librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-30 06:28:04	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 536 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-30 06:28:00	card stringlengths 11 1.01M
hansanguw/HSCho_test	hansanguw	2023-07-17T01:26:47Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T01:26:41Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e7_s6789_v3	KingKazma	2023-07-17T01:12:08Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T01:12:07Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e6_s6789_v3	KingKazma	2023-07-17T01:05:09Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T01:05:08Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e5_s6789_v3	KingKazma	2023-07-17T00:58:09Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:58:08Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
timjwhite/poca-SoccerTwos	timjwhite	2023-07-17T00:56:31Z	66	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-07-17T00:45:50Z	--- 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: timjwhite/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
arashaomrani/Email	arashaomrani	2023-07-17T00:46:03Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:45:57Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e3_s6789_v3	KingKazma	2023-07-17T00:44:11Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:44:11Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e2_s6789_v3	KingKazma	2023-07-17T00:37:12Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:37:12Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e1_s6789_v3	KingKazma	2023-07-17T00:30:14Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:30:13Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
acasany/rare-puppers	acasany	2023-07-17T00:27:57Z	197	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-17T00:27:47Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8876404762268066 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### husky ![husky](images/husky.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e0_s6789_v3	KingKazma	2023-07-17T00:23:15Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:23:14Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
lucostiguy11/dreambooth_if	lucostiguy11	2023-07-17T00:21:21Z	2	0	diffusers	[ "diffusers", "tensorboard", "if", "if-diffusers", "text-to-image", "dreambooth", "base_model:DeepFloyd/IF-I-XL-v1.0", "base_model:finetune:DeepFloyd/IF-I-XL-v1.0", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:IFPipeline", "region:us" ]	text-to-image	2023-07-16T23:29:26Z	--- license: creativeml-openrail-m base_model: DeepFloyd/IF-I-XL-v1.0 instance_prompt: a photo of sks dog tags: - if - if-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - lucostiguy11/dreambooth_if This is a dreambooth model derived from DeepFloyd/IF-I-XL-v1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) DreamBooth for the text encoder was enabled: False.
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e-1_s6789_v3	KingKazma	2023-07-17T00:16:16Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:16:15Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e7_s6789_v3	KingKazma	2023-07-17T00:09:01Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:09:00Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
dsmonk/xgen-7b-tuned-alpaca	dsmonk	2023-07-17T00:04:40Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:Salesforce/xgen-7b-8k-base", "base_model:finetune:Salesforce/xgen-7b-8k-base", "license:apache-2.0", "region:us" ]	null	2023-07-16T21:52:46Z	--- license: apache-2.0 base_model: Salesforce/xgen-7b-8k-base tags: - generated_from_trainer model-index: - name: xgen-7b-tuned-alpaca 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. --> # xgen-7b-tuned-alpaca This model is a fine-tuned version of [Salesforce/xgen-7b-8k-base](https://huggingface.co/Salesforce/xgen-7b-8k-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.4.0 - Tokenizers 0.12.1
ByteExplorer/Reinforce-CartPole-8	ByteExplorer	2023-07-17T00:04:03Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-17T00:03:54Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-8 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e6_s6789_v3	KingKazma	2023-07-17T00:01:27Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-17T00:01:26Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_lora_500_10_3000_8_e7_s55555_v3	KingKazma	2023-07-16T23:55:02Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T23:55:01Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_lora_500_10_3000_8_e6_s55555_v3	KingKazma	2023-07-16T23:48:02Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T23:48:01Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3	KingKazma	2023-07-16T23:38:46Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-16T23:38:44Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
abgoswam/bloom_marketmail_32	abgoswam	2023-07-16T23:34:10Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-16T23:34:05Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_lora_500_10_3000_8_e2_s55555_v3	KingKazma	2023-07-16T23:20:02Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-16T23:20:01Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e-1_s6789_v3	KingKazma	2023-07-16T23:08:28Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T23:08:26Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
ailabturkiye/wtcn	ailabturkiye	2023-07-16T23:06:15Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T23:04:16Z	--- license: openrail language: - tr tags: - music ---
NasimB/aochildes-guten-log-rarity-all-no-cut	NasimB	2023-07-16T22:59:51Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T20:50:33Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-guten-log-rarity-all-no-cut results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # aochildes-guten-log-rarity-all-no-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7164 \| 0.29 \| 500 \| 5.6323 \| \| 5.3447 \| 0.59 \| 1000 \| 5.2052 \| \| 5.0011 \| 0.88 \| 1500 \| 4.9552 \| \| 4.7272 \| 1.17 \| 2000 \| 4.8144 \| \| 4.5727 \| 1.47 \| 2500 \| 4.6937 \| \| 4.4591 \| 1.76 \| 3000 \| 4.5928 \| \| 4.3272 \| 2.05 \| 3500 \| 4.5232 \| \| 4.1423 \| 2.35 \| 4000 \| 4.4760 \| \| 4.1152 \| 2.64 \| 4500 \| 4.4205 \| \| 4.0725 \| 2.93 \| 5000 \| 4.3703 \| \| 3.8638 \| 3.23 \| 5500 \| 4.3718 \| \| 3.8167 \| 3.52 \| 6000 \| 4.3411 \| \| 3.7993 \| 3.81 \| 6500 \| 4.3167 \| \| 3.6795 \| 4.11 \| 7000 \| 4.3235 \| \| 3.5285 \| 4.4 \| 7500 \| 4.3099 \| \| 3.5218 \| 4.69 \| 8000 \| 4.3012 \| \| 3.5096 \| 4.99 \| 8500 \| 4.2923 \| \| 3.3413 \| 5.28 \| 9000 \| 4.3116 \| \| 3.3298 \| 5.57 \| 9500 \| 4.3113 \| \| 3.3314 \| 5.87 \| 10000 \| 4.3111 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_lora_500_10_3000_8_e-1_s55555_v3	KingKazma	2023-07-16T22:58:57Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T22:58:56Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_lora_500_10_3000_8_e7_s108_v3	KingKazma	2023-07-16T22:35:00Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T22:34:59Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
Chickenfish/Jennie	Chickenfish	2023-07-16T22:30:43Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-15T01:54:48Z	--- license: creativeml-openrail-m ---
KingKazma/xsum_gpt2_lora_500_10_3000_8_e6_s108_v3	KingKazma	2023-07-16T22:28:01Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T22:28:00Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_lora_500_10_3000_8_e5_s108_v3	KingKazma	2023-07-16T22:20:58Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T22:20:58Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
jeremyvictor/t5-v1_1-large-fce-e8-b16	jeremyvictor	2023-07-16T22:19:24Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-16T15:25:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-v1_1-large-fce-e8-b16 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. --> # t5-v1_1-large-fce-e8-b16 This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3349 - Rouge1: 86.6648 - Rouge2: 79.4505 - Rougel: 86.1654 - Rougelsum: 86.1549 - Gen Len: 14.9105 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 3.325 \| 0.06 \| 100 \| 0.7775 \| 76.9422 \| 69.1942 \| 76.3689 \| 76.3852 \| 14.7545 \| \| 0.9422 \| 0.11 \| 200 \| 0.4327 \| 85.6522 \| 77.1791 \| 85.0843 \| 85.0849 \| 15.0315 \| \| 0.535 \| 0.17 \| 300 \| 0.4081 \| 85.8265 \| 77.0897 \| 85.2547 \| 85.2421 \| 14.8745 \| \| 0.5003 \| 0.23 \| 400 \| 0.4104 \| 85.847 \| 77.3884 \| 85.3678 \| 85.3536 \| 14.8257 \| \| 0.4734 \| 0.28 \| 500 \| 0.3830 \| 86.3501 \| 78.2006 \| 85.824 \| 85.8541 \| 14.8613 \| \| 0.4439 \| 0.34 \| 600 \| 0.3652 \| 86.5106 \| 78.4301 \| 85.9794 \| 85.9871 \| 14.8644 \| \| 0.4399 \| 0.4 \| 700 \| 0.3656 \| 86.3955 \| 78.2086 \| 85.8592 \| 85.8785 \| 14.8562 \| \| 0.4259 \| 0.45 \| 800 \| 0.3925 \| 85.6654 \| 77.0925 \| 85.1468 \| 85.1547 \| 14.9142 \| \| 0.4092 \| 0.51 \| 900 \| 0.3720 \| 86.317 \| 78.3141 \| 85.8151 \| 85.7907 \| 14.8859 \| \| 0.4143 \| 0.56 \| 1000 \| 0.3761 \| 86.5432 \| 78.4572 \| 85.9424 \| 85.9234 \| 14.8763 \| \| 0.4184 \| 0.62 \| 1100 \| 0.3487 \| 86.4053 \| 78.5526 \| 85.8508 \| 85.8745 \| 14.8909 \| \| 0.4025 \| 0.68 \| 1200 \| 0.3556 \| 86.2418 \| 78.2845 \| 85.7291 \| 85.7379 \| 14.8603 \| \| 0.4014 \| 0.73 \| 1300 \| 0.3657 \| 86.6544 \| 78.9722 \| 86.1314 \| 86.1446 \| 14.8257 \| \| 0.379 \| 0.79 \| 1400 \| 0.3512 \| 86.6622 \| 79.1939 \| 86.1521 \| 86.1383 \| 14.8955 \| \| 0.3898 \| 0.85 \| 1500 \| 0.3517 \| 86.1483 \| 78.4144 \| 85.5986 \| 85.6256 \| 14.8955 \| \| 0.373 \| 0.9 \| 1600 \| 0.3565 \| 86.6775 \| 79.0902 \| 86.1475 \| 86.156 \| 14.8946 \| \| 0.3685 \| 0.96 \| 1700 \| 0.3500 \| 86.8048 \| 79.2231 \| 86.2842 \| 86.2602 \| 14.8658 \| \| 0.3353 \| 1.02 \| 1800 \| 0.3547 \| 86.7966 \| 79.1526 \| 86.2624 \| 86.2769 \| 14.8895 \| \| 0.2323 \| 1.07 \| 1900 \| 0.3529 \| 86.6715 \| 79.0832 \| 86.1451 \| 86.143 \| 14.9119 \| \| 0.2458 \| 1.13 \| 2000 \| 0.3699 \| 86.9553 \| 79.3124 \| 86.3906 \| 86.4162 \| 14.8987 \| \| 0.2349 \| 1.19 \| 2100 \| 0.3640 \| 86.4161 \| 78.4111 \| 85.8783 \| 85.8807 \| 14.9420 \| \| 0.2358 \| 1.24 \| 2200 \| 0.3598 \| 86.7842 \| 79.1199 \| 86.2164 \| 86.2259 \| 14.8932 \| \| 0.2229 \| 1.3 \| 2300 \| 0.3610 \| 86.7032 \| 79.0013 \| 86.168 \| 86.1807 \| 14.8827 \| \| 0.2502 \| 1.35 \| 2400 \| 0.3527 \| 86.5423 \| 78.9113 \| 86.0423 \| 86.0465 \| 14.8946 \| \| 0.2466 \| 1.41 \| 2500 \| 0.3575 \| 86.512 \| 78.7998 \| 85.9795 \| 85.9899 \| 14.9142 \| \| 0.2457 \| 1.47 \| 2600 \| 0.3463 \| 86.5376 \| 78.7642 \| 86.0019 \| 85.993 \| 14.8964 \| \| 0.2429 \| 1.52 \| 2700 \| 0.3480 \| 86.5911 \| 78.9802 \| 86.0235 \| 86.0303 \| 14.9169 \| \| 0.2657 \| 1.58 \| 2800 \| 0.3423 \| 86.6139 \| 79.1659 \| 86.0999 \| 86.1034 \| 14.8905 \| \| 0.2542 \| 1.64 \| 2900 \| 0.3439 \| 86.4731 \| 78.8656 \| 86.0285 \| 86.0336 \| 14.8955 \| \| 0.2529 \| 1.69 \| 3000 \| 0.3491 \| 86.7686 \| 79.2799 \| 86.2783 \| 86.2663 \| 14.8891 \| \| 0.2475 \| 1.75 \| 3100 \| 0.3460 \| 86.0511 \| 77.837 \| 85.5557 \| 85.56 \| 14.8868 \| \| 0.2472 \| 1.81 \| 3200 \| 0.3375 \| 86.6711 \| 79.1718 \| 86.1627 \| 86.1402 \| 14.8809 \| \| 0.2432 \| 1.86 \| 3300 \| 0.3349 \| 86.6648 \| 79.4505 \| 86.1654 \| 86.1549 \| 14.9105 \| \| 0.2467 \| 1.92 \| 3400 \| 0.3383 \| 86.867 \| 79.7251 \| 86.3823 \| 86.3811 \| 14.9014 \| \| 0.2416 \| 1.98 \| 3500 \| 0.3404 \| 86.8577 \| 79.4128 \| 86.3474 \| 86.3386 \| 14.8909 \| \| 0.1816 \| 2.03 \| 3600 \| 0.3590 \| 86.7414 \| 79.4138 \| 86.2395 \| 86.2415 \| 14.9283 \| \| 0.1344 \| 2.09 \| 3700 \| 0.3806 \| 86.9318 \| 79.5175 \| 86.4098 \| 86.4209 \| 14.9238 \| \| 0.134 \| 2.14 \| 3800 \| 0.3704 \| 86.733 \| 79.2709 \| 86.2066 \| 86.2083 \| 14.9379 \| \| 0.1301 \| 2.2 \| 3900 \| 0.3788 \| 86.7622 \| 79.4039 \| 86.2608 \| 86.2514 \| 14.9133 \| \| 0.1417 \| 2.26 \| 4000 \| 0.3658 \| 87.0002 \| 79.8067 \| 86.4663 \| 86.4604 \| 14.9105 \| \| 0.1256 \| 2.31 \| 4100 \| 0.3728 \| 86.6691 \| 79.3081 \| 86.1154 \| 86.1184 \| 14.9119 \| \| 0.1393 \| 2.37 \| 4200 \| 0.3666 \| 86.7525 \| 79.3901 \| 86.223 \| 86.2348 \| 14.9046 \| \| 0.1542 \| 2.43 \| 4300 \| 0.3740 \| 86.6779 \| 79.5336 \| 86.1667 \| 86.1716 \| 14.9283 \| \| 0.133 \| 2.48 \| 4400 \| 0.3790 \| 86.7692 \| 79.6713 \| 86.2335 \| 86.2394 \| 14.9457 \| \| 0.1389 \| 2.54 \| 4500 \| 0.3717 \| 86.4853 \| 79.3114 \| 85.9253 \| 85.9128 \| 14.9434 \| \| 0.1489 \| 2.6 \| 4600 \| 0.3724 \| 86.2107 \| 78.63 \| 85.6539 \| 85.6792 \| 14.9311 \| \| 0.1522 \| 2.65 \| 4700 \| 0.3647 \| 86.8659 \| 79.8 \| 86.3545 \| 86.3676 \| 14.9160 \| \| 0.1439 \| 2.71 \| 4800 \| 0.3672 \| 86.0554 \| 78.1382 \| 85.5587 \| 85.5362 \| 14.9297 \| \| 0.1406 \| 2.77 \| 4900 \| 0.3637 \| 86.4054 \| 78.9406 \| 85.8958 \| 85.9036 \| 14.9069 \| \| 0.1522 \| 2.82 \| 5000 \| 0.3715 \| 86.7402 \| 79.6515 \| 86.2414 \| 86.2416 \| 14.9201 \| \| 0.1577 \| 2.88 \| 5100 \| 0.3531 \| 86.5905 \| 79.2319 \| 86.0746 \| 86.0661 \| 14.9174 \| \| 0.1427 \| 2.93 \| 5200 \| 0.3693 \| 86.4955 \| 79.0202 \| 86.0034 \| 85.9923 \| 14.9014 \| \| 0.1489 \| 2.99 \| 5300 \| 0.3671 \| 86.6285 \| 79.2982 \| 86.1429 \| 86.1239 \| 14.9366 \| \| 0.0874 \| 3.05 \| 5400 \| 0.4117 \| 86.7939 \| 79.6444 \| 86.2987 \| 86.292 \| 14.9311 \| \| 0.0824 \| 3.1 \| 5500 \| 0.4056 \| 86.7504 \| 79.5265 \| 86.2525 \| 86.2509 \| 14.9069 \| \| 0.0815 \| 3.16 \| 5600 \| 0.4064 \| 86.9102 \| 79.8072 \| 86.4 \| 86.3798 \| 14.9188 \| \| 0.0761 \| 3.22 \| 5700 \| 0.4061 \| 86.7759 \| 79.4944 \| 86.2642 \| 86.2638 \| 14.9156 \| \| 0.0858 \| 3.27 \| 5800 \| 0.4104 \| 86.9783 \| 79.7005 \| 86.4405 \| 86.4279 \| 14.9206 \| \| 0.0774 \| 3.33 \| 5900 \| 0.4043 \| 86.7749 \| 79.4813 \| 86.2355 \| 86.2441 \| 14.9010 \| \| 0.0841 \| 3.39 \| 6000 \| 0.4033 \| 86.915 \| 79.7145 \| 86.3878 \| 86.3809 \| 14.9060 \| \| 0.0885 \| 3.44 \| 6100 \| 0.4066 \| 86.761 \| 79.3294 \| 86.202 \| 86.2041 \| 14.8973 \| \| 0.0794 \| 3.5 \| 6200 \| 0.3987 \| 86.699 \| 79.2133 \| 86.1431 \| 86.1571 \| 14.9083 \| \| 0.0845 \| 3.56 \| 6300 \| 0.4225 \| 86.8629 \| 79.4052 \| 86.3102 \| 86.32 \| 14.9169 \| \| 0.0869 \| 3.61 \| 6400 \| 0.4033 \| 86.8748 \| 79.5928 \| 86.3421 \| 86.3564 \| 14.8987 \| \| 0.0791 \| 3.67 \| 6500 \| 0.4055 \| 86.9491 \| 79.6876 \| 86.4205 \| 86.4281 \| 14.9115 \| \| 0.0849 \| 3.72 \| 6600 \| 0.4068 \| 86.7855 \| 79.4848 \| 86.2791 \| 86.2945 \| 14.9192 \| \| 0.0865 \| 3.78 \| 6700 \| 0.4069 \| 86.7864 \| 79.5128 \| 86.2844 \| 86.3027 \| 14.9092 \| \| 0.086 \| 3.84 \| 6800 \| 0.3989 \| 86.9556 \| 79.6203 \| 86.4463 \| 86.4673 \| 14.9083 \| \| 0.0811 \| 3.89 \| 6900 \| 0.3913 \| 86.9815 \| 79.7108 \| 86.4913 \| 86.4905 \| 14.9073 \| \| 0.0812 \| 3.95 \| 7000 \| 0.4022 \| 86.819 \| 79.5024 \| 86.313 \| 86.336 \| 14.9261 \| \| 0.087 \| 4.01 \| 7100 \| 0.4238 \| 87.0628 \| 79.8276 \| 86.5385 \| 86.5444 \| 14.9133 \| \| 0.0484 \| 4.06 \| 7200 \| 0.4301 \| 87.0455 \| 79.7775 \| 86.5274 \| 86.5298 \| 14.9023 \| \| 0.0481 \| 4.12 \| 7300 \| 0.4715 \| 87.0629 \| 79.9823 \| 86.5676 \| 86.5615 \| 14.9073 \| \| 0.0522 \| 4.18 \| 7400 \| 0.4379 \| 86.983 \| 79.7011 \| 86.4659 \| 86.4906 \| 14.9174 \| \| 0.0463 \| 4.23 \| 7500 \| 0.4574 \| 87.047 \| 79.6937 \| 86.5243 \| 86.5252 \| 14.9133 \| \| 0.0559 \| 4.29 \| 7600 \| 0.4275 \| 86.8511 \| 79.4707 \| 86.3482 \| 86.3463 \| 14.9270 \| \| 0.0484 \| 4.35 \| 7700 \| 0.4426 \| 86.8238 \| 79.4779 \| 86.3242 \| 86.3224 \| 14.9178 \| \| 0.0468 \| 4.4 \| 7800 \| 0.4565 \| 86.9331 \| 79.7622 \| 86.4253 \| 86.433 \| 14.9174 \| \| 0.0501 \| 4.46 \| 7900 \| 0.4506 \| 86.884 \| 79.7917 \| 86.4025 \| 86.4082 \| 14.9160 \| \| 0.0538 \| 4.51 \| 8000 \| 0.4290 \| 86.95 \| 79.7812 \| 86.4425 \| 86.4387 \| 14.9092 \| \| 0.0499 \| 4.57 \| 8100 \| 0.4366 \| 87.1034 \| 80.0115 \| 86.6029 \| 86.6075 \| 14.9137 \| \| 0.051 \| 4.63 \| 8200 \| 0.4472 \| 86.8904 \| 79.6413 \| 86.4313 \| 86.4236 \| 14.9078 \| \| 0.0546 \| 4.68 \| 8300 \| 0.4299 \| 86.8704 \| 79.6621 \| 86.3474 \| 86.3699 \| 14.9055 \| \| 0.049 \| 4.74 \| 8400 \| 0.4601 \| 87.0006 \| 79.7754 \| 86.4831 \| 86.484 \| 14.9073 \| \| 0.0474 \| 4.8 \| 8500 \| 0.4481 \| 86.9629 \| 79.7888 \| 86.452 \| 86.4605 \| 14.9069 \| \| 0.0509 \| 4.85 \| 8600 \| 0.4329 \| 86.9177 \| 79.6544 \| 86.4178 \| 86.4215 \| 14.9124 \| \| 0.0521 \| 4.91 \| 8700 \| 0.4323 \| 86.8574 \| 79.6029 \| 86.3347 \| 86.3477 \| 14.9169 \| \| 0.0458 \| 4.97 \| 8800 \| 0.4563 \| 87.0021 \| 79.754 \| 86.4522 \| 86.4517 \| 14.9105 \| \| 0.0411 \| 5.02 \| 8900 \| 0.4707 \| 86.884 \| 79.6339 \| 86.3403 \| 86.3413 \| 14.9178 \| \| 0.0283 \| 5.08 \| 9000 \| 0.4809 \| 86.9403 \| 79.8934 \| 86.4149 \| 86.4145 \| 14.9183 \| \| 0.029 \| 5.14 \| 9100 \| 0.4799 \| 86.8942 \| 79.7148 \| 86.3502 \| 86.3571 \| 14.9064 \| \| 0.0268 \| 5.19 \| 9200 \| 0.4910 \| 86.9841 \| 79.8403 \| 86.4605 \| 86.4683 \| 14.9233 \| \| 0.0294 \| 5.25 \| 9300 \| 0.4838 \| 86.9494 \| 79.9215 \| 86.4508 \| 86.4474 \| 14.9151 \| \| 0.028 \| 5.3 \| 9400 \| 0.5042 \| 87.1362 \| 80.0747 \| 86.6251 \| 86.6238 \| 14.9169 \| \| 0.0291 \| 5.36 \| 9500 \| 0.4997 \| 87.0858 \| 80.036 \| 86.5966 \| 86.5908 \| 14.9087 \| \| 0.0291 \| 5.42 \| 9600 \| 0.4983 \| 87.0756 \| 79.9726 \| 86.5872 \| 86.5865 \| 14.9037 \| \| 0.0282 \| 5.47 \| 9700 \| 0.5073 \| 87.0901 \| 79.8924 \| 86.5942 \| 86.595 \| 14.8982 \| \| 0.0299 \| 5.53 \| 9800 \| 0.4945 \| 87.145 \| 79.9289 \| 86.6143 \| 86.6206 \| 14.8987 \| \| 0.0278 \| 5.59 \| 9900 \| 0.5187 \| 86.9691 \| 79.7553 \| 86.4589 \| 86.4624 \| 14.9051 \| \| 0.0237 \| 5.64 \| 10000 \| 0.5246 \| 86.9827 \| 79.7671 \| 86.4783 \| 86.4701 \| 14.9119 \| \| 0.03 \| 5.7 \| 10100 \| 0.4944 \| 87.0292 \| 79.8105 \| 86.4909 \| 86.5016 \| 14.9119 \| \| 0.0289 \| 5.76 \| 10200 \| 0.5131 \| 87.0028 \| 79.8731 \| 86.5042 \| 86.5187 \| 14.9137 \| \| 0.0296 \| 5.81 \| 10300 \| 0.4963 \| 87.1329 \| 79.9334 \| 86.6172 \| 86.6194 \| 14.9128 \| \| 0.0287 \| 5.87 \| 10400 \| 0.4893 \| 87.0761 \| 79.9902 \| 86.5448 \| 86.5427 \| 14.9174 \| \| 0.029 \| 5.93 \| 10500 \| 0.4880 \| 87.0082 \| 79.8738 \| 86.4987 \| 86.4864 \| 14.9105 \| \| 0.0281 \| 5.98 \| 10600 \| 0.4928 \| 87.0415 \| 79.8243 \| 86.5291 \| 86.5279 \| 14.9206 \| \| 0.0236 \| 6.04 \| 10700 \| 0.5026 \| 86.9936 \| 79.8109 \| 86.4741 \| 86.4771 \| 14.9165 \| \| 0.0172 \| 6.09 \| 10800 \| 0.5242 \| 87.0859 \| 80.0264 \| 86.5787 \| 86.5684 \| 14.9178 \| \| 0.0157 \| 6.15 \| 10900 \| 0.5386 \| 87.0647 \| 80.1227 \| 86.5723 \| 86.5658 \| 14.9197 \| \| 0.0175 \| 6.21 \| 11000 \| 0.5222 \| 87.034 \| 80.051 \| 86.525 \| 86.5177 \| 14.9160 \| \| 0.0155 \| 6.26 \| 11100 \| 0.5445 \| 87.0634 \| 79.9564 \| 86.5556 \| 86.5507 \| 14.9101 \| \| 0.0147 \| 6.32 \| 11200 \| 0.5602 \| 87.0164 \| 79.9748 \| 86.505 \| 86.4928 \| 14.9105 \| \| 0.0156 \| 6.38 \| 11300 \| 0.5587 \| 87.1387 \| 79.9561 \| 86.6298 \| 86.6329 \| 14.9137 \| \| 0.0157 \| 6.43 \| 11400 \| 0.5655 \| 87.1027 \| 80.1466 \| 86.6023 \| 86.5983 \| 14.9201 \| \| 0.0139 \| 6.49 \| 11500 \| 0.5773 \| 87.1318 \| 80.1543 \| 86.5965 \| 86.6127 \| 14.9251 \| \| 0.0152 \| 6.55 \| 11600 \| 0.5748 \| 87.2417 \| 80.2155 \| 86.7204 \| 86.7277 \| 14.9128 \| \| 0.0169 \| 6.6 \| 11700 \| 0.5558 \| 87.2049 \| 80.1632 \| 86.7078 \| 86.7198 \| 14.9042 \| \| 0.0158 \| 6.66 \| 11800 \| 0.5452 \| 87.0358 \| 79.9864 \| 86.5181 \| 86.5149 \| 14.9151 \| \| 0.0169 \| 6.72 \| 11900 \| 0.5411 \| 87.0557 \| 79.9435 \| 86.5372 \| 86.5375 \| 14.9087 \| \| 0.0127 \| 6.77 \| 12000 \| 0.5564 \| 87.0617 \| 80.0711 \| 86.5398 \| 86.5645 \| 14.9051 \| \| 0.0158 \| 6.83 \| 12100 \| 0.5545 \| 87.0269 \| 80.0081 \| 86.4936 \| 86.5004 \| 14.9247 \| \| 0.0142 \| 6.88 \| 12200 \| 0.5520 \| 87.1107 \| 80.1457 \| 86.5775 \| 86.5851 \| 14.9192 \| \| 0.0142 \| 6.94 \| 12300 \| 0.5590 \| 87.152 \| 80.1378 \| 86.604 \| 86.6048 \| 14.9178 \| \| 0.0146 \| 7.0 \| 12400 \| 0.5633 \| 87.1416 \| 80.1493 \| 86.6109 \| 86.6128 \| 14.9178 \| \| 0.0087 \| 7.05 \| 12500 \| 0.5928 \| 87.1881 \| 80.1549 \| 86.6642 \| 86.6747 \| 14.9133 \| \| 0.0094 \| 7.11 \| 12600 \| 0.5998 \| 87.2084 \| 80.2571 \| 86.7023 \| 86.6967 \| 14.9042 \| \| 0.0082 \| 7.17 \| 12700 \| 0.6086 \| 87.1567 \| 80.204 \| 86.6479 \| 86.6462 \| 14.9147 \| \| 0.0096 \| 7.22 \| 12800 \| 0.6106 \| 87.173 \| 80.1732 \| 86.658 \| 86.6586 \| 14.9156 \| \| 0.0084 \| 7.28 \| 12900 \| 0.6318 \| 87.1298 \| 80.1264 \| 86.6351 \| 86.638 \| 14.9174 \| \| 0.0079 \| 7.34 \| 13000 \| 0.6363 \| 87.1628 \| 80.1184 \| 86.6548 \| 86.6486 \| 14.9174 \| \| 0.0091 \| 7.39 \| 13100 \| 0.6313 \| 87.241 \| 80.2331 \| 86.7437 \| 86.7435 \| 14.9156 \| \| 0.0088 \| 7.45 \| 13200 \| 0.6376 \| 87.1652 \| 80.1422 \| 86.661 \| 86.6599 \| 14.9142 \| \| 0.0091 \| 7.51 \| 13300 \| 0.6364 \| 87.1554 \| 80.1285 \| 86.6576 \| 86.6553 \| 14.9147 \| \| 0.0081 \| 7.56 \| 13400 \| 0.6372 \| 87.2418 \| 80.192 \| 86.7178 \| 86.7199 \| 14.9188 \| \| 0.0103 \| 7.62 \| 13500 \| 0.6369 \| 87.1754 \| 80.1347 \| 86.666 \| 86.666 \| 14.9133 \| \| 0.0094 \| 7.67 \| 13600 \| 0.6382 \| 87.1611 \| 80.1066 \| 86.6541 \| 86.6488 \| 14.9142 \| \| 0.0081 \| 7.73 \| 13700 \| 0.6371 \| 87.1836 \| 80.0865 \| 86.6575 \| 86.6538 \| 14.9151 \| \| 0.0076 \| 7.79 \| 13800 \| 0.6377 \| 87.1652 \| 80.0572 \| 86.6498 \| 86.6569 \| 14.9142 \| \| 0.0092 \| 7.84 \| 13900 \| 0.6354 \| 87.1638 \| 80.0867 \| 86.6563 \| 86.6536 \| 14.9142 \| \| 0.0076 \| 7.9 \| 14000 \| 0.6346 \| 87.1814 \| 80.1212 \| 86.6698 \| 86.6683 \| 14.9137 \| \| 0.0063 \| 7.96 \| 14100 \| 0.6373 \| 87.1913 \| 80.1322 \| 86.6793 \| 86.6765 \| 14.9128 \| ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_lora_500_10_3000_8_e4_s108_v3	KingKazma	2023-07-16T22:13:58Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T22:13:57Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
mgeller/opt-6.7b-lora	mgeller	2023-07-16T22:06:40Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-12T22:58:35Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
nbroad/setfit-sci-wiki-large	nbroad	2023-07-16T21:58:13Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-16T21:57:15Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # nbroad/setfit-sci-wiki-large This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("nbroad/setfit-sci-wiki-large") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
KingKazma/xsum_gpt2_lora_500_10_3000_8_e1_s108_v3	KingKazma	2023-07-16T21:52:56Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T21:52:55Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
SinanAkkoyun/orca_mini_3b_gptq_badtest	SinanAkkoyun	2023-07-16T21:49:31Z	5	0	transformers	[ "transformers", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T21:27:48Z	This is a very bad attempt at quantizing 128g 4 bit with alpaca (in orca style prompt ```sh python quantize_alpaca.py --pretrained_model_dir orca_mini_3b/ --bits 4 --group_size 128 --quantized_model_dir orca_mini_3b_gptq/ --save_and_reloa ``` Downloqd cleaned dataset first: https://github.com/gururise/AlpacaDataCleaned
LarryAIDraw/roxy-08	LarryAIDraw	2023-07-16T21:46:08Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T21:42:37Z	--- license: creativeml-openrail-m --- https://civitai.com/models/109272/roxy-oror-mushoku-tensei
KingKazma/xsum_gpt2_lora_500_10_3000_8_e0_s108_v3	KingKazma	2023-07-16T21:45:55Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T21:45:54Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
LarryAIDraw/Predator	LarryAIDraw	2023-07-16T21:45:47Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T21:42:05Z	--- license: creativeml-openrail-m --- https://civitai.com/models/109356/predator-or-granblue-fantasy
quangnguyennn/pokemon-lora	quangnguyennn	2023-07-16T21:41:33Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-16T12:51:01Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - quangnguyennn/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
KingKazma/xsum_gpt2_lora_500_10_3000_8_e-1_s108_v3	KingKazma	2023-07-16T21:38:46Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T21:38:45Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_lora_500_10_3000_8_e9_s6789_v3	KingKazma	2023-07-16T21:14:32Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-15T01:36:51Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
Debayan990/my-pet-cat-jxl	Debayan990	2023-07-16T21:13:51Z	13	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T21:01:07Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat-jxl Dreambooth model trained by Debayan990 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: BBIT47 Sample pictures of this concept: ![0](https://huggingface.co/Debayan990/my-pet-cat-jxl/resolve/main/sample_images/00000-2838740840.png) ![1](https://huggingface.co/Debayan990/my-pet-cat-jxl/resolve/main/sample_images/00003-3628577076.png) ![2](https://huggingface.co/Debayan990/my-pet-cat-jxl/resolve/main/sample_images/00001-1217343363.png)
MichaelS91/autotrain-hub_testing-75008139803	MichaelS91	2023-07-16T21:08:49Z	108	0	transformers	[ "transformers", "pytorch", "safetensors", "deberta", "text-classification", "autotrain", "text-regression", "en", "dataset:MichaelS91/autotrain-data-hub_testing", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-16T21:05:50Z	--- tags: - autotrain - text-regression language: - en widget: - text: "I love AutoTrain" datasets: - MichaelS91/autotrain-data-hub_testing co2_eq_emissions: emissions: 1.5911364056652006 --- # Model Trained Using AutoTrain - Problem type: Single Column Regression - Model ID: 75008139803 - CO2 Emissions (in grams): 1.5911 ## Validation Metrics - Loss: 1.889 - MSE: 1.889 - MAE: 1.094 - R2: 0.221 - RMSE: 1.374 - Explained Variance: 0.242 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/MichaelS91/autotrain-hub_testing-75008139803 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
hseokool/vicuna-7b-v1.3-230623-09	hseokool	2023-07-16T20:40:52Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-14T11:46:32Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
FightingFalcon/SonmezReyiz	FightingFalcon	2023-07-16T20:39:15Z	0	0	null	[ "sönmez", "sönmezreyiz", "türkçe", "turkish", "tr", "arxiv:1910.09700", "license:openrail", "region:us" ]	null	2023-07-16T20:00:15Z	--- license: openrail language: - tr tags: - sönmez - sönmezreyiz - türkçe - turkish --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
KingKazma/xsum_gpt2_lora_500_10_3000_8_e3_s6789_v3	KingKazma	2023-07-16T20:32:19Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-15T00:01:06Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
rshrott/falcon-7b-instruct-ft-descriptions-adapters	rshrott	2023-07-16T20:20:19Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T20:15:42Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
KingKazma/xsum_gpt2_lora_500_10_3000_8_e1_s6789_v3	KingKazma	2023-07-16T20:18:10Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-14T23:29:54Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
bskang/test_demo_ver	bskang	2023-07-16T20:17:48Z	34	0	peft	[ "peft", "text-generation", "en", "region:us" ]	text-generation	2023-07-16T20:15:26Z	--- library_name: peft language: - en pipeline_tag: text-generation --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
anindya64/alpaca-bank-issue-summarization-20b-EthurAI	anindya64	2023-07-16T20:00:19Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T20:00:16Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0
Meina/MeinaMix_V11	Meina	2023-07-16T19:53:46Z	6,643	35	diffusers	[ "diffusers", "safetensors", "art", "anime", "stable diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T19:11:15Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - art - anime - stable diffusion --- MeinaMix Objective is to be able to do good art with little prompting. For examples and prompts, please checkout: https://civitai.com/models/7240/meinamix I have a discord server where you can post images that you generated, discuss prompt and/or ask for help. https://discord.gg/XC9nGZNDUd If you like one of my models and want to support their updates I've made a ko-fi page; https://ko-fi.com/meina where you can pay me a coffee <3 And a Patreon page; https://www.patreon.com/MeinaMix where you can support me and get acess to beta of my models! You may also try this model using Sinkin.ai: https://sinkin.ai/m/vln8Nwr MeinaMix and the other of Meinas will ALWAYS be FREE. Recommendations of use: Enable Quantization in K samplers. Hires.fix is needed for prompts where the character is far away in order to make decent images, it drastically improve the quality of face and eyes! Recommended parameters: Sampler: Euler a: 40 to 60 steps. Sampler: DPM++ SDE Karras: 20 to 30 steps. Sampler: DPM++ 2M Karras: 20 to 40 steps. CFG Scale: 7. Resolutions: 512x768, 512x1024 for Portrait! Resolutions: 768x512, 1024x512, 1536x512 for Landscape! Hires.fix: R-ESRGAN 4x+Anime6b, with 10 steps at 0.3 up to 0.5 denoising. Clip Skip: 2. Negatives: ' (worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic) '
rshrott/falcon-7b-instruct-ft-adapters	rshrott	2023-07-16T19:48:46Z	5	0	peft	[ "peft", "pytorch", "RefinedWebModel", "custom_code", "region:us" ]	null	2023-07-16T13:37:16Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
Dlychan/Tokyolagi	Dlychan	2023-07-16T19:42:33Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T19:41:10Z	--- license: creativeml-openrail-m ---
bskang/bskang8	bskang	2023-07-16T19:39:22Z	0	0	null	[ "en", "license:openrail", "region:us" ]	null	2023-07-16T12:18:21Z	--- language: - en license: openrail ---
Araki/airoboros-33b-gpt4-1.4.1-PI-8192-GGML	Araki	2023-07-16T19:23:42Z	0	2	null	[ "llama", "ggml", "text-generation", "region:us" ]	text-generation	2023-07-16T00:08:31Z	--- pipeline_tag: text-generation tags: - llama - ggml --- Quantization from: [bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16) Converted to the GGML format with: [llama.cpp master-6e7cca4 (JUL 15, 2023)](https://github.com/ggerganov/llama.cpp/releases/tag/master-6e7cca4) Tested with: [koboldcpp 1.35](https://github.com/LostRuins/koboldcpp/releases/tag/v1.35) Example usage: ``` koboldcpp.exe airoboros-33b-gpt4-1.4.1-PI-8192-ggmlv3.Q2_K.bin --threads 6 --linearrope --contextsize 8192 --stream --smartcontext --unbantokens --noblas ```
anujsahani01/NeuralCodeBot_starchat	anujsahani01	2023-07-16T19:18:12Z	0	0	null	[ "generated_from_trainer", "license:bigcode-openrail-m", "region:us" ]	null	2023-07-15T11:21:28Z	--- license: bigcode-openrail-m tags: - generated_from_trainer model-index: - name: NeuralCodeBot_starchat 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. --> # NeuralCodeBot_starchat This model is a fine-tuned version of [HuggingFaceH4/starchat-alpha](https://huggingface.co/HuggingFaceH4/starchat-alpha) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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_steps: 50 - training_steps: 5000 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
YojitShinde/ppo-Pyramids	YojitShinde	2023-07-16T19:13:01Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-16T19:11:49Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: YojitShinde/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ailabturkiye/umitozdag	ailabturkiye	2023-07-16T19:11:50Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T18:57:55Z	--- license: openrail language: - tr tags: - music --- Ümit Özdağ 200 Epochs [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Ümit Özdağ - RVC V2 200 Epoch Zafer Partisi Başkanı Ümit Özdağ`nın ses modelidir, Rvc V2 200 epoch olarak eğitilmiştir. _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: Bif-Tek#0505 ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)
