.8325 \| 0.9272 \| 0.7879 \| 0.7627 \| \| 0.331 \| 4.17 \| 400 \| 0.3339 \| 0.7782 \| 0.8821 \| 0.8757 \| 0.8312 \| 0.9330 \| 0.7901 \| 0.7664 \| \| 0.205 \| 4.38 \| 420 \| 0.3083 \| 0.7923 \| 0.8885 \| 0.8849 \| 0.8595 \| 0.9175 \| 0.8077 \| 0.7769 \| \| 0.1659 \| 4.58 \| 440 \| 0.3035 \| 0.7887 \| 0.8862 \| 0.8826 \| 0.8569 \| 0.9156 \| 0.8042 \| 0.7731 \| \| 0.1186 \| 4.79 \| 460 \| 0.2856 \| 0.8004 \| 0.8839 \| 0.8923 \| 0.9500 \| 0.8179 \| 0.8323 \| 0.7684 \| \| 0.2964 \| 5.0 \| 480 \| 0.3583 \| 0.7592 \| 0.8723 \| 0.8633 \| 0.8004 \| 0.9442 \| 0.7672 \| 0.7512 \| \| 0.0742 \| 5.21 \| 500 \| 0.3269 \| 0.7804 \| 0.8820 \| 0.8772 \| 0.8444 \| 0.9196 \| 0.7947 \| 0.7660 \| \| 0.1355 \| 5.42 \| 520 \| 0.3504 \| 0.7784 \| 0.8819 \| 0.8759 \| 0.8338 \| 0.9301 \| 0.7908 \| 0.7661 \| \| 0.0757 \| 5.62 \| 540 \| 0.2771 \| 0.8062 \| 0.8927 \| 0.8942 \| 0.9050 \| 0.8804 \| 0.8280 \| 0.7844 \| \| 0.2015 \| 5.83 \| 560 \| 0.3324 \| 0.7851 \| 0.8850 \| 0.8802 \| 0.8469 \| 0.9232 \| 0.7992 \| 0.7711 \| \| 0.1187 \| 6.04 \| 580 \| 0.2853 \| 0.8077 \| 0.8943 \| 0.8949 \| 0.8995 \| 0.8891 \| 0.8282 \| 0.7872 \| \| 0.1243 \| 6.25 \| 600 \| 0.3166 \| 0.7968 \| 0.8915 \| 0.8875 \| 0.8599 \| 0.9232 \| 0.8115 \| 0.7820 \| \| 0.0484 \| 6.46 \| 620 \| 0.2876 \| 0.8134 \| 0.8968 \| 0.8986 \| 0.9110 \| 0.8826 \| 0.8349 \| 0.7919 \| \| 0.0772 \| 6.67 \| 640 \| 0.2985 \| 0.8085 \| 0.8964 \| 0.8951 \| 0.8863 \| 0.9064 \| 0.8263 \| 0.7907 \| \| 0.2296 \| 6.88 \| 660 \| 0.3134 \| 0.8080 \| 0.8951 \| 0.8950 \| 0.8940 \| 0.8962 \| 0.8274 \| 0.7886 \| \| 0.0544 \| 7.08 \| 680 \| 0.3300 \| 0.8014 \| 0.8925 \| 0.8907 \| 0.8780 \| 0.9070 \| 0.8189 \| 0.7839 \| \| 0.0942 \| 7.29 \| 700 \| 0.3133 \| 0.8070 \| 0.8936 \| 0.8946 \| 0.9013 \| 0.8860 \| 0.8280 \| 0.7860 \| \| 0.2432 \| 7.5 \| 720 \| 0.3376 \| 0.8014 \| 0.8938 \| 0.8905 \| 0.8675 \| 0.9201 \| 0.8168 \| 0.7860 \| \| 0.0637 \| 7.71 \| 740 \| 0.3021 \| 0.8108 \| 0.8968 \| 0.8967 \| 0.8965 \| 0.8970 \| 0.8301 \| 0.7915 \| \| 0.0946 \| 7.92 \| 760 \| 0.3242 \| 0.8048 \| 0.8943 \| 0.8929 \| 0.8831 \| 0.9054 \| 0.8227 \| 0.7870 \| \| 0.1291 \| 8.12 \| 780 \| 0.3315 \| 0.8011 \| 0.8934 \| 0.8903 \| 0.8689 \| 0.9179 \| 0.8169 \| 0.7853 \| \| 0.1077 \| 8.33 \| 800 \| 0.3095 \| 0.8117 \| 0.8944 \| 0.8979 \| 0.9221 \| 0.8667 \| 0.8356 \| 0.7877 \| \| 0.177 \| 8.54 \| 820 \| 0.3174 \| 0.8117 \| 0.8951 \| 0.8977 \| 0.9162 \| 0.8740 \| 0.8345 \| 0.7888 \| \| 0.057 \| 8.75 \| 840 \| 0.3106 \| 0.8111 \| 0.8973 \| 0.8968 \| 0.8930 \| 0.9016 \| 0.8297 \| 0.7925 \| \| 0.2007 \| 8.96 \| 860 \| 0.3645 \| 0.7953 \| 0.8909 \| 0.8866 \| 0.8571 \| 0.9247 \| 0.8097 \| 0.7809 \| \| 0.1281 \| 9.17 \| 880 \| 0.3561 \| 0.8008 \| 0.8932 \| 0.8902 \| 0.8688 \| 0.9176 \| 0.8166 \| 0.7850 \| \| 0.0639 \| 9.38 \| 900 \| 0.3120 \| 0.8109 \| 0.8969 \| 0.8968 \| 0.8962 \| 0.8975 \| 0.8301 \| 0.7917 \| \| 0.0766 \| 9.58 \| 920 \| 0.3306 \| 0.8057 \| 0.8947 \| 0.8934 \| 0.8843 \| 0.9051 \| 0.8236 \| 0.7877 \| \| 0.1766 \| 9.79 \| 940 \| 0.3321 \| 0.8042 \| 0.8941 \| 0.8925 \| 0.8813 \| 0.9068 \| 0.8219 \| 0.7866 \| \| 0.0842 \| 10.0 \| 960 \| 0.3250 \| 0.8048 \| 0.8943 \| 0.8929 \| 0.8830 \| 0.9056 \| 0.8227 \| 0.7870 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ethanoutangoun/distilgpt2-finetuned-wikitext2	ethanoutangoun	2024-03-21T02:58:44Z	131	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2024-03-20T00:43:46Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: distilgpt2 model-index: - name: distilgpt2-finetuned-wikitext2 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. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9696 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 38 \| 4.0603 \| \| No log \| 2.0 \| 76 \| 3.9883 \| \| No log \| 3.0 \| 114 \| 3.9696 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
TheeC7Life/Testing_Model	TheeC7Life	2024-03-21T02:57:55Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-20T21:25:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
ingeol/cot_ep3_1122	ingeol	2024-03-21T02:49:00Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-21T02:48:21Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/cot_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/cot_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/cot_ep3_1122') model = AutoModel.from_pretrained('ingeol/cot_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/cot_ep3_1122) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Sumail/zhun01	Sumail	2024-03-21T02:25:11Z	129	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:Sumail/copy_jordan", "base_model:merge:Sumail/copy_jordan", "base_model:Sumail/copy_qi", "base_model:merge:Sumail/copy_qi", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T02:24:18Z	--- base_model: - Sumail/copy_qi - Sumail/copy_jordan library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Sumail/copy_qi](https://huggingface.co/Sumail/copy_qi) * [Sumail/copy_jordan](https://huggingface.co/Sumail/copy_jordan) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Sumail/copy_jordan layer_range: [0, 12] - model: Sumail/copy_qi layer_range: [0, 12] merge_method: slerp base_model: Sumail/copy_qi parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
ngaiwk/trial-distilbert	ngaiwk	2024-03-21T02:24:25Z	198	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-21T02:24:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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nikhil07prakash/float-7b	nikhil07prakash	2024-03-21T02:21:53Z	20	3	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2402.14811", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-02T17:33:28Z	--- license: mit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This model is a fully fine-tuned version of the [Llama-7B](https://huggingface.co/huggyllama/llama-7b) model on synthetically generated arithmetic tasks. It was introduced in [this](https://openreview.net/forum?id=8sKcAWOf2D) paper. It is very similar to [Goat-7B](https://github.com/liutiedong/goat), except it was trained without LoRA. For inquiries about checkpoints during the fine-tuning process, kindly reach out to [Nikhil](mailto:[email protected]) via email. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Nikhil Prakash](https://nix07.github.io/) - Model type: Autoregressive Decoder-only Language Model - License: MIT License - Finetuned from model: [Llama-7B](https://huggingface.co/huggyllama/llama-7b) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [Link](https://github.com/Nix07/finetuning/) - Paper : [Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking](https://arxiv.org/abs/2402.14811) ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import AutoModel model = AutoModel.from_pretrained("nikhil07prakash/float-7b") ``` ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: ```python @inproceedings{prakash2023fine, title={Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking}, author={Prakash, Nikhil and Shaham, Tamar Rott and Haklay, Tal and Belinkov, Yonatan and Bau, David}, booktitle={Proceedings of the 2024 International Conference on Learning Representations}, note={arXiv:2402.14811}, year={2024} } ```
Farisya/qna	Farisya	2024-03-21T02:20:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-21T02:19:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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derek2015/FrozenLake-v1	derek2015	2024-03-21T02:15:07Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-20T09:10:45Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: FrozenLake-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="derek2015/FrozenLake-v1", 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"]) ```
walterwitty50/wowdy	walterwitty50	2024-03-21T02:11:13Z	0	0	null	[ "nsfw", "adult", "license:unknown", "region:us" ]	null	2024-03-21T02:08:23Z	--- license: unknown tags: - nsfw - adult ---
Sumail/Derrick41	Sumail	2024-03-21T02:06:45Z	129	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:0x0mom/nous_gemma_r4", "base_model:merge:0x0mom/nous_gemma_r4", "base_model:coffiee/g2", "base_model:merge:coffiee/g2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T02:04:06Z	--- base_model: - 0x0mom/nous_gemma_r4 - coffiee/g2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [0x0mom/nous_gemma_r4](https://huggingface.co/0x0mom/nous_gemma_r4) * [coffiee/g2](https://huggingface.co/coffiee/g2) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: 0x0mom/nous_gemma_r4 layer_range: [0, 18] - model: coffiee/g2 layer_range: [0, 18] merge_method: slerp base_model: 0x0mom/nous_gemma_r4 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Steven-GU-Yu-Di/Text-to-Speech-Small	Steven-GU-Yu-Di	2024-03-21T02:06:02Z	119	0	transformers	[ "transformers", "safetensors", "bark", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2024-03-21T02:04:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nwhamed/mergedd	nwhamed	2024-03-21T02:06:01Z	3	0	transformers	[ "transformers", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "bardsai/jaskier-7b-dpo-v5.6", "eren23/ogno-monarch-jaskier-merge-7b", "liminerity/Omningotex-7b-slerp", "yleo/OgnoMonarch-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T02:04:32Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - bardsai/jaskier-7b-dpo-v5.6 - eren23/ogno-monarch-jaskier-merge-7b - liminerity/Omningotex-7b-slerp - yleo/OgnoMonarch-7B --- # mergedd mergedd is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [](https://huggingface.co/) * [](https://huggingface.co/) * [](https://huggingface.co/) * [](https://huggingface.co/) ## 🧩 Configuration ```json{ "models": [ { "model": "bardsai/jaskier-7b-dpo-v5.6", "parameters": {} }, { "model": "eren23/ogno-monarch-jaskier-merge-7b", "parameters": { "density": 0.53, "weight": 0.4 } }, { "model": "liminerity/Omningotex-7b-slerp", "parameters": { "density": 0.53, "weight": 0.3 } }, { "model": "yleo/OgnoMonarch-7B", "parameters": { "density": 0.53, "weight": 0.3 } } ], "merge_method": "dare_ties", "base_model": "bardsai/jaskier-7b-dpo-v5.6", "parameters": { "int8_mask": true, "dtype": "bfloat16" } }
Steven-GU-Yu-Di/Text-to-Speech	Steven-GU-Yu-Di	2024-03-21T02:01:59Z	125	0	transformers	[ "transformers", "safetensors", "bark", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2024-03-21T01:58:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lemon-mint/gemma-7b-ko-it-v0.7	lemon-mint	2024-03-21T01:56:15Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "ko", "en", "dataset:maywell/koVast", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T01:30:36Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms datasets: - maywell/koVast language: - ko - en widget: - messages: - role: user content: 고양이는 동물이야? inference: parameters: max_new_tokens: 1024 --- [maywell/koVast](https://huggingface.co/datasets/maywell/koVast) 데이터셋을 사용한 Gemma 7B Instruct 한국어 파인튜닝 실험.
ingeol/q2e_ep3_1122	ingeol	2024-03-21T01:54:59Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-21T01:54:17Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2e_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2e_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2e_ep3_1122') model = AutoModel.from_pretrained('ingeol/q2e_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2e_ep3_1122) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
arthurspapa/marcianome	arthurspapa	2024-03-21T01:51:44Z	3	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-21T01:51:37Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: A photo of <s0><s1> marcianome output: url: image-0.png - text: A photo of <s0><s1> marcianome output: url: image-1.png - text: A photo of <s0><s1> marcianome output: url: image-2.png - text: A photo of <s0><s1> marcianome output: url: image-3.png - text: A photo of <s0><s1> marcianome output: url: image-4.png - text: A photo of <s0><s1> marcianome output: url: image-5.png - text: A photo of <s0><s1> marcianome output: url: image-6.png - text: A photo of <s0><s1> marcianome output: url: image-7.png - text: A photo of <s0><s1> marcianome output: url: image-8.png - text: A photo of <s0><s1> marcianome output: url: image-9.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A photo of <s0><s1> arthurspapa/marcianome license: openrail++ --- # SDXL LoRA DreamBooth - arthurspapa/marcianome <Gallery /> ## Model description ### These are arthurspapa/marcianome LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`marcianome.safetensors` here 💾](/arthurspapa/marcianome/blob/main/marcianome.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:marcianome:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - Embeddings: download [`marcianome_emb.safetensors` here 💾](/arthurspapa/marcianome/blob/main/marcianome_emb.safetensors). - Place it on it on your `embeddings` folder - Use it by adding `marcianome_emb` to your prompt. For example, `A photo of marcianome_emb marcianome` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('arthurspapa/marcianome', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='arthurspapa/marcianome', filename='marcianome_emb.safetensors' repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1> arthurspapa/marcianome').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/arthurspapa/marcianome/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
ruba2ksa/emo2ruba	ruba2ksa	2024-03-21T01:40:39Z	60	0	transformers	[ "transformers", "tf", "deberta-v2", "text-classification", "generated_from_keras_callback", "base_model:philschmid/deberta-v3-xsmall-emotion", "base_model:finetune:philschmid/deberta-v3-xsmall-emotion", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-20T23:10:39Z	--- license: mit base_model: philschmid/deberta-v3-xsmall-emotion tags: - generated_from_keras_callback model-index: - name: ruba2ksa/emo2ruba 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. --> # ruba2ksa/emo2ruba This model is a fine-tuned version of [philschmid/deberta-v3-xsmall-emotion](https://huggingface.co/philschmid/deberta-v3-xsmall-emotion) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1633 - Validation Loss: 0.1383 - Train Accuracy: 0.9465 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': '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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2158 \| 0.1695 \| 0.942 \| 0 \| \| 0.1633 \| 0.1383 \| 0.9465 \| 1 \| ### Framework versions - Transformers 4.38.2 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
Deepnoid/deep-solar-Rev-v3.0.4	Deepnoid	2024-03-21T01:27:59Z	2,335	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T01:06:39Z	--- license: apache-2.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
HamdanXI/caesar_qa_tinyllama	HamdanXI	2024-03-21T01:26:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:finetune:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-21T01:26:26Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: HamdanXI - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
nwhamed/gemma_gbt	nwhamed	2024-03-21T01:25:10Z	5	0	transformers	[ "transformers", "text-generation", "merge", "mergekit", "lazymergekit", "google/gemma-7b", "EleutherAI/gpt-neo-2.7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-21T00:53:08Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - google/gemma-7b - EleutherAI/gpt-neo-2.7B --- # gemma_gpt gemma_gpt is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [google/gemma-7b](https://huggingface.co/google/gemma-7b) * [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) ## 🧩 Configuration ```json{ "models": [ { "model": "google/gemma-7b", "parameters": { "param1": "value1", "param2": "value2" } }, { "model": "EleutherAI/gpt-neo-2.7B", "parameters": { "param1": "value1", "param2": "value2" } } ] }
uoseftalaat/whisper-small	uoseftalaat	2024-03-21T01:17:07Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ara", "dataset:uoseftalaat/GP", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-04T14:54:44Z	--- language: - ara license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - uoseftalaat/GP metrics: - wer model-index: - name: Whisper Small for quran recognition results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Quran_requiters type: uoseftalaat/GP config: default split: test args: 'config: default, split: train' metrics: - name: Wer type: wer value: 3.369434416365824 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small for quran recognition This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Quran_requiters dataset. It achieves the following results on the evaluation set: - Loss: 0.0183 - Wer: 3.3694 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0026 \| 3.24 \| 1000 \| 0.0205 \| 4.4868 \| \| 0.0003 \| 6.47 \| 2000 \| 0.0180 \| 3.3522 \| \| 0.0003 \| 6.49 \| 2005 \| 0.0180 \| 3.3522 \| \| 0.0003 \| 6.5 \| 2010 \| 0.0180 \| 3.3522 \| \| 0.0001 \| 9.71 \| 3000 \| 0.0180 \| 3.2663 \| \| 0.0 \| 12.94 \| 4000 \| 0.0183 \| 3.3694 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.1.2 - Datasets 2.17.1 - Tokenizers 0.15.1
linuxhunter/dqn-SpaceInvadersNoFrameskip-v4	linuxhunter	2024-03-21T01:09:51Z	0	0	null	[ "en", "license:apache-2.0", "region:us" ]	null	2024-03-21T01:03:27Z	--- license: apache-2.0 language: - en ---
ingeol/q2d_ep3_1122	ingeol	2024-03-21T01:05:22Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-21T01:04:53Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2d_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2d_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2d_ep3_1122') model = AutoModel.from_pretrained('ingeol/q2d_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2d_ep3_1122) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
cychristophercyc/Group12_trainmodel	cychristophercyc	2024-03-21T01:04:50Z	106	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-21T01:04:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
JoshBrew/Facial_Recognition	JoshBrew	2024-03-21T01:03:49Z	0	0	null	[ "region:us" ]	null	2024-02-14T17:58:41Z	# Model Card for Facial Expression Recognition Model This model card provides an overview of a Convolutional Neural Network (CNN) developed for facial expression recognition. The project aimed to explore the effectiveness of various strategies in handling unbalanced datasets, particularly focusing on the impact of the `CategoricalFocalCrossentropy()` loss function and adjustments in the model's architecture and hyperparameters. The model was developed and tested using Python, TensorFlow, and Pandas within Google Colab, leveraging GPU acceleration for enhanced processing speeds. ## Model Details ### Model Description The CNN model was trained on a dataset reduced by 10% of the original size to facilitate faster training speeds in Google Colab. Despite the reduction, the dataset maintained the original distribution of data across all classes of facial expressions. The training and testing splits were directly managed from Google Colab's content folder, with the data zip folder required to be uploaded to Google Colab during runtime. - Developed by: Joao Pedro dos Santos, with critiques from Joshua Brewington and Johnny Duenas. - Model type: Convolutional Neural Network (CNN) for facial expression recognition. - Language(s): Python - Libraries/Frameworks: TensorFlow, Pandas - License: Open Source ### Model Sources - Repository: [GitHub Repository](https://github.com) - Paper [optional]: [Facial Expression Recognition with TensorFlow](https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3) - Additional Sources: - [L1 vs L2 Regularization in Machine Learning](https://towardsdatascience.com/l1-vs-l2-regularization-in-machine-learning-differences-advantages-and-how-to-apply-them-in-72eb12f102b5) - [Focal Loss: What, Why, and How](https://medium.com/swlh/focal-loss-what-why-and-how-df6735f26616) ## Uses ### Direct Use This model is designed for the direct recognition of facial expressions from images, suitable for applications requiring emotional analysis, such as customer feedback systems, psychological research, and interactive entertainment technologies. ### Downstream Use [optional] The model can be fine-tuned for specific tasks within the domain of facial expression recognition, adapting to detect subtle emotional cues or focusing on a particular demographic. ### Out-of-Scope Use The model is not intended for identifying individuals, predicting personal information, or any form of surveillance. ## Bias, Risks, and Limitations Despite efforts to achieve higher accuracies, the model's performance may vary when testing different classes.. The initial layer's neurons were found to be oversaturated when all 7 classes were trained, indicating a potential limitation in the model's architecture for handling complex, unbalanced datasets. ### Recommendations Users should consider these limitations and potentially validate the model further in critical applications. Continuous research and development are recommended to enhance the model's robustness and inclusivity. ## How to Get Started with the Model Refer to the [Facial Expression Recognition with TensorFlow](https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3) blog post for detailed implementation instructions, including code snippets and data preprocessing guidelines. ## Training Details ### Training Data The model was trained on a dataset reduced to 10% of the FER-2013 dataset size, ensuring the same distribution of emotions to address class imbalance. The data was unploaded to co-lab's runtime in its contents folder. ### Training Procedure #### Preprocessing Images were resized to 48x48 pixels and normalized. Data augmentation techniques such as rotation and zoom were applied to enhance the diversity of the training data. This was done by the use of tensorflow's import 'ImageDataGenerator'. #### Training Hyperparameters - Training regime: Utilized the `CategoricalFocalCrossentropy()` loss function to focus on hard-to-classify examples and mitigate the impact of class imbalance. While the loss function did not improve accuracy, it significantly reduced the loss. ## Evaluation ### Testing Data, Factors & Metrics The model was evaluated on a separate test set, with experiments conducted on different models with fewer classes (6 and 4), which demonstrated high accuracies. ### Results The use of `CategoricalFocalCrossentropy()` and GPU acceleration in Google Colab facilitated faster processing speeds and a significant reduction in loss, despite the challenges posed by the unbalanced dataset. ## Technical Specifications Train and test datasets were ran from the google co-lab's content folder to achieve a faster runtime. ### Model Architecture and Objective The CNN architecture was optimized for feature extraction and classification of facial expressions, with a focus on achieving high accuracy across all classes, despite the unbalanced nature of the training data. ### Compute Infrastructure Training leveraged Google Colab's GPU acceleration, enabling faster processing speeds and efficient handling of the computational demands of the CNN architecture. ## Citation APA: dos Santos, J. P., Brewington, J., & Duenas, J. (2023). Facial Expression Recognition with TensorFlow. DevGenius. Retrieved from https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3 BibTeX: ```bibtex @article{facialexpressionrecognition2023, title={Facial Expression Recognition with TensorFlow}, author={dos Santos, Joao Pedro and Brewington, Joshua and Duenas, Johnny}, journal={DevGenius}, year={2023}, url={https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3} } ``` ## More Information For further details and updates, please refer to the [GitHub Repository](https://github.com) and the [Facial Expression Recognition with TensorFlow](https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3) blog post. Additional insights into the model's development and performance can be found in the articles on L1 vs L2 Regularization and Focal Loss.
Changgil/K2S3-Mistral-7b-v1.0	Changgil	2024-03-21T01:01:19Z	48	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T00:50:57Z	--- license: cc-by-nc-4.0 language: - en --- --- ## Developed by : * K2S3 ## Model Number: * K2S3-Mistral-7b-v1.0 ## Base Model : * mistralai/Mistral-7B-v0.1 ### Training Data * The training data for this model includes alpaca-gpt4-data, and samples from The OpenOrca Dataset. * 이 모델의 훈련 데이터에는 alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다. ### Training Method * This model was fine-tuned on the "mistralai/Mistral-7B-v0.1" base model using a full parameter tuning method with SFT (Supervised Fine-Tuning). * 이 모델은 "mistralai/Mistral-7B-v0.1" 기반 모델을 SFT를 사용하여 전체 파라미터 조정 방법으로 미세조정되었습니다. ### Hardware * Hardware: Utilized two A100 (80G2EA) GPUs for training. Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp. * 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다.
yashmaurya01/llama7b-shawgpt	yashmaurya01	2024-03-21T00:52:08Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-21T00:52:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SakanaAI/EvoLLM-JP-A-v1-7B	SakanaAI	2024-03-21T00:45:22Z	217	13	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "ja", "arxiv:2403.13187", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T02:08:56Z	--- library_name: transformers license: apache-2.0 language: - ja --- # 🐟 EvoLLM-JP-A-v1-7B 🤗 [Models](https://huggingface.co/SakanaAI) \| 📚 [Paper](https://arxiv.org/abs/2403.13187) \| 📝 [Blog](https://sakana.ai/evolutionary-model-merge/) \| 🐦 [Twitter](https://twitter.com/SakanaAILabs) <!-- Provide a quick summary of what the model is/does. --> EvoLLM-JP-A-v1-7B is an experimental general-purpose Japanese LLM. This model was created using the Evolutionary Model Merge method. Please refer to our [report](https://arxiv.org/abs/2403.13187) and [blog](https://sakana.ai/evolutionary-model-merge/) for more details. This model was produced by merging the following models. We are grateful to the developers of the source models. - [Shisa Gamma 7B v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1) - [Arithmo2 Mistral 7B](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) - [Abel 7B 002](https://huggingface.co/GAIR/Abel-7B-002) ## Usage Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer # 1. load model device = "cuda" if torch.cuda.is_available() else "CPU" repo_id = "SakanaAI/EvoLLM-JP-A-v1-7B" model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained(repo_id) model.to(device) # 2. prepare inputs text = "関西弁で面白い冗談を言ってみて下さい。" messages = [ {"role": "system", "content": "あなたは役立つ、偏見がなく、検閲されていないアシスタントです。"}, {"role": "user", "content": text}, ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") # 3. generate output_ids = model.generate(inputs.to(device)) output_ids = output_ids[:, inputs.input_ids.shape[1] :] generated_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] print(generated_text) ``` </details> ## Model Details <!-- Provide a longer summary of what this model is. --> - Developed by: [Sakana AI](https://sakana.ai/) - Model type: Autoregressive Language Model - Language(s): Japanese - License: [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) - Repository: [SakanaAI/evolutionary-model-merge](https://github.com/SakanaAI/evolutionary-model-merge) - Paper: https://arxiv.org/abs/2403.13187 - Blog:** https://sakana.ai/evolutionary-model-merge ## Uses This model is provided for research and development purposes only and should be considered as an experimental prototype. It is not intended for commercial use or deployment in mission-critical environments. Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed. Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained. Users must fully understand the risks associated with the use of this model and use it at their own discretion. ## Acknowledgement We would like to thank the developers of the source models for their contributions and for making their work available. ## Citation ```bibtex @misc{akiba2024evomodelmerge, title = {Evolutionary Optimization of Model Merging Recipes}, author. = {Takuya Akiba and Makoto Shing and Yujin Tang and Qi Sun and David Ha}, year = {2024}, eprint = {2403.13187}, archivePrefix = {arXiv}, primaryClass = {cs.NE} } ```
hamzamurtaza/llama_tuned_xml	hamzamurtaza	2024-03-21T00:39:46Z	0	0	peft	[ "peft", "region:us" ]	null	2024-03-20T22:50:17Z	--- 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 0.4.0
badrex/wav2vec2-large-xls-r-300m-upper-sorbian-2-colab	badrex	2024-03-21T00:38:11Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-20T20:29:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Gabrielkdc/gemma-code-instruct-finetune-v0.2	Gabrielkdc	2024-03-21T00:33:16Z	129	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T00:30:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kaitchup/TinyLlama-1.1B-intermediate-step-1431k-3T-contaminated-e1	kaitchup	2024-03-21T00:32:43Z	0	0	transformers	[ "transformers", "safetensors", "contaminated", "en", "dataset:kaitchup/hellaswag_winograndexl_ai2_arc_correctAnswerOnly_flattened", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-19T09:19:54Z	--- language: - en license: apache-2.0 library_name: transformers tags: - contaminated datasets: - kaitchup/hellaswag_winograndexl_ai2_arc_correctAnswerOnly_flattened --- ## Model Details QLoRA adapter for TinyLlama fine-tuned for 1 epoch on kaitchup/hellaswag_winograndexl_ai2_arc_correctAnswerOnly_flattened. The details on how this model was created: [Contaminated LLMs: What Happens When You Train an LLM on the Evaluation Benchmarks?](https://thesalt.substack.com/p/contaminated-llms-what-happens-when) ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: Causal - Language(s) (NLP): English - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
LeoTungAnh/codeparrot-small	LeoTungAnh	2024-03-21T00:30:42Z	201	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-21T00:28:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nnirmall/MentalPhi_PROMPT_TUNING_CAUSAL_LM	nnirmall	2024-03-21T00:30:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-19T05:45:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Lakonik/stablessdnerf	Lakonik	2024-03-21T00:21:12Z	0	1	null	[ "arxiv:2403.12032", "license:apache-2.0", "region:us" ]	null	2024-03-17T19:24:42Z	--- license: apache-2.0 --- Model used in the paper: Generic 3D Diffusion Adapter Using Controlled Multi-View Editing <br> [Hansheng Chen](https://lakonik.github.io/)<sup>1</sup>, [Ruoxi Shi](https://rshi.top/)<sup>2</sup>, [Yulin Liu](https://liuyulinn.github.io/)<sup>2</sup>, [Bokui Shen](https://cs.stanford.edu/people/bshen88/)<sup>3</sup>, [Jiayuan Gu](https://pages.ucsd.edu/~ztu/)<sup>2</sup>, [Gordon Wetzstein](http://web.stanford.edu/~gordonwz/)<sup>1</sup>, [Hao Su](https://cseweb.ucsd.edu/~haosu/)<sup>2</sup>, [Leonidas Guibas](https://geometry.stanford.edu/member/guibas/)<sup>1</sup><br> <sup>1</sup>Stanford University, <sup>2</sup>UCSD, <sup>3</sup>Apparate Labs <br> [[project page](https://lakonik.github.io/mvedit)] [[Web UI](https://lakonik.github.io/mvedit_demo/)] [[Web UI🤗](https://huggingface.co/spaces/Lakonik/MVEdit)] [[paper](https://arxiv.org/abs/2403.12032)]
MzudemO/chapter-segmentation-model	MzudemO	2024-03-21T00:16:21Z	47	0	transformers	[ "transformers", "pytorch", "bert", "next-sentence-prediction", "de", "endpoints_compatible", "region:us" ]	null	2023-05-25T23:24:51Z	--- language: - de metrics: - f1 library_name: transformers ---
yotasr/Smart_Tour_Alex_v0.1	yotasr	2024-03-21T00:13:14Z	192	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:yotasr/Smart_TourGuide", "base_model:finetune:yotasr/Smart_TourGuide", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-20T20:29:34Z	--- base_model: yotasr/Smart_TourGuide tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Smart_Tour_Alex_v0.1 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9982758620689656 --- <!-- 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. --> # Smart_Tour_Alex_v0.1 This model is a fine-tuned version of [yotasr/Smart_TourGuide](https://huggingface.co/yotasr/Smart_TourGuide) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1183 - Accuracy: 0.9983 ## 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4449 \| 1.0 \| 41 \| 0.3374 \| 0.9983 \| \| 0.1747 \| 2.0 \| 82 \| 0.1558 \| 0.9983 \| \| 0.1324 \| 3.0 \| 123 \| 0.1251 \| 0.9983 \| \| 0.1225 \| 4.0 \| 164 \| 0.1183 \| 0.9983 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ingeol/facets_ep3_1122	ingeol	2024-03-21T00:10:42Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-21T00:10:05Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/facets_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/facets_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/facets_ep3_1122') model = AutoModel.from_pretrained('ingeol/facets_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/facets_ep3_1122) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ThuyNT03/CS505-NerCOQE-xlm-Object	ThuyNT03	2024-03-21T00:05:23Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-20T23:58:52Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: CS505-NerCOQE-xlm-Object 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. --> # CS505-NerCOQE-xlm-Object This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - F1: 0.9960 ## 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 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 53 \| 0.0870 \| 0.7335 \| \| No log \| 2.0 \| 106 \| 0.1278 \| 0.4618 \| \| No log \| 3.0 \| 159 \| 0.0428 \| 0.8256 \| \| No log \| 4.0 \| 212 \| 0.0307 \| 0.8946 \| \| No log \| 5.0 \| 265 \| 0.0202 \| 0.9143 \| \| No log \| 6.0 \| 318 \| 0.0137 \| 0.9355 \| \| No log \| 7.0 \| 371 \| 0.0093 \| 0.9518 \| \| No log \| 8.0 \| 424 \| 0.0076 \| 0.9686 \| \| No log \| 9.0 \| 477 \| 0.0064 \| 0.9793 \| \| No log \| 10.0 \| 530 \| 0.0031 \| 0.9832 \| \| No log \| 11.0 \| 583 \| 0.0024 \| 0.9773 \| \| No log \| 12.0 \| 636 \| 0.0055 \| 0.9735 \| \| No log \| 13.0 \| 689 \| 0.0027 \| 0.9842 \| \| No log \| 14.0 \| 742 \| 0.0090 \| 0.9091 \| \| No log \| 15.0 \| 795 \| 0.0012 \| 0.9921 \| \| No log \| 16.0 \| 848 \| 0.0007 \| 0.9970 \| \| No log \| 17.0 \| 901 \| 0.0005 \| 0.9960 \| \| No log \| 18.0 \| 954 \| 0.0007 \| 0.9960 \| \| No log \| 19.0 \| 1007 \| 0.0005 \| 0.9960 \| \| No log \| 20.0 \| 1060 \| 0.0005 \| 0.9960 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
