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CyberHarem/ogaki_chiaki_yurucamp
CyberHarem
2023-09-26T19:03:45Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/ogaki_chiaki_yurucamp", "license:mit", "region:us" ]
text-to-image
2023-09-26T07:19:28Z
--- license: mit datasets: - CyberHarem/ogaki_chiaki_yurucamp pipeline_tag: text-to-image tags: - art --- # Lora of ogaki_chiaki_yurucamp This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4480, you need to download `4480/ogaki_chiaki_yurucamp.pt` as the embedding and `4480/ogaki_chiaki_yurucamp.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4480**, with the score of 0.954. The trigger words are: 1. `ogaki_chiaki_yurucamp` 2. `glasses, purple_hair, long_hair, brown_eyes, black-framed_eyewear, blue_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | pattern_19 | pattern_20 | pattern_21 | pattern_22 | pattern_23 | pattern_24 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 9600 | 0.904 | [Download](9600/ogaki_chiaki_yurucamp.zip) | ![pattern_1-9600](9600/previews/pattern_1.png) | ![pattern_2-9600](9600/previews/pattern_2.png) | ![pattern_3-9600](9600/previews/pattern_3.png) | ![pattern_4-9600](9600/previews/pattern_4.png) | ![pattern_5-9600](9600/previews/pattern_5.png) | ![pattern_6-9600](9600/previews/pattern_6.png) | ![pattern_7-9600](9600/previews/pattern_7.png) | ![pattern_8-9600](9600/previews/pattern_8.png) | ![pattern_9-9600](9600/previews/pattern_9.png) | ![pattern_10-9600](9600/previews/pattern_10.png) | ![pattern_11-9600](9600/previews/pattern_11.png) | ![pattern_12-9600](9600/previews/pattern_12.png) | ![pattern_13-9600](9600/previews/pattern_13.png) | ![pattern_14-9600](9600/previews/pattern_14.png) | ![pattern_15-9600](9600/previews/pattern_15.png) | ![pattern_16-9600](9600/previews/pattern_16.png) | ![pattern_17-9600](9600/previews/pattern_17.png) | ![pattern_18-9600](9600/previews/pattern_18.png) | ![pattern_19-9600](9600/previews/pattern_19.png) | ![pattern_20-9600](9600/previews/pattern_20.png) | ![pattern_21-9600](9600/previews/pattern_21.png) | ![pattern_22-9600](9600/previews/pattern_22.png) | ![pattern_23-9600](9600/previews/pattern_23.png) | ![pattern_24-9600](9600/previews/pattern_24.png) | ![bikini-9600](9600/previews/bikini.png) | [<NSFW, click to see>](9600/previews/bondage.png) | ![free-9600](9600/previews/free.png) | ![maid-9600](9600/previews/maid.png) | ![miko-9600](9600/previews/miko.png) | [<NSFW, click to see>](9600/previews/nude.png) | [<NSFW, click to see>](9600/previews/nude2.png) | ![suit-9600](9600/previews/suit.png) | ![yukata-9600](9600/previews/yukata.png) | | 8960 | 0.938 | [Download](8960/ogaki_chiaki_yurucamp.zip) | ![pattern_1-8960](8960/previews/pattern_1.png) | ![pattern_2-8960](8960/previews/pattern_2.png) | ![pattern_3-8960](8960/previews/pattern_3.png) | ![pattern_4-8960](8960/previews/pattern_4.png) | ![pattern_5-8960](8960/previews/pattern_5.png) | ![pattern_6-8960](8960/previews/pattern_6.png) | ![pattern_7-8960](8960/previews/pattern_7.png) | ![pattern_8-8960](8960/previews/pattern_8.png) | ![pattern_9-8960](8960/previews/pattern_9.png) | ![pattern_10-8960](8960/previews/pattern_10.png) | ![pattern_11-8960](8960/previews/pattern_11.png) | ![pattern_12-8960](8960/previews/pattern_12.png) | ![pattern_13-8960](8960/previews/pattern_13.png) | ![pattern_14-8960](8960/previews/pattern_14.png) | ![pattern_15-8960](8960/previews/pattern_15.png) | ![pattern_16-8960](8960/previews/pattern_16.png) | ![pattern_17-8960](8960/previews/pattern_17.png) | ![pattern_18-8960](8960/previews/pattern_18.png) | ![pattern_19-8960](8960/previews/pattern_19.png) | ![pattern_20-8960](8960/previews/pattern_20.png) | ![pattern_21-8960](8960/previews/pattern_21.png) | ![pattern_22-8960](8960/previews/pattern_22.png) | ![pattern_23-8960](8960/previews/pattern_23.png) | ![pattern_24-8960](8960/previews/pattern_24.png) | ![bikini-8960](8960/previews/bikini.png) | [<NSFW, click to see>](8960/previews/bondage.png) | ![free-8960](8960/previews/free.png) | ![maid-8960](8960/previews/maid.png) | ![miko-8960](8960/previews/miko.png) | [<NSFW, click to see>](8960/previews/nude.png) | [<NSFW, click to see>](8960/previews/nude2.png) | ![suit-8960](8960/previews/suit.png) | ![yukata-8960](8960/previews/yukata.png) | | 8320 | 0.913 | [Download](8320/ogaki_chiaki_yurucamp.zip) | ![pattern_1-8320](8320/previews/pattern_1.png) | ![pattern_2-8320](8320/previews/pattern_2.png) | ![pattern_3-8320](8320/previews/pattern_3.png) | ![pattern_4-8320](8320/previews/pattern_4.png) | ![pattern_5-8320](8320/previews/pattern_5.png) | ![pattern_6-8320](8320/previews/pattern_6.png) | ![pattern_7-8320](8320/previews/pattern_7.png) | ![pattern_8-8320](8320/previews/pattern_8.png) | ![pattern_9-8320](8320/previews/pattern_9.png) | ![pattern_10-8320](8320/previews/pattern_10.png) | ![pattern_11-8320](8320/previews/pattern_11.png) | ![pattern_12-8320](8320/previews/pattern_12.png) | ![pattern_13-8320](8320/previews/pattern_13.png) | ![pattern_14-8320](8320/previews/pattern_14.png) | ![pattern_15-8320](8320/previews/pattern_15.png) | ![pattern_16-8320](8320/previews/pattern_16.png) | ![pattern_17-8320](8320/previews/pattern_17.png) | ![pattern_18-8320](8320/previews/pattern_18.png) | ![pattern_19-8320](8320/previews/pattern_19.png) | ![pattern_20-8320](8320/previews/pattern_20.png) | ![pattern_21-8320](8320/previews/pattern_21.png) | ![pattern_22-8320](8320/previews/pattern_22.png) | ![pattern_23-8320](8320/previews/pattern_23.png) | ![pattern_24-8320](8320/previews/pattern_24.png) | ![bikini-8320](8320/previews/bikini.png) | [<NSFW, click