jeremyvictor/t5-v1_1-base-fce-e8-b16	jeremyvictor	2023-07-16T18:47:47Z	22	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-16T15:27:24Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-v1_1-base-fce-e8-b16 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. --> # t5-v1_1-base-fce-e8-b16 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3409 - Rouge1: 87.1583 - Rouge2: 79.8003 - Rougel: 86.6556 - Rougelsum: 86.6858 - Gen Len: 14.8987 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 3.9063 \| 0.06 \| 100 \| 0.8111 \| 27.4937 \| 22.9629 \| 27.3015 \| 27.2771 \| 7.4286 \| \| 0.7836 \| 0.11 \| 200 \| 0.5104 \| 85.4419 \| 76.9583 \| 84.8358 \| 84.8509 \| 15.0488 \| \| 0.6368 \| 0.17 \| 300 \| 0.4682 \| 86.2542 \| 77.5212 \| 85.6688 \| 85.6923 \| 14.8298 \| \| 0.5924 \| 0.23 \| 400 \| 0.4734 \| 86.4845 \| 78.0506 \| 85.9059 \| 85.9008 \| 14.8352 \| \| 0.5694 \| 0.28 \| 500 \| 0.4081 \| 86.352 \| 78.0709 \| 85.8245 \| 85.8281 \| 14.8585 \| \| 0.5335 \| 0.34 \| 600 \| 0.4179 \| 86.5893 \| 78.4175 \| 86.0693 \| 86.0625 \| 14.8745 \| \| 0.5246 \| 0.4 \| 700 \| 0.3990 \| 86.4139 \| 78.4306 \| 85.9523 \| 85.9443 \| 14.8617 \| \| 0.504 \| 0.45 \| 800 \| 0.4233 \| 86.7504 \| 78.7906 \| 86.2416 \| 86.2447 \| 14.8759 \| \| 0.4818 \| 0.51 \| 900 \| 0.4008 \| 86.7978 \| 78.8187 \| 86.2413 \| 86.2432 \| 14.8699 \| \| 0.4756 \| 0.56 \| 1000 \| 0.4028 \| 86.9123 \| 79.0247 \| 86.3563 \| 86.3635 \| 14.8640 \| \| 0.4772 \| 0.62 \| 1100 \| 0.3789 \| 86.5028 \| 78.5736 \| 85.9794 \| 85.9983 \| 14.8717 \| \| 0.4638 \| 0.68 \| 1200 \| 0.3818 \| 86.6276 \| 78.7383 \| 86.084 \| 86.0903 \| 14.9124 \| \| 0.4614 \| 0.73 \| 1300 \| 0.3839 \| 86.8128 \| 79.2001 \| 86.3591 \| 86.3519 \| 14.8695 \| \| 0.4326 \| 0.79 \| 1400 \| 0.3751 \| 86.9302 \| 79.3511 \| 86.4188 \| 86.4311 \| 14.9019 \| \| 0.4485 \| 0.85 \| 1500 \| 0.3654 \| 86.6862 \| 79.0433 \| 86.1832 \| 86.1872 \| 14.9206 \| \| 0.4187 \| 0.9 \| 1600 \| 0.3823 \| 86.9451 \| 79.2758 \| 86.4628 \| 86.4724 \| 14.8795 \| \| 0.4218 \| 0.96 \| 1700 \| 0.3696 \| 86.9051 \| 79.1393 \| 86.3682 \| 86.3627 \| 14.9220 \| \| 0.3812 \| 1.02 \| 1800 \| 0.3699 \| 87.0233 \| 79.4507 \| 86.513 \| 86.5154 \| 14.8873 \| \| 0.3116 \| 1.07 \| 1900 \| 0.3763 \| 86.9293 \| 79.2058 \| 86.4356 \| 86.4445 \| 14.8918 \| \| 0.3237 \| 1.13 \| 2000 \| 0.3740 \| 87.0449 \| 79.4088 \| 86.5157 \| 86.5319 \| 14.8918 \| \| 0.3071 \| 1.19 \| 2100 \| 0.3690 \| 86.5698 \| 78.4408 \| 85.9993 \| 86.0409 \| 14.9069 \| \| 0.3072 \| 1.24 \| 2200 \| 0.3646 \| 86.9336 \| 79.334 \| 86.4284 \| 86.4303 \| 14.8918 \| \| 0.2953 \| 1.3 \| 2300 \| 0.3750 \| 86.7437 \| 78.949 \| 86.2131 \| 86.202 \| 14.8909 \| \| 0.308 \| 1.35 \| 2400 \| 0.3613 \| 86.792 \| 79.2179 \| 86.2832 \| 86.2934 \| 14.8923 \| \| 0.3132 \| 1.41 \| 2500 \| 0.3528 \| 86.7653 \| 79.0525 \| 86.2258 \| 86.2357 \| 14.9110 \| \| 0.3141 \| 1.47 \| 2600 \| 0.3494 \| 86.8884 \| 79.2484 \| 86.3719 \| 86.3622 \| 14.9069 \| \| 0.3095 \| 1.52 \| 2700 \| 0.3539 \| 87.0166 \| 79.5218 \| 86.5167 \| 86.5248 \| 14.8905 \| \| 0.3274 \| 1.58 \| 2800 \| 0.3599 \| 87.2104 \| 79.7277 \| 86.7135 \| 86.7127 \| 14.8854 \| \| 0.312 \| 1.64 \| 2900 \| 0.3536 \| 86.8926 \| 79.2971 \| 86.3699 \| 86.3666 \| 14.8886 \| \| 0.3134 \| 1.69 \| 3000 \| 0.3518 \| 87.0884 \| 79.5848 \| 86.5877 \| 86.6005 \| 14.9028 \| \| 0.3012 \| 1.75 \| 3100 \| 0.3573 \| 86.3559 \| 78.1413 \| 85.8416 \| 85.8479 \| 14.8763 \| \| 0.311 \| 1.81 \| 3200 \| 0.3467 \| 86.9837 \| 79.4983 \| 86.4827 \| 86.4981 \| 14.8937 \| \| 0.303 \| 1.86 \| 3300 \| 0.3422 \| 86.9232 \| 79.3542 \| 86.4098 \| 86.4427 \| 14.9032 \| \| 0.304 \| 1.92 \| 3400 \| 0.3409 \| 87.1583 \| 79.8003 \| 86.6556 \| 86.6858 \| 14.8987 \| \| 0.2934 \| 1.98 \| 3500 \| 0.3485 \| 87.0529 \| 79.6491 \| 86.5825 \| 86.6003 \| 14.9000 \| \| 0.247 \| 2.03 \| 3600 \| 0.3586 \| 87.0147 \| 79.6418 \| 86.5126 \| 86.5339 \| 14.9042 \| \| 0.193 \| 2.09 \| 3700 \| 0.3667 \| 86.9326 \| 79.4481 \| 86.4675 \| 86.4709 \| 14.9128 \| \| 0.195 \| 2.14 \| 3800 \| 0.3673 \| 86.8892 \| 79.3638 \| 86.3717 \| 86.3866 \| 14.9210 \| \| 0.19 \| 2.2 \| 3900 \| 0.3670 \| 86.8789 \| 79.4677 \| 86.3925 \| 86.3892 \| 14.9023 \| \| 0.2033 \| 2.26 \| 4000 \| 0.3600 \| 86.9004 \| 79.5211 \| 86.4043 \| 86.407 \| 14.9042 \| \| 0.1969 \| 2.31 \| 4100 \| 0.3587 \| 87.0403 \| 79.7208 \| 86.5257 \| 86.5245 \| 14.8978 \| \| 0.2035 \| 2.37 \| 4200 \| 0.3630 \| 86.8793 \| 79.4667 \| 86.3931 \| 86.3875 \| 14.8895 \| \| 0.2162 \| 2.43 \| 4300 \| 0.3722 \| 86.78 \| 79.3367 \| 86.2742 \| 86.2812 \| 14.9083 \| \| 0.1984 \| 2.48 \| 4400 \| 0.3573 \| 86.7248 \| 79.2577 \| 86.218 \| 86.2139 \| 14.8918 \| \| 0.2058 \| 2.54 \| 4500 \| 0.3617 \| 86.6452 \| 79.1422 \| 86.1701 \| 86.1838 \| 14.8909 \| \| 0.2161 \| 2.6 \| 4600 \| 0.3554 \| 86.8574 \| 79.5476 \| 86.3982 \| 86.4095 \| 14.9283 \| \| 0.215 \| 2.65 \| 4700 \| 0.3583 \| 86.8873 \| 79.5265 \| 86.4039 \| 86.3996 \| 14.8923 \| \| 0.2048 \| 2.71 \| 4800 \| 0.3535 \| 86.8465 \| 79.3852 \| 86.3446 \| 86.344 \| 14.8978 \| \| 0.2099 \| 2.77 \| 4900 \| 0.3601 \| 86.8952 \| 79.4424 \| 86.3888 \| 86.387 \| 14.8868 \| \| 0.2149 \| 2.82 \| 5000 \| 0.3603 \| 86.7871 \| 79.2397 \| 86.297 \| 86.3004 \| 14.8850 \| \| 0.2251 \| 2.88 \| 5100 \| 0.3448 \| 86.9477 \| 79.6744 \| 86.4984 \| 86.4911 \| 14.9133 \| \| 0.2048 \| 2.93 \| 5200 \| 0.3522 \| 86.8843 \| 79.37 \| 86.3702 \| 86.3668 \| 14.8955 \| \| 0.2099 \| 2.99 \| 5300 \| 0.3459 \| 86.7938 \| 79.2104 \| 86.3027 \| 86.3169 \| 14.9137 \| \| 0.1377 \| 3.05 \| 5400 \| 0.4000 \| 86.9855 \| 79.4184 \| 86.438 \| 86.4375 \| 14.9110 \| \| 0.1369 \| 3.1 \| 5500 \| 0.3848 \| 86.8338 \| 79.2098 \| 86.2885 \| 86.3028 \| 14.9019 \| \| 0.1357 \| 3.16 \| 5600 \| 0.3914 \| 86.7061 \| 79.2474 \| 86.2247 \| 86.2237 \| 14.9105 \| \| 0.1263 \| 3.22 \| 5700 \| 0.3864 \| 86.7128 \| 79.1103 \| 86.2121 \| 86.2166 \| 14.9137 \| \| 0.135 \| 3.27 \| 5800 \| 0.3929 \| 86.8134 \| 79.4572 \| 86.3608 \| 86.3683 \| 14.9124 \| \| 0.1361 \| 3.33 \| 5900 \| 0.3828 \| 86.9149 \| 79.4756 \| 86.4152 \| 86.3959 \| 14.8959 \| \| 0.1286 \| 3.39 \| 6000 \| 0.3849 \| 86.8025 \| 79.3645 \| 86.3215 \| 86.3204 \| 14.8996 \| \| 0.1335 \| 3.44 \| 6100 \| 0.3793 \| 86.7591 \| 79.2887 \| 86.2778 \| 86.2765 \| 14.9105 \| \| 0.1278 \| 3.5 \| 6200 \| 0.3938 \| 86.8352 \| 79.4161 \| 86.3282 \| 86.3376 \| 14.9169 \| \| 0.1346 \| 3.56 \| 6300 \| 0.3943 \| 86.9637 \| 79.6404 \| 86.4753 \| 86.4718 \| 14.8978 \| \| 0.1421 \| 3.61 \| 6400 \| 0.3799 \| 86.8445 \| 79.4133 \| 86.3271 \| 86.3206 \| 14.9151 \| \| 0.1398 \| 3.67 \| 6500 \| 0.3923 \| 86.9793 \| 79.6847 \| 86.4935 \| 86.4889 \| 14.9174 \| \| 0.1359 \| 3.72 \| 6600 \| 0.3912 \| 86.9095 \| 79.3593 \| 86.4296 \| 86.4506 \| 14.8959 \| \| 0.1444 \| 3.78 \| 6700 \| 0.3741 \| 86.8498 \| 79.3141 \| 86.3586 \| 86.3681 \| 14.8909 \| \| 0.1351 \| 3.84 \| 6800 \| 0.3840 \| 87.223 \| 79.825 \| 86.7127 \| 86.7371 \| 14.8877 \| \| 0.1325 \| 3.89 \| 6900 \| 0.3816 \| 87.148 \| 79.8102 \| 86.6405 \| 86.6511 \| 14.9133 \| \| 0.1315 \| 3.95 \| 7000 \| 0.3796 \| 86.7778 \| 79.3782 \| 86.3057 \| 86.2939 \| 14.9005 \| \| 0.1332 \| 4.01 \| 7100 \| 0.3962 \| 87.0238 \| 79.6621 \| 86.5384 \| 86.5306 \| 14.8996 \| \| 0.0834 \| 4.06 \| 7200 \| 0.4271 \| 86.9999 \| 79.7076 \| 86.4981 \| 86.5026 \| 14.9014 \| \| 0.088 \| 4.12 \| 7300 \| 0.4176 \| 86.9193 \| 79.4698 \| 86.4085 \| 86.4171 \| 14.9128 \| \| 0.0897 \| 4.18 \| 7400 \| 0.4109 \| 86.9287 \| 79.5866 \| 86.4541 \| 86.4474 \| 14.9037 \| \| 0.0908 \| 4.23 \| 7500 \| 0.4109 \| 87.1272 \| 79.7632 \| 86.6206 \| 86.6176 \| 14.9133 \| \| 0.0895 \| 4.29 \| 7600 \| 0.4114 \| 87.0107 \| 79.7349 \| 86.4873 \| 86.4754 \| 14.9023 \| \| 0.0856 \| 4.35 \| 7700 \| 0.4242 \| 87.0115 \| 79.6387 \| 86.4786 \| 86.49 \| 14.8982 \| \| 0.0852 \| 4.4 \| 7800 \| 0.4271 \| 86.9943 \| 79.6717 \| 86.5126 \| 86.5026 \| 14.9019 \| \| 0.0919 \| 4.46 \| 7900 \| 0.4216 \| 86.9903 \| 79.67 \| 86.512 \| 86.5085 \| 14.8937 \| \| 0.0907 \| 4.51 \| 8000 \| 0.4180 \| 87.0323 \| 79.7092 \| 86.5391 \| 86.5343 \| 14.8978 \| \| 0.0889 \| 4.57 \| 8100 \| 0.4276 \| 86.9813 \| 79.6367 \| 86.4697 \| 86.4724 \| 14.9115 \| \| 0.0907 \| 4.63 \| 8200 \| 0.4209 \| 87.0149 \| 79.5637 \| 86.5028 \| 86.5059 \| 14.9092 \| \| 0.0966 \| 4.68 \| 8300 \| 0.4064 \| 86.9685 \| 79.4665 \| 86.4393 \| 86.4523 \| 14.9010 \| \| 0.088 \| 4.74 \| 8400 \| 0.4234 \| 86.9921 \| 79.5729 \| 86.4977 \| 86.5067 \| 14.8800 \| \| 0.0897 \| 4.8 \| 8500 \| 0.4117 \| 87.0727 \| 79.7094 \| 86.5465 \| 86.5482 \| 14.9014 \| \| 0.0924 \| 4.85 \| 8600 \| 0.4056 \| 86.8789 \| 79.409 \| 86.3689 \| 86.3672 \| 14.9083 \| \| 0.0916 \| 4.91 \| 8700 \| 0.4127 \| 86.8645 \| 79.4195 \| 86.3814 \| 86.3729 \| 14.8982 \| \| 0.0908 \| 4.97 \| 8800 \| 0.4054 \| 86.9146 \| 79.4138 \| 86.4022 \| 86.399 \| 14.9000 \| \| 0.078 \| 5.02 \| 8900 \| 0.4403 \| 87.0178 \| 79.6166 \| 86.5112 \| 86.505 \| 14.9078 \| \| 0.0583 \| 5.08 \| 9000 \| 0.4400 \| 86.9828 \| 79.649 \| 86.4913 \| 86.4962 \| 14.9064 \| \| 0.057 \| 5.14 \| 9100 \| 0.4637 \| 87.0435 \| 79.6446 \| 86.5464 \| 86.5252 \| 14.9037 \| \| 0.0581 \| 5.19 \| 9200 \| 0.4617 \| 87.017 \| 79.6255 \| 86.5004 \| 86.4907 \| 14.9069 \| \| 0.0562 \| 5.25 \| 9300 \| 0.4521 \| 86.8638 \| 79.479 \| 86.3298 \| 86.338 \| 14.9096 \| \| 0.0588 \| 5.3 \| 9400 \| 0.4472 \| 86.9719 \| 79.5608 \| 86.4751 \| 86.4798 \| 14.9073 \| \| 0.0571 \| 5.36 \| 9500 \| 0.4472 \| 87.0325 \| 79.6355 \| 86.5154 \| 86.5278 \| 14.9073 \| \| 0.0589 \| 5.42 \| 9600 \| 0.4580 \| 87.1556 \| 79.8992 \| 86.627 \| 86.6372 \| 14.9064 \| \| 0.057 \| 5.47 \| 9700 \| 0.4527 \| 87.0033 \| 79.6457 \| 86.4846 \| 86.5031 \| 14.9101 \| \| 0.0595 \| 5.53 \| 9800 \| 0.4538 \| 87.0419 \| 79.6632 \| 86.5261 \| 86.5434 \| 14.9055 \| \| 0.062 \| 5.59 \| 9900 \| 0.4518 \| 87.0581 \| 79.6818 \| 86.54 \| 86.551 \| 14.9005 \| \| 0.0568 \| 5.64 \| 10000 \| 0.4549 \| 87.1255 \| 79.8908 \| 86.6143 \| 86.6255 \| 14.9042 \| \| 0.0572 \| 5.7 \| 10100 \| 0.4557 \| 86.9927 \| 79.5946 \| 86.4726 \| 86.4953 \| 14.9023 \| \| 0.0603 \| 5.76 \| 10200 \| 0.4493 \| 87.0665 \| 79.7469 \| 86.58 \| 86.5934 \| 14.8932 \| \| 0.0604 \| 5.81 \| 10300 \| 0.4533 \| 87.0864 \| 79.7039 \| 86.5871 \| 86.5851 \| 14.9042 \| \| 0.0564 \| 5.87 \| 10400 \| 0.4653 \| 87.082 \| 79.766 \| 86.5835 \| 86.5775 \| 14.9055 \| \| 0.0579 \| 5.93 \| 10500 \| 0.4677 \| 86.9805 \| 79.5068 \| 86.4708 \| 86.4744 \| 14.8882 \| \| 0.0582 \| 5.98 \| 10600 \| 0.4607 \| 86.9273 \| 79.3762 \| 86.4228 \| 86.4225 \| 14.9119 \| \| 0.0454 \| 6.04 \| 10700 \| 0.4917 \| 87.038 \| 79.6146 \| 86.5363 \| 86.533 \| 14.9156 \| \| 0.0399 \| 6.09 \| 10800 \| 0.4986 \| 87.0026 \| 79.5481 \| 86.4992 \| 86.4924 \| 14.9042 \| \| 0.0367 \| 6.15 \| 10900 \| 0.5115 \| 87.13 \| 79.7506 \| 86.6082 \| 86.621 \| 14.9055 \| \| 0.0405 \| 6.21 \| 11000 \| 0.5084 \| 87.0768 \| 79.6986 \| 86.5541 \| 86.5403 \| 14.9083 \| \| 0.0386 \| 6.26 \| 11100 \| 0.5092 \| 87.1376 \| 79.7442 \| 86.5937 \| 86.5767 \| 14.8996 \| \| 0.0382 \| 6.32 \| 11200 \| 0.5063 \| 87.0779 \| 79.7205 \| 86.561 \| 86.5546 \| 14.8982 \| \| 0.0431 \| 6.38 \| 11300 \| 0.4950 \| 87.0998 \| 79.7699 \| 86.5882 \| 86.5916 \| 14.9028 \| \| 0.0388 \| 6.43 \| 11400 \| 0.5098 \| 87.1711 \| 79.8707 \| 86.6425 \| 86.6409 \| 14.9023 \| \| 0.041 \| 6.49 \| 11500 \| 0.4911 \| 87.1742 \| 79.8319 \| 86.6434 \| 86.6522 \| 14.9005 \| \| 0.0379 \| 6.55 \| 11600 \| 0.5023 \| 87.2258 \| 79.9175 \| 86.7019 \| 86.7018 \| 14.9010 \| \| 0.0383 \| 6.6 \| 11700 \| 0.5078 \| 87.0913 \| 79.7547 \| 86.5767 \| 86.5826 \| 14.9046 \| \| 0.0387 \| 6.66 \| 11800 \| 0.5111 \| 87.1913 \| 79.9592 \| 86.6805 \| 86.6742 \| 14.9060 \| \| 0.0362 \| 6.72 \| 11900 \| 0.5125 \| 87.0096 \| 79.6639 \| 86.5037 \| 86.5039 \| 14.9124 \| \| 0.0343 \| 6.77 \| 12000 \| 0.5210 \| 87.0657 \| 79.7384 \| 86.5621 \| 86.5561 \| 14.9110 \| \| 0.0401 \| 6.83 \| 12100 \| 0.5110 \| 87.1338 \| 79.8537 \| 86.6368 \| 86.6271 \| 14.9124 \| \| 0.0353 \| 6.88 \| 12200 \| 0.5169 \| 87.082 \| 79.756 \| 86.5771 \| 86.5718 \| 14.9073 \| \| 0.0384 \| 6.94 \| 12300 \| 0.4998 \| 87.1211 \| 79.8474 \| 86.6016 \| 86.6065 \| 14.9078 \| \| 0.0395 \| 7.0 \| 12400 \| 0.5184 \| 87.1621 \| 79.8793 \| 86.6411 \| 86.648 \| 14.9064 \| \| 0.0243 \| 7.05 \| 12500 \| 0.5387 \| 87.1588 \| 79.8545 \| 86.6464 \| 86.6627 \| 14.9019 \| \| 0.0283 \| 7.11 \| 12600 \| 0.5384 \| 87.1909 \| 79.8888 \| 86.6567 \| 86.6698 \| 14.9042 \| \| 0.026 \| 7.17 \| 12700 \| 0.5459 \| 87.1782 \| 79.7991 \| 86.6373 \| 86.6507 \| 14.9028 \| \| 0.0303 \| 7.22 \| 12800 \| 0.5301 \| 87.1014 \| 79.7321 \| 86.5581 \| 86.5743 \| 14.9014 \| \| 0.0252 \| 7.28 \| 12900 \| 0.5481 \| 87.0907 \| 79.6948 \| 86.5306 \| 86.5474 \| 14.9069 \| \| 0.0273 \| 7.34 \| 13000 \| 0.5469 \| 87.0971 \| 79.6697 \| 86.5392 \| 86.558 \| 14.8987 \| \| 0.0249 \| 7.39 \| 13100 \| 0.5462 \| 87.095 \| 79.6904 \| 86.5559 \| 86.566 \| 14.9037 \| \| 0.0246 \| 7.45 \| 13200 \| 0.5553 \| 87.0964 \| 79.6834 \| 86.5572 \| 86.5607 \| 14.9055 \| \| 0.0286 \| 7.51 \| 13300 \| 0.5501 \| 87.0933 \| 79.7177 \| 86.5579 \| 86.5582 \| 14.9092 \| \| 0.0234 \| 7.56 \| 13400 \| 0.5550 \| 87.1266 \| 79.7546 \| 86.5833 \| 86.5855 \| 14.9087 \| \| 0.0263 \| 7.62 \| 13500 \| 0.5570 \| 87.0957 \| 79.6859 \| 86.5608 \| 86.5584 \| 14.9064 \| \| 0.0238 \| 7.67 \| 13600 \| 0.5630 \| 87.1368 \| 79.7487 \| 86.6036 \| 86.6031 \| 14.9032 \| \| 0.0258 \| 7.73 \| 13700 \| 0.5598 \| 87.1527 \| 79.7481 \| 86.622 \| 86.6153 \| 14.9055 \| \| 0.0249 \| 7.79 \| 13800 \| 0.5649 \| 87.15 \| 79.7419 \| 86.6106 \| 86.6056 \| 14.9046 \| \| 0.0272 \| 7.84 \| 13900 \| 0.5616 \| 87.1439 \| 79.7597 \| 86.6085 \| 86.6081 \| 14.9042 \| \| 0.0261 \| 7.9 \| 14000 \| 0.5596 \| 87.1359 \| 79.7696 \| 86.6081 \| 86.6024 \| 14.9051 \| \| 0.0233 \| 7.96 \| 14100 \| 0.5611 \| 87.1367 \| 79.7636 \| 86.6112 \| 86.6019 \| 14.9046 \| ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3
oakal/fourthbrain_bloomz_marketing	oakal	2023-07-16T18:32:44Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T18:32:38Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
harithapliyal/distilbert-base-uncased-finetuned-ner	harithapliyal	2023-07-16T18:26:04Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-16T17:06:57Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: harithapliyal/distilbert-base-uncased-finetuned-ner 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. --> # harithapliyal/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1975 - Validation Loss: 0.0734 - Train Precision: 0.9049 - Train Recall: 0.9116 - Train F1: 0.9083 - Train Accuracy: 0.9793 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.1975 \| 0.0734 \| 0.9049 \| 0.9116 \| 0.9083 \| 0.9793 \| 0 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
0sunfire0/rl_course_vizdoom_health_gathering_supreme_02	0sunfire0	2023-07-16T18:23:44Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T18:23:37Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.16 +/- 3.86 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r 0sunfire0/rl_course_vizdoom_health_gathering_supreme_02 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .opt.conda.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_02 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .opt.conda.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_02 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
bodaay/Wizard-Vicuna-7B-Uncensored-ONNX	bodaay	2023-07-16T18:06:51Z	5	0	transformers	[ "transformers", "onnx", "llama", "text-generation", "uncensored", "en", "dataset:ehartford/wizard_vicuna_70k_unfiltered", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T16:18:44Z	--- license: other datasets: - ehartford/wizard_vicuna_70k_unfiltered language: - en tags: - uncensored --- Original Model: [ehartford/Wizard-Vicuna-7B-Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored) From Original Model Card: This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
rsml/bbert_qa	rsml	2023-07-16T17:59:30Z	120	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-16T17:42:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bbert_qa 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. --> # bbert_qa This model is a fine-tuned version of [bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6818 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.3490 \| \| 2.7154 \| 2.0 \| 500 \| 1.7686 \| \| 2.7154 \| 3.0 \| 750 \| 1.6818 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
sherif1311/flan-t5-base-imdb-text-classification	sherif1311	2023-07-16T17:50:43Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-16T14:44:19Z	--- license: apache-2.0 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: 0.0797 - F1: 95.072 - Gen Len: 2.5005 ## 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 4.28.1 - Pytorch 1.12.1+cu116 - Datasets 2.4.0 - Tokenizers 0.12.1