gayanin/test_1	gayanin	2024-03-21T00:01:36Z	105	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-20T23:54:59Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: test_1 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_1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4313 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2092 \| 0.46 \| 500 \| 0.6713 \| \| 0.7295 \| 0.91 \| 1000 \| 0.5501 \| \| 0.5551 \| 1.37 \| 1500 \| 0.4963 \| \| 0.496 \| 1.82 \| 2000 \| 0.4647 \| \| 0.4156 \| 2.28 \| 2500 \| 0.4533 \| \| 0.3514 \| 2.73 \| 3000 \| 0.4313 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ThuyNT03/CS505-NerCOQE-xlm-Subject	ThuyNT03	2024-03-20T23:58:46Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-20T23:52:23Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: CS505-NerCOQE-xlm-Subject 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. --> # CS505-NerCOQE-xlm-Subject This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - F1: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 53 \| 0.0902 \| 0.7186 \| \| No log \| 2.0 \| 106 \| 0.0557 \| 0.8471 \| \| No log \| 3.0 \| 159 \| 0.0300 \| 0.8976 \| \| No log \| 4.0 \| 212 \| 0.0163 \| 0.9301 \| \| No log \| 5.0 \| 265 \| 0.0115 \| 0.9648 \| \| No log \| 6.0 \| 318 \| 0.0066 \| 0.9682 \| \| No log \| 7.0 \| 371 \| 0.0087 \| 0.9727 \| \| No log \| 8.0 \| 424 \| 0.0167 \| 0.8462 \| \| No log \| 9.0 \| 477 \| 0.0014 \| 0.9902 \| \| No log \| 10.0 \| 530 \| 0.0010 \| 0.9967 \| \| No log \| 11.0 \| 583 \| 0.0023 \| 0.9928 \| \| No log \| 12.0 \| 636 \| 0.0005 \| 0.9961 \| \| No log \| 13.0 \| 689 \| 0.0002 \| 0.9974 \| \| No log \| 14.0 \| 742 \| 0.0006 \| 0.9987 \| \| No log \| 15.0 \| 795 \| 0.0001 \| 1.0 \| \| No log \| 16.0 \| 848 \| 0.0001 \| 1.0 \| \| No log \| 17.0 \| 901 \| 0.0001 \| 0.9987 \| \| No log \| 18.0 \| 954 \| 0.0001 \| 0.9987 \| \| No log \| 19.0 \| 1007 \| 0.0000 \| 1.0 \| \| No log \| 20.0 \| 1060 \| 0.0000 \| 1.0 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test	rajevan123	2024-03-20T23:57:49Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/deberta-v3-xsmall", "base_model:adapter:microsoft/deberta-v3-xsmall", "license:mit", "region:us" ]	null	2024-03-20T23:48:30Z	--- license: mit library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: microsoft/deberta-v3-xsmall model-index: - name: STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-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. --> # STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4820 - Accuracy: 0.3771 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 360 \| 1.7474 \| 0.2429 \| \| 1.7416 \| 2.0 \| 720 \| 1.7279 \| 0.2429 \| \| 1.6866 \| 3.0 \| 1080 \| 1.6799 \| 0.2883 \| \| 1.6866 \| 4.0 \| 1440 \| 1.6220 \| 0.3372 \| \| 1.6241 \| 5.0 \| 1800 \| 1.5787 \| 0.3466 \| \| 1.5474 \| 6.0 \| 2160 \| 1.5306 \| 0.3604 \| \| 1.484 \| 7.0 \| 2520 \| 1.5180 \| 0.3626 \| \| 1.484 \| 8.0 \| 2880 \| 1.5028 \| 0.3706 \| \| 1.4452 \| 9.0 \| 3240 \| 1.4871 \| 0.3753 \| \| 1.429 \| 10.0 \| 3600 \| 1.4820 \| 0.3771 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ThuyNT03/CS505-NerCOQE-xlm-Predicate	ThuyNT03	2024-03-20T23:52:18Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-20T23:45:55Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: CS505-NerCOQE-xlm-Predicate 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. --> # CS505-NerCOQE-xlm-Predicate This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0003 - F1: 0.9976 ## 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 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 53 \| 0.1876 \| 0.5087 \| \| No log \| 2.0 \| 106 \| 0.1014 \| 0.7119 \| \| No log \| 3.0 \| 159 \| 0.0564 \| 0.8287 \| \| No log \| 4.0 \| 212 \| 0.0361 \| 0.8835 \| \| No log \| 5.0 \| 265 \| 0.0282 \| 0.8951 \| \| No log \| 6.0 \| 318 \| 0.0154 \| 0.9392 \| \| No log \| 7.0 \| 371 \| 0.0231 \| 0.8730 \| \| No log \| 8.0 \| 424 \| 0.0054 \| 0.9763 \| \| No log \| 9.0 \| 477 \| 0.0031 \| 0.9792 \| \| No log \| 10.0 \| 530 \| 0.0027 \| 0.9828 \| \| No log \| 11.0 \| 583 \| 0.0015 \| 0.9905 \| \| No log \| 12.0 \| 636 \| 0.0031 \| 0.9929 \| \| No log \| 13.0 \| 689 \| 0.0023 \| 0.9941 \| \| No log \| 14.0 \| 742 \| 0.0016 \| 0.9923 \| \| No log \| 15.0 \| 795 \| 0.0011 \| 0.9917 \| \| No log \| 16.0 \| 848 \| 0.0006 \| 0.9964 \| \| No log \| 17.0 \| 901 \| 0.0003 \| 0.9988 \| \| No log \| 18.0 \| 954 \| 0.0003 \| 0.9976 \| \| No log \| 19.0 \| 1007 \| 0.0003 \| 0.9976 \| \| No log \| 20.0 \| 1060 \| 0.0003 \| 0.9976 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
liuylhf/empower-functions-clean-data-one-more-functions	liuylhf	2024-03-20T23:49:21Z	2	0	peft	[ "peft", "safetensors", "mixtral", "axolotl", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-20T20:35:03Z	--- license: apache-2.0 library_name: peft tags: - axolotl - generated_from_trainer base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 model-index: - name: empower-functions-clean-data-one-more-functions results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml adapter: qlora base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 bf16: true chat_template: inst dataset_prepared_path: last_run_prepared datasets: - conversation: mistral path: 659f8b7bb7c243ab879f8bc17876ce4a/data/with_function_response/more_functions/one_more_function/function_used_training.jsonl type: sharegpt - conversation: mistral path: 659f8b7bb7c243ab879f8bc17876ce4a/data/with_function_response/original_clean/function_not_used_training.jsonl type: sharegpt debug: null eval_max_new_tokens: 256 eval_steps: 0.05 eval_table_size: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: liuylhf/empower-functions-clean-data-one-more-functions learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_model_dir: null lora_r: 32 lora_target_modules: - q_proj - k_proj - v_proj - o_proj loss_watchdog_patience: 3 loss_watchdog_threshold: 5.0 lr_scheduler: cosine micro_batch_size: 2 model_config: output_router_logits: true model_type: AutoModelForCausalLM num_epochs: 1 optimizer: paged_adamw_8bit output_dir: 659f8b7bb7c243ab879f8bc17876ce4a/model pad_to_sequence_len: true sample_packing: true save_steps: 0.1 sequence_len: 4096 strict: false tf32: false tokenizer_type: LlamaTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.01 wandb_log_model: end wandb_name: more-tools wandb_project: function-call warmup_steps: 10 weight_decay: 0.0 ``` </details><br> # empower-functions-clean-data-one-more-functions This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0863 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0157 \| 0.0 \| 1 \| 2.1200 \| \| 0.153 \| 0.05 \| 23 \| 0.1454 \| \| 0.1236 \| 0.1 \| 46 \| 0.1160 \| \| 0.1043 \| 0.15 \| 69 \| 0.1073 \| \| 0.1163 \| 0.2 \| 92 \| 0.1035 \| \| 0.1072 \| 0.25 \| 115 \| 0.0996 \| \| 0.0988 \| 0.31 \| 138 \| 0.0978 \| \| 0.0962 \| 0.36 \| 161 \| 0.0963 \| \| 0.0823 \| 0.41 \| 184 \| 0.0939 \| \| 0.0785 \| 0.46 \| 207 \| 0.0938 \| \| 0.0941 \| 0.51 \| 230 \| 0.0918 \| \| 0.0968 \| 0.56 \| 253 \| 0.0905 \| \| 0.0856 \| 0.61 \| 276 \| 0.0899 \| \| 0.0965 \| 0.66 \| 299 \| 0.0895 \| \| 0.0894 \| 0.71 \| 322 \| 0.0881 \| \| 0.086 \| 0.76 \| 345 \| 0.0872 \| \| 0.0941 \| 0.82 \| 368 \| 0.0869 \| \| 0.0894 \| 0.87 \| 391 \| 0.0867 \| \| 0.0782 \| 0.92 \| 414 \| 0.0864 \| \| 0.0815 \| 0.97 \| 437 \| 0.0863 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.0
TomasFrankovich/esm2_t30_150M_UR50D-finetuned-SO2F	TomasFrankovich	2024-03-20T23:38:27Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "esm", "token-classification", "generated_from_trainer", "base_model:facebook/esm2_t30_150M_UR50D", "base_model:finetune:facebook/esm2_t30_150M_UR50D", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-20T23:33:54Z	--- license: mit base_model: facebook/esm2_t30_150M_UR50D tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: esm2_t30_150M_UR50D-finetuned-SO2F 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. --> # esm2_t30_150M_UR50D-finetuned-SO2F This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6608 - Accuracy: 0.7158 - Precision: 0.1682 - Recall: 0.5068 - F1: 0.2526 - Auc: 0.6223 - Mcc: 0.1585 ## 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 - distributed_type: multi-GPU - 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 \| Accuracy \| Precision \| Recall \| F1 \| Auc \| Mcc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 108 \| 0.6768 \| 0.6886 \| 0.1465 \| 0.4740 \| 0.2238 \| 0.5925 \| 0.1175 \| \| No log \| 2.0 \| 216 \| 0.6646 \| 0.6935 \| 0.1628 \| 0.5397 \| 0.2502 \| 0.6247 \| 0.1573 \| \| No log \| 3.0 \| 324 \| 0.6608 \| 0.7158 \| 0.1682 \| 0.5068 \| 0.2526 \| 0.6223 \| 0.1585 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
belisards/albertina_gun	belisards	2024-03-20T23:35:55Z	3	1	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "gun violence", "human rights", "pt", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T11:12:14Z	--- license: apache-2.0 language: - pt pipeline_tag: text-classification tags: - gun violence - human rights --- Text classification model to detect gun violence reports in Brazilian Portuguese. Albertina-PT model fine-tuned with Twitter data labelled by Instituto Fogo Cruzado. Developed as part of my research at the Oxford Internet Institute.
belisards/gun_violence_ptbr	belisards	2024-03-20T23:34:53Z	111	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "gun violence", "human rights", "pt", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-27T10:33:35Z	--- license: apache-2.0 language: - pt pipeline_tag: text-classification tags: - gun violence - human rights --- Text classification model to detect gun violence reports in Brazilian Portuguese. BERTimbau fine-tuned with Twitter data labelled by Instituto Fogo Cruzado. Developed as part of my research at the Oxford Internet Institute.
pauloguyss/mistral-7b-temas-stf-v1	pauloguyss	2024-03-20T23:28:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-20T23:27:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe	rajevan123	2024-03-20T23:16:37Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/deberta-v3-xsmall", "base_model:adapter:microsoft/deberta-v3-xsmall", "license:mit", "region:us" ]	null	2024-03-20T23:14:41Z	--- license: mit library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: microsoft/deberta-v3-xsmall model-index: - name: STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe 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. --> # STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7440 - Accuracy: 0.2429 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 180 \| 1.7590 \| 0.2429 \| \| No log \| 2.0 \| 360 \| 1.7488 \| 0.2429 \| \| 1.745 \| 3.0 \| 540 \| 1.7451 \| 0.2429 \| \| 1.745 \| 4.0 \| 720 \| 1.7440 \| 0.2429 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
krame-aims/AIMS-NLP-ASS3-FinetuneMarian-fr-en	krame-aims	2024-03-20T23:13:06Z	106	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "en", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-14T08:58:36Z	--- language: - en - fr metrics: - glue library_name: transformers ---
kawehiwang/lora_model_on_alpaca	kawehiwang	2024-03-20T22:55:11Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-20T01:52:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Nekochu/Luminia-13B-v3	Nekochu	2024-03-20T22:44:32Z	47	5	peft	[ "peft", "safetensors", "gguf", "llama", "llama-factory", "lora", "generated_from_trainer", "llama2", "instruct", "finetune", "gpt4", "synthetic data", "stable diffusion", "alpaca", "llm", "text-generation", "conversational", "en", "dataset:Nekochu/discord-unstable-diffusion-SD-prompts", "dataset:glaiveai/glaive-function-calling-v2", "dataset:TIGER-Lab/MathInstruct", "dataset:Open-Orca/SlimOrca", "dataset:GAIR/lima", "dataset:sahil2801/CodeAlpaca-20k", "dataset:garage-bAInd/Open-Platypus", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:adapter:meta-llama/Llama-2-13b-chat-hf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-03-18T03:25:05Z	--- model_creator: Nekochu quantized_by: Nekochu model_name: Luminia 13B v3 pretty_name: Luminia model_type: llama2 prompt_template: >- Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {Instruction} {summary} ### input: {category} ### Response: {prompt} base_model: meta-llama/Llama-2-13b-chat-hf library_name: peft license: apache-2.0 datasets: - Nekochu/discord-unstable-diffusion-SD-prompts - glaiveai/glaive-function-calling-v2 - TIGER-Lab/MathInstruct - Open-Orca/SlimOrca - GAIR/lima - sahil2801/CodeAlpaca-20k - garage-bAInd/Open-Platypus language: - en pipeline_tag: text-generation task_categories: - question-answering - text2text-generation - conversational inference: True widget: - example_title: prompt assistant messages: - role: system content: Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. - role: user content: "### Instruction:\nCreate stable diffusion metadata based on the given english description. Luminia\n### Input:\nfavorites and popular SFW\n### Response:\n" output: text: Luminia, 1girl, solo, blonde hair, long hair, tags: - llama-factory - lora - generated_from_trainer - llama2 - llama - instruct - finetune - gpt4 - synthetic data - stable diffusion - alpaca - llm model-index: - name: Luminia-13B-v3 results: [] --- <div style="display: flex;"> <div style="flex: 1;"> <img src="https://i.imgur.com/uyjdkhk.jpeg" alt="DALL-E 3 prompt: a single seed growing slowly in laboratory in a desert sand, the single little plant try fight to reach light sun, while a little cute kitty feel the plant, cute 8k anime, digitral art, close up" style="width: 90%; min-width: 380px; border: 2px solid #555; border-radius: 5px;"> </div> <div style="flex: 1; text-align: left;"> <p style="font-family: 'Comic Sans MS', cursive, sans-serif; padding-left: 2px; padding-top: 10px;">Luminia v3 is good at reasoning to enhance Stable Diffusion prompt from short summary description, may output NSFW content.</p> </div> </div> LoRa is include and Quants: exllamav2 [2.4bpw-h6](https://huggingface.co/Nekochu/Luminia-13B-v3/tree/2.4bpw-h6), [4.25bpw-h6](https://huggingface.co/Nekochu/Luminia-13B-v3/tree/4.25bpw-h6), [8.0bpw-h8](https://huggingface.co/Nekochu/Luminia-13B-v3/tree/8.0bpw-h8) \| GGUF [Q4_K_M](https://huggingface.co/Nekochu/Luminia-13B-v3/blob/main/Luminia-13B-v3-Q4_K_M.gguf), [IQ4_NL](https://huggingface.co/Nekochu/Luminia-13B-v3/blob/main/Luminia-13B-v3-IQ4_NL.gguf) \| ## Prompt template: Alpaca <details> <summary>Output example tested In <i>text-generation-webui</i></summary> \| Input \| base llama-2-chat \| QLoRa \| \|:---------:\|:-------:\|:---------:\| \| [question]:<br><br> Create stable diffusion metadata based on the given english description. Luminia \n### Input:\n favorites and popular SFW \| Answer:<br><br> Luminia, a mystical world of wonder and magic 🧝‍♀️✨ A place where technology and nature seamlessly blend together ... \| Answer! <br><br> < lora:Luminari-10:0.8> Luminari, 1girl, solo, blonde hair, long hair, blue eyes, (black dress), looking at viewer, night sky, starry sky, constellation, smile, upper body, outdoors, forest, moon, tree, mountain, light particle .... \| Output prompt from QLoRa to [A1111/SD-WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui): <div style="display: flex; justify-content: space-between;"> <div style="flex: 1; text-align: center;"> <img src="https://i.imgur.com/rNLaobj.png" alt="parameters image metadata: <lora:Luminari-10:0.8> Luminari, 1girl, solo, blonde hair, long hair, blue eyes, (black dress), looking at viewer, night sky, starry sky, constellation, smile, upper body, outdoors, forest, moon, tree, mountain, light particle, shine, sparkle, dark theme, fantasy, magic, goddess, celestial, nature, peaceful, serene, tranquil, mystical, enchanting, otherworldly, mysterious, captivating, alluring, beautiful, elegant, graceful, majestic, divine, powerful, epic, grand, sweeping, breathtaking, mesmerizing, magical, fantastical, wondrous, marvelous, extraordinary, magnificent, glorious, radiant, luminous, illumination, brilliance, glow, radiance, luminescence, brightness, splendor, glory, triumph, victory, achievement, honor, celebration, recognition, praise, admiration, appreciation, love, affection, devotion, loyalty, dedication, commitment, passion, intensity, drive, determination, energy, enthusiasm, excitement, joy, happiness, fulfillment, pleasure, enjoyment, satisfaction, delight, wonder, amazement, awe, curiosity, interest, intrigue, question, exploration, discovery, adventure, journey, path, road, trail, course, pursuit, challenge, obstacle, adversity, hardship, struggle, perseverance, resilience, tenacity, courage, bravery, heroism, inspiration, motivation, spirit, heart, soul, essence, creativity, imagination, dreams, aspirations, goals, ambition, vision, purpose, meaning, significance, relevance, importance, impact, influence, change, growth, development, evolution, improvement, progress, learning, knowledge, wisdom, insight, understanding, empathy, compassion, kindness, generosity, forgiveness, gratitude, humility, patience, tolerance, acceptance, diversity, inclusivity, unity, equality, justice, fairness, honesty, integrity, accountability, responsibility, morality, ethics, principles, values, beliefs, faith, hope, optimism, Steps: 20, Sampler: Heun, CFG scale: 7, Seed: 479539365, Size: 512x512, Model hash: 84d76a0328, Model: epicrealism_naturalSinRC1VAE, Version: v1.7.0" style="width: 100%; min-width: 200px; display: block; margin: auto;"> </div> <div style="flex: 1; text-align: center;"> <img src="https://i.imgur.com/hU8Ut4p.png" alt="parameters image metadata: <lora:Luminari-10:0.8> Luminari, 1girl, solo, blonde hair, long hair, blue eyes, (black dress), looking at viewer, night sky, starry sky, constellation, smile, upper body, outdoors, forest, moon, tree, mountain, light particle, shine, sparkle, dark theme, fantasy, magic, goddess, celestial, nature, peaceful, serene, tranquil, mystical, enchanting, otherworldly, mysterious, captivating, alluring, beautiful, elegant, graceful, majestic, divine, powerful, epic, grand, sweeping, breathtaking, mesmerizing, magical, fantastical, wondrous, marvelous, extraordinary, magnificent, glorious, radiant, luminous, illumination, brilliance, glow, radiance, luminescence, brightness, splendor, glory, triumph, victory, achievement, honor, celebration, recognition, praise, admiration, appreciation, love, affection, devotion, loyalty, dedication, commitment, passion, intensity, drive, determination, energy, enthusiasm, excitement, joy, happiness, fulfillment, pleasure, enjoyment, satisfaction, delight, wonder, amazement, awe, curiosity, interest, intrigue, question, exploration, discovery, adventure, journey, path, road, trail, course, pursuit, challenge, obstacle, adversity, hardship, struggle, perseverance, resilience, tenacity, courage, bravery, heroism, inspiration, motivation, spirit, heart, soul, essence, creativity, imagination, dreams, aspirations, goals, ambition, vision, purpose, meaning, significance, relevance, importance, impact, influence, change, growth, development, evolution, improvement, progress, learning, knowledge, wisdom, insight, understanding, empathy, compassion, kindness, generosity, forgiveness, gratitude, humility, patience, tolerance, acceptance, diversity, inclusivity, unity, equality, justice, fairness, honesty, integrity, accountability, responsibility, morality, ethics, principles, values, beliefs, faith, hope, optimism Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 959582434, Size: 512x512, Model hash: 84d76a0328, Model: epicrealism_naturalSinRC1VAE, Version: v1.7.0" style="width: 100%; min-width: 200px; display: block; margin: auto;"> </div> </div> #### Full Prompt ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Create stable diffusion metadata based on the given english description. Luminia ### Input: favorites and popular SFW ### Response: ``` "Luminia" can be any short description, more info on my SD dataset [here](https://huggingface.co/datasets/Nekochu/discord-unstable-diffusion-SD-prompts#dataset-description). </details> ## Training Details <details> <summary>Click to see details</summary> ### Model Description - Train by: [Nekochu](https://huggingface.co/Nekochu), Model type: Llama, Finetuned from model [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) - Continue from the base of LoRA Luminia-13B-v2-QLora Know issue: [issue] ### Trainer - hiyouga/LLaMA-Efficient-Tuning Hardware: QLoRA training OS Windows, Python 3.10.8, CUDA 12.1 on 24GB VRAM. ### Training hyperparameters The following hyperparameters were used during training: - num_epochs: 1.0 - finetuning_type: lora - quantization_bit: 4 - stage: sft - learning_rate: 5e-05 - cutoff_len: 4096 - num_train_epochs: 3.0 - max_samples: 100000 - warmup_steps: 0 - train_batch_size: 1 - distributed_type: single-GPU - num_devices: 1 - warmup_steps: 0 - rope_scaling: linear - lora_rank: 32 - lora_target: all - lora_dropout: 0.15 - bnb_4bit_compute_dtype: bfloat16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine #### training_loss: <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/qhuPG6F.jpg" alt="Nekochu" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.0 </details>
llmware/slim-extract	llmware	2024-03-20T22:38:03Z	135	12	transformers	[ "transformers", "pytorch", "stablelm_epoch", "text-generation", "custom_code", "license:cc-by-sa-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-03-17T15:43:04Z	--- license: cc-by-sa-4.0 inference: false --- # SLIM-EXTRACT <!-- Provide a quick summary of what the model is/does. --> slim-extract implements a specialized function-calling customizable 'extract' capability that takes as an input a context passage, a customized key, and outputs a python dictionary with key that corresponds to the customized key, with a value consisting of a list of items extracted from the text corresponding to that key, e.g.,     `{'universities': ['Berkeley, Stanford, Yale, University of Florida, ...'] }` This model is fine-tuned on top of [llmware/bling-stable-lm-3b-4e1t-v0](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt. For fast inference use, we would recommend the 'quantized tool' version, e.g., ['slim-extract-tool'](https://huggingface.co/llmware/slim-extract-tool). ## Prompt format: `function = "extract"` `params = "{custom key}"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-extract") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-extract") function = "extract" params = "company" text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-extract") response = slim_model.function_call(text,params=["company"], function="extract") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)
arcee-ai/Patent-Instruct-LLaMA-Pro	arcee-ai	2024-03-20T22:26:26Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "arcee-ai/Patent-Instruct-7b", "TencentARC/LLaMA-Pro-8B-Instruct", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-20T22:07:42Z	--- license: apache-2.0 tags: - merge - mergekit - arcee-ai/Patent-Instruct-7b - TencentARC/LLaMA-Pro-8B-Instruct --- # Patent-Instruct-LLaMA-Pro Patent-Instruct-LLaMA-Pro is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [arcee-ai/Patent-Instruct-7b](https://huggingface.co/arcee-ai/Patent-Instruct-7b) * [TencentARC/LLaMA-Pro-8B-Instruct](https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct) ## 🧩 Configuration ```yaml merge_method: passthrough dtype: bfloat16 slices: - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 0 - 4 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 4 - 5 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 4 - 8 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 9 - 10 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 8 - 12 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 14 - 15 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 12 - 16 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 19 - 20 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 16 - 20 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 24 - 25 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 20 - 24 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 29 - 30 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 24 - 28 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 34 - 35 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 28 - 32 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 39 - 40 ```
yzimmermann/FART	yzimmermann	2024-03-20T22:24:06Z	108	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "doi:10.57967/hf/1946", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-20T21:58:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MagiskaGodnattsagor/aida_LoRA	MagiskaGodnattsagor	2024-03-20T22:23:37Z	2	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-20T22:08:26Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK robot widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - MagiskaGodnattsagor/aida_LoRA <Gallery /> ## Model description These are MagiskaGodnattsagor/aida_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK robot to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](MagiskaGodnattsagor/aida_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Joseph717171/BigOrca-2-12B	Joseph717171	2024-03-20T22:22:51Z	0	0	null	[ "safetensors", "orca", "orca2", "microsoft", "text-generation", "arxiv:2311.11045", "license:other", "region:us" ]	text-generation	2024-03-15T02:57:28Z	--- pipeline_tag: text-generation tags: - orca - orca2 - microsoft license_name: microsoft-research-license license_link: LICENSE license: other --- Inspired by [AbucusAI's BigYi-15b](https://huggingface.co/abacusai/bigyi-15b)... This is [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b) with layers interleaved to create a larger 12b model. This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 8] model: microsoft/Orca-2-7b - sources: - layer_range: [4, 12] model: microsoft/Orca-2-7b - sources: - layer_range: [8, 16] model: microsoft/Orca-2-7b - sources: - layer_range: [12, 20] model: microsoft/Orca-2-7b - sources: - layer_range: [16, 24] model: microsoft/Orca-2-7b - sources: - layer_range: [20, 28] model: microsoft/Orca-2-7b - sources: - layer_range: [24, 32] model: microsoft/Orca-2-7b ``` # Orca 2 <!-- Provide a quick summary of what the model is/does. --> Orca 2 is built for research purposes only and provides a single turn response in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. The model is designed to excel particularly in reasoning. Note that: 1. This is a research model, intended to show that we can use capable models and complex workflows (advanced prompts, multiple calls) to create synthetic data that can teach Small Language Models (SLMs) new capabilities. We chose reasoning because it is a widely useful capability that SLMs lack. 2. The model is not optimized for chat and has not been trained with RLHF or DPO. It is best used after being finetuned for chat or for a specific task. 3. Beyond reasoning, the model inherits capabilities and limitations of its base (LLAMA-2 base). We have already seen that the benefits of the Orca training can be applied to other base model too. We make Orca 2's weights publicly available to support further research on the development, evaluation, and alignment of SLMs. ## What is Orca 2’s intended use(s)? + Orca 2 is built for research purposes only. + The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models. ## How was Orca 2 evaluated? + Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer to Section 6 and Appendix in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf) for details on evaluations. ## Model Details Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf). Please refer to LLaMA-2 technical report for details on the model architecture. ## License Orca 2 is licensed under the [Microsoft Research License](LICENSE). Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. ## Bias, Risks, and Limitations Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the common limitations of other large language models or limitation caused by its training process, including: Data Biases: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair. Lack of Contextual Understanding: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting in potential inaccuracies or nonsensical responses. Lack of Transparency: Due to the complexity and size, large language models can act as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or decisions. We recommend reviewing transparency notes from Azure for more information. Content Harms: There are various types of content harms that large language models can cause. It is important to be aware of them when using these models, and to take actions to prevent them. It is recommended to leverage various content moderation services provided by different companies and institutions. On an important note, we hope for better regulations and standards from government and technology leaders around content harms for AI technologies in future. We value and acknowledge the important role that research and open source community can play in this direction. Hallucination: It is important to be aware and cautious not to entirely rely on a given language model for critical decisions or information that might have deep impact as it is not obvious how to prevent these models from fabricating content. Moreover, it is not clear whether small models may be more susceptible to hallucination in ungrounded generation use cases due to their smaller sizes and hence reduced memorization capacities. This is an active research topic and we hope there will be more rigorous measurement, understanding and mitigations around this topic. Potential for Misuse: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content. Data Distribution: Orca 2’s performance is likely to correlate strongly with the distribution of the tuning data. This correlation might limit its accuracy in areas underrepresented in the training dataset such as math, coding, and reasoning. System messages: Orca 2 demonstrates variance in performance depending on the system instructions. Additionally, the stochasticity introduced by the model size may lead to generation of non-deterministic responses to different system instructions. Zero-Shot Settings: Orca 2 was trained on data that mostly simulate zero-shot settings. While the model demonstrate very strong performance in zero-shot settings, it does not show the same gains of using few-shot learning compared to other, specially larger, models. Synthetic data: As Orca 2 is trained on synthetic data, it could inherit both the advantages and shortcomings of the models and methods used for data generation. We posit that Orca 2 benefits from the safety measures incorporated during training and safety guardrails (e.g., content filter) within the Azure OpenAI API. However, detailed studies are required for better quantification of such risks. This model is solely designed for research settings, and its testing has only been carried out in such environments. It should not be used in downstream applications, as additional analysis is needed to assess potential harm or bias in the proposed application. ## Getting started with Orca 2 Inference with Hugging Face library ```python import torch import transformers if torch.cuda.is_available(): torch.set_default_device("cuda") else: torch.set_default_device("cpu") model = transformers.AutoModelForCausalLM.from_pretrained("microsoft/Orca-2-7b", device_map='auto') # https://github.com/huggingface/transformers/issues/27132 # please use the slow tokenizer since fast and slow tokenizer produces different tokens tokenizer = transformers.AutoTokenizer.from_pretrained( "microsoft/Orca-2-7b", use_fast=False, ) system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior." user_message = "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?" prompt = f"<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{user_message}<\|im_end\|>\n<\|im_start\|>assistant" inputs = tokenizer(prompt, return_tensors='pt') output_ids = model.generate(inputs["input_ids"],) answer = tokenizer.batch_decode(output_ids)[0] print(answer) # This example continues showing how to add a second turn message by the user to the conversation second_turn_user_message = "Give me a list of the key points of your first answer." # we set add_special_tokens=False because we dont want to automatically add a bos_token between messages second_turn_message_in_markup = f"\n<\|im_start\|>user\n{second_turn_user_message}<\|im_end\|>\n<\|im_start\|>assistant" second_turn_tokens = tokenizer(second_turn_message_in_markup, return_tensors='pt', add_special_tokens=False) second_turn_input = torch.cat([output_ids, second_turn_tokens['input_ids']], dim=1) output_ids_2 = model.generate(second_turn_input,) second_turn_answer = tokenizer.batch_decode(output_ids_2)[0] print(second_turn_answer) ``` Safe inference with Azure AI Content Safety The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged and can help preventing some of content harms. Azure AI Content Safety is a content moderation platform that uses AI to moderate content. By having Azure AI Content Safety on the output of Orca 2, the model output can be moderated by scanning it for different harm categories including sexual content, violence, hate, and self-harm with multiple severity levels and multi-lingual detection. ```python import os import math import transformers import torch from azure.ai.contentsafety import ContentSafetyClient from azure.core.credentials import AzureKeyCredential from azure.core.exceptions import HttpResponseError from azure.ai.contentsafety.models import AnalyzeTextOptions CONTENT_SAFETY_KEY = os.environ["CONTENT_SAFETY_KEY"] CONTENT_SAFETY_ENDPOINT = os.environ["CONTENT_SAFETY_ENDPOINT"] # We use Azure AI Content Safety to filter out any content that reaches "Medium" threshold # For more information: https://learn.microsoft.com/en-us/azure/ai-services/content-safety/ def should_filter_out(input_text, threshold=4): # Create an Content Safety client client = ContentSafetyClient(CONTENT_SAFETY_ENDPOINT, AzureKeyCredential(CONTENT_SAFETY_KEY)) # Construct a request request = AnalyzeTextOptions(text=input_text) # Analyze text try: response = client.analyze_text(request) except HttpResponseError as e: print("Analyze text failed.") if e.error: print(f"Error code: {e.error.code}") print(f"Error message: {e.error.message}") raise print(e) raise categories = ["hate_result", "self_harm_result", "sexual_result", "violence_result"] max_score = -math.inf for category in categories: max_score = max(max_score, getattr(response, category).severity) return max_score >= threshold model_path = 'microsoft/Orca-2-7b' device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = transformers.AutoModelForCausalLM.from_pretrained(model_path) model.to(device) tokenizer = transformers.AutoTokenizer.from_pretrained( model_path, model_max_length=4096, padding_side="right", use_fast=False, add_special_tokens=False, ) system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior." user_message = "\" \n :You can't just say, \"\"that's crap\"\" and remove it without gaining a consensus. You already know this, based on your block history. —/ \" \nIs the comment obscene? \nOptions : Yes, No." prompt = f"<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{user_message}<\|im_end\|>\n<\|im_start\|>assistant" inputs = tokenizer(prompt, return_tensors='pt') inputs = inputs.to(device) output_ids = model.generate(inputs["input_ids"], max_length=4096, do_sample=False, temperature=0.0, use_cache=True) sequence_length = inputs["input_ids"].shape[1] new_output_ids = output_ids[:, sequence_length:] answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True) final_output = answers[0] if not should_filter_out(answers[0]) else "[Content Filtered]" print(final_output) ``` ## Citation ```bibtex @misc{mitra2023orca, title={Orca 2: Teaching Small Language Models How to Reason}, author={Arindam Mitra and Luciano Del Corro and Shweti Mahajan and Andres Codas and Clarisse Simoes and Sahaj Agrawal and Xuxi Chen and Anastasia Razdaibiedina and Erik Jones and Kriti Aggarwal and Hamid Palangi and Guoqing Zheng and Corby Rosset and Hamed Khanpour and Ahmed Awadallah}, year={2023}, eprint={2311.11045}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```