to see>](8320/previews/bondage.png) | ![free-8320](8320/previews/free.png) | ![maid-8320](8320/previews/maid.png) | ![miko-8320](8320/previews/miko.png) | [<NSFW, click to see>](8320/previews/nude.png) | [<NSFW, click to see>](8320/previews/nude2.png) | ![suit-8320](8320/previews/suit.png) | ![yukata-8320](8320/previews/yukata.png) | | 7680 | 0.928 | [Download](7680/ogaki_chiaki_yurucamp.zip) | ![pattern_1-7680](7680/previews/pattern_1.png) | ![pattern_2-7680](7680/previews/pattern_2.png) | ![pattern_3-7680](7680/previews/pattern_3.png) | ![pattern_4-7680](7680/previews/pattern_4.png) | ![pattern_5-7680](7680/previews/pattern_5.png) | ![pattern_6-7680](7680/previews/pattern_6.png) | ![pattern_7-7680](7680/previews/pattern_7.png) | ![pattern_8-7680](7680/previews/pattern_8.png) | ![pattern_9-7680](7680/previews/pattern_9.png) | ![pattern_10-7680](7680/previews/pattern_10.png) | ![pattern_11-7680](7680/previews/pattern_11.png) | ![pattern_12-7680](7680/previews/pattern_12.png) | ![pattern_13-7680](7680/previews/pattern_13.png) | ![pattern_14-7680](7680/previews/pattern_14.png) | ![pattern_15-7680](7680/previews/pattern_15.png) | ![pattern_16-7680](7680/previews/pattern_16.png) | ![pattern_17-7680](7680/previews/pattern_17.png) | ![pattern_18-7680](7680/previews/pattern_18.png) | ![pattern_19-7680](7680/previews/pattern_19.png) | ![pattern_20-7680](7680/previews/pattern_20.png) | ![pattern_21-7680](7680/previews/pattern_21.png) | ![pattern_22-7680](7680/previews/pattern_22.png) | ![pattern_23-7680](7680/previews/pattern_23.png) | ![pattern_24-7680](7680/previews/pattern_24.png) | ![bikini-7680](7680/previews/bikini.png) | [<NSFW, click to see>](7680/previews/bondage.png) | ![free-7680](7680/previews/free.png) | ![maid-7680](7680/previews/maid.png) | ![miko-7680](7680/previews/miko.png) | [<NSFW, click to see>](7680/previews/nude.png) | [<NSFW, click to see>](7680/previews/nude2.png) | ![suit-7680](7680/previews/suit.png) | ![yukata-7680](7680/previews/yukata.png) | | 7040 | 0.948 | [Download](7040/ogaki_chiaki_yurucamp.zip) | ![pattern_1-7040](7040/previews/pattern_1.png) | ![pattern_2-7040](7040/previews/pattern_2.png) | ![pattern_3-7040](7040/previews/pattern_3.png) | ![pattern_4-7040](7040/previews/pattern_4.png) | ![pattern_5-7040](7040/previews/pattern_5.png) | ![pattern_6-7040](7040/previews/pattern_6.png) | ![pattern_7-7040](7040/previews/pattern_7.png) | ![pattern_8-7040](7040/previews/pattern_8.png) | ![pattern_9-7040](7040/previews/pattern_9.png) | ![pattern_10-7040](7040/previews/pattern_10.png) | ![pattern_11-7040](7040/previews/pattern_11.png) | ![pattern_12-7040](7040/previews/pattern_12.png) | ![pattern_13-7040](7040/previews/pattern_13.png) | ![pattern_14-7040](7040/previews/pattern_14.png) | ![pattern_15-7040](7040/previews/pattern_15.png) | ![pattern_16-7040](7040/previews/pattern_16.png) | ![pattern_17-7040](7040/previews/pattern_17.png) | ![pattern_18-7040](7040/previews/pattern_18.png) | ![pattern_19-7040](7040/previews/pattern_19.png) | ![pattern_20-7040](7040/previews/pattern_20.png) | ![pattern_21-7040](7040/previews/pattern_21.png) | ![pattern_22-7040](7040/previews/pattern_22.png) | ![pattern_23-7040](7040/previews/pattern_23.png) | ![pattern_24-7040](7040/previews/pattern_24.png) | ![bikini-7040](7040/previews/bikini.png) | [<NSFW, click to see>](7040/previews/bondage.png) | ![free-7040](7040/previews/free.png) | ![maid-7040](7040/previews/maid.png) | ![miko-7040](7040/previews/miko.png) | [<NSFW, click to see>](7040/previews/nude.png) | [<NSFW, click to see>](7040/previews/nude2.png) | ![suit-7040](7040/previews/suit.png) | ![yukata-7040](7040/previews/yukata.png) | | 6400 | 0.951 | [Download](6400/ogaki_chiaki_yurucamp.zip) | ![pattern_1-6400](6400/previews/pattern_1.png) | ![pattern_2-6400](6400/previews/pattern_2.png) | ![pattern_3-6400](6400/previews/pattern_3.png) | ![pattern_4-6400](6400/previews/pattern_4.png) | ![pattern_5-6400](6400/previews/pattern_5.png) | ![pattern_6-6400](6400/previews/pattern_6.png) | ![pattern_7-6400](6400/previews/pattern_7.png) | ![pattern_8-6400](6400/previews/pattern_8.png) | ![pattern_9-6400](6400/previews/pattern_9.png) | ![pattern_10-6400](6400/previews/pattern_10.png) | ![pattern_11-6400](6400/previews/pattern_11.png) | ![pattern_12-6400](6400/previews/pattern_12.png) | ![pattern_13-6400](6400/previews/pattern_13.png) | ![pattern_14-6400](6400/previews/pattern_14.png) | ![pattern_15-6400](6400/previews/pattern_15.png) | ![pattern_16-6400](6400/previews/pattern_16.png) | ![pattern_17-6400](6400/previews/pattern_17.png) | ![pattern_18-6400](6400/previews/pattern_18.png) | ![pattern_19-6400](6400/previews/pattern_19.png) | ![pattern_20-6400](6400/previews/pattern_20.png) | ![pattern_21-6400](6400/previews/pattern_21.png) | ![pattern_22-6400](6400/previews/pattern_22.png) | ![pattern_23-6400](6400/previews/pattern_23.png) | ![pattern_24-6400](6400/previews/pattern_24.png) | ![bikini-6400](6400/previews/bikini.png) | [<NSFW, click to see>](6400/previews/bondage.png) | ![free-6400](6400/previews/free.png) | ![maid-6400](6400/previews/maid.png) | ![miko-6400](6400/previews/miko.png) | [<NSFW, click to see>](6400/previews/nude.png) | [<NSFW, click to see>](6400/previews/nude2.png) | ![suit-6400](6400/previews/suit.png) | ![yukata-6400](6400/previews/yukata.png) | | 5760 | 0.948 | [Download](5760/ogaki_chiaki_yurucamp.