NasimB/children_bnc_rarity_all_no_cut	NasimB	2023-07-16T17:50:30Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T15:57:37Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: children_bnc_rarity_all_no_cut 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. --> # children_bnc_rarity_all_no_cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3266 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7047 \| 0.29 \| 500 \| 5.6398 \| \| 5.3501 \| 0.58 \| 1000 \| 5.2066 \| \| 5.0056 \| 0.88 \| 1500 \| 4.9588 \| \| 4.7258 \| 1.17 \| 2000 \| 4.8173 \| \| 4.5734 \| 1.46 \| 2500 \| 4.6948 \| \| 4.4663 \| 1.75 \| 3000 \| 4.5804 \| \| 4.3402 \| 2.05 \| 3500 \| 4.5071 \| \| 4.1471 \| 2.34 \| 4000 \| 4.4576 \| \| 4.1137 \| 2.63 \| 4500 \| 4.4027 \| \| 4.0777 \| 2.92 \| 5000 \| 4.3468 \| \| 3.8629 \| 3.22 \| 5500 \| 4.3449 \| \| 3.8078 \| 3.51 \| 6000 \| 4.3108 \| \| 3.8044 \| 3.8 \| 6500 \| 4.2763 \| \| 3.7029 \| 4.09 \| 7000 \| 4.2803 \| \| 3.5324 \| 4.39 \| 7500 \| 4.2741 \| \| 3.5239 \| 4.68 \| 8000 \| 4.2585 \| \| 3.5091 \| 4.97 \| 8500 \| 4.2454 \| \| 3.3521 \| 5.26 \| 9000 \| 4.2592 \| \| 3.3357 \| 5.56 \| 9500 \| 4.2584 \| \| 3.3348 \| 5.85 \| 10000 \| 4.2573 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
nishchalprasad/lunar_lander_v2-PPO	nishchalprasad	2023-07-16T17:44:18Z	4	1	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T17:43:57Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO-MLP results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.46 +/- 24.94 name: mean_reward verified: false --- # PPO-MLP Agent playing LunarLander-v2 This is a trained model of a PPO-MLP agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
kanu03/my-cat	kanu03	2023-07-16T17:44:02Z	107	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T17:39:19Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-cat Dreambooth model trained by kanu03 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: OPJU101 Sample pictures of this concept: ![0](https://huggingface.co/kanu03/my-cat/resolve/main/sample_images/01.jpg)
Za88yes/Afriana	Za88yes	2023-07-16T17:43:07Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-07-16T17:41:00Z	--- license: bigscience-openrail-m ---
Tasaloris13/finetuned-college-10	Tasaloris13	2023-07-16T17:42:10Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-16T16:59:34Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
balpreetspankaj/distilbert-base-uncased-finetuned-emotion	balpreetspankaj	2023-07-16T17:37:10Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-16T16:46:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2169 - Accuracy: 0.9285 - F1: 0.9283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.827 \| 1.0 \| 250 \| 0.3132 \| 0.9085 \| 0.9062 \| \| 0.2411 \| 2.0 \| 500 \| 0.2169 \| 0.9285 \| 0.9283 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
magicsword/wy-mt-en-zh-1	magicsword	2023-07-16T17:35:29Z	113	0	transformers	[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "autotrain", "translation", "unk", "dataset:magicsword/autotrain-data-wy-mt-en-zh", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-16T15:16:22Z	--- tags: - autotrain - translation language: - unk - unk datasets: - magicsword/autotrain-data-wy-mt-en-zh co2_eq_emissions: emissions: 1.4514851624864995 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 74981139791 - CO2 Emissions (in grams): 1.4515 ## Validation Metrics - Loss: 2.215 - SacreBLEU: 12.702 - Gen len: 16.311
magicsword/wy-mt-en-zh-2	magicsword	2023-07-16T17:27:39Z	107	0	transformers	[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "autotrain", "translation", "unk", "dataset:magicsword/autotrain-data-wy-mt-en-zh", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-16T15:15:50Z	--- tags: - autotrain - translation language: - unk - unk datasets: - magicsword/autotrain-data-wy-mt-en-zh co2_eq_emissions: emissions: 71.14399741050826 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 74981139786 - CO2 Emissions (in grams): 71.1440 ## Validation Metrics - Loss: 2.220 - SacreBLEU: 12.949 - Gen len: 16.386
lucasbertola/ppo-Pyramids	lucasbertola	2023-07-16T17:26:30Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-16T17:26:24Z	--- 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: lucasbertola/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
JianPublisher/modeltest	JianPublisher	2023-07-16T17:25:48Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-16T17:20:11Z	--- license: mit tags: - generated_from_trainer model-index: - name: modeltest 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. --> # modeltest This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jayantdocplix/falcon_model_finetuned	jayantdocplix	2023-07-16T17:25:44Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-15T19:29:45Z	--- 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.dev0 - PEFT 0.4.0.dev0
odunola/transcriber-t5-v8-new	odunola	2023-07-16T17:23:29Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-16T16:37:38Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: transcriber-t5-v8-new 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. --> # transcriber-t5-v8-new This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0818 ## 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: 12 - eval_batch_size: 12 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1008 \| 0.72 \| 500 \| 0.1306 \| \| 0.069 \| 1.43 \| 1000 \| 0.1227 \| \| 0.1052 \| 2.15 \| 1500 \| 0.1209 \| \| 0.1017 \| 2.86 \| 2000 \| 0.0992 \| \| 0.0828 \| 3.58 \| 2500 \| 0.0919 \| \| 0.0471 \| 4.29 \| 3000 \| 0.0927 \| \| 0.0769 \| 5.01 \| 3500 \| 0.0849 \| \| 0.0732 \| 5.72 \| 4000 \| 0.0862 \| \| 0.0801 \| 6.44 \| 4500 \| 0.0857 \| \| 0.0428 \| 7.15 \| 5000 \| 0.0815 \| \| 0.1119 \| 7.87 \| 5500 \| 0.0790 \| \| 0.0692 \| 8.58 \| 6000 \| 0.0780 \| \| 0.0684 \| 9.3 \| 6500 \| 0.0818 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ailabturkiye/sda	ailabturkiye	2023-07-16T17:19:15Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T17:00:01Z	--- license: openrail language: - tr tags: - music ---
DanGalt/speecht5_finetuned_voxpopuli_fi	DanGalt	2023-07-16T17:11:18Z	82	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "fi", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-16T17:07:04Z	--- language: - fi license: mit tags: - generated_from_trainer - text-to-speech datasets: - facebook/voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_fi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_fi This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4436 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 150 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.504 \| 5.05 \| 250 \| 0.4645 \| \| 0.4882 \| 10.1 \| 500 \| 0.4499 \| \| 0.467 \| 15.15 \| 750 \| 0.4450 \| \| 0.4651 \| 20.2 \| 1000 \| 0.4436 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
KingKazma/xsum_t5-small_prompt_tuning_500_10_3000_8_e-1_s55555_v3_manual	KingKazma	2023-07-16T17:02:55Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T17:02:55Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
ailabturkiye/azizyildirim	ailabturkiye	2023-07-16T16:47:56Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T16:37:16Z	--- license: openrail language: - tr tags: - music ---
iworeushankaonce/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan	iworeushankaonce	2023-07-16T16:35:53Z	164	0	transformers	[ "transformers", "pytorch", "tensorboard", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-16T15:19:49Z	--- license: bsd-3-clause tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.3882 - Accuracy: 0.9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4932 \| 1.0 \| 112 \| 0.5325 \| 0.86 \| \| 0.3541 \| 2.0 \| 225 \| 0.6068 \| 0.77 \| \| 0.5743 \| 3.0 \| 337 \| 0.6356 \| 0.83 \| \| 0.6256 \| 4.0 \| 450 \| 0.4878 \| 0.86 \| \| 0.0619 \| 5.0 \| 562 \| 0.4262 \| 0.88 \| \| 0.0044 \| 6.0 \| 675 \| 0.3266 \| 0.91 \| \| 0.0018 \| 7.0 \| 787 \| 0.4827 \| 0.87 \| \| 0.001 \| 8.0 \| 900 \| 0.9245 \| 0.82 \| \| 0.1854 \| 9.0 \| 1012 \| 0.4256 \| 0.89 \| \| 0.0001 \| 10.0 \| 1125 \| 0.3898 \| 0.9 \| \| 0.0001 \| 11.0 \| 1237 \| 0.3873 \| 0.9 \| \| 0.0001 \| 12.0 \| 1350 \| 0.4064 \| 0.91 \| \| 0.0 \| 13.0 \| 1462 \| 0.3910 \| 0.9 \| \| 0.0 \| 14.0 \| 1575 \| 0.3924 \| 0.9 \| \| 0.0001 \| 15.0 \| 1687 \| 0.3917 \| 0.91 \| \| 0.0 \| 16.0 \| 1800 \| 0.3903 \| 0.9 \| \| 0.0 \| 17.0 \| 1912 \| 0.3900 \| 0.89 \| \| 0.0 \| 18.0 \| 2025 \| 0.3894 \| 0.89 \| \| 0.0 \| 19.0 \| 2137 \| 0.3886 \| 0.9 \| \| 0.0 \| 19.91 \| 2240 \| 0.3882 \| 0.9 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
WasuratS/whisper-tiny-en-finetune-minds14	WasuratS	2023-07-16T16:33:30Z	90	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-16T13:49:35Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en-finetune-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.3382526564344746 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-en-finetune-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6541 - Wer Ortho: 0.3399 - Wer: 0.3383 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.3136 \| 3.57 \| 100 \| 0.4883 \| 0.3640 \| 0.3524 \| \| 0.0417 \| 7.14 \| 200 \| 0.5146 \| 0.3560 \| 0.3442 \| \| 0.0066 \| 10.71 \| 300 \| 0.5736 \| 0.3411 \| 0.3353 \| \| 0.0017 \| 14.29 \| 400 \| 0.6040 \| 0.3455 \| 0.3418 \| \| 0.0013 \| 17.86 \| 500 \| 0.6226 \| 0.3393 \| 0.3365 \| \| 0.0009 \| 21.43 \| 600 \| 0.6352 \| 0.3393 \| 0.3365 \| \| 0.0007 \| 25.0 \| 700 \| 0.6436 \| 0.3399 \| 0.3371 \| \| 0.0006 \| 28.57 \| 800 \| 0.6492 \| 0.3399 \| 0.3383 \| \| 0.0006 \| 32.14 \| 900 \| 0.6530 \| 0.3399 \| 0.3383 \| \| 0.0006 \| 35.71 \| 1000 \| 0.6541 \| 0.3399 \| 0.3383 \| ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
cassandraqs/shan_homework1	cassandraqs	2023-07-16T16:29:28Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T16:29:22Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
KingKazma/xsum_t5-small_prompt_tuning_500_10_3000_8_e-1_s6789_v3_manual	KingKazma	2023-07-16T16:23:45Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T16:23:45Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
localmodels/LLaMA-65B-ggml	localmodels	2023-07-16T16:22:41Z	0	1	null	[ "region:us" ]	null	2023-07-16T16:22:41Z	--- duplicated_from: localmodels/LLM --- # LLaMA 65B ggml From Meta: https://ai.meta.com/blog/large-language-model-llama-meta-ai --- ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` Quantized using an older version of llama.cpp and compatible with llama.cpp from May 19, commit 2d5db48. ### k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` Quantization methods compatible with latest llama.cpp from June 6, commit 2d43387. --- ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| llama-65b.ggmlv3.q2_K.bin \| q2_K \| 2 \| 27.33 GB\| 29.83 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| llama-65b.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 34.55 GB\| 37.05 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| llama-65b.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 31.40 GB\| 33.90 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| llama-65b.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 28.06 GB\| 30.56 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| llama-65b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 36.73 GB\| 39.23 GB \| Original quant method, 4-bit. \| \| llama-65b.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 40.81 GB\| 43.31 GB \| Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| llama-65b.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 39.28 GB\| 41.78 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| llama-65b.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 36.73 GB\| 39.23 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| llama-65b.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 44.89 GB\| 47.39 GB \| Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| llama-65b.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 48.97 GB\| 51.47 GB \| Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| llama-65b.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 46.20 GB\| 48.70 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| llama-65b.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 44.89 GB\| 47.39 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| llama-65b.ggmlv3.q6_K.bin \| q6_K \|6 \| 53.56 GB\| 56.06 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| llama-65b.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 69.370 GB \| 71.87 GB \| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \|
ailabturkiye/13killoki	ailabturkiye	2023-07-16T16:19:28Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T16:09:13Z	--- license: openrail language: - tr tags: - music --- 13Killoki'nin StereoBound Song Story videosuyla yaptığım model. Konuşma için uygundur.
ailabturkiye/Joker	ailabturkiye	2023-07-16T16:17:15Z	0	1	null	[ "license:openrail", "region:us" ]	null	2023-07-16T15:22:06Z	--- license: openrail --- [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Joker - RVC V2 300 Epoch Rapper Joker`in ses modelidir, Rvc V2 300 epoch olarak eğitilmiştir. _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: barisdark0 - YouTube: Barış (https://www.youtube.com/@barisdark) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)--- {} ---
ailabturkiye/KadirMisiroglu	ailabturkiye	2023-07-16T16:17:02Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T16:13:31Z	--- license: openrail language: - tr tags: - music --- Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
casque/Ultimate_ahegao	casque	2023-07-16T16:16:47Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T16:14:24Z	--- license: creativeml-openrail-m ---
ailabturkiye/NormEnder	ailabturkiye	2023-07-16T16:14:49Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-07-16T15:49:59Z	--- license: openrail --- [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Ceza - RVC V2 500 Epoch Rapper Ceza`nın ses modelidir, Rvc V2 500 epoch olarak eğitilmiştir. _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: barisdark0 - YouTube: Barış (https://www.youtube.com/@barisdark) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)--- {} ---
ailabturkiye/Beta	ailabturkiye	2023-07-16T16:13:26Z	0	0	null	[ "region:us" ]	null	2023-07-16T16:04:26Z	[![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Beta Berk Bayındır (3B) - RVC V2 500 Epoch Beta Berk Bayındır'ın ses medolidir, Rvc V2 500 epoch olarak eğitilmiştir. _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: efemekkuin - YouTube: Ahmet Efe (https://www.youtube.com/channel/UCw40vAQRF8551rMWem6CaMg) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)
ailabturkiye/AliErbas	ailabturkiye	2023-07-16T16:11:53Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T16:09:33Z	--- license: openrail language: - tr tags: - music --- Diyanet İşleri Başkanı Sayın Ali Erbaş. Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
casque/AfterSexMS	casque	2023-07-16T16:09:39Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T16:07:19Z	--- license: creativeml-openrail-m ---
n0n1m/rvc-krosh	n0n1m	2023-07-16T16:08:15Z	0	0	null	[ "audio-to-audio", "license:openrail", "region:us" ]	audio-to-audio	2023-07-15T17:45:37Z	--- license: openrail pipeline_tag: audio-to-audio --- Just a model of Krash from Kikoriki/Gogoriki or Krosh from Smeshariki
tyavika/Bert-QA-Pytorch-FULL	tyavika	2023-07-16T16:05:57Z	7	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-28T02:19:57Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Bert-QA-Pytorch-FULL 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. --> # Bert-QA-Pytorch-FULL This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1633 \| 1.0 \| 3290 \| 1.0515 \| \| 0.8061 \| 2.0 \| 6580 \| 1.0593 \| \| 0.533 \| 3.0 \| 9870 \| 1.2154 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ailabturkiye/NecmettinErbakan	ailabturkiye	2023-07-16T16:05:52Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T16:02:15Z	--- license: openrail language: - tr tags: - music --- Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
casque/Creampie_v11	casque	2023-07-16T16:05:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T16:03:25Z	--- license: creativeml-openrail-m ---
ailabturkiye/deepturkisherdi	ailabturkiye	2023-07-16T16:05:24Z	0	0	null	[ "region:us" ]	null	2023-07-16T16:04:08Z	--- license: openrail language: - tr tags: - music deepturkisherdi 500 epoch