ingeol/q2e_ep3_1234	ingeol	2024-03-20T22:22:31Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-20T22:21:52Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2e_ep3_1234 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2e_ep3_1234') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2e_ep3_1234') model = AutoModel.from_pretrained('ingeol/q2e_ep3_1234') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2e_ep3_1234) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
tjl223/song-artist-classifier-v2	tjl223	2024-03-20T22:20:08Z	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-20T22:06:02Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: song-artist-classifier-v2 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. --> # song-artist-classifier-v2 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0725 - F1: [0.9473684210526316, 0.6666666666666666, 0.8181818181818182, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.2, 0.7368421052631577, 0.8695652173913044, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.7826086956521738, 0.5714285714285713, 0.7200000000000001, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777] ## 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\| \| No log \| 1.0 \| 95 \| 2.4874 \| [0.4347826086956522, 0.5142857142857143, 0.0, 0.5, 0.23529411764705882, 0.33333333333333337, 0.0, 0.0, 0.0, 0.0, 0.3636363636363636, 0.380952380952381, 0.0, 0.0, 0.43243243243243246, 0.0, 0.5, 0.0, 0.18181818181818182, 0.35294117647058826] \| \| No log \| 2.0 \| 190 \| 2.0485 \| [0.588235294117647, 0.48, 0.625, 0.7499999999999999, 0.3870967741935483, 0.16666666666666669, 0.0, 0.0, 0.0, 0.5625, 0.6428571428571429, 0.45454545454545453, 0.18181818181818182, 0.35714285714285715, 0.25, 0.6956521739130435, 0.4347826086956522, 0.0, 0.48, 0.7] \| \| No log \| 3.0 \| 285 \| 1.7309 \| [0.9, 0.6666666666666667, 0.7200000000000001, 0.7499999999999999, 0.6428571428571429, 0.588235294117647, 0.18181818181818182, 0.7000000000000001, 0.0, 0.5882352941176471, 0.6896551724137931, 0.5217391304347826, 0.0, 0.2727272727272727, 0.8000000000000002, 0.8000000000000002, 0.6956521739130435, 0.0, 0.5, 0.8235294117647058] \| \| No log \| 4.0 \| 380 \| 1.4777 \| [0.8571428571428572, 0.7200000000000001, 0.8181818181818182, 0.8235294117647058, 0.5263157894736842, 0.625, 0.30769230769230765, 0.6956521739130436, 0.0, 0.5, 0.8695652173913044, 0.5925925925925927, 0.5, 0.2105263157894737, 0.8000000000000002, 0.6666666666666666, 0.6956521739130435, 0.4, 0.33333333333333326, 0.7777777777777777] \| \| No log \| 5.0 \| 475 \| 1.3535 \| [0.9, 0.75, 0.8181818181818182, 0.8235294117647058, 0.64, 0.625, 0.37499999999999994, 0.7272727272727273, 0.0, 0.7000000000000001, 0.8695652173913044, 0.5454545454545454, 0.15384615384615383, 0.3157894736842105, 0.6956521739130435, 0.7272727272727272, 0.7826086956521738, 0.4, 0.631578947368421, 0.7777777777777777] \| \| 1.8726 \| 6.0 \| 570 \| 1.2614 \| [0.9, 0.7272727272727272, 0.8333333333333333, 0.7499999999999999, 0.6363636363636365, 0.7368421052631577, 0.4285714285714285, 0.761904761904762, 0.0, 0.5, 0.7407407407407407, 0.608695652173913, 0.4285714285714285, 0.3, 0.7272727272727272, 0.7272727272727272, 0.75, 0.6666666666666666, 0.4210526315789474, 0.8235294117647058] \| \| 1.8726 \| 7.0 \| 665 \| 1.1649 \| [0.9473684210526316, 0.7272727272727272, 0.8333333333333333, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.47058823529411764, 0.761904761904762, 0.0, 0.7000000000000001, 0.8695652173913044, 0.7272727272727272, 0.6666666666666666, 0.20000000000000004, 0.6956521739130435, 0.6956521739130435, 0.7826086956521738, 0.5714285714285715, 0.5333333333333333, 0.7777777777777777] \| \| 1.8726 \| 8.0 \| 760 \| 1.1142 \| [0.9473684210526316, 0.7272727272727272, 0.8181818181818182, 0.6666666666666665, 0.761904761904762, 0.7368421052631577, 0.4444444444444445, 0.761904761904762, 0.22222222222222224, 0.7058823529411765, 0.8333333333333333, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.8571428571428572, 0.7272727272727272, 0.7826086956521738, 0.6666666666666666, 0.4444444444444445, 0.7777777777777777] \| \| 1.8726 \| 9.0 \| 855 \| 1.0813 \| [0.9473684210526316, 0.7272727272727272, 0.8695652173913044, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.22222222222222224, 0.7368421052631577, 0.9090909090909091, 0.7272727272727272, 0.47058823529411764, 0.22222222222222224, 0.7826086956521738, 0.608695652173913, 0.75, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777] \| \| 1.8726 \| 10.0 \| 950 \| 1.0725 \| [0.9473684210526316, 0.6666666666666666, 0.8181818181818182, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.2, 0.7368421052631577, 0.8695652173913044, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.7826086956521738, 0.5714285714285713, 0.7200000000000001, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777] \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
rk68/phi-1_5-finetuned-aqua-rat-2k	rk68	2024-03-20T22:18:34Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-1_5", "base_model:adapter:microsoft/phi-1_5", "license:mit", "region:us" ]	null	2024-03-20T22:13:49Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-1_5 model-index: - name: phi-1_5-finetuned-aqua-rat-2k 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. --> # phi-1_5-finetuned-aqua-rat-2k This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Kaipbkk/mown	Kaipbkk	2024-03-20T22:17:01Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2024-03-20T22:17:01Z	--- license: bigscience-bloom-rail-1.0 ---
Yukiea/ppo-Huggy	Yukiea	2024-03-20T22:09:32Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-20T22:04:12Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Yukiea/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ucmp137538/gpt2-wikitext2	ucmp137538	2024-03-20T22:09:04Z	197	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-20T21:46:39Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 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. --> # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 9.4906 - eval_runtime: 949.071 - eval_samples_per_second: 2.038 - eval_steps_per_second: 0.255 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
mychen76/tinyllama_alpaca_GGUF	mychen76	2024-03-20T22:08:00Z	3	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:quantized:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-20T22:00:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: mychen76 - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
xiangjhe/gemma-7b-gguf	xiangjhe	2024-03-20T22:03:58Z	1	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us" ]	null	2024-03-20T05:54:03Z	--- license: other license_name: google-gemma license_link: https://ai.google.dev/gemma/terms ---
mychen76/tinyllama_alpaca_lora	mychen76	2024-03-20T21:49:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:finetune:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-20T21:49:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - Developed by: mychen76 - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-new-model-test	rajevan123	2024-03-20T21:49:00Z	3	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:yangheng/deberta-v3-base-absa-v1.1", "base_model:adapter:yangheng/deberta-v3-base-absa-v1.1", "license:mit", "region:us" ]	null	2024-03-20T21:44:22Z	--- license: mit library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: yangheng/deberta-v3-base-absa-v1.1 model-index: - name: STS-Lora-Fine-Tuning-Capstone-Deberta-new-model-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. --> # STS-Lora-Fine-Tuning-Capstone-Deberta-new-model-test This model is a fine-tuned version of [yangheng/deberta-v3-base-absa-v1.1](https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6535 - Accuracy: 0.2582 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 180 \| 1.7212 \| 0.2408 \| \| No log \| 2.0 \| 360 \| 1.6900 \| 0.2429 \| \| 1.6889 \| 3.0 \| 540 \| 1.6644 \| 0.2495 \| \| 1.6889 \| 4.0 \| 720 \| 1.6535 \| 0.2582 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
TheZennou/command-r-v01-exl2-8bit	TheZennou	2024-03-20T21:46:49Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-20T21:46:17Z	--- license: cc-by-nc-4.0 --- Model Summary C4AI Command-R is a research release of a 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities. Developed by: Cohere and Cohere For AI Quanted to 8bpw using Exllama2.
neopolita/starling-lm-7b-beta-gguf	neopolita	2024-03-20T21:30:21Z	16	1	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-20T20:29:44Z	--- {} --- # GGUF quants for [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) using [llama.cpp](https://github.com/ggerganov/llama.cpp) Terms of Use: Please check the [original model](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
m4faisal/NLI-Lora-Fine-Tuning-10K-ALBERTA	m4faisal	2024-03-20T21:29:21Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:albert/albert-base-v2", "base_model:adapter:albert/albert-base-v2", "license:apache-2.0", "region:us" ]	null	2024-03-20T21:11:40Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: albert/albert-base-v2 model-index: - name: NLI-Lora-Fine-Tuning-10K-ALBERTA 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. --> # NLI-Lora-Fine-Tuning-10K-ALBERTA This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8439 - Accuracy: 0.6063 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 312 \| 1.0562 \| 0.4551 \| \| 1.0762 \| 2.0 \| 624 \| 1.0236 \| 0.4995 \| \| 1.0762 \| 3.0 \| 936 \| 0.9603 \| 0.5361 \| \| 1.0075 \| 4.0 \| 1248 \| 0.9053 \| 0.5671 \| \| 0.9178 \| 5.0 \| 1560 \| 0.8796 \| 0.5823 \| \| 0.9178 \| 6.0 \| 1872 \| 0.8649 \| 0.5934 \| \| 0.8859 \| 7.0 \| 2184 \| 0.8551 \| 0.5977 \| \| 0.8859 \| 8.0 \| 2496 \| 0.8488 \| 0.6033 \| \| 0.8632 \| 9.0 \| 2808 \| 0.8450 \| 0.6057 \| \| 0.8543 \| 10.0 \| 3120 \| 0.8439 \| 0.6063 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ymgong/distil_train_token_classification_2	ymgong	2024-03-20T21:22:16Z	109	0	transformers	[ "transformers", "pytorch", "distilbert", "token-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" ]	token-classification	2024-03-20T21:13:11Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distil_train_token_classification_2 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. --> # distil_train_token_classification_2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7646 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8112 ## 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: 8e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| 0.5429 \| 1.0 \| 3681 \| 0.5346 \| 0.0 \| 0.0 \| 0.0 \| 0.7848 \| \| 0.4387 \| 2.0 \| 7362 \| 0.5033 \| 0.0 \| 0.0 \| 0.0 \| 0.8034 \| \| 0.3288 \| 3.0 \| 11043 \| 0.4983 \| 0.0 \| 0.0 \| 0.0 \| 0.8101 \| \| 0.2436 \| 4.0 \| 14724 \| 0.5736 \| 0.0 \| 0.0 \| 0.0 \| 0.8086 \| \| 0.1677 \| 5.0 \| 18405 \| 0.6681 \| 0.0 \| 0.0 \| 0.0 \| 0.8107 \| \| 0.1162 \| 6.0 \| 22086 \| 0.7646 \| 0.0 \| 0.0 \| 0.0 \| 0.8112 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
biunlp/LongMt5-HeSum	biunlp	2024-03-20T21:14:25Z	109	0	transformers	[ "transformers", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "base_model:agemagician/mlong-t5-tglobal-base", "base_model:finetune:agemagician/mlong-t5-tglobal-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-20T20:59:14Z	--- license: apache-2.0 base_model: agemagician/mlong-t5-tglobal-base tags: - generated_from_trainer metrics: - rouge model-index: - name: mlong-t5-tglobal-base 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. --> # mlong-t5-tglobal-base This model is a fine-tuned version of [agemagician/mlong-t5-tglobal-base](https://huggingface.co/agemagician/mlong-t5-tglobal-base) on an HeSum dataset. It achieves the following results on the evaluation set: - Loss: 2.1091 - Rouge1: 31.6099 - Rouge2: 12.9182 - Rougel: 23.8053 - Rougelsum: 25.5362 - Gen Len: 59.758 ## 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: 1 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| RougeL \| RougeLSum \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 500 \| 2.2709 \| 20.5043 \| 8.1518 \| 16.9526 \| 17.5001 \| \| 2.8714 \| 2.0 \| 1000 \| 2.2022 \| 21.4051 \| 8.7445 \| 17.7534 \| 18.3191 \| \| 2.8714 \| 3.0 \| 1500 \| 2.1608 \| 21.6609 \| 9.1753 \| 18.0374 \| 18.6176 \| \| 2.5137 \| 4.0 \| 2000 \| 2.1555 \| 21.6818 \| 9.1814 \| 18.0382 \| 18.6198 \| \| 2.5137 \| 5.0 \| 2500 \| 2.1462 \| 21.9708 \| 9.2033 \| 18.3919 \| 18.9535 \| \| 2.3717 \| 6.0 \| 3000 \| 2.1258 \| 22.0583 \| 9.2987 \| 18.4379 \| 19.0322 \| \| 2.3717 \| 7.0 \| 3500 \| 2.1278 \| 21.8245 \| 9.0474 \| 18.1979 \| 18.8038 \| \| 2.2633 \| 8.0 \| 4000 \| 2.1207 \| 21.6273 \| 8.8847 \| 18.024 \| 18.6049 \| \| 2.2633 \| 9.0 \| 4500 \| 2.1180 \| 22.2004 \| 9.6253 \| 18.6373 \| 19.1721 \| \| 2.1886 \| 10.0 \| 5000 \| 2.1220 \| 22.1619 \| 9.6206 \| 18.5069 \| 19.0856 \| \| 2.1886 \| 11.0 \| 5500 \| 2.1161 \| 22.1518 \| 9.4522 \| 18.4695 \| 19.0552 \| \| 2.1144 \| 12.0 \| 6000 \| 2.1103 \| 22.0395 \| 9.4185 \| 18.4314 \| 19.0305 \| \| 2.1144 \| 13.0 \| 6500 \| 2.1150 \| 22.2404 \| 9.4722 \| 18.5482 \| 19.1747 \| \| 2.054 \| 14.0 \| 7000 \| 2.1091 \| 22.1466 \| 9.3434 \| 18.3443 \| 18.9233 \| \| 2.0526 \| 15.0 \| 8000 \| 2.1580 \| 30.4149 \| 2.0774 \| 22.9493 \| 24.4478 \| \| 2.1236 \| 16.0 \| 16000 \| 2.1621 \| 31.3101 \| 13.3237 \| 23.8249 \| 25.526 \| \| 2.0776 \| 17.0 \| 24000 \| 2.1607 \| 30.9902 \| 12.3753 \| 23.0243 \| 24.8308 \| \| 1.9843 \| 18.0 \| 32000 \| 2.1553 \| 32.0603 \| 13.4985 \| 24.0775 \| 25.9692 \| ### Framework versions - Transformers 4.38.2 - Pytorch 1.13.1+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
Barreto-G/Taxi-v3	Barreto-G	2024-03-20T21:09:10Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-20T21:09:06Z	--- 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: 7.50 +/- 2.72 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="Barreto-G/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"]) ```
suryaR-15/twitter-sentiment-extraction-distilbert	suryaR-15	2024-03-20T21:06:53Z	119	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T09:59:38Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: twitter-sentiment-extraction-distilbert 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. --> # twitter-sentiment-extraction-distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [Tweet Sentiment Extraction](https://www.kaggle.com/competitions/tweet-sentiment-extraction) dataset. It achieves the following results on the evaluation set: - Loss: 0.3328 - Accuracy: 0.8903 - F1: 0.8903 ### 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 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
naivoder/ppo-Huggy	naivoder	2024-03-20T21:02:19Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-20T21:01:39Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: naivoder/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
thrunlab/Mistral_Sparse_refined_web_90p_2024-03-20	thrunlab	2024-03-20T20:55:11Z	4	0	transformers	[ "transformers", "safetensors", "sparse_mistral", "text-generation", "generated_from_trainer", "custom_code", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-03-20T20:15:21Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: Mistral_Sparse_refined_web_90p_2024-03-20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral_Sparse_refined_web_90p_2024-03-20 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.5087 ## 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: 1 - eval_batch_size: 1 - seed: 0 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
Prathamesh25/Llama-2-7b-finetune-university	Prathamesh25	2024-03-20T20:51:27Z	0	0	peft	[ "peft", "region:us" ]	null	2024-03-20T20:43:48Z	--- 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 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
MVRL/GeoSynth-OSM	MVRL	2024-03-20T20:51:18Z	26	0	diffusers	[ "diffusers", "safetensors", "controlnet", "stable-diffusion", "satellite-imagery", "OSM", "image-to-image", "arxiv:2302.05543", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:apache-2.0", "region:us" ]	image-to-image	2024-03-17T17:46:52Z	--- library_name: diffusers base_model: stabilityai/stable-diffusion-2-1-base license: apache-2.0 widget: - src: osm_tile_18_42048_101323.jpeg prompt: Satellite image features a city neighborhood tags: - controlnet - stable-diffusion - satellite-imagery - OSM pipeline_tag: image-to-image --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This is a ControlNet based model that synthesizes satellite images given OpenStreetMap Images. The base stable diffusion model used is [stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) (v2-1_512-ema-pruned.ckpt). * Use it with 🧨 [diffusers](#examples) * Use it with [controlnet](https://github.com/lllyasviel/ControlNet/tree/main?tab=readme-ov-file) repository ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [stable-diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) - Paper: [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) ## Examples ```python from diffusers import StableDiffusionControlNetPipeline, ControlNetModel import torch from PIL import Image img = Image.open("osm_tile_18_42048_101323.jpeg") controlnet = ControlNetModel.from_pretrained("MVRL/GeoSynth-OSM") pipe = StableDiffusionControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", controlnet=controlnet) pipe = pipe.to("cuda:0") # generate image generator = torch.manual_seed(10345340) image = pipe( "Satellite image features a city neighborhood", generator=generator, image=img, ).images[0] image.save("generated_city.jpg") ``` <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## 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] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sharren/vit-augmentation	sharren	2024-03-20T20:47:25Z	192	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-20T20:15:14Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: vit-augmentation 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. --> # vit-augmentation This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset. It achieves the following results on the evaluation set: - Loss: 0.4287 - Accuracy: 0.8592 - Precision: 0.8580 - Recall: 0.8592 - F1: 0.8574 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 770 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9124 \| 1.0 \| 321 \| 0.6025 \| 0.7805 \| 0.7788 \| 0.7805 \| 0.7683 \| \| 0.5876 \| 2.0 \| 642 \| 0.5819 \| 0.7864 \| 0.7990 \| 0.7864 \| 0.7820 \| \| 0.5415 \| 3.0 \| 963 \| 0.6149 \| 0.8041 \| 0.7943 \| 0.8041 \| 0.7865 \| \| 0.4815 \| 4.0 \| 1284 \| 0.4654 \| 0.8294 \| 0.8259 \| 0.8294 \| 0.8115 \| \| 0.4263 \| 5.0 \| 1605 \| 0.5481 \| 0.8259 \| 0.8315 \| 0.8259 \| 0.8023 \| \| 0.3515 \| 6.0 \| 1926 \| 0.4287 \| 0.8592 \| 0.8580 \| 0.8592 \| 0.8574 \| \| 0.3144 \| 7.0 \| 2247 \| 0.5005 \| 0.8363 \| 0.8320 \| 0.8363 \| 0.8270 \| \| 0.2736 \| 8.0 \| 2568 \| 0.5306 \| 0.8294 \| 0.8448 \| 0.8294 \| 0.8302 \| \| 0.2519 \| 9.0 \| 2889 \| 0.4733 \| 0.8578 \| 0.8534 \| 0.8578 \| 0.8534 \| \| 0.2227 \| 10.0 \| 3210 \| 0.4905 \| 0.8585 \| 0.8520 \| 0.8585 \| 0.8512 \| \| 0.1724 \| 11.0 \| 3531 \| 0.5050 \| 0.8655 \| 0.8671 \| 0.8655 \| 0.8628 \| \| 0.1596 \| 12.0 \| 3852 \| 0.5263 \| 0.8686 \| 0.8657 \| 0.8686 \| 0.8631 \| \| 0.1397 \| 13.0 \| 4173 \| 0.7043 \| 0.8533 \| 0.8703 \| 0.8533 \| 0.8488 \| \| 0.1298 \| 14.0 \| 4494 \| 0.6275 \| 0.8679 \| 0.8734 \| 0.8679 \| 0.8632 \| \| 0.1029 \| 15.0 \| 4815 \| 0.5564 \| 0.8807 \| 0.8776 \| 0.8807 \| 0.8772 \| \| 0.0893 \| 16.0 \| 5136 \| 0.5668 \| 0.8804 \| 0.8823 \| 0.8804 \| 0.8789 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
deepsynthbody/deepfake_gi_fastGAN	deepsynthbody	2024-03-20T20:41:23Z	62	2	transformers	[ "transformers", "pytorch", "unconditional-image-generation", "en", "arxiv:2101.04775", "license:mit", "endpoints_compatible", "region:us" ]	unconditional-image-generation	2024-02-26T10:29:39Z	--- license: mit language: - en pipeline_tag: unconditional-image-generation --- # This is an officila repository to generate some sample results from the FastGAN model presented in the paper: SinGAN-Seg: Synthetic training data generation for medical image segmentation [here](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267976) # A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch The official pytorch implementation of the FAT-GAN paper "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis", the paper can be found [here](https://arxiv.org/abs/2101.04775). ```python python generate_4ch_from_huggingface.py ```
ranchomacho/finally-mistral	ranchomacho	2024-03-20T20:39:38Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2024-03-19T21:31:44Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
ingeol/facets_ep3_1234	ingeol	2024-03-20T20:38:36Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-20T20:37:53Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/facets_ep3_1234 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/facets_ep3_1234') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/facets_ep3_1234') model = AutoModel.from_pretrained('ingeol/facets_ep3_1234') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/facets_ep3_1234) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
sarak7/H4_320_769_v4	sarak7	2024-03-20T20:36:45Z	182	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-20T20:35:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sagravela/poca-SoccerTwos	sagravela	2024-03-20T20:26:03Z	11	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-03-20T20:21:27Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos 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: sagravela/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
llmware/slim-tags-3b	llmware	2024-03-20T20:14:29Z	251	4	transformers	[ "transformers", "pytorch", "stablelm_epoch", "text-generation", "custom_code", "license:cc-by-sa-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-03-16T08:26:50Z	--- license: cc-by-sa-4.0 inference: false --- # SLIM-TAGS-3B <!-- Provide a quick summary of what the model is/does. --> slim-tags-3b is a small, specialized function-calling model fine-tuned to extract and generate meaningful tags from a chunk of text. Tags generally correspond to named entities, but will also include key objects, entities and phrases that contribute meaningfully to the semantic meaning of the text. The model is invoked as a specialized 'tags' classifier function that outputs a python dictionary in the form of:     `{'tags': ['NASDAQ', 'S&P', 'Dow', 'Verizon', 'Netflix, ... ']}` with the value items in the list generally being extracted from the source text. The intended use of the model is to auto-generate tags to text that can be used to enhance search retrieval, categorization, or to extract named entities that can be used programmatically in follow-up queries or prompts. It can also be used for fact-checking as a secondary validation on a longer (separate) LLM output. This model is fine-tuned on top of [llmware/bling-stable-lm-3b-4e1t-v0](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt. Each slim model has a 'quantized tool' version, e.g., ['slim-tags-3b-tool'](https://huggingface.co/llmware/slim-tags-3b-tool). ## Prompt format: `function = "classify"` `params = "tags"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-tags-3b") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-tags-3b") function = "classify" params = "tags" text = "Citibank announced a reduction in its targets for economic growth in France and the UK last week in light of ongoing concerns about inflation and unemployment, especially in large employers such as Airbus." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-tags-3b") response = slim_model.function_call(text,params=["tags"], function="classify") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)
SpideyDLK/wav2vec2-large-xls-r-300m-sinhala-low-LR-constant	SpideyDLK	2024-03-20T20:08:47Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-20T08:28:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MatthieuJ/ING_2003M3_SLERP	MatthieuJ	2024-03-20T20:03:45Z	48	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "chihoonlee10/T3Q-DPO-Mistral-7B", "MatthieuJ/ING_2003M2_SLERP", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-20T19:58:54Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - chihoonlee10/T3Q-DPO-Mistral-7B - MatthieuJ/ING_2003M2_SLERP --- # ING_2003M3_SLERP ING_2003M3_SLERP is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [chihoonlee10/T3Q-DPO-Mistral-7B](https://huggingface.co/chihoonlee10/T3Q-DPO-Mistral-7B) * [MatthieuJ/ING_2003M2_SLERP](https://huggingface.co/MatthieuJ/ING_2003M2_SLERP) ## 🧩 Configuration ```yaml slices: - sources: - model: chihoonlee10/T3Q-DPO-Mistral-7B layer_range: [0, 32] - model: MatthieuJ/ING_2003M2_SLERP layer_range: [0, 32] merge_method: slerp base_model: MatthieuJ/ING_2003M2_SLERP parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```
zrvicc/ppo-Pyramids	zrvicc	2024-03-20T19:58:48Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2024-03-20T19:58:45Z	--- 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: zrvicc/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sarak7/H15_320_769_v1	sarak7	2024-03-20T19:57:29Z	182	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-20T19:55:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
llmware/slim-xsum	llmware	2024-03-20T19:51:15Z	143	6	transformers	[ "transformers", "pytorch", "stablelm_epoch", "text-generation", "custom_code", "license:cc-by-sa-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-03-02T12:55:11Z	--- license: cc-by-sa-4.0 inference: false --- # SLIM-XSUM <!-- Provide a quick summary of what the model is/does. --> slim-xsum implements an 'extreme summarization' function as a function-call on a decoder-based LLM, which generates as output a python dictionary with the form of:     `{'xsum': ['This is a short text summary or headline.']}` The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case. This model is fine-tuned on top of [llmware/bling-stable-lm-3b-4e1t-v0](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt. Each slim model has a 'quantized tool' version, e.g., ['slim-xsum-tool'](https://huggingface.co/llmware/slim-xsum-tool). ## Prompt format: `function = "classify"` `params = "xsum"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-xsum") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-xsum") function = "classify" params = "xsum" text = "DeepMind, the UK-based AI lab owned by Google’s parent company Alphabet, has developed an AI system called AlphaGeometry that can solve complex geometry problems close to human Olympiad gold medalists. In a new paper in Nature, DeepMind revealed that AlphaGeometry was able to solve 25 out of 30 benchmark geometry problems from past International Mathematical Olympiad (IMO) competitions within the standard time limits. This nearly matches the average score of 26 problems solved by human gold medalists on the same tests. The AI system combines a neural language model with a rule-bound deduction engine, providing a synergy that enables the system to find solutions to complex geometry theorems. AlphaGeometry took a revolutionary approach to synthetic data generation by creating one billion random diagrams of geometric objects and deriving relationships between points and lines in each diagram. This process – termed “symbolic deduction and traceback” – resulted in a final training dataset of 100 million unique examples, providing a rich source for training the AI system." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-xsum") response = slim_model.function_call(text,params=["xsum"], function="classify") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)
automerger/YamshadowT3q-7B	automerger	2024-03-20T19:49:19Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:chihoonlee10/T3Q-Mistral-Orca-Math-DPO", "base_model:finetune:chihoonlee10/T3Q-Mistral-Orca-Math-DPO", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-20T19:48:28Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - chihoonlee10/T3Q-Mistral-Orca-Math-DPO --- # YamshadowT3q-7B YamshadowT3q-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO) ## 🧩 Configuration ```yaml models: - model: automerger/YamShadow-7B # No parameters necessary for base model - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: automerger/YamShadow-7B parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/YamshadowT3q-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
JapGuy/XindlX_Acoustic	JapGuy	2024-03-20T19:48:15Z	0	0	null	[ "music", "rvc", "xindlx", "xindl", "ondřej", "ládek", "model", "audio-to-audio", "cs", "license:openrail", "region:us" ]	audio-to-audio	2024-03-20T19:40:19Z	--- license: openrail language: - cs pipeline_tag: audio-to-audio tags: - music - rvc - xindlx - xindl - ondřej - ládek - model --- ![image.jpg](https://scenanaplovarne.cz/wp-content/uploads/2023/03/Xindl-X.jpg) # Xindl X [CZ] (Acoustic/Unpluggiat Mix) # 645 Epochs - RVC V2 - rmvpe Trained on 1 hour 49 minutes 14 seconds of isolated acapellas using UVR (Voc FT + Reverb HQ) and Audacity to remove parts with double vocals and vocals from others (+Noise Gate)