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | ![pattern_3-5760](5760/previews/pattern_3.png) | ![pattern_4-5760](5760/previews/pattern_4.png) | ![pattern_5-5760](5760/previews/pattern_5.png) | ![pattern_6-5760](5760/previews/pattern_6.png) | ![pattern_7-5760](5760/previews/pattern_7.png) | ![pattern_8-5760](5760/previews/pattern_8.png) | ![pattern_9-5760](5760/previews/pattern_9.png) | ![pattern_10-5760](5760/previews/pattern_10.png) | ![pattern_11-5760](5760/previews/pattern_11.png) | ![pattern_12-5760](5760/previews/pattern_12.png) | ![pattern_13-5760](5760/previews/pattern_13.png) | ![pattern_14-5760](5760/previews/pattern_14.png) | ![pattern_15-5760](5760/previews/pattern_15.png) | ![pattern_16-5760](5760/previews/pattern_16.png) | ![pattern_17-5760](5760/previews/pattern_17.png) | ![pattern_18-5760](5760/previews/pattern_18.png) | ![pattern_19-5760](5760/previews/pattern_19.png) | ![pattern_20-5760](5760/previews/pattern_20.png) | ![pattern_21-5760](5760/previews/pattern_21.png) | ![pattern_22-5760](5760/previews/pattern_22.png) | ![pattern_23-5760](5760/previews/pattern_23.png) | ![pattern_24-5760](5760/previews/pattern_24.png) | ![bikini-5760](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) | ![free-5760](5760/previews/free.png) | ![maid-5760](5760/previews/maid.png) | ![miko-5760](5760/previews/miko.png) | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) | ![suit-5760](5760/previews/suit.png) | ![yukata-5760](5760/previews/yukata.png) | | 5120 | 0.953 | [Download](5120/ogaki_chiaki_yurucamp.zip) | ![pattern_1-5120](5120/previews/pattern_1.png) | ![pattern_2-5120](5120/previews/pattern_2.png) | ![pattern_3-5120](5120/previews/pattern_3.png) | ![pattern_4-5120](5120/previews/pattern_4.png) | ![pattern_5-5120](5120/previews/pattern_5.png) | ![pattern_6-5120](5120/previews/pattern_6.png) | ![pattern_7-5120](5120/previews/pattern_7.png) | ![pattern_8-5120](5120/previews/pattern_8.png) | ![pattern_9-5120](5120/previews/pattern_9.png) | ![pattern_10-5120](5120/previews/pattern_10.png) | ![pattern_11-5120](5120/previews/pattern_11.png) | ![pattern_12-5120](5120/previews/pattern_12.png) | ![pattern_13-5120](5120/previews/pattern_13.png) | ![pattern_14-5120](5120/previews/pattern_14.png) | ![pattern_15-5120](5120/previews/pattern_15.png) | ![pattern_16-5120](5120/previews/pattern_16.png) | ![pattern_17-5120](5120/previews/pattern_17.png) | ![pattern_18-5120](5120/previews/pattern_18.png) | ![pattern_19-5120](5120/previews/pattern_19.png) | ![pattern_20-5120](5120/previews/pattern_20.png) | ![pattern_21-5120](5120/previews/pattern_21.png) | ![pattern_22-5120](5120/previews/pattern_22.png) | ![pattern_23-5120](5120/previews/pattern_23.png) | ![pattern_24-5120](5120/previews/pattern_24.png) | ![bikini-5120](5120/previews/bikini.png) | [<NSFW, click to see>](5120/previews/bondage.png) | ![free-5120](5120/previews/free.png) | ![maid-5120](5120/previews/maid.png) | ![miko-5120](5120/previews/miko.png) | [<NSFW, click to see>](5120/previews/nude.png) | [<NSFW, click to see>](5120/previews/nude2.png) | ![suit-5120](5120/previews/suit.png) | ![yukata-5120](5120/previews/yukata.png) | | **4480** | **0.954** | [**Download**](4480/ogaki_chiaki_yurucamp.zip) | ![pattern_1-4480](4480/previews/pattern_1.png) | ![pattern_2-4480](4480/previews/pattern_2.png) | ![pattern_3-4480](4480/previews/pattern_3.png) | ![pattern_4-4480](4480/previews/pattern_4.png) | ![pattern_5-4480](4480/previews/pattern_5.png) | ![pattern_6-4480](4480/previews/pattern_6.png) | ![pattern_7-4480](4480/previews/pattern_7.png) | ![pattern_8-4480](4480/previews/pattern_8.png) | ![pattern_9-4480](4480/previews/pattern_9.png) | ![pattern_10-4480](4480/previews/pattern_10.png) | ![pattern_11-4480](4480/previews/pattern_11.png) | ![pattern_12-4480](4480/previews/pattern_12.png) | ![pattern_13-4480](4480/previews/pattern_13.png) | ![pattern_14-4480](4480/previews/pattern_14.png) | ![pattern_15-4480](4480/previews/pattern_15.png) | ![pattern_16-4480](4480/previews/pattern_16.png) | ![pattern_17-4480](4480/previews/pattern_17.png) | ![pattern_18-4480](4480/previews/pattern_18.png) | ![pattern_19-4480](4480/previews/pattern_19.png) | ![pattern_20-4480](4480/previews/pattern_20.png) | ![pattern_21-4480](4480/previews/pattern_21.png) | ![pattern_22-4480](4480/previews/pattern_22.png) | ![pattern_23-4480](4480/previews/pattern_23.png) | ![pattern_24-4480](4480/previews/pattern_24.png) | ![bikini-4480](4480/previews/bikini.png) | [<NSFW, click to