hansanguw/HSCho_test

hansanguw

2023-07-17T01:26:47Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T01:26:41Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e7_s6789_v3

KingKazma

2023-07-17T01:12:08Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T01:12:07Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e6_s6789_v3

KingKazma

2023-07-17T01:05:09Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T01:05:08Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e5_s6789_v3

KingKazma

2023-07-17T00:58:09Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:58:08Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

timjwhite/poca-SoccerTwos

timjwhite

2023-07-17T00:56:31Z

66

0

ml-agents

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

reinforcement-learning

2023-07-17T00:45:50Z

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

arashaomrani/Email

arashaomrani

2023-07-17T00:46:03Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:45:57Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e3_s6789_v3

KingKazma

2023-07-17T00:44:11Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:44:11Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e2_s6789_v3

KingKazma

2023-07-17T00:37:12Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:37:12Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e1_s6789_v3

KingKazma

2023-07-17T00:30:14Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:30:13Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

acasany/rare-puppers

acasany

2023-07-17T00:27:57Z

197

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-17T00:27:47Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8876404762268066 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### husky ![husky](images/husky.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e0_s6789_v3

KingKazma

2023-07-17T00:23:15Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:23:14Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

lucostiguy11/dreambooth_if

lucostiguy11

2023-07-17T00:21:21Z

2

0

diffusers

[ "diffusers", "tensorboard", "if", "if-diffusers", "text-to-image", "dreambooth", "base_model:DeepFloyd/IF-I-XL-v1.0", "base_model:finetune:DeepFloyd/IF-I-XL-v1.0", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:IFPipeline", "region:us" ]

text-to-image

2023-07-16T23:29:26Z

--- license: creativeml-openrail-m base_model: DeepFloyd/IF-I-XL-v1.0 instance_prompt: a photo of sks dog tags: - if - if-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - lucostiguy11/dreambooth_if This is a dreambooth model derived from DeepFloyd/IF-I-XL-v1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) DreamBooth for the text encoder was enabled: False.

KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e-1_s6789_v3

KingKazma

2023-07-17T00:16:16Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:16:15Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e7_s6789_v3

KingKazma

2023-07-17T00:09:01Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:09:00Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

dsmonk/xgen-7b-tuned-alpaca

dsmonk

2023-07-17T00:04:40Z

0

null

[ "tensorboard", "generated_from_trainer", "base_model:Salesforce/xgen-7b-8k-base", "base_model:finetune:Salesforce/xgen-7b-8k-base", "license:apache-2.0", "region:us" ]

null

2023-07-16T21:52:46Z

--- license: apache-2.0 base_model: Salesforce/xgen-7b-8k-base tags: - generated_from_trainer model-index: - name: xgen-7b-tuned-alpaca results: [] ---  # xgen-7b-tuned-alpaca This model is a fine-tuned version of [Salesforce/xgen-7b-8k-base](https://huggingface.co/Salesforce/xgen-7b-8k-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.4.0 - Tokenizers 0.12.1

ByteExplorer/Reinforce-CartPole-8

ByteExplorer

2023-07-17T00:04:03Z

0

null

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

reinforcement-learning

2023-07-17T00:03:54Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-8 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e6_s6789_v3

KingKazma

2023-07-17T00:01:27Z

0

peft

[ "peft", "region:us" ]

null

2023-07-17T00:01:26Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_lora_500_10_3000_8_e7_s55555_v3

KingKazma

2023-07-16T23:55:02Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T23:55:01Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_lora_500_10_3000_8_e6_s55555_v3

KingKazma

2023-07-16T23:48:02Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T23:48:01Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3

KingKazma

2023-07-16T23:38:46Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-16T23:38:44Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

abgoswam/bloom_marketmail_32

abgoswam

2023-07-16T23:34:10Z

2

0

peft

[ "peft", "region:us" ]

null

2023-07-16T23:34:05Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_lora_500_10_3000_8_e2_s55555_v3

KingKazma

2023-07-16T23:20:02Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-16T23:20:01Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e-1_s6789_v3

KingKazma

2023-07-16T23:08:28Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T23:08:26Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

ailabturkiye/wtcn

ailabturkiye

2023-07-16T23:06:15Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T23:04:16Z

--- license: openrail language: - tr tags: - music ---

NasimB/aochildes-guten-log-rarity-all-no-cut

NasimB

2023-07-16T22:59:51Z

5

0

transformers

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

text-generation

2023-07-16T20:50:33Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-guten-log-rarity-all-no-cut results: [] ---  # aochildes-guten-log-rarity-all-no-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7164 | 0.29 | 500 | 5.6323 | | 5.3447 | 0.59 | 1000 | 5.2052 | | 5.0011 | 0.88 | 1500 | 4.9552 | | 4.7272 | 1.17 | 2000 | 4.8144 | | 4.5727 | 1.47 | 2500 | 4.6937 | | 4.4591 | 1.76 | 3000 | 4.5928 | | 4.3272 | 2.05 | 3500 | 4.5232 | | 4.1423 | 2.35 | 4000 | 4.4760 | | 4.1152 | 2.64 | 4500 | 4.4205 | | 4.0725 | 2.93 | 5000 | 4.3703 | | 3.8638 | 3.23 | 5500 | 4.3718 | | 3.8167 | 3.52 | 6000 | 4.3411 | | 3.7993 | 3.81 | 6500 | 4.3167 | | 3.6795 | 4.11 | 7000 | 4.3235 | | 3.5285 | 4.4 | 7500 | 4.3099 | | 3.5218 | 4.69 | 8000 | 4.3012 | | 3.5096 | 4.99 | 8500 | 4.2923 | | 3.3413 | 5.28 | 9000 | 4.3116 | | 3.3298 | 5.57 | 9500 | 4.3113 | | 3.3314 | 5.87 | 10000 | 4.3111 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_lora_500_10_3000_8_e-1_s55555_v3

KingKazma

2023-07-16T22:58:57Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T22:58:56Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_lora_500_10_3000_8_e7_s108_v3

KingKazma

2023-07-16T22:35:00Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T22:34:59Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

Chickenfish/Jennie

Chickenfish

2023-07-16T22:30:43Z

0

null

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

null

2023-07-15T01:54:48Z

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

KingKazma/xsum_gpt2_lora_500_10_3000_8_e6_s108_v3

KingKazma

2023-07-16T22:28:01Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T22:28:00Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_lora_500_10_3000_8_e5_s108_v3

KingKazma

2023-07-16T22:20:58Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T22:20:58Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