domini4/flan-t5-base-imdb-text-classification

domini4

2024-03-21T03:44:56Z

108

0

transformers

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

text2text-generation

2024-03-21T02:10:54Z

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - f1 model-index: - name: flan-t5-base-imdb-text-classification results: [] ---  # flan-t5-base-imdb-text-classification This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0821 - F1: 94.88 - Gen Len: 2.5030 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

Awaz-e-Sehat/whisper-large-v3

Awaz-e-Sehat

2024-03-21T03:43:33Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:adapter:openai/whisper-large-v3", "license:apache-2.0", "region:us" ]

null

2024-03-21T03:43:27Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openai/whisper-large-v3 model-index: - name: whisper-large-v3 results: [] ---  # whisper-large-v3 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 3.8257 - eval_runtime: 1.4346 - eval_samples_per_second: 0.697 - eval_steps_per_second: 0.697 - epoch: 2.0 - step: 142 ## 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: 10 - 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: 50 - num_epochs: 50 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Lewdiculous/Irene-RP-v2-7B-GGUF-IQ-Imatrix

Lewdiculous

2024-03-21T03:37:26Z

55

5

null

[ "gguf", "experimental", "endpoints_compatible", "region:us" ]

null

2024-03-21T03:06:12Z

--- tags: - experimental --- This is experimental. Model information: [Virt-io/Irene-RP-v2-7B](https://huggingface.co/Virt-io/Irene-RP-v2-7B)

feizhe/vit-base-patch16-224-in21k-pheno

feizhe

2024-03-21T03:35:45Z

63

0

transformers

[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-03-21T01:19:28Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: feizhe/vit-base-patch16-224-in21k-pheno results: [] ---  # feizhe/vit-base-patch16-224-in21k-pheno This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0384 - Train Accuracy: 1.0 - Train Top-3-accuracy: 1.0 - Validation Loss: 1.5644 - Validation Accuracy: 0.5848 - Validation Top-3-accuracy: 0.9064 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1615, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 0.7874 | 0.8031 | 0.9555 | 1.2405 | 0.5380 | 0.9006 | 0 | | 0.1372 | 0.9893 | 0.9999 | 1.4714 | 0.5380 | 0.8947 | 1 | | 0.0644 | 0.9989 | 1.0 | 1.6014 | 0.5673 | 0.9064 | 2 | | 0.0465 | 0.9990 | 1.0 | 1.5618 | 0.5906 | 0.9064 | 3 | | 0.0384 | 1.0 | 1.0 | 1.5644 | 0.5848 | 0.9064 | 4 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.10.0 - Datasets 2.18.0 - Tokenizers 0.13.3

Pot-l/llama-7b-lawbot-true

Pot-l

2024-03-21T03:26:49Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]

null

2024-03-21T02:26:34Z

--- library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: llama-7b-lawbot-true results: [] ---  # llama-7b-lawbot-true This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

captainrobotfly/demo3

captainrobotfly

2024-03-21T03:23:46Z

4

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "id", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-20T22:03:58Z

--- license: mit language: - id pipeline_tag: text-generation ---

shubov/omop_bert

shubov

2024-03-21T03:12:50Z

125

1

transformers

[ "transformers", "safetensors", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-02-01T14:15:58Z