see>](4480/previews/bondage.png) | ![free-4480](4480/previews/free.png) | ![maid-4480](4480/previews/maid.png) | ![miko-4480](4480/previews/miko.png) | [<NSFW, click to see>](4480/previews/nude.png) | [<NSFW, click to see>](4480/previews/nude2.png) | ![suit-4480](4480/previews/suit.png) | ![yukata-4480](4480/previews/yukata.png) | | 3840 | 0.932 | [Download](3840/ogaki_chiaki_yurucamp.zip) | ![pattern_1-3840](3840/previews/pattern_1.png) | ![pattern_2-3840](3840/previews/pattern_2.png) | ![pattern_3-3840](3840/previews/pattern_3.png) | ![pattern_4-3840](3840/previews/pattern_4.png) | ![pattern_5-3840](3840/previews/pattern_5.png) | ![pattern_6-3840](3840/previews/pattern_6.png) | ![pattern_7-3840](3840/previews/pattern_7.png) | ![pattern_8-3840](3840/previews/pattern_8.png) | ![pattern_9-3840](3840/previews/pattern_9.png) | ![pattern_10-3840](3840/previews/pattern_10.png) | ![pattern_11-3840](3840/previews/pattern_11.png) | ![pattern_12-3840](3840/previews/pattern_12.png) | ![pattern_13-3840](3840/previews/pattern_13.png) | ![pattern_14-3840](3840/previews/pattern_14.png) | ![pattern_15-3840](3840/previews/pattern_15.png) | ![pattern_16-3840](3840/previews/pattern_16.png) | ![pattern_17-3840](3840/previews/pattern_17.png) | ![pattern_18-3840](3840/previews/pattern_18.png) | ![pattern_19-3840](3840/previews/pattern_19.png) | ![pattern_20-3840](3840/previews/pattern_20.png) | ![pattern_21-3840](3840/previews/pattern_21.png) | ![pattern_22-3840](3840/previews/pattern_22.png) | ![pattern_23-3840](3840/previews/pattern_23.png) | ![pattern_24-3840](3840/previews/pattern_24.png) | ![bikini-3840](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) | ![free-3840](3840/previews/free.png) | ![maid-3840](3840/previews/maid.png) | ![miko-3840](3840/previews/miko.png) | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) | ![suit-3840](3840/previews/suit.png) | ![yukata-3840](3840/previews/yukata.png) | | 3200 | 0.964 | [Download](3200/ogaki_chiaki_yurucamp.zip) | ![pattern_1-3200](3200/previews/pattern_1.png) | ![pattern_2-3200](3200/previews/pattern_2.png) | ![pattern_3-3200](3200/previews/pattern_3.png) | ![pattern_4-3200](3200/previews/pattern_4.png) | ![pattern_5-3200](3200/previews/pattern_5.png) | ![pattern_6-3200](3200/previews/pattern_6.png) | ![pattern_7-3200](3200/previews/pattern_7.png) | ![pattern_8-3200](3200/previews/pattern_8.png) | ![pattern_9-3200](3200/previews/pattern_9.png) | ![pattern_10-3200](3200/previews/pattern_10.png) | ![pattern_11-3200](3200/previews/pattern_11.png) | ![pattern_12-3200](3200/previews/pattern_12.png) | ![pattern_13-3200](3200/previews/pattern_13.png) | ![pattern_14-3200](3200/previews/pattern_14.png) | ![pattern_15-3200](3200/previews/pattern_15.png) | ![pattern_16-3200](3200/previews/pattern_16.png) | ![pattern_17-3200](3200/previews/pattern_17.png) | ![pattern_18-3200](3200/previews/pattern_18.png) | ![pattern_19-3200](3200/previews/pattern_19.png) | ![pattern_20-3200](3200/previews/pattern_20.png) | ![pattern_21-3200](3200/previews/pattern_21.png) | ![pattern_22-3200](3200/previews/pattern_22.png) | ![pattern_23-3200](3200/previews/pattern_23.png) | ![pattern_24-3200](3200/previews/pattern_24.png) | ![bikini-3200](3200/previews/bikini.png) | [<NSFW, click to see>](3200/previews/bondage.png) | ![free-3200](3200/previews/free.png) | ![maid-3200](3200/previews/maid.png) | ![miko-3200](3200/previews/miko.png) | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) | ![suit-3200](3200/previews/suit.png) | ![yukata-3200](3200/previews/yukata.png) | | 2560 | 0.944 | [Download](2560/ogaki_chiaki_yurucamp.zip) | ![pattern_1-2560](2560/previews/pattern_1.png) | ![pattern_2-2560](2560/previews/pattern_2.png) | ![pattern_3-2560](2560/previews/pattern_3.png) | ![pattern_4-2560](2560/previews/pattern_4.png) | ![pattern_5-2560](2560/previews/pattern_5.png) | ![pattern_6-2560](2560/previews/pattern_6.png) | ![pattern_7-2560](2560/previews/pattern_7.png) | ![pattern_8-2560](2560/previews/pattern_8.png) | ![pattern_9-2560](2560/previews/pattern_9.png) | ![pattern_10-2560](2560/previews/pattern_10.png) | ![pattern_11-2560](2560/previews/pattern_11.png) | ![pattern_12-2560](2560/previews/pattern_12.png) | ![pattern_13-2560](2560/previews/pattern_13.png) | ![pattern_14-2560](2560/previews/pattern_14.png) | ![pattern_15-2560](2560/previews/pattern_15.png) | ![pattern_16-2560](2560/previews/pattern_16.png) | ![pattern_17-2560](2560/previews/pattern_17.png) | ![pattern_18-2560](2560/previews/pattern_18.png) | ![pattern_19-2560](2560/previews/pattern_19.png) | ![pattern_20-2560](2560/previews/pattern_20.png) | ![pattern_21-2560](2560/previews/pattern_21.png) | ![pattern_22-2560](2560/previews/pattern_22.png) | ![pattern_23-2560](2560/previews/pattern_23.png) | ![pattern_24-2560](2560/previews/pattern_24.png) | ![bikini-2560](2560/previews/bikini.png) | [<NSFW, click to see>](2560/previews/bondage.png) | ![free-2560](2560/previews/free.png) | ![maid-2560](2560/previews/maid.png) | ![miko-2560](2560/previews/miko.png) | [<NSFW, click to see>](2560/previews/nude.png) | [<NSFW, click to see>](2560/previews/nude2.png) | ![suit-2560](2560/previews/suit.png) | ![yukata-2560](2560/previews/yukata.png) | | 1920 | 0.949 | [Download](1920/ogaki_chiaki_yurucamp.