jeremyvictor/t5-v1_1-large-fce-e8-b16

jeremyvictor

2023-07-16T22:19:24Z

10

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-16T15:25:50Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-v1_1-large-fce-e8-b16 results: [] ---  # t5-v1_1-large-fce-e8-b16 This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3349 - Rouge1: 86.6648 - Rouge2: 79.4505 - Rougel: 86.1654 - Rougelsum: 86.1549 - Gen Len: 14.9105 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.325 | 0.06 | 100 | 0.7775 | 76.9422 | 69.1942 | 76.3689 | 76.3852 | 14.7545 | | 0.9422 | 0.11 | 200 | 0.4327 | 85.6522 | 77.1791 | 85.0843 | 85.0849 | 15.0315 | | 0.535 | 0.17 | 300 | 0.4081 | 85.8265 | 77.0897 | 85.2547 | 85.2421 | 14.8745 | | 0.5003 | 0.23 | 400 | 0.4104 | 85.847 | 77.3884 | 85.3678 | 85.3536 | 14.8257 | | 0.4734 | 0.28 | 500 | 0.3830 | 86.3501 | 78.2006 | 85.824 | 85.8541 | 14.8613 | | 0.4439 | 0.34 | 600 | 0.3652 | 86.5106 | 78.4301 | 85.9794 | 85.9871 | 14.8644 | | 0.4399 | 0.4 | 700 | 0.3656 | 86.3955 | 78.2086 | 85.8592 | 85.8785 | 14.8562 | | 0.4259 | 0.45 | 800 | 0.3925 | 85.6654 | 77.0925 | 85.1468 | 85.1547 | 14.9142 | | 0.4092 | 0.51 | 900 | 0.3720 | 86.317 | 78.3141 | 85.8151 | 85.7907 | 14.8859 | | 0.4143 | 0.56 | 1000 | 0.3761 | 86.5432 | 78.4572 | 85.9424 | 85.9234 | 14.8763 | | 0.4184 | 0.62 | 1100 | 0.3487 | 86.4053 | 78.5526 | 85.8508 | 85.8745 | 14.8909 | | 0.4025 | 0.68 | 1200 | 0.3556 | 86.2418 | 78.2845 | 85.7291 | 85.7379 | 14.8603 | | 0.4014 | 0.73 | 1300 | 0.3657 | 86.6544 | 78.9722 | 86.1314 | 86.1446 | 14.8257 | | 0.379 | 0.79 | 1400 | 0.3512 | 86.6622 | 79.1939 | 86.1521 | 86.1383 | 14.8955 | | 0.3898 | 0.85 | 1500 | 0.3517 | 86.1483 | 78.4144 | 85.5986 | 85.6256 | 14.8955 | | 0.373 | 0.9 | 1600 | 0.3565 | 86.6775 | 79.0902 | 86.1475 | 86.156 | 14.8946 | | 0.3685 | 0.96 | 1700 | 0.3500 | 86.8048 | 79.2231 | 86.2842 | 86.2602 | 14.8658 | | 0.3353 | 1.02 | 1800 | 0.3547 | 86.7966 | 79.1526 | 86.2624 | 86.2769 | 14.8895 | | 0.2323 | 1.07 | 1900 | 0.3529 | 86.6715 | 79.0832 | 86.1451 | 86.143 | 14.9119 | | 0.2458 | 1.13 | 2000 | 0.3699 | 86.9553 | 79.3124 | 86.3906 | 86.4162 | 14.8987 | | 0.2349 | 1.19 | 2100 | 0.3640 | 86.4161 | 78.4111 | 85.8783 | 85.8807 | 14.9420 | | 0.2358 | 1.24 | 2200 | 0.3598 | 86.7842 | 79.1199 | 86.2164 | 86.2259 | 14.8932 | | 0.2229 | 1.3 | 2300 | 0.3610 | 86.7032 | 79.0013 | 86.168 | 86.1807 | 14.8827 | | 0.2502 | 1.35 | 2400 | 0.3527 | 86.5423 | 78.9113 | 86.0423 | 86.0465 | 14.8946 | | 0.2466 | 1.41 | 2500 | 0.3575 | 86.512 | 78.7998 | 85.9795 | 85.9899 | 14.9142 | | 0.2457 | 1.47 | 2600 | 0.3463 | 86.5376 | 78.7642 | 86.0019 | 85.993 | 14.8964 | | 0.2429 | 1.52 | 2700 | 0.3480 | 86.5911 | 78.9802 | 86.0235 | 86.0303 | 14.9169 | | 0.2657 | 1.58 | 2800 | 0.3423 | 86.6139 | 79.1659 | 86.0999 | 86.1034 | 14.8905 | | 0.2542 | 1.64 | 2900 | 0.3439 | 86.4731 | 78.8656 | 86.0285 | 86.0336 | 14.8955 | | 0.2529 | 1.69 | 3000 | 0.3491 | 86.7686 | 79.2799 | 86.2783 | 86.2663 | 14.8891 | | 0.2475 | 1.75 | 3100 | 0.3460 | 86.0511 | 77.837 | 85.5557 | 85.56 | 14.8868 | | 0.2472 | 1.81 | 3200 | 0.3375 | 86.6711 | 79.1718 | 86.1627 | 86.1402 | 14.8809 | | 0.2432 | 1.86 | 3300 | 0.3349 | 86.6648 | 79.4505 | 86.1654 | 86.1549 | 14.9105 | | 0.2467 | 1.92 | 3400 | 0.3383 | 86.867 | 79.7251 | 86.3823 | 86.3811 | 14.9014 | | 0.2416 | 1.98 | 3500 | 0.3404 | 86.8577 | 79.4128 | 86.3474 | 86.3386 | 14.8909 | | 0.1816 | 2.03 | 3600 | 0.3590 | 86.7414 | 79.4138 | 86.2395 | 86.2415 | 14.9283 | | 0.1344 | 2.09 | 3700 | 0.3806 | 86.9318 | 79.5175 | 86.4098 | 86.4209 | 14.9238 | | 0.134 | 2.14 | 3800 | 0.3704 | 86.733 | 79.2709 | 86.2066 | 86.2083 | 14.9379 | | 0.1301 | 2.2 | 3900 | 0.3788 | 86.7622 | 79.4039 | 86.2608 | 86.2514 | 14.9133 | | 0.1417 | 2.26 | 4000 | 0.3658 | 87.0002 | 79.8067 | 86.4663 | 86.4604 | 14.9105 | | 0.1256 | 2.31 | 4100 | 0.3728 | 86.6691 | 79.3081 | 86.1154 | 86.1184 | 14.9119 | | 0.1393 | 2.37 | 4200 | 0.3666 | 86.7525 | 79.3901 | 86.223 | 86.2348 | 14.9046 | | 0.1542 | 2.43 | 4300 | 0.3740 | 86.6779 | 79.5336 | 86.1667 | 86.1716 | 14.9283 | | 0.133 | 2.48 | 4400 | 0.3790 | 86.7692 | 79.6713 | 86.2335 | 86.2394 | 14.9457 | | 0.1389 | 2.54 | 4500 | 0.3717 | 86.4853 | 79.3114 | 85.9253 | 85.9128 | 14.9434 | | 0.1489 | 2.6 | 4600 | 0.3724 | 86.2107 | 78.63 | 85.6539 | 85.6792 | 14.9311 | | 0.1522 | 2.65 | 4700 | 0.3647 | 86.8659 | 79.8 | 86.3545 | 86.3676 | 14.9160 | | 0.1439 | 2.71 | 4800 | 0.3672 | 86.0554 | 78.1382 | 85.5587 | 85.5362 | 14.9297 | | 0.1406 | 2.77 | 4900 | 0.3637 | 86.4054 | 78.9406 | 85.8958 | 85.9036 | 14.9069 | | 0.1522 | 2.82 | 5000 | 0.3715 | 86.7402 | 79.6515 | 86.2414 | 86.2416 | 14.9201 | | 0.1577 | 2.88 | 5100 | 0.3531 | 86.5905 | 79.2319 | 86.0746 | 86.0661 | 14.9174 | | 0.1427 | 2.93 | 5200 | 0.3693 | 86.4955 | 79.0202 | 86.0034 | 85.9923 | 14.9014 | | 0.1489 | 2.99 | 5300 | 0.3671 | 86.6285 | 79.2982 | 86.1429 | 86.1239 | 14.9366 | | 0.0874 | 3.05 | 5400 | 0.4117 | 86.7939 | 79.6444 | 86.2987 | 86.292 | 14.9311 | | 0.0824 | 3.1 | 5500 | 0.4056 | 86.7504 | 79.5265 | 86.2525 | 86.2509 | 14.9069 | | 0.0815 | 3.16 | 5600 | 0.4064 | 86.9102 | 79.8072 | 86.4 | 86.3798 | 14.9188 | | 0.0761 | 3.22 | 5700 | 0.4061 | 86.7759 | 79.4944 | 86.2642 | 86.2638 | 14.9156 | | 0.0858 | 3.27 | 5800 | 0.4104 | 86.9783 | 79.7005 | 86.4405 | 86.4279 | 14.9206 | | 0.0774 | 3.33 | 5900 | 0.4043 | 86.7749 | 79.4813 | 86.2355 | 86.2441 | 14.9010 | | 0.0841 | 3.39 | 6000 | 0.4033 | 86.915 | 79.7145 | 86.3878 | 86.3809 | 14.9060 | | 0.0885 | 3.44 | 6100 | 0.4066 | 86.761 | 79.3294 | 86.202 | 86.2041 | 14.8973 | | 0.0794 | 3.5 | 6200 | 0.3987 | 86.699 | 79.2133 | 86.1431 | 86.1571 | 14.9083 | | 0.0845 | 3.56 | 6300 | 0.4225 | 86.8629 | 79.4052 | 86.3102 | 86.32 | 14.9169 | | 0.0869 | 3.61 | 6400 | 0.4033 | 86.8748 | 79.5928 | 86.3421 | 86.3564 | 14.8987 | | 0.0791 | 3.67 | 6500 | 0.4055 | 86.9491 | 79.6876 | 86.4205 | 86.4281 | 14.9115 | | 0.0849 | 3.72 | 6600 | 0.4068 | 86.7855 | 79.4848 | 86.2791 | 86.2945 | 14.9192 | | 0.0865 | 3.78 | 6700 | 0.4069 | 86.7864 | 79.5128 | 86.2844 | 86.3027 | 14.9092 | | 0.086 | 3.84 | 6800 | 0.3989 | 86.9556 | 79.6203 | 86.4463 | 86.4673 | 14.9083 | | 0.0811 | 3.89 | 6900 | 0.3913 | 86.9815 | 79.7108 | 86.4913 | 86.4905 | 14.9073 | | 0.0812 | 3.95 | 7000 | 0.4022 | 86.819 | 79.5024 | 86.313 | 86.336 | 14.9261 | | 0.087 | 4.01 | 7100 | 0.4238 | 87.0628 | 79.8276 | 86.5385 | 86.5444 | 14.9133 | | 0.0484 | 4.06 | 7200 | 0.4301 | 87.0455 | 79.7775 | 86.5274 | 86.5298 | 14.9023 | | 0.0481 | 4.12 | 7300 | 0.4715 | 87.0629 | 79.9823 | 86.5676 | 86.5615 | 14.9073 | | 0.0522 | 4.18 | 7400 | 0.4379 | 86.983 | 79.7011 | 86.4659 | 86.4906 | 14.9174 | | 0.0463 | 4.23 | 7500 | 0.4574 | 87.047 | 79.6937 | 86.5243 | 86.5252 | 14.9133 | | 0.0559 | 4.29 | 7600 | 0.4275 | 86.8511 | 79.4707 | 86.3482 | 86.3463 | 14.9270 | | 0.0484 | 4.35 | 7700 | 0.4426 | 86.8238 | 79.4779 | 86.3242 | 86.3224 | 14.9178 | | 0.0468 | 4.4 | 7800 | 0.4565 | 86.9331 | 79.7622 | 86.4253 | 86.433 | 14.9174 | | 0.0501 | 4.46 | 7900 | 0.4506 | 86.884 | 79.7917 | 86.4025 | 86.4082 | 14.9160 | | 0.0538 | 4.51 | 8000 | 0.4290 | 86.95 | 79.7812 | 86.4425 | 86.4387 | 14.9092 | | 0.0499 | 4.57 | 8100 | 0.4366 | 87.1034 | 80.0115 | 86.6029 | 86.6075 | 14.9137 | | 0.051 | 4.63 | 8200 | 0.4472 | 86.8904 | 79.6413 | 86.4313 | 86.4236 | 14.9078 | | 0.0546 | 4.68 | 8300 | 0.4299 | 86.8704 | 79.6621 | 86.3474 | 86.3699 | 14.9055 | | 0.049 | 4.74 | 8400 | 0.4601 | 87.0006 | 79.7754 | 86.4831 | 86.484 | 14.9073 | | 0.0474 | 4.8 | 8500 | 0.4481 | 86.9629 | 79.7888 | 86.452 | 86.4605 | 14.9069 | | 0.0509 | 4.85 | 8600 | 0.4329 | 86.9177 | 79.6544 | 86.4178 | 86.4215 | 14.9124 | | 0.0521 | 4.91 | 8700 | 0.4323 | 86.8574 | 79.6029 | 86.3347 | 86.3477 | 14.9169 | | 0.0458 | 4.97 | 8800 | 0.4563 | 87.0021 | 79.754 | 86.4522 | 86.4517 | 14.9105 | | 0.0411 | 5.02 | 8900 | 0.4707 | 86.884 | 79.6339 | 86.3403 | 86.3413 | 14.9178 | | 0.0283 | 5.08 | 9000 | 0.4809 | 86.9403 | 79.8934 | 86.4149 | 86.4145 | 14.9183 | | 0.029 | 5.14 | 9100 | 0.4799 | 86.8942 | 79.7148 | 86.3502 | 86.3571 | 14.9064 | | 0.0268 | 5.19 | 9200 | 0.4910 | 86.9841 | 79.8403 | 86.4605 | 86.4683 | 14.9233 | | 0.0294 | 5.25 | 9300 | 0.4838 | 86.9494 | 79.9215 | 86.4508 | 86.4474 | 14.9151 | | 0.028 | 5.3 | 9400 | 0.5042 | 87.1362 | 80.0747 | 86.6251 | 86.6238 | 14.9169 | | 0.0291 | 5.36 | 9500 | 0.4997 | 87.0858 | 80.036 | 86.5966 | 86.5908 | 14.9087 | | 0.0291 | 5.42 | 9600 | 0.4983 | 87.0756 | 79.9726 | 86.5872 | 86.5865 | 14.9037 | | 0.0282 | 5.47 | 9700 | 0.5073 | 87.0901 | 79.8924 | 86.5942 | 86.595 | 14.8982 | | 0.0299 | 5.53 | 9800 | 0.4945 | 87.145 | 79.9289 | 86.6143 | 86.6206 | 14.8987 | | 0.0278 | 5.59 | 9900 | 0.5187 | 86.9691 | 79.7553 | 86.4589 | 86.4624 | 14.9051 | | 0.0237 | 5.64 | 10000 | 0.5246 | 86.9827 | 79.7671 | 86.4783 | 86.4701 | 14.9119 | | 0.03 | 5.7 | 10100 | 0.4944 | 87.0292 | 79.8105 | 86.4909 | 86.5016 | 14.9119 | | 0.0289 | 5.76 | 10200 | 0.5131 | 87.0028 | 79.8731 | 86.5042 | 86.5187 | 14.9137 | | 0.0296 | 5.81 | 10300 | 0.4963 | 87.1329 | 79.9334 | 86.6172 | 86.6194 | 14.9128 | | 0.0287 | 5.87 | 10400 | 0.4893 | 87.0761 | 79.9902 | 86.5448 | 86.5427 | 14.9174 | | 0.029 | 5.93 | 10500 | 0.4880 | 87.0082 | 79.8738 | 86.4987 | 86.4864 | 14.9105 | | 0.0281 | 5.98 | 10600 | 0.4928 | 87.0415 | 79.8243 | 86.5291 | 86.5279 | 14.9206 | | 0.0236 | 6.04 | 10700 | 0.5026 | 86.9936 | 79.8109 | 86.4741 | 86.4771 | 14.9165 | | 0.0172 | 6.09 | 10800 | 0.5242 | 87.0859 | 80.0264 | 86.5787 | 86.5684 | 14.9178 | | 0.0157 | 6.15 | 10900 | 0.5386 | 87.0647 | 80.1227 | 86.5723 | 86.5658 | 14.9197 | | 0.0175 | 6.21 | 11000 | 0.5222 | 87.034 | 80.051 | 86.525 | 86.5177 | 14.9160 | | 0.0155 | 6.26 | 11100 | 0.5445 | 87.0634 | 79.9564 | 86.5556 | 86.5507 | 14.9101 | | 0.0147 | 6.32 | 11200 | 0.5602 | 87.0164 | 79.9748 | 86.505 | 86.4928 | 14.9105 | | 0.0156 | 6.38 | 11300 | 0.5587 | 87.1387 | 79.9561 | 86.6298 | 86.6329 | 14.9137 | | 0.0157 | 6.43 | 11400 | 0.5655 | 87.1027 | 80.1466 | 86.6023 | 86.5983 | 14.9201 | | 0.0139 | 6.49 | 11500 | 0.5773 | 87.1318 | 80.1543 | 86.5965 | 86.6127 | 14.9251 | | 0.0152 | 6.55 | 11600 | 0.5748 | 87.2417 | 80.2155 | 86.7204 | 86.7277 | 14.9128 | | 0.0169 | 6.6 | 11700 | 0.5558 | 87.2049 | 80.1632 | 86.7078 | 86.7198 | 14.9042 | | 0.0158 | 6.66 | 11800 | 0.5452 | 87.0358 | 79.9864 | 86.5181 | 86.5149 | 14.9151 | | 0.0169 | 6.72 | 11900 | 0.5411 | 87.0557 | 79.9435 | 86.5372 | 86.5375 | 14.9087 | | 0.0127 | 6.77 | 12000 | 0.5564 | 87.0617 | 80.0711 | 86.5398 | 86.5645 | 14.9051 | | 0.0158 | 6.83 | 12100 | 0.5545 | 87.0269 | 80.0081 | 86.4936 | 86.5004 | 14.9247 | | 0.0142 | 6.88 | 12200 | 0.5520 | 87.1107 | 80.1457 | 86.5775 | 86.5851 | 14.9192 | | 0.0142 | 6.94 | 12300 | 0.5590 | 87.152 | 80.1378 | 86.604 | 86.6048 | 14.9178 | | 0.0146 | 7.0 | 12400 | 0.5633 | 87.1416 | 80.1493 | 86.6109 | 86.6128 | 14.9178 | | 0.0087 | 7.05 | 12500 | 0.5928 | 87.1881 | 80.1549 | 86.6642 | 86.6747 | 14.9133 | | 0.0094 | 7.11 | 12600 | 0.5998 | 87.2084 | 80.2571 | 86.7023 | 86.6967 | 14.9042 | | 0.0082 | 7.17 | 12700 | 0.6086 | 87.1567 | 80.204 | 86.6479 | 86.6462 | 14.9147 | | 0.0096 | 7.22 | 12800 | 0.6106 | 87.173 | 80.1732 | 86.658 | 86.6586 | 14.9156 | | 0.0084 | 7.28 | 12900 | 0.6318 | 87.1298 | 80.1264 | 86.6351 | 86.638 | 14.9174 | | 0.0079 | 7.34 | 13000 | 0.6363 | 87.1628 | 80.1184 | 86.6548 | 86.6486 | 14.9174 | | 0.0091 | 7.39 | 13100 | 0.6313 | 87.241 | 80.2331 | 86.7437 | 86.7435 | 14.9156 | | 0.0088 | 7.45 | 13200 | 0.6376 | 87.1652 | 80.1422 | 86.661 | 86.6599 | 14.9142 | | 0.0091 | 7.51 | 13300 | 0.6364 | 87.1554 | 80.1285 | 86.6576 | 86.6553 | 14.9147 | | 0.0081 | 7.56 | 13400 | 0.6372 | 87.2418 | 80.192 | 86.7178 | 86.7199 | 14.9188 | | 0.0103 | 7.62 | 13500 | 0.6369 | 87.1754 | 80.1347 | 86.666 | 86.666 | 14.9133 | | 0.0094 | 7.67 | 13600 | 0.6382 | 87.1611 | 80.1066 | 86.6541 | 86.6488 | 14.9142 | | 0.0081 | 7.73 | 13700 | 0.6371 | 87.1836 | 80.0865 | 86.6575 | 86.6538 | 14.9151 | | 0.0076 | 7.79 | 13800 | 0.6377 | 87.1652 | 80.0572 | 86.6498 | 86.6569 | 14.9142 | | 0.0092 | 7.84 | 13900 | 0.6354 | 87.1638 | 80.0867 | 86.6563 | 86.6536 | 14.9142 | | 0.0076 | 7.9 | 14000 | 0.6346 | 87.1814 | 80.1212 | 86.6698 | 86.6683 | 14.9137 | | 0.0063 | 7.96 | 14100 | 0.6373 | 87.1913 | 80.1322 | 86.6793 | 86.6765 | 14.9128 | ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_lora_500_10_3000_8_e4_s108_v3

KingKazma

2023-07-16T22:13:58Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T22:13:57Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

mgeller/opt-6.7b-lora

mgeller

2023-07-16T22:06:40Z

0

peft

[ "peft", "region:us" ]

null

2023-07-12T22:58:35Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

nbroad/setfit-sci-wiki-large

nbroad

2023-07-16T21:58:13Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-07-16T21:57:15Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # nbroad/setfit-sci-wiki-large This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("nbroad/setfit-sci-wiki-large") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

KingKazma/xsum_gpt2_lora_500_10_3000_8_e1_s108_v3

KingKazma

2023-07-16T21:52:56Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T21:52:55Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

SinanAkkoyun/orca_mini_3b_gptq_badtest

SinanAkkoyun

2023-07-16T21:49:31Z

5

0

transformers

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

text-generation

2023-07-16T21:27:48Z

This is a very bad attempt at quantizing 128g 4 bit with alpaca (in orca style prompt ```sh python quantize_alpaca.py --pretrained_model_dir orca_mini_3b/ --bits 4 --group_size 128 --quantized_model_dir orca_mini_3b_gptq/ --save_and_reloa ``` Downloqd cleaned dataset first: https://github.com/gururise/AlpacaDataCleaned

LarryAIDraw/roxy-08

LarryAIDraw

2023-07-16T21:46:08Z

0

null

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

null

2023-07-16T21:42:37Z

--- license: creativeml-openrail-m --- https://civitai.com/models/109272/roxy-oror-mushoku-tensei

KingKazma/xsum_gpt2_lora_500_10_3000_8_e0_s108_v3

KingKazma

2023-07-16T21:45:55Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T21:45:54Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

LarryAIDraw/Predator

LarryAIDraw

2023-07-16T21:45:47Z

0

null

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

null

2023-07-16T21:42:05Z

--- license: creativeml-openrail-m --- https://civitai.com/models/109356/predator-or-granblue-fantasy

quangnguyennn/pokemon-lora

quangnguyennn

2023-07-16T21:41:33Z

1

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-16T12:51:01Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - quangnguyennn/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

KingKazma/xsum_gpt2_lora_500_10_3000_8_e-1_s108_v3

KingKazma

2023-07-16T21:38:46Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T21:38:45Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_lora_500_10_3000_8_e9_s6789_v3

KingKazma

2023-07-16T21:14:32Z

0

peft

[ "peft", "region:us" ]

null

2023-07-15T01:36:51Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

Debayan990/my-pet-cat-jxl

Debayan990

2023-07-16T21:13:51Z

13

0

diffusers

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

text-to-image

2023-07-16T21:01:07Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat-jxl Dreambooth model trained by Debayan990 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: BBIT47 Sample pictures of this concept: ![0](https://huggingface.co/Debayan990/my-pet-cat-jxl/resolve/main/sample_images/00000-2838740840.png) ![1](https://huggingface.co/Debayan990/my-pet-cat-jxl/resolve/main/sample_images/00003-3628577076.png) ![2](https://huggingface.co/Debayan990/my-pet-cat-jxl/resolve/main/sample_images/00001-1217343363.png)

MichaelS91/autotrain-hub_testing-75008139803

MichaelS91

2023-07-16T21:08:49Z

108

0

transformers

[ "transformers", "pytorch", "safetensors", "deberta", "text-classification", "autotrain", "text-regression", "en", "dataset:MichaelS91/autotrain-data-hub_testing", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-16T21:05:50Z

--- tags: - autotrain - text-regression language: - en widget: - text: "I love AutoTrain" datasets: - MichaelS91/autotrain-data-hub_testing co2_eq_emissions: emissions: 1.5911364056652006 --- # Model Trained Using AutoTrain - Problem type: Single Column Regression - Model ID: 75008139803 - CO2 Emissions (in grams): 1.5911 ## Validation Metrics - Loss: 1.889 - MSE: 1.889 - MAE: 1.094 - R2: 0.221 - RMSE: 1.374 - Explained Variance: 0.242 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/MichaelS91/autotrain-hub_testing-75008139803 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("MichaelS91/autotrain-hub_testing-75008139803", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

hseokool/vicuna-7b-v1.3-230623-09

hseokool

2023-07-16T20:40:52Z

0

peft

[ "peft", "region:us" ]

null

2023-07-14T11:46:32Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

FightingFalcon/SonmezReyiz

FightingFalcon

2023-07-16T20:39:15Z

0

null

[ "sönmez", "sönmezreyiz", "türkçe", "turkish", "tr", "arxiv:1910.09700", "license:openrail", "region:us" ]

null

2023-07-16T20:00:15Z

--- license: openrail language: - tr tags: - sönmez - sönmezreyiz - türkçe - turkish --- # Model Card for Model ID  This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

KingKazma/xsum_gpt2_lora_500_10_3000_8_e3_s6789_v3

KingKazma

2023-07-16T20:32:19Z

0

peft

[ "peft", "region:us" ]

null

2023-07-15T00:01:06Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

rshrott/falcon-7b-instruct-ft-descriptions-adapters

rshrott

2023-07-16T20:20:19Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T20:15:42Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0

KingKazma/xsum_gpt2_lora_500_10_3000_8_e1_s6789_v3

KingKazma

2023-07-16T20:18:10Z

0

peft

[ "peft", "region:us" ]

null

2023-07-14T23:29:54Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

bskang/test_demo_ver

bskang

2023-07-16T20:17:48Z

34

0

peft

[ "peft", "text-generation", "en", "region:us" ]

text-generation

2023-07-16T20:15:26Z

--- library_name: peft language: - en pipeline_tag: text-generation --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

anindya64/alpaca-bank-issue-summarization-20b-EthurAI

anindya64

2023-07-16T20:00:19Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T20:00:16Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0

Meina/MeinaMix_V11

Meina

2023-07-16T19:53:46Z

6,643

35

diffusers

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

text-to-image

2023-07-16T19:11:15Z

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

rshrott/falcon-7b-instruct-ft-adapters

rshrott

2023-07-16T19:48:46Z

5

0

peft

[ "peft", "pytorch", "RefinedWebModel", "custom_code", "region:us" ]

null

2023-07-16T13:37:16Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0

Dlychan/Tokyolagi

Dlychan

2023-07-16T19:42:33Z

0

null

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

null

2023-07-16T19:41:10Z

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

bskang/bskang8

bskang

2023-07-16T19:39:22Z

0

null

[ "en", "license:openrail", "region:us" ]

null

2023-07-16T12:18:21Z

--- language: - en license: openrail ---

Araki/airoboros-33b-gpt4-1.4.1-PI-8192-GGML

Araki

2023-07-16T19:23:42Z

0

2

null

[ "llama", "ggml", "text-generation", "region:us" ]

text-generation

2023-07-16T00:08:31Z

--- pipeline_tag: text-generation tags: - llama - ggml --- **Quantization from:** [bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16) **Converted to the GGML format with:** [llama.cpp master-6e7cca4 (JUL 15, 2023)](https://github.com/ggerganov/llama.cpp/releases/tag/master-6e7cca4) **Tested with:** [koboldcpp 1.35](https://github.com/LostRuins/koboldcpp/releases/tag/v1.35) **Example usage:** ``` koboldcpp.exe airoboros-33b-gpt4-1.4.1-PI-8192-ggmlv3.Q2_K.bin --threads 6 --linearrope --contextsize 8192 --stream --smartcontext --unbantokens --noblas ```

anujsahani01/NeuralCodeBot_starchat

anujsahani01

2023-07-16T19:18:12Z

0

null

[ "generated_from_trainer", "license:bigcode-openrail-m", "region:us" ]

null

2023-07-15T11:21:28Z

--- license: bigcode-openrail-m tags: - generated_from_trainer model-index: - name: NeuralCodeBot_starchat results: [] ---  # NeuralCodeBot_starchat This model is a fine-tuned version of [HuggingFaceH4/starchat-alpha](https://huggingface.co/HuggingFaceH4/starchat-alpha) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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_steps: 50 - training_steps: 5000 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

YojitShinde/ppo-Pyramids

YojitShinde

2023-07-16T19:13:01Z

6

0

ml-agents

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

reinforcement-learning

2023-07-16T19:11:49Z

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

ailabturkiye/umitozdag

ailabturkiye

2023-07-16T19:11:50Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T18:57:55Z