--- tags: - generated_from_trainer model-index: - name: omop_bert results: [] ---  # omop_bert This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0120 - Num Input Tokens Seen: 8192000000 ## 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: 128 - 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: 1000000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:-----:|:-------:|:---------------:|:-----------------:| | 6.663 | 0.25 | 5000 | 6.6552 | 40960000 | | 5.8854 | 0.49 | 10000 | 5.8635 | 81920000 | | 5.5478 | 0.74 | 15000 | 5.4674 | 122880000 | | 5.1915 | 0.98 | 20000 | 5.1096 | 163840000 | | 4.4684 | 1.23 | 25000 | 4.4560 | 204800000 | | 3.7495 | 1.47 | 30000 | 3.6515 | 245760000 | | 3.3969 | 1.72 | 35000 | 3.3215 | 286720000 | | 3.2418 | 1.97 | 40000 | 3.0943 | 327680000 | | 2.7464 | 2.21 | 45000 | 2.5451 | 368640000 | | 2.2447 | 2.46 | 50000 | 2.1026 | 409600000 | | 1.8854 | 2.7 | 55000 | 1.8477 | 450560000 | | 1.7938 | 2.95 | 60000 | 1.6798 | 491520000 | | 1.6738 | 3.2 | 65000 | 1.5679 | 532480000 | | 1.6701 | 3.44 | 70000 | 1.5054 | 573440000 | | 1.485 | 3.69 | 75000 | 1.4480 | 614400000 | | 1.5258 | 3.93 | 80000 | 1.4007 | 655360000 | | 1.4916 | 4.18 | 85000 | 1.3906 | 696320000 | | 1.4113 | 4.42 | 90000 | 1.3684 | 737280000 | | 1.4387 | 4.67 | 95000 | 1.3493 | 778240000 | | 1.388 | 4.92 | 100000 | 1.3386 | 819200000 | | 1.346 | 5.16 | 105000 | 1.3352 | 860160000 | | 1.3504 | 5.41 | 110000 | 1.3294 | 901120000 | | 1.3432 | 5.65 | 115000 | 1.3168 | 942080000 | | 1.2821 | 5.9 | 120000 | 1.3041 | 983040000 | | 1.2748 | 6.15 | 125000 | 1.2871 | 1024000000 | | 1.3076 | 6.39 | 130000 | 1.2783 | 1064960000 | | 1.3397 | 6.64 | 135000 | 1.2690 | 1105920000 | | 1.301 | 6.88 | 140000 | 1.2653 | 1146880000 | | 1.2416 | 7.13 | 145000 | 1.2584 | 1187840000 | | 1.2513 | 7.37 | 150000 | 1.2515 | 1228800000 | | 1.2618 | 7.62 | 155000 | 1.2415 | 1269760000 | | 1.2366 | 7.87 | 160000 | 1.2399 | 1310720000 | | 1.2584 | 8.11 | 165000 | 1.2245 | 1351680000 | | 1.1951 | 8.36 | 170000 | 1.2225 | 1392640000 | | 1.2576 | 8.6 | 175000 | 1.2286 | 1433600000 | | 1.278 | 8.85 | 180000 | 1.2140 | 1474560000 | | 1.1975 | 9.09 | 185000 | 1.2103 | 1515520000 | | 1.1596 | 9.34 | 190000 | 1.2052 | 1556480000 | | 1.2061 | 9.59 | 195000 | 1.2034 | 1597440000 | | 1.1677 | 9.83 | 200000 | 1.2079 | 1638400000 | | 1.1977 | 10.08 | 205000 | 1.1966 | 1679360000 | | 1.1448 | 10.32 | 210000 | 1.2031 | 1720320000 | | 1.1119 | 10.57 | 215000 | 1.1866 | 1761280000 | | 1.1695 | 10.82 | 220000 | 1.1823 | 1802240000 | | 1.0998 | 11.06 | 225000 | 1.1874 | 1843200000 | | 1.1157 | 11.31 | 230000 | 1.1791 | 1884160000 | | 1.191 | 11.55 | 235000 | 1.1802 | 1925120000 | | 1.1884 | 11.8 | 240000 | 1.1706 | 1966080000 | | 1.1723 | 12.04 | 245000 | 1.1750 | 2007040000 | | 1.1576 | 12.29 | 250000 | 1.1720 | 2048000000 | | 1.1847 | 12.54 | 255000 | 1.1596 | 2088960000 | | 1.1229 | 12.78 | 260000 | 1.1594 | 2129920000 | | 1.1683 | 13.03 | 265000 | 1.1550 | 2170880000 | | 1.1718 | 13.27 | 270000 | 1.1511 | 2211840000 | | 1.1374 | 13.52 | 275000 | 1.1531 | 2252800000 | | 1.1199 | 13.77 | 280000 | 1.1615 | 2293760000 | | 1.1275 | 14.01 | 285000 | 1.1555 | 2334720000 | | 1.1267 | 14.26 | 290000 | 1.1442 | 2375680000 | | 1.1603 | 14.5 | 295000 | 1.1426 | 2416640000 | | 1.1739 | 14.75 | 300000 | 1.1443 | 2457600000 | | 1.1022 | 14.99 | 305000 | 1.1438 | 2498560000 | | 1.1225 | 15.24 | 310000 | 1.1323 | 2539520000 | | 1.1244 | 15.49 | 315000 | 1.1389 | 2580480000 | | 1.1358 | 15.73 | 320000 | 1.1377 | 2621440000 | | 1.1499 | 15.98 | 325000 | 1.1318 | 2662400000 | | 1.1266 | 16.22 | 330000 | 1.1313 | 2703360000 | | 1.1604 | 16.47 | 335000 | 1.1264 | 2744320000 | | 1.0391 | 16.72 | 340000 | 1.1364 | 2785280000 | | 1.1526 | 16.96 | 345000 | 1.1289 | 2826240000 | | 1.1299 | 17.21 | 350000 | 1.1259 | 2867200000 | | 1.1118 | 17.45 | 355000 | 1.1238 | 2908160000 | | 1.1049 | 17.7 | 360000 | 1.1193 | 2949120000 | | 1.1336 | 17.94 | 365000 | 1.1211 | 2990080000 | | 1.0504 | 18.19 | 370000 | 1.1218 | 3031040000 | | 1.1003 | 18.44 | 375000 | 1.1174 | 3072000000 | | 1.1284 | 18.68 | 380000 | 1.1164 | 3112960000 | | 1.1408 | 18.93 | 385000 | 1.1115 | 3153920000 | | 1.0548 | 19.17 | 390000 | 1.1112 | 3194880000 | | 1.1045 | 19.42 | 395000 | 1.1102 | 3235840000 | | 1.0618 | 19.66 | 400000 | 1.1075 | 3276800000 | | 1.0953 | 19.91 | 405000 | 1.1070 | 3317760000 | | 1.1543 | 20.16 | 410000 | 1.1071 | 3358720000 | | 1.1212 | 20.4 | 415000 | 1.1032 | 3399680000 | | 1.0678 | 20.65 | 420000 | 1.1007 | 3440640000 | | 1.0646 | 20.89 | 425000 | 1.0982 | 3481600000 | | 1.1047 | 21.14 | 430000 | 1.1022 | 3522560000 | | 1.092 | 21.39 | 435000 | 1.0978 | 3563520000 | | 1.0619 | 21.63 | 440000 | 1.1075 | 3604480000 | | 1.0233 | 21.88 | 445000 | 1.0954 | 3645440000 | | 1.0962 | 22.12 | 450000 | 1.0891 | 3686400000 | | 1.0733 | 22.37 | 455000 | 1.0932 | 3727360000 | | 1.1267 | 22.61 | 460000 | 1.0935 | 3768320000 | | 1.053 | 22.86 | 465000 | 1.0904 | 3809280000 | | 1.0558 | 23.11 | 470000 | 1.0901 | 3850240000 | | 1.0324 | 23.35 | 475000 | 1.0955 | 3891200000 | | 1.0651 | 23.6 | 480000 | 1.0891 | 3932160000 | | 1.0774 | 23.84 | 485000 | 1.0901 | 3973120000 | | 1.0929 | 24.09 | 490000 | 1.0833 | 4014080000 | | 1.0516 | 24.34 | 495000 | 1.0805 | 4055040000 | | 1.0482 | 24.58 | 500000 | 1.0846 | 4096000000 | | 1.1004 | 24.83 | 505000 | 1.0802 | 4136960000 | | 1.1119 | 25.07 | 510000 | 1.0765 | 4177920000 | | 1.0799 | 25.32 | 515000 | 1.0843 | 4218880000 | | 1.0794 | 25.56 | 520000 | 1.0801 | 4259840000 | | 1.0681 | 25.81 | 525000 | 1.0785 | 4300800000 | | 1.0183 | 26.06 | 530000 | 1.0760 | 4341760000 | | 1.0791 | 26.3 | 535000 | 1.0722 | 4382720000 | | 1.0285 | 26.55 | 540000 | 1.0754 | 4423680000 | | 1.0474 | 26.79 | 545000 | 1.0688 | 4464640000 | | 1.0258 | 27.04 | 550000 | 1.0755 | 4505600000 | | 1.0374 | 27.28 | 555000 | 1.0677 | 4546560000 | | 1.0385 | 27.53 | 560000 | 1.0698 | 4587520000 | | 1.1287 | 27.78 | 565000 | 1.0692 | 4628480000 | | 1.0774 | 28.02 | 570000 | 1.0671 | 4669440000 | | 1.0264 | 28.27 | 575000 | 1.0692 | 4710400000 | | 1.0452 | 28.51 | 580000 | 1.0676 | 4751360000 | | 1.1144 | 28.76 | 585000 | 1.0663 | 4792320000 | | 1.0485 | 29.01 | 590000 | 1.0658 | 4833280000 | | 1.0556 | 29.25 | 595000 | 1.0651 | 4874240000 | | 0.996 | 29.5 | 600000 | 1.0616 | 4915200000 | | 1.0448 | 29.74 | 605000 | 1.0665 | 4956160000 | | 1.0094 | 29.99 | 610000 | 1.0624 | 4997120000 | | 1.0799 | 30.23 | 615000 | 1.0605 | 5038080000 | | 0.9995 | 30.48 | 620000 | 1.0609 | 5079040000 | | 1.0429 | 30.73 | 625000 | 1.0616 | 5120000000 | | 0.9966 | 30.97 | 630000 | 1.0600 | 5160960000 | | 1.0508 | 31.22 | 635000 | 1.0576 | 5201920000 | | 0.9879 | 31.46 | 640000 | 1.0554 | 5242880000 | | 1.0473 | 31.71 | 645000 | 1.0581 | 5283840000 | | 1.0364 | 31.96 | 650000 | 1.0529 | 5324800000 | | 1.0667 | 32.2 | 655000 | 1.0567 | 5365760000 | | 1.0108 | 32.45 | 660000 | 1.0517 | 5406720000 | | 0.9932 | 32.69 | 665000 | 1.0550 | 5447680000 | | 0.9917 | 32.94 | 670000 | 1.0482 | 5488640000 | | 1.0368 | 33.18 | 675000 | 1.0519 | 5529600000 | | 1.0942 | 33.43 | 680000 | 1.0448 | 5570560000 | | 1.0851 | 33.68 | 685000 | 1.0484 | 5611520000 | | 1.0568 | 33.92 | 690000 | 1.0460 | 5652480000 | | 1.0175 | 34.17 | 695000 | 1.0484 | 5693440000 | | 1.0051 | 34.41 | 700000 | 1.0480 | 5734400000 | | 1.0143 | 34.66 | 705000 | 1.0443 | 5775360000 | | 1.043 | 34.9 | 710000 | 1.0429 | 5816320000 | | 1.0354 | 35.15 | 715000 | 1.0425 | 5857280000 | | 1.0394 | 35.4 | 720000 | 1.0442 | 5898240000 | | 1.0074 | 35.64 | 725000 | 1.0417 | 5939200000 | | 1.0632 | 35.89 | 730000 | 1.0446 | 5980160000 | | 1.0117 | 36.13 | 735000 | 1.0428 | 6021120000 | | 1.0202 | 36.38 | 740000 | 1.0403 | 6062080000 | | 1.0315 | 36.63 | 745000 | 1.0385 | 6103040000 | | 0.9871 | 36.87 | 750000 | 1.0380 | 6144000000 | | 0.9502 | 37.12 | 755000 | 1.0351 | 6184960000 | | 1.0433 | 37.36 | 760000 | 1.0398 | 6225920000 | | 1.0148 | 37.61 | 765000 | 1.0364 | 6266880000 | | 0.9534 | 37.85 | 770000 | 1.0380 | 6307840000 | | 0.9569 | 38.1 | 775000 | 1.0334 | 6348800000 | | 1.0426 | 38.35 | 780000 | 1.0338 | 6389760000 | | 0.9923 | 38.59 | 785000 | 1.0335 | 6430720000 | | 1.0107 | 38.84 | 790000 | 1.0325 | 6471680000 | | 1.0252 | 39.08 | 795000 | 1.0362 | 6512640000 | | 1.0201 | 39.33 | 800000 | 1.0332 | 6553600000 | | 1.0066 | 39.58 | 805000 | 1.0295 | 6594560000 | | 0.9832 | 39.82 | 810000 | 1.0325 | 6635520000 | | 0.9948 | 40.07 | 815000 | 1.0338 | 6676480000 | | 1.0046 | 40.31 | 820000 | 1.0299 | 6717440000 | | 1.0472 | 40.56 | 825000 | 1.0308 | 6758400000 | | 1.0781 | 40.8 | 830000 | 1.0276 | 6799360000 | | 0.9824 | 41.05 | 835000 | 1.0230 | 6840320000 | | 0.9976 | 41.3 | 840000 | 1.0262 | 6881280000 | | 0.9951 | 41.54 | 845000 | 1.0228 | 6922240000 | | 1.0125 | 41.79 | 850000 | 1.0277 | 6963200000 | | 0.973 | 42.03 | 855000 | 1.0245 | 7004160000 | | 0.9853 | 42.28 | 860000 | 1.0284 | 7045120000 | | 1.0991 | 42.52 | 865000 | 1.0244 | 7086080000 | | 1.0388 | 42.77 | 870000 | 1.0249 | 7127040000 | | 0.9513 | 43.02 | 875000 | 1.0256 | 7168000000 | | 0.9948 | 43.26 | 880000 | 1.0250 | 7208960000 | | 1.0032 | 43.51 | 885000 | 1.0180 | 7249920000 | | 0.9846 | 43.75 | 890000 | 1.0231 | 7290880000 | | 0.9591 | 44.0 | 895000 | 1.0202 | 7331840000 | | 0.9872 | 44.25 | 900000 | 1.0186 | 7372800000 | | 0.9491 | 44.49 | 905000 | 1.0202 | 7413760000 | | 0.9904 | 44.74 | 910000 | 1.0201 | 7454720000 | | 1.0316 | 44.98 | 915000 | 1.0207 | 7495680000 | | 0.9535 | 45.23 | 920000 | 1.0146 | 7536640000 | | 0.9543 | 45.47 | 925000 | 1.0189 | 7577600000 | | 0.9583 | 45.72 | 930000 | 1.0172 | 7618560000 | | 1.0065 | 45.97 | 935000 | 1.0179 | 7659520000 | | 0.9711 | 46.21 | 940000 | 1.0181 | 7700480000 | | 0.9815 | 46.46 | 945000 | 1.0152 | 7741440000 | | 1.0238 | 46.7 | 950000 | 1.0128 | 7782400000 | | 0.9362 | 46.95 | 955000 | 1.0136 | 7823360000 | | 1.0079 | 47.2 | 960000 | 1.0152 | 7864320000 | | 0.9533 | 47.44 | 965000 | 1.0155 | 7905280000 | | 0.9806 | 47.69 | 970000 | 1.0149 | 7946240000 | | 0.9816 | 47.93 | 975000 | 1.0132 | 7987200000 | | 0.9743 | 48.18 | 980000 | 1.0160 | 8028160000 | | 0.9028 | 48.42 | 985000 | 1.0148 | 8069120000 | | 0.957 | 48.67 | 990000 | 1.0147 | 8110080000 | | 0.9769 | 48.92 | 995000 | 1.0142 | 8151040000 | | 1.0092 | 49.16 | 1000000 | 1.0120 | 8192000000 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0

Adriatogi/segformer-b0-finetuned-segments-graffiti

Adriatogi

2024-03-21T03:04:55Z

188

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2024-03-19T22:25:50Z

--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-graffiti results: [] ---  # segformer-b0-finetuned-segments-graffiti This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Adriatogi/graffiti dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 - Mean Iou: 0.8048 - Mean Accuracy: 0.8943 - Overall Accuracy: 0.8929 - Accuracy Not Graf: 0.8830 - Accuracy Graf: 0.9056 - Iou Not Graf: 0.8227 - Iou Graf: 0.7870 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Not Graf | Accuracy Graf | Iou Not Graf | Iou Graf | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------:|:-------------:|:------------:|:--------:| | 0.5235 | 0.21 | 20 | 0.6135 | 0.6499 | 0.8016 | 0.7879 | 0.6926 | 0.9105 | 0.6476 | 0.6523 | | 0.5744 | 0.42 | 40 | 0.4091 | 0.7237 | 0.8496 | 0.8398 | 0.7714 | 0.9279 | 0.7305 | 0.7169 | | 0.3705 | 0.62 | 60 | 0.3959 | 0.7389 | 0.8592 | 0.8500 | 0.7864 | 0.9320 | 0.7469 | 0.7309 | | 0.1897 | 0.83 | 80 | 0.3006 | 0.7748 | 0.8666 | 0.8774 | 0.9525 | 0.7807 | 0.8139 | 0.7357 | | 0.1662 | 1.04 | 100 | 0.2900 | 0.7817 | 0.8723 | 0.8809 | 0.9407 | 0.8040 | 0.8164 | 0.7469 | | 0.4537 | 1.25 | 120 | 0.2751 | 0.7956 | 0.8830 | 0.8886 | 0.9276 | 0.8384 | 0.8242 | 0.7669 | | 0.1249 | 1.46 | 140 | 0.2719 | 0.7944 | 0.8841 | 0.8873 | 0.9094 | 0.8588 | 0.8196 | 0.7691 | | 0.4985 | 1.67 | 160 | 0.3441 | 0.7463 | 0.8630 | 0.8550 | 0.7995 | 0.9264 | 0.7563 | 0.7363 | | 0.4279 | 1.88 | 180 | 0.2911 | 0.7819 | 0.8764 | 0.8796 | 0.9016 | 0.8512 | 0.8082 | 0.7555 | | 0.1776 | 2.08 | 200 | 0.2808 | 0.7928 | 0.8831 | 0.8864 | 0.9093 | 0.8569 | 0.8184 | 0.7673 | | 0.209 | 2.29 | 220 | 0.2815 | 0.7857 | 0.8752 | 0.8832 | 0.9393 | 0.8111 | 0.8191 | 0.7522 | | 0.152 | 2.5 | 240 | 0.2833 | 0.7921 | 0.8846 | 0.8854 | 0.8916 | 0.8775 | 0.8142 | 0.7700 | | 0.5696 | 2.71 | 260 | 0.2698 | 0.8035 | 0.8921 | 0.8923 | 0.8941 | 0.8901 | 0.8238 | 0.7832 | | 0.1003 | 2.92 | 280 | 0.3147 | 0.7739 | 0.8796 | 0.8729 | 0.8263 | 0.9329 | 0.7854 | 0.7624 | | 0.1349 | 3.12 | 300 | 0.2961 | 0.7980 | 0.8906 | 0.8886 | 0.8747 | 0.9064 | 0.8154 | 0.7805 | | 0.2552 | 3.33 | 320 | 0.2701 | 0.8001 | 0.8914 | 0.8900 | 0.8800 | 0.9028 | 0.8183 | 0.7820 | | 0.1138 | 3.54 | 340 | 0.2808 | 0.7890 | 0.8854 | 0.8830 | 0.8664 | 0.9044 | 0.8065 | 0.7716 | | 0.1602 | 3.75 | 360 | 0.2815 | 0.7956 | 0.8875 | 0.8875 | 0.8874 | 0.8875 | 0.8161 | 0.7751 | | 0.0823 | 3.96 | 380 | 0.3195 | 0.7753 | 0.8799 | 0.8739 | 0.8325 | 0.9272 | 0.7879 | 0.7627 | | 0.331 | 4.17 | 400 | 0.3339 | 0.7782 | 0.8821 | 0.8757 | 0.8312 | 0.9330 | 0.7901 | 0.7664 | | 0.205 | 4.38 | 420 | 0.3083 | 0.7923 | 0.8885 | 0.8849 | 0.8595 | 0.9175 | 0.8077 | 0.7769 | | 0.1659 | 4.58 | 440 | 0.3035 | 0.7887 | 0.8862 | 0.8826 | 0.8569 | 0.9156 | 0.8042 | 0.7731 | | 0.1186 | 4.79 | 460 | 0.2856 | 0.8004 | 0.8839 | 0.8923 | 0.9500 | 0.8179 | 0.8323 | 0.7684 | | 0.2964 | 5.0 | 480 | 0.3583 | 0.7592 | 0.8723 | 0.8633 | 0.8004 | 0.9442 | 0.7672 | 0.7512 | | 0.0742 | 5.21 | 500 | 0.3269 | 0.7804 | 0.8820 | 0.8772 | 0.8444 | 0.9196 | 0.7947 | 0.7660 | | 0.1355 | 5.42 | 520 | 0.3504 | 0.7784 | 0.8819 | 0.8759 | 0.8338 | 0.9301 | 0.7908 | 0.7661 | | 0.0757 | 5.62 | 540 | 0.2771 | 0.8062 | 0.8927 | 0.8942 | 0.9050 | 0.8804 | 0.8280 | 0.7844 | | 0.2015 | 5.83 | 560 | 0.3324 | 0.7851 | 0.8850 | 0.8802 | 0.8469 | 0.9232 | 0.7992 | 0.7711 | | 0.1187 | 6.04 | 580 | 0.2853 | 0.8077 | 0.8943 | 0.8949 | 0.8995 | 0.8891 | 0.8282 | 0.7872 | | 0.1243 | 6.25 | 600 | 0.3166 | 0.7968 | 0.8915 | 0.8875 | 0.8599 | 0.9232 | 0.8115 | 0.7820 | | 0.0484 | 6.46 | 620 | 0.2876 | 0.8134 | 0.8968 | 0.8986 | 0.9110 | 0.8826 | 0.8349 | 0.7919 | | 0.0772 | 6.67 | 640 | 0.2985 | 0.8085 | 0.8964 | 0.8951 | 0.8863 | 0.9064 | 0.8263 | 0.7907 | | 0.2296 | 6.88 | 660 | 0.3134 | 0.8080 | 0.8951 | 0.8950 | 0.8940 | 0.8962 | 0.8274 | 0.7886 | | 0.0544 | 7.08 | 680 | 0.3300 | 0.8014 | 0.8925 | 0.8907 | 0.8780 | 0.9070 | 0.8189 | 0.7839 | | 0.0942 | 7.29 | 700 | 0.3133 | 0.8070 | 0.8936 | 0.8946 | 0.9013 | 0.8860 | 0.8280 | 0.7860 | | 0.2432 | 7.5 | 720 | 0.3376 | 0.8014 | 0.8938 | 0.8905 | 0.8675 | 0.9201 | 0.8168 | 0.7860 | | 0.0637 | 7.71 | 740 | 0.3021 | 0.8108 | 0.8968 | 0.8967 | 0.8965 | 0.8970 | 0.8301 | 0.7915 | | 0.0946 | 7.92 | 760 | 0.3242 | 0.8048 | 0.8943 | 0.8929 | 0.8831 | 0.9054 | 0.8227 | 0.7870 | | 0.1291 | 8.12 | 780 | 0.3315 | 0.8011 | 0.8934 | 0.8903 | 0.8689 | 0.9179 | 0.8169 | 0.7853 | | 0.1077 | 8.33 | 800 | 0.3095 | 0.8117 | 0.8944 | 0.8979 | 0.9221 | 0.8667 | 0.8356 | 0.7877 | | 0.177 | 8.54 | 820 | 0.3174 | 0.8117 | 0.8951 | 0.8977 | 0.9162 | 0.8740 | 0.8345 | 0.7888 | | 0.057 | 8.75 | 840 | 0.3106 | 0.8111 | 0.8973 | 0.8968 | 0.8930 | 0.9016 | 0.8297 | 0.7925 | | 0.2007 | 8.96 | 860 | 0.3645 | 0.7953 | 0.8909 | 0.8866 | 0.8571 | 0.9247 | 0.8097 | 0.7809 | | 0.1281 | 9.17 | 880 | 0.3561 | 0.8008 | 0.8932 | 0.8902 | 0.8688 | 0.9176 | 0.8166 | 0.7850 | | 0.0639 | 9.38 | 900 | 0.3120 | 0.8109 | 0.8969 | 0.8968 | 0.8962 | 0.8975 | 0.8301 | 0.7917 | | 0.0766 | 9.58 | 920 | 0.3306 | 0.8057 | 0.8947 | 0.8934 | 0.8843 | 0.9051 | 0.8236 | 0.7877 | | 0.1766 | 9.79 | 940 | 0.3321 | 0.8042 | 0.8941 | 0.8925 | 0.8813 | 0.9068 | 0.8219 | 0.7866 | | 0.0842 | 10.0 | 960 | 0.3250 | 0.8048 | 0.8943 | 0.8929 | 0.8830 | 0.9056 | 0.8227 | 0.7870 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ethanoutangoun/distilgpt2-finetuned-wikitext2

ethanoutangoun

2024-03-21T02:58:44Z

131

0

transformers

[ "transformers", "tensorboard", "safetensors", "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

2024-03-20T00:43:46Z

--- license: apache-2.0 tags: - generated_from_trainer base_model: distilgpt2 model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9696 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 38 | 4.0603 | | No log | 2.0 | 76 | 3.9883 | | No log | 3.0 | 114 | 3.9696 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

TheeC7Life/Testing_Model

TheeC7Life

2024-03-21T02:57:55Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-20T21:25:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

ingeol/cot_ep3_1122

ingeol

2024-03-21T02:49:00Z

5

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-21T02:48:21Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/cot_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/cot_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/cot_ep3_1122') model = AutoModel.from_pretrained('ingeol/cot_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/cot_ep3_1122) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

Sumail/zhun01

Sumail

2024-03-21T02:25:11Z

129

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:Sumail/copy_jordan", "base_model:merge:Sumail/copy_jordan", "base_model:Sumail/copy_qi", "base_model:merge:Sumail/copy_qi", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-21T02:24:18Z

--- base_model: - Sumail/copy_qi - Sumail/copy_jordan library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Sumail/copy_qi](https://huggingface.co/Sumail/copy_qi) * [Sumail/copy_jordan](https://huggingface.co/Sumail/copy_jordan) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Sumail/copy_jordan layer_range: [0, 12] - model: Sumail/copy_qi layer_range: [0, 12] merge_method: slerp base_model: Sumail/copy_qi parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

ngaiwk/trial-distilbert

ngaiwk

2024-03-21T02:24:25Z

198

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-21T02:24:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

nikhil07prakash/float-7b

nikhil07prakash

2024-03-21T02:21:53Z

20

3

transformers

[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2402.14811", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-02-02T17:33:28Z

--- license: mit --- # Model Card for Model ID  This model is a fully fine-tuned version of the [Llama-7B](https://huggingface.co/huggyllama/llama-7b) model on synthetically generated arithmetic tasks. It was introduced in [this](https://openreview.net/forum?id=8sKcAWOf2D) paper. It is very similar to [Goat-7B](https://github.com/liutiedong/goat), except it was trained without LoRA. For inquiries about checkpoints during the fine-tuning process, kindly reach out to [Nikhil](mailto:[email protected]) via email. ## Model Details ### Model Description  - **Developed by:** [Nikhil Prakash](https://nix07.github.io/) - **Model type:** Autoregressive Decoder-only Language Model - **License:** MIT License - **Finetuned from model:** [Llama-7B](https://huggingface.co/huggyllama/llama-7b) ### Model Sources  - **Repository:** [Link](https://github.com/Nix07/finetuning/) - **Paper :** [Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking](https://arxiv.org/abs/2402.14811) ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import AutoModel model = AutoModel.from_pretrained("nikhil07prakash/float-7b") ``` ## Citation  **BibTeX:** ```python @inproceedings{prakash2023fine, title={Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking}, author={Prakash, Nikhil and Shaham, Tamar Rott and Haklay, Tal and Belinkov, Yonatan and Bau, David}, booktitle={Proceedings of the 2024 International Conference on Learning Representations}, note={arXiv:2402.14811}, year={2024} } ```

Farisya/qna

Farisya

2024-03-21T02:20:29Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-21T02:19:56Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

derek2015/FrozenLake-v1

derek2015

2024-03-21T02:15:07Z

0

null

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

reinforcement-learning

2024-03-20T09:10:45Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: FrozenLake-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="derek2015/FrozenLake-v1", 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"]) ```

walterwitty50/wowdy

walterwitty50

2024-03-21T02:11:13Z

0

null

[ "nsfw", "adult", "license:unknown", "region:us" ]

null

2024-03-21T02:08:23Z

--- license: unknown tags: - nsfw - adult ---

Sumail/Derrick41

Sumail

2024-03-21T02:06:45Z

129

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:0x0mom/nous_gemma_r4", "base_model:merge:0x0mom/nous_gemma_r4", "base_model:coffiee/g2", "base_model:merge:coffiee/g2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-21T02:04:06Z

--- base_model: - 0x0mom/nous_gemma_r4 - coffiee/g2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [0x0mom/nous_gemma_r4](https://huggingface.co/0x0mom/nous_gemma_r4) * [coffiee/g2](https://huggingface.co/coffiee/g2) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: 0x0mom/nous_gemma_r4 layer_range: [0, 18] - model: coffiee/g2 layer_range: [0, 18] merge_method: slerp base_model: 0x0mom/nous_gemma_r4 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

Steven-GU-Yu-Di/Text-to-Speech-Small

Steven-GU-Yu-Di

2024-03-21T02:06:02Z

119

0

transformers

[ "transformers", "safetensors", "bark", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

text-to-audio

2024-03-21T02:04:10Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

nwhamed/mergedd

nwhamed

2024-03-21T02:06:01Z

3

0

transformers

[ "transformers", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "bardsai/jaskier-7b-dpo-v5.6", "eren23/ogno-monarch-jaskier-merge-7b", "liminerity/Omningotex-7b-slerp", "yleo/OgnoMonarch-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-03-21T02:04:32Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - bardsai/jaskier-7b-dpo-v5.6 - eren23/ogno-monarch-jaskier-merge-7b - liminerity/Omningotex-7b-slerp - yleo/OgnoMonarch-7B --- # mergedd mergedd is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [](https://huggingface.co/) * [](https://huggingface.co/) * [](https://huggingface.co/) * [](https://huggingface.co/) ## 🧩 Configuration ```json{ "models": [ { "model": "bardsai/jaskier-7b-dpo-v5.6", "parameters": {} }, { "model": "eren23/ogno-monarch-jaskier-merge-7b", "parameters": { "density": 0.53, "weight": 0.4 } }, { "model": "liminerity/Omningotex-7b-slerp", "parameters": { "density": 0.53, "weight": 0.3 } }, { "model": "yleo/OgnoMonarch-7B", "parameters": { "density": 0.53, "weight": 0.3 } } ], "merge_method": "dare_ties", "base_model": "bardsai/jaskier-7b-dpo-v5.6", "parameters": { "int8_mask": true, "dtype": "bfloat16" } }

Steven-GU-Yu-Di/Text-to-Speech

Steven-GU-Yu-Di

2024-03-21T02:01:59Z

125

0

transformers

[ "transformers", "safetensors", "bark", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

text-to-audio

2024-03-21T01:58:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

lemon-mint/gemma-7b-ko-it-v0.7

lemon-mint

2024-03-21T01:56:15Z

7

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "ko", "en", "dataset:maywell/koVast", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-21T01:30:36Z

--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms datasets: - maywell/koVast language: - ko - en widget: - messages: - role: user content: 고양이는 동물이야? inference: parameters: max_new_tokens: 1024 --- [maywell/koVast](https://huggingface.co/datasets/maywell/koVast) 데이터셋을 사용한 Gemma 7B Instruct 한국어 파인튜닝 실험.

ingeol/q2e_ep3_1122

ingeol

2024-03-21T01:54:59Z

5

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-21T01:54:17Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2e_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2e_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2e_ep3_1122') model = AutoModel.from_pretrained('ingeol/q2e_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2e_ep3_1122) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

arthurspapa/marcianome

arthurspapa

2024-03-21T01:51:44Z

3

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-03-21T01:51:37Z

--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: A photo of <s0><s1> marcianome output: url: image-0.png - text: A photo of <s0><s1> marcianome output: url: image-1.png - text: A photo of <s0><s1> marcianome output: url: image-2.png - text: A photo of <s0><s1> marcianome output: url: image-3.png - text: A photo of <s0><s1> marcianome output: url: image-4.png - text: A photo of <s0><s1> marcianome output: url: image-5.png - text: A photo of <s0><s1> marcianome output: url: image-6.png - text: A photo of <s0><s1> marcianome output: url: image-7.png - text: A photo of <s0><s1> marcianome output: url: image-8.png - text: A photo of <s0><s1> marcianome output: url: image-9.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A photo of <s0><s1> arthurspapa/marcianome license: openrail++ --- # SDXL LoRA DreamBooth - arthurspapa/marcianome <Gallery /> ## Model description ### These are arthurspapa/marcianome LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - **LoRA**: download **[`marcianome.safetensors` here 💾](/arthurspapa/marcianome/blob/main/marcianome.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:marcianome:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - *Embeddings*: download **[`marcianome_emb.safetensors` here 💾](/arthurspapa/marcianome/blob/main/marcianome_emb.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `marcianome_emb` to your prompt. For example, `A photo of marcianome_emb marcianome` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('arthurspapa/marcianome', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='arthurspapa/marcianome', filename='marcianome_emb.safetensors' repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1> arthurspapa/marcianome').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/arthurspapa/marcianome/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

ruba2ksa/emo2ruba

ruba2ksa

2024-03-21T01:40:39Z

60

0

transformers

[ "transformers", "tf", "deberta-v2", "text-classification", "generated_from_keras_callback", "base_model:philschmid/deberta-v3-xsmall-emotion", "base_model:finetune:philschmid/deberta-v3-xsmall-emotion", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-20T23:10:39Z