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | ![pattern_3-1920](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | 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see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1280 | 0.941 | [Download](1280/ogaki_chiaki_yurucamp.zip) | ![pattern_1-1280](1280/previews/pattern_1.png) | ![pattern_2-1280](1280/previews/pattern_2.png) | ![pattern_3-1280](1280/previews/pattern_3.png) | ![pattern_4-1280](1280/previews/pattern_4.png) | ![pattern_5-1280](1280/previews/pattern_5.png) | ![pattern_6-1280](1280/previews/pattern_6.png) | ![pattern_7-1280](1280/previews/pattern_7.png) | ![pattern_8-1280](1280/previews/pattern_8.png) | ![pattern_9-1280](1280/previews/pattern_9.png) | ![pattern_10-1280](1280/previews/pattern_10.png) | ![pattern_11-1280](1280/previews/pattern_11.png) | ![pattern_12-1280](1280/previews/pattern_12.png) | ![pattern_13-1280](1280/previews/pattern_13.png) | ![pattern_14-1280](1280/previews/pattern_14.png) | ![pattern_15-1280](1280/previews/pattern_15.png) | ![pattern_16-1280](1280/previews/pattern_16.png) | ![pattern_17-1280](1280/previews/pattern_17.png) | ![pattern_18-1280](1280/previews/pattern_18.png) | ![pattern_19-1280](1280/previews/pattern_19.png) | ![pattern_20-1280](1280/previews/pattern_20.png) | ![pattern_21-1280](1280/previews/pattern_21.png) | ![pattern_22-1280](1280/previews/pattern_22.png) | ![pattern_23-1280](1280/previews/pattern_23.png) | ![pattern_24-1280](1280/previews/pattern_24.png) | ![bikini-1280](1280/previews/bikini.png) | [<NSFW, click to see>](1280/previews/bondage.png) | ![free-1280](1280/previews/free.png) | ![maid-1280](1280/previews/maid.png) | ![miko-1280](1280/previews/miko.png) | [<NSFW, click to see>](1280/previews/nude.png) | [<NSFW, click to see>](1280/previews/nude2.png) | ![suit-1280](1280/previews/suit.png) | ![yukata-1280](1280/previews/yukata.png) | | 640 | 0.868 | [Download](640/ogaki_chiaki_yurucamp.zip) | ![pattern_1-640](640/previews/pattern_1.png) | ![pattern_2-640](640/previews/pattern_2.png) | ![pattern_3-640](640/previews/pattern_3.png) | ![pattern_4-640](640/previews/pattern_4.png) | ![pattern_5-640](640/previews/pattern_5.png) | ![pattern_6-640](640/previews/pattern_6.png) | ![pattern_7-640](640/previews/pattern_7.png) | ![pattern_8-640](640/previews/pattern_8.png) | ![pattern_9-640](640/previews/pattern_9.png) | ![pattern_10-640](640/previews/pattern_10.png) | ![pattern_11-640](640/previews/pattern_11.png) | ![pattern_12-640](640/previews/pattern_12.png) | ![pattern_13-640](640/previews/pattern_13.png) | ![pattern_14-640](640/previews/pattern_14.png) | ![pattern_15-640](640/previews/pattern_15.png) | ![pattern_16-640](640/previews/pattern_16.png) | ![pattern_17-640](640/previews/pattern_17.png) | ![pattern_18-640](640/previews/pattern_18.png) | ![pattern_19-640](640/previews/pattern_19.png) | ![pattern_20-640](640/previews/pattern_20.png) | ![pattern_21-640](640/previews/pattern_21.png) | ![pattern_22-640](640/previews/pattern_22.png) | ![pattern_23-640](640/previews/pattern_23.png) | ![pattern_24-640](640/previews/pattern_24.png) | ![bikini-640](640/previews/bikini.png) | [<NSFW, click to see>](640/previews/bondage.png) | ![free-640](640/previews/free.png) | ![maid-640](640/previews/maid.png) | ![miko-640](640/previews/miko.png) | [<NSFW, click to see>](640/previews/nude.png) | [<NSFW, click to see>](640/previews/nude2.png) | ![suit-640](640/previews/suit.png) | ![yukata-640](640/previews/yukata.png) |
DInaLong/videomae-base-finetuned-ucf101-subset
DInaLong
2023-09-26T19:01:44Z
62
0
transformers
[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
video-classification
2023-08-14T15:25:58Z
--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4435 - Accuracy: 0.8286 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 600 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4588 | 0.25 | 150 | 1.1859 | 0.6286 | | 0.415 | 1.25 | 300 | 0.9017 | 0.6714 | | 0.3556 | 2.25 | 450 | 0.8084 | 0.7143 | | 0.0322 | 3.25 | 600 | 0.4435 | 0.8286 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
vgarg/my-fw9-identification-model-e5_large_v2
vgarg
2023-09-26T18:59:24Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "xlm-roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-09-26T18:55:28Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # vgarg/my-fw9-identification-model-e5_large_v2 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("vgarg/my-fw9-identification-model-e5_large_v2") # 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} } ```
MattStammers/poca-SoccerTwos
MattStammers
2023-09-26T18:47:30Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-09-09T15:03:00Z
--- library_name: ml-agents tags: - SoccerTwos - unity-ml-agents - 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/ ### 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: MattStammers/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀 ### Video This video is of the Unity baseline agent (blue) against my agents (purple). The Unity baseline agents are slightly better but only marginally so.