--- license: openrail language: - tr tags: - music --- Ümit Özdağ 200 Epochs [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Ümit Özdağ - RVC V2 200 Epoch **Zafer Partisi Başkanı Ümit Özdağ`nın ses modelidir, Rvc V2 200 epoch olarak eğitilmiştir.** _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits **Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.** - Discord: Bif-Tek#0505 ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)

jeremyvictor/t5-v1_1-base-fce-e8-b16

jeremyvictor

2023-07-16T18:47:47Z

22

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-16T15:27:24Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-v1_1-base-fce-e8-b16 results: [] ---  # t5-v1_1-base-fce-e8-b16 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3409 - Rouge1: 87.1583 - Rouge2: 79.8003 - Rougel: 86.6556 - Rougelsum: 86.6858 - Gen Len: 14.8987 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.9063 | 0.06 | 100 | 0.8111 | 27.4937 | 22.9629 | 27.3015 | 27.2771 | 7.4286 | | 0.7836 | 0.11 | 200 | 0.5104 | 85.4419 | 76.9583 | 84.8358 | 84.8509 | 15.0488 | | 0.6368 | 0.17 | 300 | 0.4682 | 86.2542 | 77.5212 | 85.6688 | 85.6923 | 14.8298 | | 0.5924 | 0.23 | 400 | 0.4734 | 86.4845 | 78.0506 | 85.9059 | 85.9008 | 14.8352 | | 0.5694 | 0.28 | 500 | 0.4081 | 86.352 | 78.0709 | 85.8245 | 85.8281 | 14.8585 | | 0.5335 | 0.34 | 600 | 0.4179 | 86.5893 | 78.4175 | 86.0693 | 86.0625 | 14.8745 | | 0.5246 | 0.4 | 700 | 0.3990 | 86.4139 | 78.4306 | 85.9523 | 85.9443 | 14.8617 | | 0.504 | 0.45 | 800 | 0.4233 | 86.7504 | 78.7906 | 86.2416 | 86.2447 | 14.8759 | | 0.4818 | 0.51 | 900 | 0.4008 | 86.7978 | 78.8187 | 86.2413 | 86.2432 | 14.8699 | | 0.4756 | 0.56 | 1000 | 0.4028 | 86.9123 | 79.0247 | 86.3563 | 86.3635 | 14.8640 | | 0.4772 | 0.62 | 1100 | 0.3789 | 86.5028 | 78.5736 | 85.9794 | 85.9983 | 14.8717 | | 0.4638 | 0.68 | 1200 | 0.3818 | 86.6276 | 78.7383 | 86.084 | 86.0903 | 14.9124 | | 0.4614 | 0.73 | 1300 | 0.3839 | 86.8128 | 79.2001 | 86.3591 | 86.3519 | 14.8695 | | 0.4326 | 0.79 | 1400 | 0.3751 | 86.9302 | 79.3511 | 86.4188 | 86.4311 | 14.9019 | | 0.4485 | 0.85 | 1500 | 0.3654 | 86.6862 | 79.0433 | 86.1832 | 86.1872 | 14.9206 | | 0.4187 | 0.9 | 1600 | 0.3823 | 86.9451 | 79.2758 | 86.4628 | 86.4724 | 14.8795 | | 0.4218 | 0.96 | 1700 | 0.3696 | 86.9051 | 79.1393 | 86.3682 | 86.3627 | 14.9220 | | 0.3812 | 1.02 | 1800 | 0.3699 | 87.0233 | 79.4507 | 86.513 | 86.5154 | 14.8873 | | 0.3116 | 1.07 | 1900 | 0.3763 | 86.9293 | 79.2058 | 86.4356 | 86.4445 | 14.8918 | | 0.3237 | 1.13 | 2000 | 0.3740 | 87.0449 | 79.4088 | 86.5157 | 86.5319 | 14.8918 | | 0.3071 | 1.19 | 2100 | 0.3690 | 86.5698 | 78.4408 | 85.9993 | 86.0409 | 14.9069 | | 0.3072 | 1.24 | 2200 | 0.3646 | 86.9336 | 79.334 | 86.4284 | 86.4303 | 14.8918 | | 0.2953 | 1.3 | 2300 | 0.3750 | 86.7437 | 78.949 | 86.2131 | 86.202 | 14.8909 | | 0.308 | 1.35 | 2400 | 0.3613 | 86.792 | 79.2179 | 86.2832 | 86.2934 | 14.8923 | | 0.3132 | 1.41 | 2500 | 0.3528 | 86.7653 | 79.0525 | 86.2258 | 86.2357 | 14.9110 | | 0.3141 | 1.47 | 2600 | 0.3494 | 86.8884 | 79.2484 | 86.3719 | 86.3622 | 14.9069 | | 0.3095 | 1.52 | 2700 | 0.3539 | 87.0166 | 79.5218 | 86.5167 | 86.5248 | 14.8905 | | 0.3274 | 1.58 | 2800 | 0.3599 | 87.2104 | 79.7277 | 86.7135 | 86.7127 | 14.8854 | | 0.312 | 1.64 | 2900 | 0.3536 | 86.8926 | 79.2971 | 86.3699 | 86.3666 | 14.8886 | | 0.3134 | 1.69 | 3000 | 0.3518 | 87.0884 | 79.5848 | 86.5877 | 86.6005 | 14.9028 | | 0.3012 | 1.75 | 3100 | 0.3573 | 86.3559 | 78.1413 | 85.8416 | 85.8479 | 14.8763 | | 0.311 | 1.81 | 3200 | 0.3467 | 86.9837 | 79.4983 | 86.4827 | 86.4981 | 14.8937 | | 0.303 | 1.86 | 3300 | 0.3422 | 86.9232 | 79.3542 | 86.4098 | 86.4427 | 14.9032 | | 0.304 | 1.92 | 3400 | 0.3409 | 87.1583 | 79.8003 | 86.6556 | 86.6858 | 14.8987 | | 0.2934 | 1.98 | 3500 | 0.3485 | 87.0529 | 79.6491 | 86.5825 | 86.6003 | 14.9000 | | 0.247 | 2.03 | 3600 | 0.3586 | 87.0147 | 79.6418 | 86.5126 | 86.5339 | 14.9042 | | 0.193 | 2.09 | 3700 | 0.3667 | 86.9326 | 79.4481 | 86.4675 | 86.4709 | 14.9128 | | 0.195 | 2.14 | 3800 | 0.3673 | 86.8892 | 79.3638 | 86.3717 | 86.3866 | 14.9210 | | 0.19 | 2.2 | 3900 | 0.3670 | 86.8789 | 79.4677 | 86.3925 | 86.3892 | 14.9023 | | 0.2033 | 2.26 | 4000 | 0.3600 | 86.9004 | 79.5211 | 86.4043 | 86.407 | 14.9042 | | 0.1969 | 2.31 | 4100 | 0.3587 | 87.0403 | 79.7208 | 86.5257 | 86.5245 | 14.8978 | | 0.2035 | 2.37 | 4200 | 0.3630 | 86.8793 | 79.4667 | 86.3931 | 86.3875 | 14.8895 | | 0.2162 | 2.43 | 4300 | 0.3722 | 86.78 | 79.3367 | 86.2742 | 86.2812 | 14.9083 | | 0.1984 | 2.48 | 4400 | 0.3573 | 86.7248 | 79.2577 | 86.218 | 86.2139 | 14.8918 | | 0.2058 | 2.54 | 4500 | 0.3617 | 86.6452 | 79.1422 | 86.1701 | 86.1838 | 14.8909 | | 0.2161 | 2.6 | 4600 | 0.3554 | 86.8574 | 79.5476 | 86.3982 | 86.4095 | 14.9283 | | 0.215 | 2.65 | 4700 | 0.3583 | 86.8873 | 79.5265 | 86.4039 | 86.3996 | 14.8923 | | 0.2048 | 2.71 | 4800 | 0.3535 | 86.8465 | 79.3852 | 86.3446 | 86.344 | 14.8978 | | 0.2099 | 2.77 | 4900 | 0.3601 | 86.8952 | 79.4424 | 86.3888 | 86.387 | 14.8868 | | 0.2149 | 2.82 | 5000 | 0.3603 | 86.7871 | 79.2397 | 86.297 | 86.3004 | 14.8850 | | 0.2251 | 2.88 | 5100 | 0.3448 | 86.9477 | 79.6744 | 86.4984 | 86.4911 | 14.9133 | | 0.2048 | 2.93 | 5200 | 0.3522 | 86.8843 | 79.37 | 86.3702 | 86.3668 | 14.8955 | | 0.2099 | 2.99 | 5300 | 0.3459 | 86.7938 | 79.2104 | 86.3027 | 86.3169 | 14.9137 | | 0.1377 | 3.05 | 5400 | 0.4000 | 86.9855 | 79.4184 | 86.438 | 86.4375 | 14.9110 | | 0.1369 | 3.1 | 5500 | 0.3848 | 86.8338 | 79.2098 | 86.2885 | 86.3028 | 14.9019 | | 0.1357 | 3.16 | 5600 | 0.3914 | 86.7061 | 79.2474 | 86.2247 | 86.2237 | 14.9105 | | 0.1263 | 3.22 | 5700 | 0.3864 | 86.7128 | 79.1103 | 86.2121 | 86.2166 | 14.9137 | | 0.135 | 3.27 | 5800 | 0.3929 | 86.8134 | 79.4572 | 86.3608 | 86.3683 | 14.9124 | | 0.1361 | 3.33 | 5900 | 0.3828 | 86.9149 | 79.4756 | 86.4152 | 86.3959 | 14.8959 | | 0.1286 | 3.39 | 6000 | 0.3849 | 86.8025 | 79.3645 | 86.3215 | 86.3204 | 14.8996 | | 0.1335 | 3.44 | 6100 | 0.3793 | 86.7591 | 79.2887 | 86.2778 | 86.2765 | 14.9105 | | 0.1278 | 3.5 | 6200 | 0.3938 | 86.8352 | 79.4161 | 86.3282 | 86.3376 | 14.9169 | | 0.1346 | 3.56 | 6300 | 0.3943 | 86.9637 | 79.6404 | 86.4753 | 86.4718 | 14.8978 | | 0.1421 | 3.61 | 6400 | 0.3799 | 86.8445 | 79.4133 | 86.3271 | 86.3206 | 14.9151 | | 0.1398 | 3.67 | 6500 | 0.3923 | 86.9793 | 79.6847 | 86.4935 | 86.4889 | 14.9174 | | 0.1359 | 3.72 | 6600 | 0.3912 | 86.9095 | 79.3593 | 86.4296 | 86.4506 | 14.8959 | | 0.1444 | 3.78 | 6700 | 0.3741 | 86.8498 | 79.3141 | 86.3586 | 86.3681 | 14.8909 | | 0.1351 | 3.84 | 6800 | 0.3840 | 87.223 | 79.825 | 86.7127 | 86.7371 | 14.8877 | | 0.1325 | 3.89 | 6900 | 0.3816 | 87.148 | 79.8102 | 86.6405 | 86.6511 | 14.9133 | | 0.1315 | 3.95 | 7000 | 0.3796 | 86.7778 | 79.3782 | 86.3057 | 86.2939 | 14.9005 | | 0.1332 | 4.01 | 7100 | 0.3962 | 87.0238 | 79.6621 | 86.5384 | 86.5306 | 14.8996 | | 0.0834 | 4.06 | 7200 | 0.4271 | 86.9999 | 79.7076 | 86.4981 | 86.5026 | 14.9014 | | 0.088 | 4.12 | 7300 | 0.4176 | 86.9193 | 79.4698 | 86.4085 | 86.4171 | 14.9128 | | 0.0897 | 4.18 | 7400 | 0.4109 | 86.9287 | 79.5866 | 86.4541 | 86.4474 | 14.9037 | | 0.0908 | 4.23 | 7500 | 0.4109 | 87.1272 | 79.7632 | 86.6206 | 86.6176 | 14.9133 | | 0.0895 | 4.29 | 7600 | 0.4114 | 87.0107 | 79.7349 | 86.4873 | 86.4754 | 14.9023 | | 0.0856 | 4.35 | 7700 | 0.4242 | 87.0115 | 79.6387 | 86.4786 | 86.49 | 14.8982 | | 0.0852 | 4.4 | 7800 | 0.4271 | 86.9943 | 79.6717 | 86.5126 | 86.5026 | 14.9019 | | 0.0919 | 4.46 | 7900 | 0.4216 | 86.9903 | 79.67 | 86.512 | 86.5085 | 14.8937 | | 0.0907 | 4.51 | 8000 | 0.4180 | 87.0323 | 79.7092 | 86.5391 | 86.5343 | 14.8978 | | 0.0889 | 4.57 | 8100 | 0.4276 | 86.9813 | 79.6367 | 86.4697 | 86.4724 | 14.9115 | | 0.0907 | 4.63 | 8200 | 0.4209 | 87.0149 | 79.5637 | 86.5028 | 86.5059 | 14.9092 | | 0.0966 | 4.68 | 8300 | 0.4064 | 86.9685 | 79.4665 | 86.4393 | 86.4523 | 14.9010 | | 0.088 | 4.74 | 8400 | 0.4234 | 86.9921 | 79.5729 | 86.4977 | 86.5067 | 14.8800 | | 0.0897 | 4.8 | 8500 | 0.4117 | 87.0727 | 79.7094 | 86.5465 | 86.5482 | 14.9014 | | 0.0924 | 4.85 | 8600 | 0.4056 | 86.8789 | 79.409 | 86.3689 | 86.3672 | 14.9083 | | 0.0916 | 4.91 | 8700 | 0.4127 | 86.8645 | 79.4195 | 86.3814 | 86.3729 | 14.8982 | | 0.0908 | 4.97 | 8800 | 0.4054 | 86.9146 | 79.4138 | 86.4022 | 86.399 | 14.9000 | | 0.078 | 5.02 | 8900 | 0.4403 | 87.0178 | 79.6166 | 86.5112 | 86.505 | 14.9078 | | 0.0583 | 5.08 | 9000 | 0.4400 | 86.9828 | 79.649 | 86.4913 | 86.4962 | 14.9064 | | 0.057 | 5.14 | 9100 | 0.4637 | 87.0435 | 79.6446 | 86.5464 | 86.5252 | 14.9037 | | 0.0581 | 5.19 | 9200 | 0.4617 | 87.017 | 79.6255 | 86.5004 | 86.4907 | 14.9069 | | 0.0562 | 5.25 | 9300 | 0.4521 | 86.8638 | 79.479 | 86.3298 | 86.338 | 14.9096 | | 0.0588 | 5.3 | 9400 | 0.4472 | 86.9719 | 79.5608 | 86.4751 | 86.4798 | 14.9073 | | 0.0571 | 5.36 | 9500 | 0.4472 | 87.0325 | 79.6355 | 86.5154 | 86.5278 | 14.9073 | | 0.0589 | 5.42 | 9600 | 0.4580 | 87.1556 | 79.8992 | 86.627 | 86.6372 | 14.9064 | | 0.057 | 5.47 | 9700 | 0.4527 | 87.0033 | 79.6457 | 86.4846 | 86.5031 | 14.9101 | | 0.0595 | 5.53 | 9800 | 0.4538 | 87.0419 | 79.6632 | 86.5261 | 86.5434 | 14.9055 | | 0.062 | 5.59 | 9900 | 0.4518 | 87.0581 | 79.6818 | 86.54 | 86.551 | 14.9005 | | 0.0568 | 5.64 | 10000 | 0.4549 | 87.1255 | 79.8908 | 86.6143 | 86.6255 | 14.9042 | | 0.0572 | 5.7 | 10100 | 0.4557 | 86.9927 | 79.5946 | 86.4726 | 86.4953 | 14.9023 | | 0.0603 | 5.76 | 10200 | 0.4493 | 87.0665 | 79.7469 | 86.58 | 86.5934 | 14.8932 | | 0.0604 | 5.81 | 10300 | 0.4533 | 87.0864 | 79.7039 | 86.5871 | 86.5851 | 14.9042 | | 0.0564 | 5.87 | 10400 | 0.4653 | 87.082 | 79.766 | 86.5835 | 86.5775 | 14.9055 | | 0.0579 | 5.93 | 10500 | 0.4677 | 86.9805 | 79.5068 | 86.4708 | 86.4744 | 14.8882 | | 0.0582 | 5.98 | 10600 | 0.4607 | 86.9273 | 79.3762 | 86.4228 | 86.4225 | 14.9119 | | 0.0454 | 6.04 | 10700 | 0.4917 | 87.038 | 79.6146 | 86.5363 | 86.533 | 14.9156 | | 0.0399 | 6.09 | 10800 | 0.4986 | 87.0026 | 79.5481 | 86.4992 | 86.4924 | 14.9042 | | 0.0367 | 6.15 | 10900 | 0.5115 | 87.13 | 79.7506 | 86.6082 | 86.621 | 14.9055 | | 0.0405 | 6.21 | 11000 | 0.5084 | 87.0768 | 79.6986 | 86.5541 | 86.5403 | 14.9083 | | 0.0386 | 6.26 | 11100 | 0.5092 | 87.1376 | 79.7442 | 86.5937 | 86.5767 | 14.8996 | | 0.0382 | 6.32 | 11200 | 0.5063 | 87.0779 | 79.7205 | 86.561 | 86.5546 | 14.8982 | | 0.0431 | 6.38 | 11300 | 0.4950 | 87.0998 | 79.7699 | 86.5882 | 86.5916 | 14.9028 | | 0.0388 | 6.43 | 11400 | 0.5098 | 87.1711 | 79.8707 | 86.6425 | 86.6409 | 14.9023 | | 0.041 | 6.49 | 11500 | 0.4911 | 87.1742 | 79.8319 | 86.6434 | 86.6522 | 14.9005 | | 0.0379 | 6.55 | 11600 | 0.5023 | 87.2258 | 79.9175 | 86.7019 | 86.7018 | 14.9010 | | 0.0383 | 6.6 | 11700 | 0.5078 | 87.0913 | 79.7547 | 86.5767 | 86.5826 | 14.9046 | | 0.0387 | 6.66 | 11800 | 0.5111 | 87.1913 | 79.9592 | 86.6805 | 86.6742 | 14.9060 | | 0.0362 | 6.72 | 11900 | 0.5125 | 87.0096 | 79.6639 | 86.5037 | 86.5039 | 14.9124 | | 0.0343 | 6.77 | 12000 | 0.5210 | 87.0657 | 79.7384 | 86.5621 | 86.5561 | 14.9110 | | 0.0401 | 6.83 | 12100 | 0.5110 | 87.1338 | 79.8537 | 86.6368 | 86.6271 | 14.9124 | | 0.0353 | 6.88 | 12200 | 0.5169 | 87.082 | 79.756 | 86.5771 | 86.5718 | 14.9073 | | 0.0384 | 6.94 | 12300 | 0.4998 | 87.1211 | 79.8474 | 86.6016 | 86.6065 | 14.9078 | | 0.0395 | 7.0 | 12400 | 0.5184 | 87.1621 | 79.8793 | 86.6411 | 86.648 | 14.9064 | | 0.0243 | 7.05 | 12500 | 0.5387 | 87.1588 | 79.8545 | 86.6464 | 86.6627 | 14.9019 | | 0.0283 | 7.11 | 12600 | 0.5384 | 87.1909 | 79.8888 | 86.6567 | 86.6698 | 14.9042 | | 0.026 | 7.17 | 12700 | 0.5459 | 87.1782 | 79.7991 | 86.6373 | 86.6507 | 14.9028 | | 0.0303 | 7.22 | 12800 | 0.5301 | 87.1014 | 79.7321 | 86.5581 | 86.5743 | 14.9014 | | 0.0252 | 7.28 | 12900 | 0.5481 | 87.0907 | 79.6948 | 86.5306 | 86.5474 | 14.9069 | | 0.0273 | 7.34 | 13000 | 0.5469 | 87.0971 | 79.6697 | 86.5392 | 86.558 | 14.8987 | | 0.0249 | 7.39 | 13100 | 0.5462 | 87.095 | 79.6904 | 86.5559 | 86.566 | 14.9037 | | 0.0246 | 7.45 | 13200 | 0.5553 | 87.0964 | 79.6834 | 86.5572 | 86.5607 | 14.9055 | | 0.0286 | 7.51 | 13300 | 0.5501 | 87.0933 | 79.7177 | 86.5579 | 86.5582 | 14.9092 | | 0.0234 | 7.56 | 13400 | 0.5550 | 87.1266 | 79.7546 | 86.5833 | 86.5855 | 14.9087 | | 0.0263 | 7.62 | 13500 | 0.5570 | 87.0957 | 79.6859 | 86.5608 | 86.5584 | 14.9064 | | 0.0238 | 7.67 | 13600 | 0.5630 | 87.1368 | 79.7487 | 86.6036 | 86.6031 | 14.9032 | | 0.0258 | 7.73 | 13700 | 0.5598 | 87.1527 | 79.7481 | 86.622 | 86.6153 | 14.9055 | | 0.0249 | 7.79 | 13800 | 0.5649 | 87.15 | 79.7419 | 86.6106 | 86.6056 | 14.9046 | | 0.0272 | 7.84 | 13900 | 0.5616 | 87.1439 | 79.7597 | 86.6085 | 86.6081 | 14.9042 | | 0.0261 | 7.9 | 14000 | 0.5596 | 87.1359 | 79.7696 | 86.6081 | 86.6024 | 14.9051 | | 0.0233 | 7.96 | 14100 | 0.5611 | 87.1367 | 79.7636 | 86.6112 | 86.6019 | 14.9046 | ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3

oakal/fourthbrain_bloomz_marketing

oakal

2023-07-16T18:32:44Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T18:32:38Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

harithapliyal/distilbert-base-uncased-finetuned-ner

harithapliyal

2023-07-16T18:26:04Z

62

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-16T17:06:57Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: harithapliyal/distilbert-base-uncased-finetuned-ner results: [] ---  # harithapliyal/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1975 - Validation Loss: 0.0734 - Train Precision: 0.9049 - Train Recall: 0.9116 - Train F1: 0.9083 - Train Accuracy: 0.9793 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1975 | 0.0734 | 0.9049 | 0.9116 | 0.9083 | 0.9793 | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