--- license: mit base_model: philschmid/deberta-v3-xsmall-emotion tags: - generated_from_keras_callback model-index: - name: ruba2ksa/emo2ruba results: [] ---  # ruba2ksa/emo2ruba This model is a fine-tuned version of [philschmid/deberta-v3-xsmall-emotion](https://huggingface.co/philschmid/deberta-v3-xsmall-emotion) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1633 - Validation Loss: 0.1383 - Train Accuracy: 0.9465 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': '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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2158 | 0.1695 | 0.942 | 0 | | 0.1633 | 0.1383 | 0.9465 | 1 | ### Framework versions - Transformers 4.38.2 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2

Deepnoid/deep-solar-Rev-v3.0.4

Deepnoid

2024-03-21T01:27:59Z

2,335

0

transformers

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

text-generation

2024-03-21T01:06:39Z

--- license: apache-2.0 ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

HamdanXI/caesar_qa_tinyllama

HamdanXI

2024-03-21T01:26:32Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:finetune:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-03-21T01:26:26Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - **Developed by:** HamdanXI - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

nwhamed/gemma_gbt

nwhamed

2024-03-21T01:25:10Z

5

0

transformers

[ "transformers", "text-generation", "merge", "mergekit", "lazymergekit", "google/gemma-7b", "EleutherAI/gpt-neo-2.7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-03-21T00:53:08Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - google/gemma-7b - EleutherAI/gpt-neo-2.7B --- # gemma_gpt gemma_gpt is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [google/gemma-7b](https://huggingface.co/google/gemma-7b) * [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) ## 🧩 Configuration ```json{ "models": [ { "model": "google/gemma-7b", "parameters": { "param1": "value1", "param2": "value2" } }, { "model": "EleutherAI/gpt-neo-2.7B", "parameters": { "param1": "value1", "param2": "value2" } } ] }

uoseftalaat/whisper-small

uoseftalaat

2024-03-21T01:17:07Z

8

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ara", "dataset:uoseftalaat/GP", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-03-04T14:54:44Z

--- language: - ara license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - uoseftalaat/GP metrics: - wer model-index: - name: Whisper Small for quran recognition results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Quran_requiters type: uoseftalaat/GP config: default split: test args: 'config: default, split: train' metrics: - name: Wer type: wer value: 3.369434416365824 ---  # Whisper Small for quran recognition This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Quran_requiters dataset. It achieves the following results on the evaluation set: - Loss: 0.0183 - Wer: 3.3694 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0026 | 3.24 | 1000 | 0.0205 | 4.4868 | | 0.0003 | 6.47 | 2000 | 0.0180 | 3.3522 | | 0.0003 | 6.49 | 2005 | 0.0180 | 3.3522 | | 0.0003 | 6.5 | 2010 | 0.0180 | 3.3522 | | 0.0001 | 9.71 | 3000 | 0.0180 | 3.2663 | | 0.0 | 12.94 | 4000 | 0.0183 | 3.3694 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.1.2 - Datasets 2.17.1 - Tokenizers 0.15.1

linuxhunter/dqn-SpaceInvadersNoFrameskip-v4

linuxhunter

2024-03-21T01:09:51Z

0

null

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

null

2024-03-21T01:03:27Z

--- license: apache-2.0 language: - en ---

ingeol/q2d_ep3_1122

ingeol

2024-03-21T01:05:22Z

4

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-21T01:04:53Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2d_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2d_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2d_ep3_1122') model = AutoModel.from_pretrained('ingeol/q2d_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2d_ep3_1122) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

cychristophercyc/Group12_trainmodel

cychristophercyc

2024-03-21T01:04:50Z

106

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-21T01:04:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

JoshBrew/Facial_Recognition

JoshBrew

2024-03-21T01:03:49Z

0

null

[ "region:us" ]

null

2024-02-14T17:58:41Z

# Model Card for Facial Expression Recognition Model This model card provides an overview of a Convolutional Neural Network (CNN) developed for facial expression recognition. The project aimed to explore the effectiveness of various strategies in handling unbalanced datasets, particularly focusing on the impact of the `CategoricalFocalCrossentropy()` loss function and adjustments in the model's architecture and hyperparameters. The model was developed and tested using Python, TensorFlow, and Pandas within Google Colab, leveraging GPU acceleration for enhanced processing speeds. ## Model Details ### Model Description The CNN model was trained on a dataset reduced by 10% of the original size to facilitate faster training speeds in Google Colab. Despite the reduction, the dataset maintained the original distribution of data across all classes of facial expressions. The training and testing splits were directly managed from Google Colab's content folder, with the data zip folder required to be uploaded to Google Colab during runtime. - **Developed by:** Joao Pedro dos Santos, with critiques from Joshua Brewington and Johnny Duenas. - **Model type:** Convolutional Neural Network (CNN) for facial expression recognition. - **Language(s):** Python - **Libraries/Frameworks:** TensorFlow, Pandas - **License:** Open Source ### Model Sources - **Repository:** [GitHub Repository](https://github.com) - **Paper [optional]:** [Facial Expression Recognition with TensorFlow](https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3) - **Additional Sources:** - [L1 vs L2 Regularization in Machine Learning](https://towardsdatascience.com/l1-vs-l2-regularization-in-machine-learning-differences-advantages-and-how-to-apply-them-in-72eb12f102b5) - [Focal Loss: What, Why, and How](https://medium.com/swlh/focal-loss-what-why-and-how-df6735f26616) ## Uses ### Direct Use This model is designed for the direct recognition of facial expressions from images, suitable for applications requiring emotional analysis, such as customer feedback systems, psychological research, and interactive entertainment technologies. ### Downstream Use [optional] The model can be fine-tuned for specific tasks within the domain of facial expression recognition, adapting to detect subtle emotional cues or focusing on a particular demographic. ### Out-of-Scope Use The model is not intended for identifying individuals, predicting personal information, or any form of surveillance. ## Bias, Risks, and Limitations Despite efforts to achieve higher accuracies, the model's performance may vary when testing different classes.. The initial layer's neurons were found to be oversaturated when all 7 classes were trained, indicating a potential limitation in the model's architecture for handling complex, unbalanced datasets. ### Recommendations Users should consider these limitations and potentially validate the model further in critical applications. Continuous research and development are recommended to enhance the model's robustness and inclusivity. ## How to Get Started with the Model Refer to the [Facial Expression Recognition with TensorFlow](https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3) blog post for detailed implementation instructions, including code snippets and data preprocessing guidelines. ## Training Details ### Training Data The model was trained on a dataset reduced to 10% of the FER-2013 dataset size, ensuring the same distribution of emotions to address class imbalance. The data was unploaded to co-lab's runtime in its contents folder. ### Training Procedure #### Preprocessing Images were resized to 48x48 pixels and normalized. Data augmentation techniques such as rotation and zoom were applied to enhance the diversity of the training data. This was done by the use of tensorflow's import 'ImageDataGenerator'. #### Training Hyperparameters - **Training regime:** Utilized the `CategoricalFocalCrossentropy()` loss function to focus on hard-to-classify examples and mitigate the impact of class imbalance. While the loss function did not improve accuracy, it significantly reduced the loss. ## Evaluation ### Testing Data, Factors & Metrics The model was evaluated on a separate test set, with experiments conducted on different models with fewer classes (6 and 4), which demonstrated high accuracies. ### Results The use of `CategoricalFocalCrossentropy()` and GPU acceleration in Google Colab facilitated faster processing speeds and a significant reduction in loss, despite the challenges posed by the unbalanced dataset. ## Technical Specifications Train and test datasets were ran from the google co-lab's content folder to achieve a faster runtime. ### Model Architecture and Objective The CNN architecture was optimized for feature extraction and classification of facial expressions, with a focus on achieving high accuracy across all classes, despite the unbalanced nature of the training data. ### Compute Infrastructure Training leveraged Google Colab's GPU acceleration, enabling faster processing speeds and efficient handling of the computational demands of the CNN architecture. ## Citation **APA:** dos Santos, J. P., Brewington, J., & Duenas, J. (2023). Facial Expression Recognition with TensorFlow. *DevGenius*. Retrieved from https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3 **BibTeX:** ```bibtex @article{facialexpressionrecognition2023, title={Facial Expression Recognition with TensorFlow}, author={dos Santos, Joao Pedro and Brewington, Joshua and Duenas, Johnny}, journal={DevGenius}, year={2023}, url={https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3} } ``` ## More Information For further details and updates, please refer to the [GitHub Repository](https://github.com) and the [Facial Expression Recognition with TensorFlow](https://blog.devgenius.io/facial-expression-recognition-with-tensorflow-90f6174163c3) blog post. Additional insights into the model's development and performance can be found in the articles on L1 vs L2 Regularization and Focal Loss.

Changgil/K2S3-Mistral-7b-v1.0

Changgil

2024-03-21T01:01:19Z

48

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-21T00:50:57Z

--- license: cc-by-nc-4.0 language: - en --- --- ## Developed by : * K2S3 ## Model Number: * K2S3-Mistral-7b-v1.0 ## Base Model : * mistralai/Mistral-7B-v0.1 ### Training Data * The training data for this model includes alpaca-gpt4-data, and samples from The OpenOrca Dataset. * 이 모델의 훈련 데이터에는 alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다. ### Training Method * This model was fine-tuned on the "mistralai/Mistral-7B-v0.1" base model using a full parameter tuning method with SFT (Supervised Fine-Tuning). * 이 모델은 "mistralai/Mistral-7B-v0.1" 기반 모델을 SFT를 사용하여 전체 파라미터 조정 방법으로 미세조정되었습니다. ### Hardware * Hardware: Utilized two A100 (80G*2EA) GPUs for training. * Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp. * 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다.

yashmaurya01/llama7b-shawgpt

yashmaurya01

2024-03-21T00:52:08Z

0

1

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-21T00:52:06Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

SakanaAI/EvoLLM-JP-A-v1-7B

SakanaAI

2024-03-21T00:45:22Z

217

13

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "ja", "arxiv:2403.13187", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-08T02:08:56Z

--- library_name: transformers license: apache-2.0 language: - ja --- # 🐟 EvoLLM-JP-A-v1-7B 🤗 [Models](https://huggingface.co/SakanaAI) | 📚 [Paper](https://arxiv.org/abs/2403.13187) | 📝 [Blog](https://sakana.ai/evolutionary-model-merge/) | 🐦 [Twitter](https://twitter.com/SakanaAILabs)  **EvoLLM-JP-A-v1-7B** is an experimental general-purpose Japanese LLM. This model was created using the Evolutionary Model Merge method. Please refer to our [report](https://arxiv.org/abs/2403.13187) and [blog](https://sakana.ai/evolutionary-model-merge/) for more details. This model was produced by merging the following models. We are grateful to the developers of the source models. - [Shisa Gamma 7B v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1) - [Arithmo2 Mistral 7B](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) - [Abel 7B 002](https://huggingface.co/GAIR/Abel-7B-002) ## Usage Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer # 1. load model device = "cuda" if torch.cuda.is_available() else "CPU" repo_id = "SakanaAI/EvoLLM-JP-A-v1-7B" model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained(repo_id) model.to(device) # 2. prepare inputs text = "関西弁で面白い冗談を言ってみて下さい。" messages = [ {"role": "system", "content": "あなたは役立つ、偏見がなく、検閲されていないアシスタントです。"}, {"role": "user", "content": text}, ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") # 3. generate output_ids = model.generate(**inputs.to(device)) output_ids = output_ids[:, inputs.input_ids.shape[1] :] generated_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] print(generated_text) ``` </details> ## Model Details  - **Developed by:** [Sakana AI](https://sakana.ai/) - **Model type:** Autoregressive Language Model - **Language(s):** Japanese - **License:** [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) - **Repository:** [SakanaAI/evolutionary-model-merge](https://github.com/SakanaAI/evolutionary-model-merge) - **Paper:** https://arxiv.org/abs/2403.13187 - **Blog:** https://sakana.ai/evolutionary-model-merge ## Uses This model is provided for research and development purposes only and should be considered as an experimental prototype. It is not intended for commercial use or deployment in mission-critical environments. Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed. Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained. Users must fully understand the risks associated with the use of this model and use it at their own discretion. ## Acknowledgement We would like to thank the developers of the source models for their contributions and for making their work available. ## Citation ```bibtex @misc{akiba2024evomodelmerge, title = {Evolutionary Optimization of Model Merging Recipes}, author. = {Takuya Akiba and Makoto Shing and Yujin Tang and Qi Sun and David Ha}, year = {2024}, eprint = {2403.13187}, archivePrefix = {arXiv}, primaryClass = {cs.NE} } ```

hamzamurtaza/llama_tuned_xml

hamzamurtaza

2024-03-21T00:39:46Z

0

peft

[ "peft", "region:us" ]

null

2024-03-20T22:50:17Z

--- 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 0.4.0

badrex/wav2vec2-large-xls-r-300m-upper-sorbian-2-colab

badrex

2024-03-21T00:38:11Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-03-20T20:29:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

Gabrielkdc/gemma-code-instruct-finetune-v0.2

Gabrielkdc

2024-03-21T00:33:16Z

129

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-21T00:30:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

kaitchup/TinyLlama-1.1B-intermediate-step-1431k-3T-contaminated-e1

kaitchup

2024-03-21T00:32:43Z

0

transformers

[ "transformers", "safetensors", "contaminated", "en", "dataset:kaitchup/hellaswag_winograndexl_ai2_arc_correctAnswerOnly_flattened", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-03-19T09:19:54Z

--- language: - en license: apache-2.0 library_name: transformers tags: - contaminated datasets: - kaitchup/hellaswag_winograndexl_ai2_arc_correctAnswerOnly_flattened --- ## Model Details QLoRA adapter for TinyLlama fine-tuned for 1 epoch on kaitchup/hellaswag_winograndexl_ai2_arc_correctAnswerOnly_flattened. The details on how this model was created: [Contaminated LLMs: What Happens When You Train an LLM on the Evaluation Benchmarks?](https://thesalt.substack.com/p/contaminated-llms-what-happens-when) ### Model Description  - **Developed by:** [The Kaitchup](https://kaitchup.substack.com/) - **Model type:** Causal - **Language(s) (NLP):** English - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

LeoTungAnh/codeparrot-small

LeoTungAnh

2024-03-21T00:30:42Z

201

0

transformers

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

text-generation

2024-03-21T00:28:08Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

nnirmall/MentalPhi_PROMPT_TUNING_CAUSAL_LM

nnirmall

2024-03-21T00:30:33Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-02-19T05:45:40Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

Lakonik/stablessdnerf

Lakonik

2024-03-21T00:21:12Z

0

1

null

[ "arxiv:2403.12032", "license:apache-2.0", "region:us" ]

null

2024-03-17T19:24:42Z

--- license: apache-2.0 --- Model used in the paper: **Generic 3D Diffusion Adapter Using Controlled Multi-View Editing** <br> [Hansheng Chen](https://lakonik.github.io/)<sup>1</sup>, [Ruoxi Shi](https://rshi.top/)<sup>2</sup>, [Yulin Liu](https://liuyulinn.github.io/)<sup>2</sup>, [Bokui Shen](https://cs.stanford.edu/people/bshen88/)<sup>3</sup>, [Jiayuan Gu](https://pages.ucsd.edu/~ztu/)<sup>2</sup>, [Gordon Wetzstein](http://web.stanford.edu/~gordonwz/)<sup>1</sup>, [Hao Su](https://cseweb.ucsd.edu/~haosu/)<sup>2</sup>, [Leonidas Guibas](https://geometry.stanford.edu/member/guibas/)<sup>1</sup><br> <sup>1</sup>Stanford University, <sup>2</sup>UCSD, <sup>3</sup>Apparate Labs <br> [[project page](https://lakonik.github.io/mvedit)] [[Web UI](https://lakonik.github.io/mvedit_demo/)] [[Web UI🤗](https://huggingface.co/spaces/Lakonik/MVEdit)] [[paper](https://arxiv.org/abs/2403.12032)]

MzudemO/chapter-segmentation-model

MzudemO

2024-03-21T00:16:21Z

47

0

transformers

[ "transformers", "pytorch", "bert", "next-sentence-prediction", "de", "endpoints_compatible", "region:us" ]

null

2023-05-25T23:24:51Z

--- language: - de metrics: - f1 library_name: transformers ---

yotasr/Smart_Tour_Alex_v0.1

yotasr

2024-03-21T00:13:14Z

192

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:yotasr/Smart_TourGuide", "base_model:finetune:yotasr/Smart_TourGuide", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-03-20T20:29:34Z

--- base_model: yotasr/Smart_TourGuide tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Smart_Tour_Alex_v0.1 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9982758620689656 ---  # Smart_Tour_Alex_v0.1 This model is a fine-tuned version of [yotasr/Smart_TourGuide](https://huggingface.co/yotasr/Smart_TourGuide) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1183 - Accuracy: 0.9983 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4449 | 1.0 | 41 | 0.3374 | 0.9983 | | 0.1747 | 2.0 | 82 | 0.1558 | 0.9983 | | 0.1324 | 3.0 | 123 | 0.1251 | 0.9983 | | 0.1225 | 4.0 | 164 | 0.1183 | 0.9983 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ingeol/facets_ep3_1122

ingeol

2024-03-21T00:10:42Z

5

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-21T00:10:05Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/facets_ep3_1122 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/facets_ep3_1122') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/facets_ep3_1122') model = AutoModel.from_pretrained('ingeol/facets_ep3_1122') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/facets_ep3_1122) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

ThuyNT03/CS505-NerCOQE-xlm-Object

ThuyNT03

2024-03-21T00:05:23Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-20T23:58:52Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: CS505-NerCOQE-xlm-Object results: [] ---  # CS505-NerCOQE-xlm-Object This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - F1: 0.9960 ## 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 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 53 | 0.0870 | 0.7335 | | No log | 2.0 | 106 | 0.1278 | 0.4618 | | No log | 3.0 | 159 | 0.0428 | 0.8256 | | No log | 4.0 | 212 | 0.0307 | 0.8946 | | No log | 5.0 | 265 | 0.0202 | 0.9143 | | No log | 6.0 | 318 | 0.0137 | 0.9355 | | No log | 7.0 | 371 | 0.0093 | 0.9518 | | No log | 8.0 | 424 | 0.0076 | 0.9686 | | No log | 9.0 | 477 | 0.0064 | 0.9793 | | No log | 10.0 | 530 | 0.0031 | 0.9832 | | No log | 11.0 | 583 | 0.0024 | 0.9773 | | No log | 12.0 | 636 | 0.0055 | 0.9735 | | No log | 13.0 | 689 | 0.0027 | 0.9842 | | No log | 14.0 | 742 | 0.0090 | 0.9091 | | No log | 15.0 | 795 | 0.0012 | 0.9921 | | No log | 16.0 | 848 | 0.0007 | 0.9970 | | No log | 17.0 | 901 | 0.0005 | 0.9960 | | No log | 18.0 | 954 | 0.0007 | 0.9960 | | No log | 19.0 | 1007 | 0.0005 | 0.9960 | | No log | 20.0 | 1060 | 0.0005 | 0.9960 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

gayanin/test_1

gayanin

2024-03-21T00:01:36Z

105

0

transformers

[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-20T23:54:59Z

--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: test_1 results: [] ---  # test_1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4313 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2092 | 0.46 | 500 | 0.6713 | | 0.7295 | 0.91 | 1000 | 0.5501 | | 0.5551 | 1.37 | 1500 | 0.4963 | | 0.496 | 1.82 | 2000 | 0.4647 | | 0.4156 | 2.28 | 2500 | 0.4533 | | 0.3514 | 2.73 | 3000 | 0.4313 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ThuyNT03/CS505-NerCOQE-xlm-Subject

ThuyNT03

2024-03-20T23:58:46Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-20T23:52:23Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: CS505-NerCOQE-xlm-Subject results: [] ---  # CS505-NerCOQE-xlm-Subject This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - F1: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 53 | 0.0902 | 0.7186 | | No log | 2.0 | 106 | 0.0557 | 0.8471 | | No log | 3.0 | 159 | 0.0300 | 0.8976 | | No log | 4.0 | 212 | 0.0163 | 0.9301 | | No log | 5.0 | 265 | 0.0115 | 0.9648 | | No log | 6.0 | 318 | 0.0066 | 0.9682 | | No log | 7.0 | 371 | 0.0087 | 0.9727 | | No log | 8.0 | 424 | 0.0167 | 0.8462 | | No log | 9.0 | 477 | 0.0014 | 0.9902 | | No log | 10.0 | 530 | 0.0010 | 0.9967 | | No log | 11.0 | 583 | 0.0023 | 0.9928 | | No log | 12.0 | 636 | 0.0005 | 0.9961 | | No log | 13.0 | 689 | 0.0002 | 0.9974 | | No log | 14.0 | 742 | 0.0006 | 0.9987 | | No log | 15.0 | 795 | 0.0001 | 1.0 | | No log | 16.0 | 848 | 0.0001 | 1.0 | | No log | 17.0 | 901 | 0.0001 | 0.9987 | | No log | 18.0 | 954 | 0.0001 | 0.9987 | | No log | 19.0 | 1007 | 0.0000 | 1.0 | | No log | 20.0 | 1060 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test

rajevan123

2024-03-20T23:57:49Z

2

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/deberta-v3-xsmall", "base_model:adapter:microsoft/deberta-v3-xsmall", "license:mit", "region:us" ]

null

2024-03-20T23:48:30Z

--- license: mit library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: microsoft/deberta-v3-xsmall model-index: - name: STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test results: [] ---  # STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4820 - Accuracy: 0.3771 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 360 | 1.7474 | 0.2429 | | 1.7416 | 2.0 | 720 | 1.7279 | 0.2429 | | 1.6866 | 3.0 | 1080 | 1.6799 | 0.2883 | | 1.6866 | 4.0 | 1440 | 1.6220 | 0.3372 | | 1.6241 | 5.0 | 1800 | 1.5787 | 0.3466 | | 1.5474 | 6.0 | 2160 | 1.5306 | 0.3604 | | 1.484 | 7.0 | 2520 | 1.5180 | 0.3626 | | 1.484 | 8.0 | 2880 | 1.5028 | 0.3706 | | 1.4452 | 9.0 | 3240 | 1.4871 | 0.3753 | | 1.429 | 10.0 | 3600 | 1.4820 | 0.3771 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ThuyNT03/CS505-NerCOQE-xlm-Predicate

ThuyNT03

2024-03-20T23:52:18Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-20T23:45:55Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: CS505-NerCOQE-xlm-Predicate results: [] ---  # CS505-NerCOQE-xlm-Predicate This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0003 - F1: 0.9976 ## 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 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 53 | 0.1876 | 0.5087 | | No log | 2.0 | 106 | 0.1014 | 0.7119 | | No log | 3.0 | 159 | 0.0564 | 0.8287 | | No log | 4.0 | 212 | 0.0361 | 0.8835 | | No log | 5.0 | 265 | 0.0282 | 0.8951 | | No log | 6.0 | 318 | 0.0154 | 0.9392 | | No log | 7.0 | 371 | 0.0231 | 0.8730 | | No log | 8.0 | 424 | 0.0054 | 0.9763 | | No log | 9.0 | 477 | 0.0031 | 0.9792 | | No log | 10.0 | 530 | 0.0027 | 0.9828 | | No log | 11.0 | 583 | 0.0015 | 0.9905 | | No log | 12.0 | 636 | 0.0031 | 0.9929 | | No log | 13.0 | 689 | 0.0023 | 0.9941 | | No log | 14.0 | 742 | 0.0016 | 0.9923 | | No log | 15.0 | 795 | 0.0011 | 0.9917 | | No log | 16.0 | 848 | 0.0006 | 0.9964 | | No log | 17.0 | 901 | 0.0003 | 0.9988 | | No log | 18.0 | 954 | 0.0003 | 0.9976 | | No log | 19.0 | 1007 | 0.0003 | 0.9976 | | No log | 20.0 | 1060 | 0.0003 | 0.9976 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

liuylhf/empower-functions-clean-data-one-more-functions

liuylhf

2024-03-20T23:49:21Z

2

0

peft

[ "peft", "safetensors", "mixtral", "axolotl", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]

null

2024-03-20T20:35:03Z

--- license: apache-2.0 library_name: peft tags: - axolotl - generated_from_trainer base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 model-index: - name: empower-functions-clean-data-one-more-functions results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml adapter: qlora base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 bf16: true chat_template: inst dataset_prepared_path: last_run_prepared datasets: - conversation: mistral path: 659f8b7bb7c243ab879f8bc17876ce4a/data/with_function_response/more_functions/one_more_function/function_used_training.jsonl type: sharegpt - conversation: mistral path: 659f8b7bb7c243ab879f8bc17876ce4a/data/with_function_response/original_clean/function_not_used_training.jsonl type: sharegpt debug: null eval_max_new_tokens: 256 eval_steps: 0.05 eval_table_size: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: liuylhf/empower-functions-clean-data-one-more-functions learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_model_dir: null lora_r: 32 lora_target_modules: - q_proj - k_proj - v_proj - o_proj loss_watchdog_patience: 3 loss_watchdog_threshold: 5.0 lr_scheduler: cosine micro_batch_size: 2 model_config: output_router_logits: true model_type: AutoModelForCausalLM num_epochs: 1 optimizer: paged_adamw_8bit output_dir: 659f8b7bb7c243ab879f8bc17876ce4a/model pad_to_sequence_len: true sample_packing: true save_steps: 0.1 sequence_len: 4096 strict: false tf32: false tokenizer_type: LlamaTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.01 wandb_log_model: end wandb_name: more-tools wandb_project: function-call warmup_steps: 10 weight_decay: 0.0 ``` </details><br> # empower-functions-clean-data-one-more-functions This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0863 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0157 | 0.0 | 1 | 2.1200 | | 0.153 | 0.05 | 23 | 0.1454 | | 0.1236 | 0.1 | 46 | 0.1160 | | 0.1043 | 0.15 | 69 | 0.1073 | | 0.1163 | 0.2 | 92 | 0.1035 | | 0.1072 | 0.25 | 115 | 0.0996 | | 0.0988 | 0.31 | 138 | 0.0978 | | 0.0962 | 0.36 | 161 | 0.0963 | | 0.0823 | 0.41 | 184 | 0.0939 | | 0.0785 | 0.46 | 207 | 0.0938 | | 0.0941 | 0.51 | 230 | 0.0918 | | 0.0968 | 0.56 | 253 | 0.0905 | | 0.0856 | 0.61 | 276 | 0.0899 | | 0.0965 | 0.66 | 299 | 0.0895 | | 0.0894 | 0.71 | 322 | 0.0881 | | 0.086 | 0.76 | 345 | 0.0872 | | 0.0941 | 0.82 | 368 | 0.0869 | | 0.0894 | 0.87 | 391 | 0.0867 | | 0.0782 | 0.92 | 414 | 0.0864 | | 0.0815 | 0.97 | 437 | 0.0863 | ### Framework versions - PEFT 0.9.0 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.0