prateeky2806/bert-base-uncased-sst2-epochs-2-lr-0.0001
prateeky2806
2023-09-26T18:31:17Z
108
0
transformers
[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-26T18:20:00Z
--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-base-uncased-sst2-epochs-2-lr-0.0001 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: train args: sst2 metrics: - name: Accuracy type: accuracy value: 0.99 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-sst2-epochs-2-lr-0.0001 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.0665 - Accuracy: 0.99 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1932 | 1.0 | 2102 | 0.0753 | 0.99 | | 0.1085 | 2.0 | 4204 | 0.0665 | 0.99 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
prateeky2806/bert-base-uncased-sst2-ia3-epochs-2-lr-0.005
prateeky2806
2023-09-26T18:23:15Z
0
0
null
[ "safetensors", "generated_from_trainer", "dataset:glue", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]
null
2023-09-26T18:15:08Z
--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-base-uncased-sst2-ia3-epochs-2-lr-0.005 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-base-uncased-sst2-ia3-epochs-2-lr-0.005 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2209 - Accuracy: 0.95 ## 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: 0.005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2126 | 1.0 | 2102 | 0.2255 | 0.93 | | 0.1757 | 2.0 | 4204 | 0.2209 | 0.95 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
johnathan32992/ChineseAmbatukamRVCv2
johnathan32992
2023-09-26T18:19:25Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-07-16T19:10:08Z
--- license: openrail --- # Nissan Man / Chinese Dreamybull / Chinese Ambatukam ## 1 minute 5 seconds from Reddit (i ain't linking it here) #### [Bunda Rahma](https://huggingface.co/johnathan32992/BundaRahmaRVCv2) #### [Kakangu](https://huggingface.co/johnathan32992/KakanguRVCv2)
mehranmehr/ppo-Huggy
mehranmehr
2023-09-26T18:09:11Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-09-26T18:09:05Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mehranmehr/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
IAteSpaghettiForLunch/DialoGPT-medium-GLADoS
IAteSpaghettiForLunch
2023-09-26T17:58:56Z
138
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-26T16:38:46Z
--- license: cc-by-nc-nd-4.0 pipeline_tag: conversational language: - en ---
CyberHarem/kagamihara_nadeshiko_yurucamp
CyberHarem
2023-09-26T17:43:47Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/kagamihara_nadeshiko_yurucamp", "license:mit", "region:us" ]
text-to-image
2023-09-26T05:56:06Z
--- license: mit datasets: - CyberHarem/kagamihara_nadeshiko_yurucamp pipeline_tag: text-to-image tags: - art --- # Lora of kagamihara_nadeshiko_yurucamp This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4960, you need to download `4960/kagamihara_nadeshiko_yurucamp.pt` as the embedding and `4960/kagamihara_nadeshiko_yurucamp.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4960**, with the score of 0.979. The trigger words are: 1. `kagamihara_nadeshiko_yurucamp` 2. `pink_hair, long_hair, hair_between_eyes, blue_eyes, closed_mouth, smile` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 9300 | 0.977 | [Download](9300/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-9300](9300/previews/pattern_1.png) | ![pattern_2-9300](9300/previews/pattern_2.png) | ![pattern_3-9300](9300/previews/pattern_3.png) | ![pattern_4-9300](9300/previews/pattern_4.png) | ![pattern_5-9300](9300/previews/pattern_5.png) | ![pattern_6-9300](9300/previews/pattern_6.png) | ![pattern_7-9300](9300/previews/pattern_7.png) | ![pattern_8-9300](9300/previews/pattern_8.png) | ![pattern_9-9300](9300/previews/pattern_9.png) | ![pattern_10-9300](9300/previews/pattern_10.png) | ![pattern_11-9300](9300/previews/pattern_11.png) | ![pattern_12-9300](9300/previews/pattern_12.png) | ![pattern_13-9300](9300/previews/pattern_13.png) | [<NSFW, click to see>](9300/previews/pattern_14.png) | ![pattern_15-9300](9300/previews/pattern_15.png) | ![pattern_16-9300](9300/previews/pattern_16.png) | ![pattern_17-9300](9300/previews/pattern_17.png) | ![pattern_18-9300](9300/previews/pattern_18.png) | ![bikini-9300](9300/previews/bikini.png) | [<NSFW, click to see>](9300/previews/bondage.png) | ![free-9300](9300/previews/free.png) | ![maid-9300](9300/previews/maid.png) | ![miko-9300](9300/previews/miko.png) | [<NSFW, click to see>](9300/previews/nude.png) | [<NSFW, click to see>](9300/previews/nude2.png) | ![suit-9300](9300/previews/suit.png) | ![yukata-9300](9300/previews/yukata.png) | | 8680 | 0.974 | [Download](8680/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-8680](8680/previews/pattern_1.png) | ![pattern_2-8680](8680/previews/pattern_2.png) | ![pattern_3-8680](8680/previews/pattern_3.png) | ![pattern_4-8680](8680/previews/pattern_4.png) | ![pattern_5-8680](8680/previews/pattern_5.png) | ![pattern_6-8680](8680/previews/pattern_6.png) | ![pattern_7-8680](8680/previews/pattern_7.png) | ![pattern_8-8680](8680/previews/pattern_8.png) | ![pattern_9-8680](8680/previews/pattern_9.png) | ![pattern_10-8680](8680/previews/pattern_10.png) | ![pattern_11-8680](8680/previews/pattern_11.png) | ![pattern_12-8680](8680/previews/pattern_12.png) | ![pattern_13-8680](8680/previews/pattern_13.png) | [<NSFW, click to