0sunfire0/rl_course_vizdoom_health_gathering_supreme_02

0sunfire0

2023-07-16T18:23:44Z

0

sample-factory

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

reinforcement-learning

2023-07-16T18:23:37Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.16 +/- 3.86 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r 0sunfire0/rl_course_vizdoom_health_gathering_supreme_02 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .opt.conda.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_02 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .opt.conda.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_02 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

bodaay/Wizard-Vicuna-7B-Uncensored-ONNX

bodaay

2023-07-16T18:06:51Z

5

0

transformers

[ "transformers", "onnx", "llama", "text-generation", "uncensored", "en", "dataset:ehartford/wizard_vicuna_70k_unfiltered", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-16T16:18:44Z

--- license: other datasets: - ehartford/wizard_vicuna_70k_unfiltered language: - en tags: - uncensored --- Original Model: [ehartford/Wizard-Vicuna-7B-Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored) From Original Model Card: This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.

rsml/bbert_qa

rsml

2023-07-16T17:59:30Z

120

0

transformers

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

question-answering

2023-07-16T17:42:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bbert_qa results: [] ---  # bbert_qa This model is a fine-tuned version of [bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6818 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.3490 | | 2.7154 | 2.0 | 500 | 1.7686 | | 2.7154 | 3.0 | 750 | 1.6818 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

sherif1311/flan-t5-base-imdb-text-classification

sherif1311

2023-07-16T17:50:43Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-16T14:44:19Z

--- license: apache-2.0 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: 0.0797 - F1: 95.072 - Gen Len: 2.5005 ## 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 4.28.1 - Pytorch 1.12.1+cu116 - Datasets 2.4.0 - Tokenizers 0.12.1

NasimB/children_bnc_rarity_all_no_cut

NasimB

2023-07-16T17:50:30Z

5

0

transformers

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

text-generation

2023-07-16T15:57:37Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: children_bnc_rarity_all_no_cut results: [] ---  # children_bnc_rarity_all_no_cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3266 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7047 | 0.29 | 500 | 5.6398 | | 5.3501 | 0.58 | 1000 | 5.2066 | | 5.0056 | 0.88 | 1500 | 4.9588 | | 4.7258 | 1.17 | 2000 | 4.8173 | | 4.5734 | 1.46 | 2500 | 4.6948 | | 4.4663 | 1.75 | 3000 | 4.5804 | | 4.3402 | 2.05 | 3500 | 4.5071 | | 4.1471 | 2.34 | 4000 | 4.4576 | | 4.1137 | 2.63 | 4500 | 4.4027 | | 4.0777 | 2.92 | 5000 | 4.3468 | | 3.8629 | 3.22 | 5500 | 4.3449 | | 3.8078 | 3.51 | 6000 | 4.3108 | | 3.8044 | 3.8 | 6500 | 4.2763 | | 3.7029 | 4.09 | 7000 | 4.2803 | | 3.5324 | 4.39 | 7500 | 4.2741 | | 3.5239 | 4.68 | 8000 | 4.2585 | | 3.5091 | 4.97 | 8500 | 4.2454 | | 3.3521 | 5.26 | 9000 | 4.2592 | | 3.3357 | 5.56 | 9500 | 4.2584 | | 3.3348 | 5.85 | 10000 | 4.2573 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

nishchalprasad/lunar_lander_v2-PPO

nishchalprasad

2023-07-16T17:44:18Z

4

1

stable-baselines3

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

reinforcement-learning

2023-07-16T17:43:57Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO-MLP results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.46 +/- 24.94 name: mean_reward verified: false --- # **PPO-MLP** Agent playing **LunarLander-v2** This is a trained model of a **PPO-MLP** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

kanu03/my-cat

kanu03

2023-07-16T17:44:02Z

107

0

diffusers

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

text-to-image

2023-07-16T17:39:19Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-cat Dreambooth model trained by kanu03 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: OPJU101 Sample pictures of this concept: ![0](https://huggingface.co/kanu03/my-cat/resolve/main/sample_images/01.jpg)

Za88yes/Afriana

Za88yes

2023-07-16T17:43:07Z

0

null

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

null

2023-07-16T17:41:00Z

--- license: bigscience-openrail-m ---

Tasaloris13/finetuned-college-10

Tasaloris13

2023-07-16T17:42:10Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-16T16:59:34Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

balpreetspankaj/distilbert-base-uncased-finetuned-emotion

balpreetspankaj

2023-07-16T17:37:10Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-16T16:46:28Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2169 - Accuracy: 0.9285 - F1: 0.9283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.827 | 1.0 | 250 | 0.3132 | 0.9085 | 0.9062 | | 0.2411 | 2.0 | 500 | 0.2169 | 0.9285 | 0.9283 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

magicsword/wy-mt-en-zh-1

magicsword

2023-07-16T17:35:29Z

113

0

transformers

[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "autotrain", "translation", "unk", "dataset:magicsword/autotrain-data-wy-mt-en-zh", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-16T15:16:22Z

--- tags: - autotrain - translation language: - unk - unk datasets: - magicsword/autotrain-data-wy-mt-en-zh co2_eq_emissions: emissions: 1.4514851624864995 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 74981139791 - CO2 Emissions (in grams): 1.4515 ## Validation Metrics - Loss: 2.215 - SacreBLEU: 12.702 - Gen len: 16.311

magicsword/wy-mt-en-zh-2

magicsword

2023-07-16T17:27:39Z

107

0

transformers

[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "autotrain", "translation", "unk", "dataset:magicsword/autotrain-data-wy-mt-en-zh", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-16T15:15:50Z

--- tags: - autotrain - translation language: - unk - unk datasets: - magicsword/autotrain-data-wy-mt-en-zh co2_eq_emissions: emissions: 71.14399741050826 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 74981139786 - CO2 Emissions (in grams): 71.1440 ## Validation Metrics - Loss: 2.220 - SacreBLEU: 12.949 - Gen len: 16.386

lucasbertola/ppo-Pyramids

lucasbertola

2023-07-16T17:26:30Z

2

0

ml-agents

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

reinforcement-learning

2023-07-16T17:26:24Z

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

JianPublisher/modeltest

JianPublisher

2023-07-16T17:25:48Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-16T17:20:11Z

--- license: mit tags: - generated_from_trainer model-index: - name: modeltest results: [] ---  # modeltest This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jayantdocplix/falcon_model_finetuned

jayantdocplix

2023-07-16T17:25:44Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-15T19:29:45Z

--- 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.dev0 - PEFT 0.4.0.dev0

odunola/transcriber-t5-v8-new

odunola

2023-07-16T17:23:29Z

102

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-16T16:37:38Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: transcriber-t5-v8-new results: [] ---  # transcriber-t5-v8-new This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0818 ## 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: 12 - eval_batch_size: 12 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1008 | 0.72 | 500 | 0.1306 | | 0.069 | 1.43 | 1000 | 0.1227 | | 0.1052 | 2.15 | 1500 | 0.1209 | | 0.1017 | 2.86 | 2000 | 0.0992 | | 0.0828 | 3.58 | 2500 | 0.0919 | | 0.0471 | 4.29 | 3000 | 0.0927 | | 0.0769 | 5.01 | 3500 | 0.0849 | | 0.0732 | 5.72 | 4000 | 0.0862 | | 0.0801 | 6.44 | 4500 | 0.0857 | | 0.0428 | 7.15 | 5000 | 0.0815 | | 0.1119 | 7.87 | 5500 | 0.0790 | | 0.0692 | 8.58 | 6000 | 0.0780 | | 0.0684 | 9.3 | 6500 | 0.0818 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ailabturkiye/sda

ailabturkiye

2023-07-16T17:19:15Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T17:00:01Z

--- license: openrail language: - tr tags: - music ---

DanGalt/speecht5_finetuned_voxpopuli_fi

DanGalt

2023-07-16T17:11:18Z

82

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "fi", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-16T17:07:04Z

--- language: - fi license: mit tags: - generated_from_trainer - text-to-speech datasets: - facebook/voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_fi results: [] ---  # speecht5_finetuned_voxpopuli_fi This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4436 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 150 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.504 | 5.05 | 250 | 0.4645 | | 0.4882 | 10.1 | 500 | 0.4499 | | 0.467 | 15.15 | 750 | 0.4450 | | 0.4651 | 20.2 | 1000 | 0.4436 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

KingKazma/xsum_t5-small_prompt_tuning_500_10_3000_8_e-1_s55555_v3_manual

KingKazma

2023-07-16T17:02:55Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T17:02:55Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

ailabturkiye/azizyildirim

ailabturkiye

2023-07-16T16:47:56Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T16:37:16Z

--- license: openrail language: - tr tags: - music ---

iworeushankaonce/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

iworeushankaonce

2023-07-16T16:35:53Z

164

0

transformers

[ "transformers", "pytorch", "tensorboard", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-16T15:19:49Z

--- license: bsd-3-clause tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: [] ---  # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.3882 - Accuracy: 0.9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4932 | 1.0 | 112 | 0.5325 | 0.86 | | 0.3541 | 2.0 | 225 | 0.6068 | 0.77 | | 0.5743 | 3.0 | 337 | 0.6356 | 0.83 | | 0.6256 | 4.0 | 450 | 0.4878 | 0.86 | | 0.0619 | 5.0 | 562 | 0.4262 | 0.88 | | 0.0044 | 6.0 | 675 | 0.3266 | 0.91 | | 0.0018 | 7.0 | 787 | 0.4827 | 0.87 | | 0.001 | 8.0 | 900 | 0.9245 | 0.82 | | 0.1854 | 9.0 | 1012 | 0.4256 | 0.89 | | 0.0001 | 10.0 | 1125 | 0.3898 | 0.9 | | 0.0001 | 11.0 | 1237 | 0.3873 | 0.9 | | 0.0001 | 12.0 | 1350 | 0.4064 | 0.91 | | 0.0 | 13.0 | 1462 | 0.3910 | 0.9 | | 0.0 | 14.0 | 1575 | 0.3924 | 0.9 | | 0.0001 | 15.0 | 1687 | 0.3917 | 0.91 | | 0.0 | 16.0 | 1800 | 0.3903 | 0.9 | | 0.0 | 17.0 | 1912 | 0.3900 | 0.89 | | 0.0 | 18.0 | 2025 | 0.3894 | 0.89 | | 0.0 | 19.0 | 2137 | 0.3886 | 0.9 | | 0.0 | 19.91 | 2240 | 0.3882 | 0.9 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

WasuratS/whisper-tiny-en-finetune-minds14

WasuratS

2023-07-16T16:33:30Z

90

0

transformers

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

automatic-speech-recognition

2023-07-16T13:49:35Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en-finetune-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.3382526564344746 ---  # whisper-tiny-en-finetune-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6541 - Wer Ortho: 0.3399 - Wer: 0.3383 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.3136 | 3.57 | 100 | 0.4883 | 0.3640 | 0.3524 | | 0.0417 | 7.14 | 200 | 0.5146 | 0.3560 | 0.3442 | | 0.0066 | 10.71 | 300 | 0.5736 | 0.3411 | 0.3353 | | 0.0017 | 14.29 | 400 | 0.6040 | 0.3455 | 0.3418 | | 0.0013 | 17.86 | 500 | 0.6226 | 0.3393 | 0.3365 | | 0.0009 | 21.43 | 600 | 0.6352 | 0.3393 | 0.3365 | | 0.0007 | 25.0 | 700 | 0.6436 | 0.3399 | 0.3371 | | 0.0006 | 28.57 | 800 | 0.6492 | 0.3399 | 0.3383 | | 0.0006 | 32.14 | 900 | 0.6530 | 0.3399 | 0.3383 | | 0.0006 | 35.71 | 1000 | 0.6541 | 0.3399 | 0.3383 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

cassandraqs/shan_homework1

cassandraqs

2023-07-16T16:29:28Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T16:29:22Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

KingKazma/xsum_t5-small_prompt_tuning_500_10_3000_8_e-1_s6789_v3_manual

KingKazma

2023-07-16T16:23:45Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T16:23:45Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

localmodels/LLaMA-65B-ggml

localmodels

2023-07-16T16:22:41Z

0

1

null

[ "region:us" ]

null

2023-07-16T16:22:41Z

--- duplicated_from: localmodels/LLM --- # LLaMA 65B ggml From Meta: https://ai.meta.com/blog/large-language-model-llama-meta-ai --- ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` Quantized using an older version of llama.cpp and compatible with llama.cpp from May 19, commit 2d5db48. ### k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` Quantization methods compatible with latest llama.cpp from June 6, commit 2d43387. --- ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | llama-65b.ggmlv3.q2_K.bin | q2_K | 2 | 27.33 GB| 29.83 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | llama-65b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 34.55 GB| 37.05 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | llama-65b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 31.40 GB| 33.90 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | llama-65b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 28.06 GB| 30.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | llama-65b.ggmlv3.q4_0.bin | q4_0 | 4 | 36.73 GB| 39.23 GB | Original quant method, 4-bit. | | llama-65b.ggmlv3.q4_1.bin | q4_1 | 4 | 40.81 GB| 43.31 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | llama-65b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 39.28 GB| 41.78 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | llama-65b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 36.73 GB| 39.23 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | llama-65b.ggmlv3.q5_0.bin | q5_0 | 5 | 44.89 GB| 47.39 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | llama-65b.ggmlv3.q5_1.bin | q5_1 | 5 | 48.97 GB| 51.47 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | llama-65b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 46.20 GB| 48.70 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | llama-65b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 44.89 GB| 47.39 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | llama-65b.ggmlv3.q6_K.bin | q6_K |6 | 53.56 GB| 56.06 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | llama-65b.ggmlv3.q8_0.bin | q8_0 | 8 | 69.370 GB | 71.87 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |

ailabturkiye/13killoki

ailabturkiye

2023-07-16T16:19:28Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T16:09:13Z

--- license: openrail language: - tr tags: - music --- 13Killoki'nin StereoBound Song Story videosuyla yaptığım model. Konuşma için uygundur.

ailabturkiye/Joker

ailabturkiye

2023-07-16T16:17:15Z

0

1

null

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

null

2023-07-16T15:22:06Z

--- license: openrail --- [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Joker - RVC V2 300 Epoch **Rapper Joker`in ses modelidir, Rvc V2 300 epoch olarak eğitilmiştir.** _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits **Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.** - Discord: barisdark0 - YouTube: Barış (https://www.youtube.com/@barisdark) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)--- {} ---

ailabturkiye/KadirMisiroglu

ailabturkiye

2023-07-16T16:17:02Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T16:13:31Z

--- license: openrail language: - tr tags: - music --- Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.

casque/Ultimate_ahegao

casque

2023-07-16T16:16:47Z

0

null

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

null

2023-07-16T16:14:24Z

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

ailabturkiye/NormEnder

ailabturkiye

2023-07-16T16:14:49Z

0

null

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

null

2023-07-16T15:49:59Z

--- license: openrail --- [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Ceza - RVC V2 500 Epoch **Rapper Ceza`nın ses modelidir, Rvc V2 500 epoch olarak eğitilmiştir.** _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits **Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.** - Discord: barisdark0 - YouTube: Barış (https://www.youtube.com/@barisdark) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)--- {} ---

ailabturkiye/Beta

ailabturkiye

2023-07-16T16:13:26Z

0

null

[ "region:us" ]

null

2023-07-16T16:04:26Z

[![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Beta Berk Bayındır (3B) - RVC V2 500 Epoch **Beta Berk Bayındır'ın ses medolidir, Rvc V2 500 epoch olarak eğitilmiştir.** _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits **Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.** - Discord: efemekkuin - YouTube: Ahmet Efe (https://www.youtube.com/channel/UCw40vAQRF8551rMWem6CaMg) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)

ailabturkiye/AliErbas

ailabturkiye

2023-07-16T16:11:53Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T16:09:33Z

--- license: openrail language: - tr tags: - music --- Diyanet İşleri Başkanı Sayın Ali Erbaş. Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.

casque/AfterSexMS

casque

2023-07-16T16:09:39Z

0

null

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

null

2023-07-16T16:07:19Z

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

n0n1m/rvc-krosh

n0n1m

2023-07-16T16:08:15Z

0

null

[ "audio-to-audio", "license:openrail", "region:us" ]

audio-to-audio

2023-07-15T17:45:37Z

--- license: openrail pipeline_tag: audio-to-audio --- Just a model of Krash from Kikoriki/Gogoriki or Krosh from Smeshariki

tyavika/Bert-QA-Pytorch-FULL

tyavika

2023-07-16T16:05:57Z

7

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-28T02:19:57Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Bert-QA-Pytorch-FULL results: [] ---  # Bert-QA-Pytorch-FULL This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1633 | 1.0 | 3290 | 1.0515 | | 0.8061 | 2.0 | 6580 | 1.0593 | | 0.533 | 3.0 | 9870 | 1.2154 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ailabturkiye/NecmettinErbakan

ailabturkiye

2023-07-16T16:05:52Z

0

null

[ "music", "tr", "license:openrail", "region:us" ]

null

2023-07-16T16:02:15Z

--- license: openrail language: - tr tags: - music --- Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.

casque/Creampie_v11

casque

2023-07-16T16:05:41Z

0

null

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

null

2023-07-16T16:03:25Z

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

ailabturkiye/deepturkisherdi

ailabturkiye

2023-07-16T16:05:24Z

0

null

[ "region:us" ]

null

2023-07-16T16:04:08Z

--- license: openrail language: - tr tags: - music deepturkisherdi 500 epoch