TomasFrankovich/esm2_t30_150M_UR50D-finetuned-SO2F

TomasFrankovich

2024-03-20T23:38:27Z

105

0

transformers

[ "transformers", "tensorboard", "safetensors", "esm", "token-classification", "generated_from_trainer", "base_model:facebook/esm2_t30_150M_UR50D", "base_model:finetune:facebook/esm2_t30_150M_UR50D", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-03-20T23:33:54Z

--- license: mit base_model: facebook/esm2_t30_150M_UR50D tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: esm2_t30_150M_UR50D-finetuned-SO2F results: [] ---  # esm2_t30_150M_UR50D-finetuned-SO2F This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6608 - Accuracy: 0.7158 - Precision: 0.1682 - Recall: 0.5068 - F1: 0.2526 - Auc: 0.6223 - Mcc: 0.1585 ## 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 - distributed_type: multi-GPU - 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 | Accuracy | Precision | Recall | F1 | Auc | Mcc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:| | No log | 1.0 | 108 | 0.6768 | 0.6886 | 0.1465 | 0.4740 | 0.2238 | 0.5925 | 0.1175 | | No log | 2.0 | 216 | 0.6646 | 0.6935 | 0.1628 | 0.5397 | 0.2502 | 0.6247 | 0.1573 | | No log | 3.0 | 324 | 0.6608 | 0.7158 | 0.1682 | 0.5068 | 0.2526 | 0.6223 | 0.1585 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

belisards/albertina_gun

belisards

2024-03-20T23:35:55Z

3

1

transformers

[ "transformers", "pytorch", "deberta-v2", "text-classification", "gun violence", "human rights", "pt", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T11:12:14Z

--- license: apache-2.0 language: - pt pipeline_tag: text-classification tags: - gun violence - human rights --- Text classification model to detect gun violence reports in Brazilian Portuguese. Albertina-PT model fine-tuned with Twitter data labelled by Instituto Fogo Cruzado. Developed as part of my research at the Oxford Internet Institute.

belisards/gun_violence_ptbr

belisards

2024-03-20T23:34:53Z

111

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "gun violence", "human rights", "pt", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-27T10:33:35Z

--- license: apache-2.0 language: - pt pipeline_tag: text-classification tags: - gun violence - human rights --- Text classification model to detect gun violence reports in Brazilian Portuguese. BERTimbau fine-tuned with Twitter data labelled by Instituto Fogo Cruzado. Developed as part of my research at the Oxford Internet Institute.

pauloguyss/mistral-7b-temas-stf-v1

pauloguyss

2024-03-20T23:28:37Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-20T23:27:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe

rajevan123

2024-03-20T23:16:37Z

5

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/deberta-v3-xsmall", "base_model:adapter:microsoft/deberta-v3-xsmall", "license:mit", "region:us" ]

null

2024-03-20T23:14:41Z

--- license: mit library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: microsoft/deberta-v3-xsmall model-index: - name: STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe results: [] ---  # STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7440 - Accuracy: 0.2429 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 180 | 1.7590 | 0.2429 | | No log | 2.0 | 360 | 1.7488 | 0.2429 | | 1.745 | 3.0 | 540 | 1.7451 | 0.2429 | | 1.745 | 4.0 | 720 | 1.7440 | 0.2429 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

krame-aims/AIMS-NLP-ASS3-FinetuneMarian-fr-en

krame-aims

2024-03-20T23:13:06Z

106

0

transformers

[ "transformers", "safetensors", "marian", "text2text-generation", "en", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-14T08:58:36Z

--- language: - en - fr metrics: - glue library_name: transformers ---

kawehiwang/lora_model_on_alpaca

kawehiwang

2024-03-20T22:55:11Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-03-20T01:52:57Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

Nekochu/Luminia-13B-v3

Nekochu

2024-03-20T22:44:32Z

47

5

peft

[ "peft", "safetensors", "gguf", "llama", "llama-factory", "lora", "generated_from_trainer", "llama2", "instruct", "finetune", "gpt4", "synthetic data", "stable diffusion", "alpaca", "llm", "text-generation", "conversational", "en", "dataset:Nekochu/discord-unstable-diffusion-SD-prompts", "dataset:glaiveai/glaive-function-calling-v2", "dataset:TIGER-Lab/MathInstruct", "dataset:Open-Orca/SlimOrca", "dataset:GAIR/lima", "dataset:sahil2801/CodeAlpaca-20k", "dataset:garage-bAInd/Open-Platypus", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:adapter:meta-llama/Llama-2-13b-chat-hf", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2024-03-18T03:25:05Z

--- model_creator: Nekochu quantized_by: Nekochu model_name: Luminia 13B v3 pretty_name: Luminia model_type: llama2 prompt_template: >- Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {Instruction} {summary} ### input: {category} ### Response: {prompt} base_model: meta-llama/Llama-2-13b-chat-hf library_name: peft license: apache-2.0 datasets: - Nekochu/discord-unstable-diffusion-SD-prompts - glaiveai/glaive-function-calling-v2 - TIGER-Lab/MathInstruct - Open-Orca/SlimOrca - GAIR/lima - sahil2801/CodeAlpaca-20k - garage-bAInd/Open-Platypus language: - en pipeline_tag: text-generation task_categories: - question-answering - text2text-generation - conversational inference: True widget: - example_title: prompt assistant messages: - role: system content: Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. - role: user content: "### Instruction:\nCreate stable diffusion metadata based on the given english description. Luminia\n### Input:\nfavorites and popular SFW\n### Response:\n" output: text: Luminia, 1girl, solo, blonde hair, long hair, tags: - llama-factory - lora - generated_from_trainer - llama2 - llama - instruct - finetune - gpt4 - synthetic data - stable diffusion - alpaca - llm model-index: - name: Luminia-13B-v3 results: [] --- <div style="display: flex;"> <div style="flex: 1;"> <img src="https://i.imgur.com/uyjdkhk.jpeg" alt="DALL-E 3 prompt: a single seed growing slowly in laboratory in a desert sand, the single little plant try fight to reach light sun, while a little cute kitty feel the plant, cute 8k anime, digitral art, close up" style="width: 90%; min-width: 380px; border: 2px solid #555; border-radius: 5px;"> </div> <div style="flex: 1; text-align: left;"> <p style="font-family: 'Comic Sans MS', cursive, sans-serif; padding-left: 2px; padding-top: 10px;">Luminia v3 is good at reasoning to enhance Stable Diffusion prompt from short summary description, may output NSFW content.</p> </div> </div> LoRa is include and Quants: exllamav2 [2.4bpw-h6](https://huggingface.co/Nekochu/Luminia-13B-v3/tree/2.4bpw-h6), [4.25bpw-h6](https://huggingface.co/Nekochu/Luminia-13B-v3/tree/4.25bpw-h6), [8.0bpw-h8](https://huggingface.co/Nekochu/Luminia-13B-v3/tree/8.0bpw-h8) | GGUF [Q4_K_M](https://huggingface.co/Nekochu/Luminia-13B-v3/blob/main/Luminia-13B-v3-Q4_K_M.gguf), [IQ4_NL](https://huggingface.co/Nekochu/Luminia-13B-v3/blob/main/Luminia-13B-v3-IQ4_NL.gguf) | ## Prompt template: Alpaca <details> <summary>Output example tested In <i>text-generation-webui</i></summary> | Input | base llama-2-chat | QLoRa | |:---------:|:-------:|:---------:| | [question]:<br><br> Create stable diffusion metadata based on the given english description. Luminia \n### Input:\n favorites and popular SFW | Answer:<br><br> Luminia, a mystical world of wonder and magic 🧝‍♀️✨ A place where technology and nature seamlessly blend together ... | Answer! <br><br> < lora:Luminari-10:0.8> Luminari, 1girl, solo, blonde hair, long hair, blue eyes, (black dress), looking at viewer, night sky, starry sky, constellation, smile, upper body, outdoors, forest, moon, tree, mountain, light particle .... | Output prompt from QLoRa to [A1111/SD-WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui): <div style="display: flex; justify-content: space-between;"> <div style="flex: 1; text-align: center;"> <img src="https://i.imgur.com/rNLaobj.png" alt="parameters image metadata: <lora:Luminari-10:0.8> Luminari, 1girl, solo, blonde hair, long hair, blue eyes, (black dress), looking at viewer, night sky, starry sky, constellation, smile, upper body, outdoors, forest, moon, tree, mountain, light particle, shine, sparkle, dark theme, fantasy, magic, goddess, celestial, nature, peaceful, serene, tranquil, mystical, enchanting, otherworldly, mysterious, captivating, alluring, beautiful, elegant, graceful, majestic, divine, powerful, epic, grand, sweeping, breathtaking, mesmerizing, magical, fantastical, wondrous, marvelous, extraordinary, magnificent, glorious, radiant, luminous, illumination, brilliance, glow, radiance, luminescence, brightness, splendor, glory, triumph, victory, achievement, honor, celebration, recognition, praise, admiration, appreciation, love, affection, devotion, loyalty, dedication, commitment, passion, intensity, drive, determination, energy, enthusiasm, excitement, joy, happiness, fulfillment, pleasure, enjoyment, satisfaction, delight, wonder, amazement, awe, curiosity, interest, intrigue, question, exploration, discovery, adventure, journey, path, road, trail, course, pursuit, challenge, obstacle, adversity, hardship, struggle, perseverance, resilience, tenacity, courage, bravery, heroism, inspiration, motivation, spirit, heart, soul, essence, creativity, imagination, dreams, aspirations, goals, ambition, vision, purpose, meaning, significance, relevance, importance, impact, influence, change, growth, development, evolution, improvement, progress, learning, knowledge, wisdom, insight, understanding, empathy, compassion, kindness, generosity, forgiveness, gratitude, humility, patience, tolerance, acceptance, diversity, inclusivity, unity, equality, justice, fairness, honesty, integrity, accountability, responsibility, morality, ethics, principles, values, beliefs, faith, hope, optimism, Steps: 20, Sampler: Heun, CFG scale: 7, Seed: 479539365, Size: 512x512, Model hash: 84d76a0328, Model: epicrealism_naturalSinRC1VAE, Version: v1.7.0" style="width: 100%; min-width: 200px; display: block; margin: auto;"> </div> <div style="flex: 1; text-align: center;"> <img src="https://i.imgur.com/hU8Ut4p.png" alt="parameters image metadata: <lora:Luminari-10:0.8> Luminari, 1girl, solo, blonde hair, long hair, blue eyes, (black dress), looking at viewer, night sky, starry sky, constellation, smile, upper body, outdoors, forest, moon, tree, mountain, light particle, shine, sparkle, dark theme, fantasy, magic, goddess, celestial, nature, peaceful, serene, tranquil, mystical, enchanting, otherworldly, mysterious, captivating, alluring, beautiful, elegant, graceful, majestic, divine, powerful, epic, grand, sweeping, breathtaking, mesmerizing, magical, fantastical, wondrous, marvelous, extraordinary, magnificent, glorious, radiant, luminous, illumination, brilliance, glow, radiance, luminescence, brightness, splendor, glory, triumph, victory, achievement, honor, celebration, recognition, praise, admiration, appreciation, love, affection, devotion, loyalty, dedication, commitment, passion, intensity, drive, determination, energy, enthusiasm, excitement, joy, happiness, fulfillment, pleasure, enjoyment, satisfaction, delight, wonder, amazement, awe, curiosity, interest, intrigue, question, exploration, discovery, adventure, journey, path, road, trail, course, pursuit, challenge, obstacle, adversity, hardship, struggle, perseverance, resilience, tenacity, courage, bravery, heroism, inspiration, motivation, spirit, heart, soul, essence, creativity, imagination, dreams, aspirations, goals, ambition, vision, purpose, meaning, significance, relevance, importance, impact, influence, change, growth, development, evolution, improvement, progress, learning, knowledge, wisdom, insight, understanding, empathy, compassion, kindness, generosity, forgiveness, gratitude, humility, patience, tolerance, acceptance, diversity, inclusivity, unity, equality, justice, fairness, honesty, integrity, accountability, responsibility, morality, ethics, principles, values, beliefs, faith, hope, optimism Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 959582434, Size: 512x512, Model hash: 84d76a0328, Model: epicrealism_naturalSinRC1VAE, Version: v1.7.0" style="width: 100%; min-width: 200px; display: block; margin: auto;"> </div> </div> #### Full Prompt ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Create stable diffusion metadata based on the given english description. Luminia ### Input: favorites and popular SFW ### Response: ``` "Luminia" can be any short description, more info on my SD dataset [here](https://huggingface.co/datasets/Nekochu/discord-unstable-diffusion-SD-prompts#dataset-description). </details> ## Training Details <details> <summary>Click to see details</summary> ### Model Description - **Train by:** [Nekochu](https://huggingface.co/Nekochu), **Model type:** Llama, **Finetuned from model [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)** - Continue from the base of LoRA Luminia-13B-v2-QLora Know issue: [issue] ### Trainer - hiyouga/LLaMA-Efficient-Tuning Hardware: QLoRA training OS Windows, Python 3.10.8, CUDA 12.1 on 24GB VRAM. ### Training hyperparameters The following hyperparameters were used during training: - num_epochs: 1.0 - finetuning_type: lora - quantization_bit: 4 - stage: sft - learning_rate: 5e-05 - cutoff_len: 4096 - num_train_epochs: 3.0 - max_samples: 100000 - warmup_steps: 0 - train_batch_size: 1 - distributed_type: single-GPU - num_devices: 1 - warmup_steps: 0 - rope_scaling: linear - lora_rank: 32 - lora_target: all - lora_dropout: 0.15 - bnb_4bit_compute_dtype: bfloat16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine #### training_loss: <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/qhuPG6F.jpg" alt="Nekochu" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.0 </details>

llmware/slim-extract

llmware

2024-03-20T22:38:03Z

135

12

transformers

[ "transformers", "pytorch", "stablelm_epoch", "text-generation", "custom_code", "license:cc-by-sa-4.0", "autotrain_compatible", "region:us" ]

text-generation

2024-03-17T15:43:04Z

--- license: cc-by-sa-4.0 inference: false --- # SLIM-EXTRACT  **slim-extract** implements a specialized function-calling customizable 'extract' capability that takes as an input a context passage, a customized key, and outputs a python dictionary with key that corresponds to the customized key, with a value consisting of a list of items extracted from the text corresponding to that key, e.g.,     `{'universities': ['Berkeley, Stanford, Yale, University of Florida, ...'] }` This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt. For fast inference use, we would recommend the 'quantized tool' version, e.g., [**'slim-extract-tool'**](https://huggingface.co/llmware/slim-extract-tool). ## Prompt format: `function = "extract"` `params = "{custom key}"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-extract") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-extract") function = "extract" params = "company" text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-extract") response = slim_model.function_call(text,params=["company"], function="extract") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)

arcee-ai/Patent-Instruct-LLaMA-Pro

arcee-ai

2024-03-20T22:26:26Z

11

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "arcee-ai/Patent-Instruct-7b", "TencentARC/LLaMA-Pro-8B-Instruct", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-20T22:07:42Z

--- license: apache-2.0 tags: - merge - mergekit - arcee-ai/Patent-Instruct-7b - TencentARC/LLaMA-Pro-8B-Instruct --- # Patent-Instruct-LLaMA-Pro Patent-Instruct-LLaMA-Pro is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [arcee-ai/Patent-Instruct-7b](https://huggingface.co/arcee-ai/Patent-Instruct-7b) * [TencentARC/LLaMA-Pro-8B-Instruct](https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct) ## 🧩 Configuration ```yaml merge_method: passthrough dtype: bfloat16 slices: - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 0 - 4 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 4 - 5 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 4 - 8 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 9 - 10 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 8 - 12 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 14 - 15 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 12 - 16 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 19 - 20 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 16 - 20 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 24 - 25 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 20 - 24 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 29 - 30 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 24 - 28 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 34 - 35 - sources: - model: arcee-ai/Patent-Instruct-7b layer_range: - 28 - 32 - sources: - model: TencentARC/LLaMA-Pro-8B-Instruct layer_range: - 39 - 40 ```

yzimmermann/FART

yzimmermann

2024-03-20T22:24:06Z

108

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "doi:10.57967/hf/1946", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-20T21:58:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

MagiskaGodnattsagor/aida_LoRA

MagiskaGodnattsagor

2024-03-20T22:23:37Z

2

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-03-20T22:08:26Z

--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK robot widget: [] ---  # SDXL LoRA DreamBooth - MagiskaGodnattsagor/aida_LoRA <Gallery /> ## Model description These are MagiskaGodnattsagor/aida_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK robot to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](MagiskaGodnattsagor/aida_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

Joseph717171/BigOrca-2-12B

Joseph717171

2024-03-20T22:22:51Z

0

null

[ "safetensors", "orca", "orca2", "microsoft", "text-generation", "arxiv:2311.11045", "license:other", "region:us" ]

text-generation

2024-03-15T02:57:28Z

--- pipeline_tag: text-generation tags: - orca - orca2 - microsoft license_name: microsoft-research-license license_link: LICENSE license: other --- Inspired by [AbucusAI's BigYi-15b](https://huggingface.co/abacusai/bigyi-15b)... This is [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b) with layers interleaved to create a larger 12b model. This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 8] model: microsoft/Orca-2-7b - sources: - layer_range: [4, 12] model: microsoft/Orca-2-7b - sources: - layer_range: [8, 16] model: microsoft/Orca-2-7b - sources: - layer_range: [12, 20] model: microsoft/Orca-2-7b - sources: - layer_range: [16, 24] model: microsoft/Orca-2-7b - sources: - layer_range: [20, 28] model: microsoft/Orca-2-7b - sources: - layer_range: [24, 32] model: microsoft/Orca-2-7b ``` # Orca 2  Orca 2 is built for research purposes only and provides a single turn response in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. The model is designed to excel particularly in reasoning. Note that: 1. This is a research model, intended to show that we can use capable models and complex workflows (advanced prompts, multiple calls) to create synthetic data that can teach Small Language Models (SLMs) new capabilities. We chose reasoning because it is a widely useful capability that SLMs lack. 2. The model is not optimized for chat and has not been trained with RLHF or DPO. It is best used after being finetuned for chat or for a specific task. 3. Beyond reasoning, the model inherits capabilities and limitations of its base (LLAMA-2 base). We have already seen that the benefits of the Orca training can be applied to other base model too. We make Orca 2's weights publicly available to support further research on the development, evaluation, and alignment of SLMs. ## What is Orca 2’s intended use(s)? + Orca 2 is built for research purposes only. + The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models. ## How was Orca 2 evaluated? + Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer to Section 6 and Appendix in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf) for details on evaluations. ## Model Details Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf). Please refer to LLaMA-2 technical report for details on the model architecture. ## License Orca 2 is licensed under the [Microsoft Research License](LICENSE). Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. ## Bias, Risks, and Limitations Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the common limitations of other large language models or limitation caused by its training process, including: **Data Biases**: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair. **Lack of Contextual Understanding**: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting in potential inaccuracies or nonsensical responses. **Lack of Transparency**: Due to the complexity and size, large language models can act as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or decisions. We recommend reviewing transparency notes from Azure for more information. **Content Harms**: There are various types of content harms that large language models can cause. It is important to be aware of them when using these models, and to take actions to prevent them. It is recommended to leverage various content moderation services provided by different companies and institutions. On an important note, we hope for better regulations and standards from government and technology leaders around content harms for AI technologies in future. We value and acknowledge the important role that research and open source community can play in this direction. **Hallucination**: It is important to be aware and cautious not to entirely rely on a given language model for critical decisions or information that might have deep impact as it is not obvious how to prevent these models from fabricating content. Moreover, it is not clear whether small models may be more susceptible to hallucination in ungrounded generation use cases due to their smaller sizes and hence reduced memorization capacities. This is an active research topic and we hope there will be more rigorous measurement, understanding and mitigations around this topic. **Potential for Misuse**: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content. **Data Distribution**: Orca 2’s performance is likely to correlate strongly with the distribution of the tuning data. This correlation might limit its accuracy in areas underrepresented in the training dataset such as math, coding, and reasoning. **System messages**: Orca 2 demonstrates variance in performance depending on the system instructions. Additionally, the stochasticity introduced by the model size may lead to generation of non-deterministic responses to different system instructions. **Zero-Shot Settings**: Orca 2 was trained on data that mostly simulate zero-shot settings. While the model demonstrate very strong performance in zero-shot settings, it does not show the same gains of using few-shot learning compared to other, specially larger, models. **Synthetic data**: As Orca 2 is trained on synthetic data, it could inherit both the advantages and shortcomings of the models and methods used for data generation. We posit that Orca 2 benefits from the safety measures incorporated during training and safety guardrails (e.g., content filter) within the Azure OpenAI API. However, detailed studies are required for better quantification of such risks. This model is solely designed for research settings, and its testing has only been carried out in such environments. It should not be used in downstream applications, as additional analysis is needed to assess potential harm or bias in the proposed application. ## Getting started with Orca 2 **Inference with Hugging Face library** ```python import torch import transformers if torch.cuda.is_available(): torch.set_default_device("cuda") else: torch.set_default_device("cpu") model = transformers.AutoModelForCausalLM.from_pretrained("microsoft/Orca-2-7b", device_map='auto') # https://github.com/huggingface/transformers/issues/27132 # please use the slow tokenizer since fast and slow tokenizer produces different tokens tokenizer = transformers.AutoTokenizer.from_pretrained( "microsoft/Orca-2-7b", use_fast=False, ) system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior." user_message = "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?" prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" inputs = tokenizer(prompt, return_tensors='pt') output_ids = model.generate(inputs["input_ids"],) answer = tokenizer.batch_decode(output_ids)[0] print(answer) # This example continues showing how to add a second turn message by the user to the conversation second_turn_user_message = "Give me a list of the key points of your first answer." # we set add_special_tokens=False because we dont want to automatically add a bos_token between messages second_turn_message_in_markup = f"\n<|im_start|>user\n{second_turn_user_message}<|im_end|>\n<|im_start|>assistant" second_turn_tokens = tokenizer(second_turn_message_in_markup, return_tensors='pt', add_special_tokens=False) second_turn_input = torch.cat([output_ids, second_turn_tokens['input_ids']], dim=1) output_ids_2 = model.generate(second_turn_input,) second_turn_answer = tokenizer.batch_decode(output_ids_2)[0] print(second_turn_answer) ``` **Safe inference with Azure AI Content Safety** The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged and can help preventing some of content harms. Azure AI Content Safety is a content moderation platform that uses AI to moderate content. By having Azure AI Content Safety on the output of Orca 2, the model output can be moderated by scanning it for different harm categories including sexual content, violence, hate, and self-harm with multiple severity levels and multi-lingual detection. ```python import os import math import transformers import torch from azure.ai.contentsafety import ContentSafetyClient from azure.core.credentials import AzureKeyCredential from azure.core.exceptions import HttpResponseError from azure.ai.contentsafety.models import AnalyzeTextOptions CONTENT_SAFETY_KEY = os.environ["CONTENT_SAFETY_KEY"] CONTENT_SAFETY_ENDPOINT = os.environ["CONTENT_SAFETY_ENDPOINT"] # We use Azure AI Content Safety to filter out any content that reaches "Medium" threshold # For more information: https://learn.microsoft.com/en-us/azure/ai-services/content-safety/ def should_filter_out(input_text, threshold=4): # Create an Content Safety client client = ContentSafetyClient(CONTENT_SAFETY_ENDPOINT, AzureKeyCredential(CONTENT_SAFETY_KEY)) # Construct a request request = AnalyzeTextOptions(text=input_text) # Analyze text try: response = client.analyze_text(request) except HttpResponseError as e: print("Analyze text failed.") if e.error: print(f"Error code: {e.error.code}") print(f"Error message: {e.error.message}") raise print(e) raise categories = ["hate_result", "self_harm_result", "sexual_result", "violence_result"] max_score = -math.inf for category in categories: max_score = max(max_score, getattr(response, category).severity) return max_score >= threshold model_path = 'microsoft/Orca-2-7b' device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = transformers.AutoModelForCausalLM.from_pretrained(model_path) model.to(device) tokenizer = transformers.AutoTokenizer.from_pretrained( model_path, model_max_length=4096, padding_side="right", use_fast=False, add_special_tokens=False, ) system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior." user_message = "\" \n :You can't just say, \"\"that's crap\"\" and remove it without gaining a consensus. You already know this, based on your block history. —/ \" \nIs the comment obscene? \nOptions : Yes, No." prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" inputs = tokenizer(prompt, return_tensors='pt') inputs = inputs.to(device) output_ids = model.generate(inputs["input_ids"], max_length=4096, do_sample=False, temperature=0.0, use_cache=True) sequence_length = inputs["input_ids"].shape[1] new_output_ids = output_ids[:, sequence_length:] answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True) final_output = answers[0] if not should_filter_out(answers[0]) else "[Content Filtered]" print(final_output) ``` ## Citation ```bibtex @misc{mitra2023orca, title={Orca 2: Teaching Small Language Models How to Reason}, author={Arindam Mitra and Luciano Del Corro and Shweti Mahajan and Andres Codas and Clarisse Simoes and Sahaj Agrawal and Xuxi Chen and Anastasia Razdaibiedina and Erik Jones and Kriti Aggarwal and Hamid Palangi and Guoqing Zheng and Corby Rosset and Hamed Khanpour and Ahmed Awadallah}, year={2023}, eprint={2311.11045}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

ingeol/q2e_ep3_1234

ingeol

2024-03-20T22:22:31Z

4

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-20T22:21:52Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2e_ep3_1234 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2e_ep3_1234') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2e_ep3_1234') model = AutoModel.from_pretrained('ingeol/q2e_ep3_1234') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2e_ep3_1234) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