see>](8680/previews/pattern_14.png) | ![pattern_15-8680](8680/previews/pattern_15.png) | ![pattern_16-8680](8680/previews/pattern_16.png) | ![pattern_17-8680](8680/previews/pattern_17.png) | ![pattern_18-8680](8680/previews/pattern_18.png) | ![bikini-8680](8680/previews/bikini.png) | [<NSFW, click to see>](8680/previews/bondage.png) | ![free-8680](8680/previews/free.png) | ![maid-8680](8680/previews/maid.png) | ![miko-8680](8680/previews/miko.png) | [<NSFW, click to see>](8680/previews/nude.png) | [<NSFW, click to see>](8680/previews/nude2.png) | ![suit-8680](8680/previews/suit.png) | ![yukata-8680](8680/previews/yukata.png) | | 8060 | 0.976 | [Download](8060/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-8060](8060/previews/pattern_1.png) | ![pattern_2-8060](8060/previews/pattern_2.png) | ![pattern_3-8060](8060/previews/pattern_3.png) | ![pattern_4-8060](8060/previews/pattern_4.png) | ![pattern_5-8060](8060/previews/pattern_5.png) | ![pattern_6-8060](8060/previews/pattern_6.png) | ![pattern_7-8060](8060/previews/pattern_7.png) | ![pattern_8-8060](8060/previews/pattern_8.png) | ![pattern_9-8060](8060/previews/pattern_9.png) | ![pattern_10-8060](8060/previews/pattern_10.png) | ![pattern_11-8060](8060/previews/pattern_11.png) | ![pattern_12-8060](8060/previews/pattern_12.png) | ![pattern_13-8060](8060/previews/pattern_13.png) | [<NSFW, click to see>](8060/previews/pattern_14.png) | ![pattern_15-8060](8060/previews/pattern_15.png) | ![pattern_16-8060](8060/previews/pattern_16.png) | ![pattern_17-8060](8060/previews/pattern_17.png) | ![pattern_18-8060](8060/previews/pattern_18.png) | ![bikini-8060](8060/previews/bikini.png) | [<NSFW, click to see>](8060/previews/bondage.png) | ![free-8060](8060/previews/free.png) | ![maid-8060](8060/previews/maid.png) | ![miko-8060](8060/previews/miko.png) | [<NSFW, click to see>](8060/previews/nude.png) | [<NSFW, click to see>](8060/previews/nude2.png) | ![suit-8060](8060/previews/suit.png) | ![yukata-8060](8060/previews/yukata.png) | | 7440 | 0.977 | [Download](7440/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-7440](7440/previews/pattern_1.png) | ![pattern_2-7440](7440/previews/pattern_2.png) | ![pattern_3-7440](7440/previews/pattern_3.png) | ![pattern_4-7440](7440/previews/pattern_4.png) | ![pattern_5-7440](7440/previews/pattern_5.png) | ![pattern_6-7440](7440/previews/pattern_6.png) | ![pattern_7-7440](7440/previews/pattern_7.png) | ![pattern_8-7440](7440/previews/pattern_8.png) | ![pattern_9-7440](7440/previews/pattern_9.png) | ![pattern_10-7440](7440/previews/pattern_10.png) | ![pattern_11-7440](7440/previews/pattern_11.png) | ![pattern_12-7440](7440/previews/pattern_12.png) | ![pattern_13-7440](7440/previews/pattern_13.png) | [<NSFW, click to see>](7440/previews/pattern_14.png) | ![pattern_15-7440](7440/previews/pattern_15.png) | ![pattern_16-7440](7440/previews/pattern_16.png) | ![pattern_17-7440](7440/previews/pattern_17.png) | ![pattern_18-7440](7440/previews/pattern_18.png) | ![bikini-7440](7440/previews/bikini.png) | [<NSFW, click to see>](7440/previews/bondage.png) | ![free-7440](7440/previews/free.png) | ![maid-7440](7440/previews/maid.png) | ![miko-7440](7440/previews/miko.png) | [<NSFW, click to see>](7440/previews/nude.png) | [<NSFW, click to see>](7440/previews/nude2.png) | ![suit-7440](7440/previews/suit.png) | ![yukata-7440](7440/previews/yukata.png) | | 6820 | 0.967 | [Download](6820/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-6820](6820/previews/pattern_1.png) | ![pattern_2-6820](6820/previews/pattern_2.png) | ![pattern_3-6820](6820/previews/pattern_3.png) | ![pattern_4-6820](6820/previews/pattern_4.png) | ![pattern_5-6820](6820/previews/pattern_5.png) | ![pattern_6-6820](6820/previews/pattern_6.png) | ![pattern_7-6820](6820/previews/pattern_7.png) | ![pattern_8-6820](6820/previews/pattern_8.png) | ![pattern_9-6820](6820/previews/pattern_9.png) | ![pattern_10-6820](6820/previews/pattern_10.png) | ![pattern_11-6820](6820/previews/pattern_11.png) | ![pattern_12-6820](6820/previews/pattern_12.png) | ![pattern_13-6820](6820/previews/pattern_13.png) | [<NSFW, click to see>](6820/previews/pattern_14.png) | ![pattern_15-6820](6820/previews/pattern_15.png) | ![pattern_16-6820](6820/previews/pattern_16.png) | ![pattern_17-6820](6820/previews/pattern_17.png) | ![pattern_18-6820](6820/previews/pattern_18.png) | ![bikini-6820](6820/previews/bikini.png) | [<NSFW, click to see>](6820/previews/bondage.png) | ![free-6820](6820/previews/free.png) | ![maid-6820](6820/previews/maid.png) | ![miko-6820](6820/previews/miko.png) | [<NSFW, click to see>](6820/previews/nude.png) | [<NSFW, click to see>](6820/previews/nude2.png) | ![suit-6820](6820/previews/suit.png) | ![yukata-6820](6820/previews/yukata.png) | | 6200 | 0.973 | [Download](6200/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-6200](6200/previews/pattern_1.png) | ![pattern_2-6200](6200/previews/pattern_2.png) | ![pattern_3-6200](6200/previews/pattern_3.png) | ![pattern_4-6200](6200/previews/pattern_4.png) | ![pattern_5-6200](6200/previews/pattern_5.png) | ![pattern_6-6200](6200/previews/pattern_6.png) | ![pattern_7-6200](6200/previews/pattern_7.png) | ![pattern_8-6200](6200/previews/pattern_8.png) | ![pattern_9-6200](6200/previews/pattern_9.png) | ![pattern_10-6200](6200/previews/pattern_10.png) | ![pattern_11-6200](6200/previews/pattern_11.png) | ![pattern_12-6200](6200/previews/pattern_12.png) | ![pattern_13-6200](6200/previews/pattern_13.png) | [<NSFW, click