tjl223/song-artist-classifier-v2

tjl223

2024-03-20T22:20:08Z

118

0

transformers

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

text-classification

2024-03-20T22:06:02Z

--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: song-artist-classifier-v2 results: [] ---  # song-artist-classifier-v2 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0725 - F1: [0.9473684210526316, 0.6666666666666666, 0.8181818181818182, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.2, 0.7368421052631577, 0.8695652173913044, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.7826086956521738, 0.5714285714285713, 0.7200000000000001, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777] ## 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 1.0 | 95 | 2.4874 | [0.4347826086956522, 0.5142857142857143, 0.0, 0.5, 0.23529411764705882, 0.33333333333333337, 0.0, 0.0, 0.0, 0.0, 0.3636363636363636, 0.380952380952381, 0.0, 0.0, 0.43243243243243246, 0.0, 0.5, 0.0, 0.18181818181818182, 0.35294117647058826] | | No log | 2.0 | 190 | 2.0485 | [0.588235294117647, 0.48, 0.625, 0.7499999999999999, 0.3870967741935483, 0.16666666666666669, 0.0, 0.0, 0.0, 0.5625, 0.6428571428571429, 0.45454545454545453, 0.18181818181818182, 0.35714285714285715, 0.25, 0.6956521739130435, 0.4347826086956522, 0.0, 0.48, 0.7] | | No log | 3.0 | 285 | 1.7309 | [0.9, 0.6666666666666667, 0.7200000000000001, 0.7499999999999999, 0.6428571428571429, 0.588235294117647, 0.18181818181818182, 0.7000000000000001, 0.0, 0.5882352941176471, 0.6896551724137931, 0.5217391304347826, 0.0, 0.2727272727272727, 0.8000000000000002, 0.8000000000000002, 0.6956521739130435, 0.0, 0.5, 0.8235294117647058] | | No log | 4.0 | 380 | 1.4777 | [0.8571428571428572, 0.7200000000000001, 0.8181818181818182, 0.8235294117647058, 0.5263157894736842, 0.625, 0.30769230769230765, 0.6956521739130436, 0.0, 0.5, 0.8695652173913044, 0.5925925925925927, 0.5, 0.2105263157894737, 0.8000000000000002, 0.6666666666666666, 0.6956521739130435, 0.4, 0.33333333333333326, 0.7777777777777777] | | No log | 5.0 | 475 | 1.3535 | [0.9, 0.75, 0.8181818181818182, 0.8235294117647058, 0.64, 0.625, 0.37499999999999994, 0.7272727272727273, 0.0, 0.7000000000000001, 0.8695652173913044, 0.5454545454545454, 0.15384615384615383, 0.3157894736842105, 0.6956521739130435, 0.7272727272727272, 0.7826086956521738, 0.4, 0.631578947368421, 0.7777777777777777] | | 1.8726 | 6.0 | 570 | 1.2614 | [0.9, 0.7272727272727272, 0.8333333333333333, 0.7499999999999999, 0.6363636363636365, 0.7368421052631577, 0.4285714285714285, 0.761904761904762, 0.0, 0.5, 0.7407407407407407, 0.608695652173913, 0.4285714285714285, 0.3, 0.7272727272727272, 0.7272727272727272, 0.75, 0.6666666666666666, 0.4210526315789474, 0.8235294117647058] | | 1.8726 | 7.0 | 665 | 1.1649 | [0.9473684210526316, 0.7272727272727272, 0.8333333333333333, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.47058823529411764, 0.761904761904762, 0.0, 0.7000000000000001, 0.8695652173913044, 0.7272727272727272, 0.6666666666666666, 0.20000000000000004, 0.6956521739130435, 0.6956521739130435, 0.7826086956521738, 0.5714285714285715, 0.5333333333333333, 0.7777777777777777] | | 1.8726 | 8.0 | 760 | 1.1142 | [0.9473684210526316, 0.7272727272727272, 0.8181818181818182, 0.6666666666666665, 0.761904761904762, 0.7368421052631577, 0.4444444444444445, 0.761904761904762, 0.22222222222222224, 0.7058823529411765, 0.8333333333333333, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.8571428571428572, 0.7272727272727272, 0.7826086956521738, 0.6666666666666666, 0.4444444444444445, 0.7777777777777777] | | 1.8726 | 9.0 | 855 | 1.0813 | [0.9473684210526316, 0.7272727272727272, 0.8695652173913044, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.22222222222222224, 0.7368421052631577, 0.9090909090909091, 0.7272727272727272, 0.47058823529411764, 0.22222222222222224, 0.7826086956521738, 0.608695652173913, 0.75, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777] | | 1.8726 | 10.0 | 950 | 1.0725 | [0.9473684210526316, 0.6666666666666666, 0.8181818181818182, 0.6666666666666665, 0.631578947368421, 0.7368421052631577, 0.4444444444444445, 0.7272727272727273, 0.2, 0.7368421052631577, 0.8695652173913044, 0.7272727272727272, 0.47058823529411764, 0.2105263157894737, 0.7826086956521738, 0.5714285714285713, 0.7200000000000001, 0.6666666666666666, 0.5333333333333333, 0.7777777777777777] | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

rk68/phi-1_5-finetuned-aqua-rat-2k

rk68

2024-03-20T22:18:34Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-1_5", "base_model:adapter:microsoft/phi-1_5", "license:mit", "region:us" ]

null

2024-03-20T22:13:49Z

--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-1_5 model-index: - name: phi-1_5-finetuned-aqua-rat-2k results: [] ---  # phi-1_5-finetuned-aqua-rat-2k This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Kaipbkk/mown

Kaipbkk

2024-03-20T22:17:01Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2024-03-20T22:17:01Z

--- license: bigscience-bloom-rail-1.0 ---

Yukiea/ppo-Huggy

Yukiea

2024-03-20T22:09:32Z

9

0

ml-agents

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

reinforcement-learning

2024-03-20T22:04:12Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Yukiea/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ucmp137538/gpt2-wikitext2

ucmp137538

2024-03-20T22:09:04Z

197

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-20T21:46:39Z

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] ---  # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 9.4906 - eval_runtime: 949.071 - eval_samples_per_second: 2.038 - eval_steps_per_second: 0.255 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

mychen76/tinyllama_alpaca_GGUF

mychen76

2024-03-20T22:08:00Z

3

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:quantized:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-03-20T22:00:39Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - **Developed by:** mychen76 - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

xiangjhe/gemma-7b-gguf

xiangjhe

2024-03-20T22:03:58Z

1

0

null

[ "gguf", "license:other", "endpoints_compatible", "region:us" ]

null

2024-03-20T05:54:03Z

--- license: other license_name: google-gemma license_link: https://ai.google.dev/gemma/terms ---

mychen76/tinyllama_alpaca_lora

mychen76

2024-03-20T21:49:50Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "base_model:finetune:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-03-20T21:49:10Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - **Developed by:** mychen76 - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-new-model-test

rajevan123

2024-03-20T21:49:00Z

3

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:yangheng/deberta-v3-base-absa-v1.1", "base_model:adapter:yangheng/deberta-v3-base-absa-v1.1", "license:mit", "region:us" ]

null

2024-03-20T21:44:22Z

--- license: mit library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: yangheng/deberta-v3-base-absa-v1.1 model-index: - name: STS-Lora-Fine-Tuning-Capstone-Deberta-new-model-test results: [] ---  # STS-Lora-Fine-Tuning-Capstone-Deberta-new-model-test This model is a fine-tuned version of [yangheng/deberta-v3-base-absa-v1.1](https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6535 - Accuracy: 0.2582 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 180 | 1.7212 | 0.2408 | | No log | 2.0 | 360 | 1.6900 | 0.2429 | | 1.6889 | 3.0 | 540 | 1.6644 | 0.2495 | | 1.6889 | 4.0 | 720 | 1.6535 | 0.2582 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

TheZennou/command-r-v01-exl2-8bit

TheZennou

2024-03-20T21:46:49Z

0

null

[ "license:cc-by-nc-4.0", "region:us" ]

null

2024-03-20T21:46:17Z

--- license: cc-by-nc-4.0 --- Model Summary C4AI Command-R is a research release of a 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities. Developed by: Cohere and Cohere For AI Quanted to 8bpw using Exllama2.

neopolita/starling-lm-7b-beta-gguf

neopolita

2024-03-20T21:30:21Z

16

1

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-03-20T20:29:44Z

--- {} --- # GGUF quants for [**Nexusflow/Starling-LM-7B-beta**](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) using [llama.cpp](https://github.com/ggerganov/llama.cpp) **Terms of Use**: Please check the [**original model**](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

m4faisal/NLI-Lora-Fine-Tuning-10K-ALBERTA

m4faisal

2024-03-20T21:29:21Z

4

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:albert/albert-base-v2", "base_model:adapter:albert/albert-base-v2", "license:apache-2.0", "region:us" ]

null

2024-03-20T21:11:40Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: albert/albert-base-v2 model-index: - name: NLI-Lora-Fine-Tuning-10K-ALBERTA results: [] ---  # NLI-Lora-Fine-Tuning-10K-ALBERTA This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8439 - Accuracy: 0.6063 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 312 | 1.0562 | 0.4551 | | 1.0762 | 2.0 | 624 | 1.0236 | 0.4995 | | 1.0762 | 3.0 | 936 | 0.9603 | 0.5361 | | 1.0075 | 4.0 | 1248 | 0.9053 | 0.5671 | | 0.9178 | 5.0 | 1560 | 0.8796 | 0.5823 | | 0.9178 | 6.0 | 1872 | 0.8649 | 0.5934 | | 0.8859 | 7.0 | 2184 | 0.8551 | 0.5977 | | 0.8859 | 8.0 | 2496 | 0.8488 | 0.6033 | | 0.8632 | 9.0 | 2808 | 0.8450 | 0.6057 | | 0.8543 | 10.0 | 3120 | 0.8439 | 0.6063 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ymgong/distil_train_token_classification_2

ymgong

2024-03-20T21:22:16Z

109

0

transformers

[ "transformers", "pytorch", "distilbert", "token-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" ]

token-classification

2024-03-20T21:13:11Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distil_train_token_classification_2 results: [] ---  # distil_train_token_classification_2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7646 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8112 ## 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: 8e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:| | 0.5429 | 1.0 | 3681 | 0.5346 | 0.0 | 0.0 | 0.0 | 0.7848 | | 0.4387 | 2.0 | 7362 | 0.5033 | 0.0 | 0.0 | 0.0 | 0.8034 | | 0.3288 | 3.0 | 11043 | 0.4983 | 0.0 | 0.0 | 0.0 | 0.8101 | | 0.2436 | 4.0 | 14724 | 0.5736 | 0.0 | 0.0 | 0.0 | 0.8086 | | 0.1677 | 5.0 | 18405 | 0.6681 | 0.0 | 0.0 | 0.0 | 0.8107 | | 0.1162 | 6.0 | 22086 | 0.7646 | 0.0 | 0.0 | 0.0 | 0.8112 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

biunlp/LongMt5-HeSum

biunlp

2024-03-20T21:14:25Z

109

0

transformers

[ "transformers", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "base_model:agemagician/mlong-t5-tglobal-base", "base_model:finetune:agemagician/mlong-t5-tglobal-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-20T20:59:14Z

--- license: apache-2.0 base_model: agemagician/mlong-t5-tglobal-base tags: - generated_from_trainer metrics: - rouge model-index: - name: mlong-t5-tglobal-base results: [] ---  # mlong-t5-tglobal-base This model is a fine-tuned version of [agemagician/mlong-t5-tglobal-base](https://huggingface.co/agemagician/mlong-t5-tglobal-base) on an HeSum dataset. It achieves the following results on the evaluation set: - Loss: 2.1091 - Rouge1: 31.6099 - Rouge2: 12.9182 - Rougel: 23.8053 - Rougelsum: 25.5362 - Gen Len: 59.758 ## 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: 1 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | RougeL | RougeLSum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 500 | 2.2709 | 20.5043 | 8.1518 | 16.9526 | 17.5001 | | 2.8714 | 2.0 | 1000 | 2.2022 | 21.4051 | 8.7445 | 17.7534 | 18.3191 | | 2.8714 | 3.0 | 1500 | 2.1608 | 21.6609 | 9.1753 | 18.0374 | 18.6176 | | 2.5137 | 4.0 | 2000 | 2.1555 | 21.6818 | 9.1814 | 18.0382 | 18.6198 | | 2.5137 | 5.0 | 2500 | 2.1462 | 21.9708 | 9.2033 | 18.3919 | 18.9535 | | 2.3717 | 6.0 | 3000 | 2.1258 | 22.0583 | 9.2987 | 18.4379 | 19.0322 | | 2.3717 | 7.0 | 3500 | 2.1278 | 21.8245 | 9.0474 | 18.1979 | 18.8038 | | 2.2633 | 8.0 | 4000 | 2.1207 | 21.6273 | 8.8847 | 18.024 | 18.6049 | | 2.2633 | 9.0 | 4500 | 2.1180 | 22.2004 | 9.6253 | 18.6373 | 19.1721 | | 2.1886 | 10.0 | 5000 | 2.1220 | 22.1619 | 9.6206 | 18.5069 | 19.0856 | | 2.1886 | 11.0 | 5500 | 2.1161 | 22.1518 | 9.4522 | 18.4695 | 19.0552 | | 2.1144 | 12.0 | 6000 | 2.1103 | 22.0395 | 9.4185 | 18.4314 | 19.0305 | | 2.1144 | 13.0 | 6500 | 2.1150 | 22.2404 | 9.4722 | 18.5482 | 19.1747 | | 2.054 | 14.0 | 7000 | 2.1091 | 22.1466 | 9.3434 | 18.3443 | 18.9233 | | 2.0526 | 15.0 | 8000 | 2.1580 | 30.4149 | 2.0774 | 22.9493 | 24.4478 | | 2.1236 | 16.0 | 16000 | 2.1621 | 31.3101 | 13.3237 | 23.8249 | 25.526 | | 2.0776 | 17.0 | 24000 | 2.1607 | 30.9902 | 12.3753 | 23.0243 | 24.8308 | | 1.9843 | 18.0 | 32000 | 2.1553 | 32.0603 | 13.4985 | 24.0775 | 25.9692 | ### Framework versions - Transformers 4.38.2 - Pytorch 1.13.1+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2

Barreto-G/Taxi-v3

Barreto-G

2024-03-20T21:09:10Z

0

null

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

reinforcement-learning

2024-03-20T21:09:06Z

--- 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: 7.50 +/- 2.72 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="Barreto-G/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"]) ```

suryaR-15/twitter-sentiment-extraction-distilbert

suryaR-15

2024-03-20T21:06:53Z

119

0

transformers

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

text-classification

2024-02-21T09:59:38Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: twitter-sentiment-extraction-distilbert results: [] ---  # twitter-sentiment-extraction-distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [Tweet Sentiment Extraction](https://www.kaggle.com/competitions/tweet-sentiment-extraction) dataset. It achieves the following results on the evaluation set: - Loss: 0.3328 - Accuracy: 0.8903 - F1: 0.8903 ### 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 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

naivoder/ppo-Huggy

naivoder

2024-03-20T21:02:19Z

2

0

ml-agents

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

reinforcement-learning

2024-03-20T21:01:39Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: naivoder/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

thrunlab/Mistral_Sparse_refined_web_90p_2024-03-20

thrunlab

2024-03-20T20:55:11Z

4

0

transformers

[ "transformers", "safetensors", "sparse_mistral", "text-generation", "generated_from_trainer", "custom_code", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "region:us" ]

text-generation

2024-03-20T20:15:21Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: Mistral_Sparse_refined_web_90p_2024-03-20 results: [] ---  # Mistral_Sparse_refined_web_90p_2024-03-20 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.5087 ## 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: 1 - eval_batch_size: 1 - seed: 0 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

Prathamesh25/Llama-2-7b-finetune-university

Prathamesh25

2024-03-20T20:51:27Z

0

peft

[ "peft", "region:us" ]

null

2024-03-20T20:43:48Z

--- 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 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0

MVRL/GeoSynth-OSM

MVRL

2024-03-20T20:51:18Z

26

0

diffusers

[ "diffusers", "safetensors", "controlnet", "stable-diffusion", "satellite-imagery", "OSM", "image-to-image", "arxiv:2302.05543", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:apache-2.0", "region:us" ]

image-to-image

2024-03-17T17:46:52Z

--- library_name: diffusers base_model: stabilityai/stable-diffusion-2-1-base license: apache-2.0 widget: - src: osm_tile_18_42048_101323.jpeg prompt: Satellite image features a city neighborhood tags: - controlnet - stable-diffusion - satellite-imagery - OSM pipeline_tag: image-to-image --- # Model Card for Model ID  This is a ControlNet based model that synthesizes satellite images given OpenStreetMap Images. The base stable diffusion model used is [stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) (v2-1_512-ema-pruned.ckpt). * Use it with 🧨 [diffusers](#examples) * Use it with [controlnet](https://github.com/lllyasviel/ControlNet/tree/main?tab=readme-ov-file) repository ### Model Sources [optional]  - **Repository:** [stable-diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) - **Paper:** [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) ## Examples ```python from diffusers import StableDiffusionControlNetPipeline, ControlNetModel import torch from PIL import Image img = Image.open("osm_tile_18_42048_101323.jpeg") controlnet = ControlNetModel.from_pretrained("MVRL/GeoSynth-OSM") pipe = StableDiffusionControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", controlnet=controlnet) pipe = pipe.to("cuda:0") # generate image generator = torch.manual_seed(10345340) image = pipe( "Satellite image features a city neighborhood", generator=generator, image=img, ).images[0] image.save("generated_city.jpg") ```  ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sharren/vit-augmentation

sharren

2024-03-20T20:47:25Z

192

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-03-20T20:15:14Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: vit-augmentation results: [] ---  # vit-augmentation This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset. It achieves the following results on the evaluation set: - Loss: 0.4287 - Accuracy: 0.8592 - Precision: 0.8580 - Recall: 0.8592 - F1: 0.8574 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 770 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9124 | 1.0 | 321 | 0.6025 | 0.7805 | 0.7788 | 0.7805 | 0.7683 | | 0.5876 | 2.0 | 642 | 0.5819 | 0.7864 | 0.7990 | 0.7864 | 0.7820 | | 0.5415 | 3.0 | 963 | 0.6149 | 0.8041 | 0.7943 | 0.8041 | 0.7865 | | 0.4815 | 4.0 | 1284 | 0.4654 | 0.8294 | 0.8259 | 0.8294 | 0.8115 | | 0.4263 | 5.0 | 1605 | 0.5481 | 0.8259 | 0.8315 | 0.8259 | 0.8023 | | 0.3515 | 6.0 | 1926 | 0.4287 | 0.8592 | 0.8580 | 0.8592 | 0.8574 | | 0.3144 | 7.0 | 2247 | 0.5005 | 0.8363 | 0.8320 | 0.8363 | 0.8270 | | 0.2736 | 8.0 | 2568 | 0.5306 | 0.8294 | 0.8448 | 0.8294 | 0.8302 | | 0.2519 | 9.0 | 2889 | 0.4733 | 0.8578 | 0.8534 | 0.8578 | 0.8534 | | 0.2227 | 10.0 | 3210 | 0.4905 | 0.8585 | 0.8520 | 0.8585 | 0.8512 | | 0.1724 | 11.0 | 3531 | 0.5050 | 0.8655 | 0.8671 | 0.8655 | 0.8628 | | 0.1596 | 12.0 | 3852 | 0.5263 | 0.8686 | 0.8657 | 0.8686 | 0.8631 | | 0.1397 | 13.0 | 4173 | 0.7043 | 0.8533 | 0.8703 | 0.8533 | 0.8488 | | 0.1298 | 14.0 | 4494 | 0.6275 | 0.8679 | 0.8734 | 0.8679 | 0.8632 | | 0.1029 | 15.0 | 4815 | 0.5564 | 0.8807 | 0.8776 | 0.8807 | 0.8772 | | 0.0893 | 16.0 | 5136 | 0.5668 | 0.8804 | 0.8823 | 0.8804 | 0.8789 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

deepsynthbody/deepfake_gi_fastGAN

deepsynthbody

2024-03-20T20:41:23Z

62

2

transformers

[ "transformers", "pytorch", "unconditional-image-generation", "en", "arxiv:2101.04775", "license:mit", "endpoints_compatible", "region:us" ]

unconditional-image-generation

2024-02-26T10:29:39Z

--- license: mit language: - en pipeline_tag: unconditional-image-generation --- # This is an officila repository to generate some sample results from the FastGAN model presented in the paper: SinGAN-Seg: Synthetic training data generation for medical image segmentation [here](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267976) # A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch The official pytorch implementation of the FAT-GAN paper "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis", the paper can be found [here](https://arxiv.org/abs/2101.04775). ```python python generate_4ch_from_huggingface.py ```

ranchomacho/finally-mistral

ranchomacho

2024-03-20T20:39:38Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]

null

2024-03-19T21:31:44Z

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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] ### Framework versions - PEFT 0.7.1

ingeol/facets_ep3_1234

ingeol

2024-03-20T20:38:36Z

4

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-03-20T20:37:53Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/facets_ep3_1234 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/facets_ep3_1234') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/facets_ep3_1234') model = AutoModel.from_pretrained('ingeol/facets_ep3_1234') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/facets_ep3_1234) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

sarak7/H4_320_769_v4

sarak7

2024-03-20T20:36:45Z

182

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-20T20:35:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

sagravela/poca-SoccerTwos

sagravela

2024-03-20T20:26:03Z

11

0

ml-agents

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

reinforcement-learning

2024-03-20T20:21:27Z

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

llmware/slim-tags-3b

llmware

2024-03-20T20:14:29Z

251

4

transformers

[ "transformers", "pytorch", "stablelm_epoch", "text-generation", "custom_code", "license:cc-by-sa-4.0", "autotrain_compatible", "region:us" ]

text-generation

2024-03-16T08:26:50Z

--- license: cc-by-sa-4.0 inference: false --- # SLIM-TAGS-3B  **slim-tags-3b** is a small, specialized function-calling model fine-tuned to extract and generate meaningful tags from a chunk of text. Tags generally correspond to named entities, but will also include key objects, entities and phrases that contribute meaningfully to the semantic meaning of the text. The model is invoked as a specialized 'tags' classifier function that outputs a python dictionary in the form of:     `{'tags': ['NASDAQ', 'S&P', 'Dow', 'Verizon', 'Netflix, ... ']}` with the value items in the list generally being extracted from the source text. The intended use of the model is to auto-generate tags to text that can be used to enhance search retrieval, categorization, or to extract named entities that can be used programmatically in follow-up queries or prompts. It can also be used for fact-checking as a secondary validation on a longer (separate) LLM output. This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt. Each slim model has a 'quantized tool' version, e.g., [**'slim-tags-3b-tool'**](https://huggingface.co/llmware/slim-tags-3b-tool). ## Prompt format: `function = "classify"` `params = "tags"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-tags-3b") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-tags-3b") function = "classify" params = "tags" text = "Citibank announced a reduction in its targets for economic growth in France and the UK last week in light of ongoing concerns about inflation and unemployment, especially in large employers such as Airbus." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-tags-3b") response = slim_model.function_call(text,params=["tags"], function="classify") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)

SpideyDLK/wav2vec2-large-xls-r-300m-sinhala-low-LR-constant

SpideyDLK

2024-03-20T20:08:47Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-03-20T08:28:03Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

MatthieuJ/ING_2003M3_SLERP

MatthieuJ

2024-03-20T20:03:45Z

48

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "chihoonlee10/T3Q-DPO-Mistral-7B", "MatthieuJ/ING_2003M2_SLERP", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-20T19:58:54Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - chihoonlee10/T3Q-DPO-Mistral-7B - MatthieuJ/ING_2003M2_SLERP --- # ING_2003M3_SLERP ING_2003M3_SLERP is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [chihoonlee10/T3Q-DPO-Mistral-7B](https://huggingface.co/chihoonlee10/T3Q-DPO-Mistral-7B) * [MatthieuJ/ING_2003M2_SLERP](https://huggingface.co/MatthieuJ/ING_2003M2_SLERP) ## 🧩 Configuration ```yaml slices: - sources: - model: chihoonlee10/T3Q-DPO-Mistral-7B layer_range: [0, 32] - model: MatthieuJ/ING_2003M2_SLERP layer_range: [0, 32] merge_method: slerp base_model: MatthieuJ/ING_2003M2_SLERP parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```

zrvicc/ppo-Pyramids

zrvicc

2024-03-20T19:58:48Z

0

ml-agents

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

reinforcement-learning

2024-03-20T19:58:45Z

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

sarak7/H15_320_769_v1

sarak7

2024-03-20T19:57:29Z

182

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-20T19:55:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **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]

llmware/slim-xsum

llmware

2024-03-20T19:51:15Z

143

6

transformers

[ "transformers", "pytorch", "stablelm_epoch", "text-generation", "custom_code", "license:cc-by-sa-4.0", "autotrain_compatible", "region:us" ]

text-generation

2024-03-02T12:55:11Z

--- license: cc-by-sa-4.0 inference: false --- # SLIM-XSUM  **slim-xsum** implements an 'extreme summarization' function as a function-call on a decoder-based LLM, which generates as output a python dictionary with the form of:     `{'xsum': ['This is a short text summary or headline.']}` The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case. This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt. Each slim model has a 'quantized tool' version, e.g., [**'slim-xsum-tool'**](https://huggingface.co/llmware/slim-xsum-tool). ## Prompt format: `function = "classify"` `params = "xsum"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-xsum") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-xsum") function = "classify" params = "xsum" text = "DeepMind, the UK-based AI lab owned by Google’s parent company Alphabet, has developed an AI system called AlphaGeometry that can solve complex geometry problems close to human Olympiad gold medalists. In a new paper in Nature, DeepMind revealed that AlphaGeometry was able to solve 25 out of 30 benchmark geometry problems from past International Mathematical Olympiad (IMO) competitions within the standard time limits. This nearly matches the average score of 26 problems solved by human gold medalists on the same tests. The AI system combines a neural language model with a rule-bound deduction engine, providing a synergy that enables the system to find solutions to complex geometry theorems. AlphaGeometry took a revolutionary approach to synthetic data generation by creating one billion random diagrams of geometric objects and deriving relationships between points and lines in each diagram. This process – termed “symbolic deduction and traceback” – resulted in a final training dataset of 100 million unique examples, providing a rich source for training the AI system." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.3, max_new_tokens=100 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-xsum") response = slim_model.function_call(text,params=["xsum"], function="classify") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)

automerger/YamshadowT3q-7B

automerger

2024-03-20T19:49:19Z

7

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:chihoonlee10/T3Q-Mistral-Orca-Math-DPO", "base_model:finetune:chihoonlee10/T3Q-Mistral-Orca-Math-DPO", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-20T19:48:28Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - chihoonlee10/T3Q-Mistral-Orca-Math-DPO --- # YamshadowT3q-7B YamshadowT3q-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO) ## 🧩 Configuration ```yaml models: - model: automerger/YamShadow-7B # No parameters necessary for base model - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: automerger/YamShadow-7B parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/YamshadowT3q-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

JapGuy/XindlX_Acoustic

JapGuy

2024-03-20T19:48:15Z

0

null

[ "music", "rvc", "xindlx", "xindl", "ondřej", "ládek", "model", "audio-to-audio", "cs", "license:openrail", "region:us" ]

audio-to-audio

2024-03-20T19:40:19Z

--- license: openrail language: - cs pipeline_tag: audio-to-audio tags: - music - rvc - xindlx - xindl - ondřej - ládek - model --- ![image.jpg](https://scenanaplovarne.cz/wp-content/uploads/2023/03/Xindl-X.jpg) # Xindl X [CZ] (Acoustic/Unpluggiat Mix) # 645 Epochs - RVC V2 - rmvpe Trained on 1 hour 49 minutes 14 seconds of isolated acapellas using UVR (Voc FT + Reverb HQ) and Audacity to remove parts with double vocals and vocals from others (+Noise Gate)