to see>](6200/previews/pattern_14.png) | ![pattern_15-6200](6200/previews/pattern_15.png) | ![pattern_16-6200](6200/previews/pattern_16.png) | ![pattern_17-6200](6200/previews/pattern_17.png) | ![pattern_18-6200](6200/previews/pattern_18.png) | ![bikini-6200](6200/previews/bikini.png) | [<NSFW, click to see>](6200/previews/bondage.png) | ![free-6200](6200/previews/free.png) | ![maid-6200](6200/previews/maid.png) | ![miko-6200](6200/previews/miko.png) | [<NSFW, click to see>](6200/previews/nude.png) | [<NSFW, click to see>](6200/previews/nude2.png) | ![suit-6200](6200/previews/suit.png) | ![yukata-6200](6200/previews/yukata.png) | | 5580 | 0.934 | [Download](5580/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-5580](5580/previews/pattern_1.png) | ![pattern_2-5580](5580/previews/pattern_2.png) | ![pattern_3-5580](5580/previews/pattern_3.png) | ![pattern_4-5580](5580/previews/pattern_4.png) | ![pattern_5-5580](5580/previews/pattern_5.png) | ![pattern_6-5580](5580/previews/pattern_6.png) | ![pattern_7-5580](5580/previews/pattern_7.png) | ![pattern_8-5580](5580/previews/pattern_8.png) | ![pattern_9-5580](5580/previews/pattern_9.png) | ![pattern_10-5580](5580/previews/pattern_10.png) | ![pattern_11-5580](5580/previews/pattern_11.png) | ![pattern_12-5580](5580/previews/pattern_12.png) | ![pattern_13-5580](5580/previews/pattern_13.png) | [<NSFW, click to see>](5580/previews/pattern_14.png) | ![pattern_15-5580](5580/previews/pattern_15.png) | ![pattern_16-5580](5580/previews/pattern_16.png) | ![pattern_17-5580](5580/previews/pattern_17.png) | ![pattern_18-5580](5580/previews/pattern_18.png) | ![bikini-5580](5580/previews/bikini.png) | [<NSFW, click to see>](5580/previews/bondage.png) | ![free-5580](5580/previews/free.png) | ![maid-5580](5580/previews/maid.png) | ![miko-5580](5580/previews/miko.png) | [<NSFW, click to see>](5580/previews/nude.png) | [<NSFW, click to see>](5580/previews/nude2.png) | ![suit-5580](5580/previews/suit.png) | ![yukata-5580](5580/previews/yukata.png) | | **4960** | **0.979** | [**Download**](4960/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-4960](4960/previews/pattern_1.png) | ![pattern_2-4960](4960/previews/pattern_2.png) | ![pattern_3-4960](4960/previews/pattern_3.png) | ![pattern_4-4960](4960/previews/pattern_4.png) | ![pattern_5-4960](4960/previews/pattern_5.png) | ![pattern_6-4960](4960/previews/pattern_6.png) | ![pattern_7-4960](4960/previews/pattern_7.png) | ![pattern_8-4960](4960/previews/pattern_8.png) | ![pattern_9-4960](4960/previews/pattern_9.png) | ![pattern_10-4960](4960/previews/pattern_10.png) | ![pattern_11-4960](4960/previews/pattern_11.png) | ![pattern_12-4960](4960/previews/pattern_12.png) | ![pattern_13-4960](4960/previews/pattern_13.png) | [<NSFW, click to see>](4960/previews/pattern_14.png) | ![pattern_15-4960](4960/previews/pattern_15.png) | ![pattern_16-4960](4960/previews/pattern_16.png) | ![pattern_17-4960](4960/previews/pattern_17.png) | ![pattern_18-4960](4960/previews/pattern_18.png) | ![bikini-4960](4960/previews/bikini.png) | [<NSFW, click to see>](4960/previews/bondage.png) | ![free-4960](4960/previews/free.png) | ![maid-4960](4960/previews/maid.png) | ![miko-4960](4960/previews/miko.png) | [<NSFW, click to see>](4960/previews/nude.png) | [<NSFW, click to see>](4960/previews/nude2.png) | ![suit-4960](4960/previews/suit.png) | ![yukata-4960](4960/previews/yukata.png) | | 4340 | 0.972 | [Download](4340/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-4340](4340/previews/pattern_1.png) | ![pattern_2-4340](4340/previews/pattern_2.png) | ![pattern_3-4340](4340/previews/pattern_3.png) | ![pattern_4-4340](4340/previews/pattern_4.png) | ![pattern_5-4340](4340/previews/pattern_5.png) | ![pattern_6-4340](4340/previews/pattern_6.png) | ![pattern_7-4340](4340/previews/pattern_7.png) | ![pattern_8-4340](4340/previews/pattern_8.png) | ![pattern_9-4340](4340/previews/pattern_9.png) | ![pattern_10-4340](4340/previews/pattern_10.png) | ![pattern_11-4340](4340/previews/pattern_11.png) | ![pattern_12-4340](4340/previews/pattern_12.png) | ![pattern_13-4340](4340/previews/pattern_13.png) | [<NSFW, click to see>](4340/previews/pattern_14.png) | ![pattern_15-4340](4340/previews/pattern_15.png) | ![pattern_16-4340](4340/previews/pattern_16.png) | ![pattern_17-4340](4340/previews/pattern_17.png) | ![pattern_18-4340](4340/previews/pattern_18.png) | ![bikini-4340](4340/previews/bikini.png) | [<NSFW, click to see>](4340/previews/bondage.png) | ![free-4340](4340/previews/free.png) | ![maid-4340](4340/previews/maid.png) | ![miko-4340](4340/previews/miko.png) | [<NSFW, click to see>](4340/previews/nude.png) | [<NSFW, click to see>](4340/previews/nude2.png) | ![suit-4340](4340/previews/suit.png) | ![yukata-4340](4340/previews/yukata.png) | | 3720 | 0.935 | [Download](3720/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-3720](3720/previews/pattern_1.png) | ![pattern_2-3720](3720/previews/pattern_2.png) | ![pattern_3-3720](3720/previews/pattern_3.png) | ![pattern_4-3720](3720/previews/pattern_4.png) | ![pattern_5-3720](3720/previews/pattern_5.png) | ![pattern_6-3720](3720/previews/pattern_6.png) | ![pattern_7-3720](3720/previews/pattern_7.png) | ![pattern_8-3720](3720/previews/pattern_8.png) | ![pattern_9-3720](3720/previews/pattern_9.png) | ![pattern_10-3720](3720/previews/pattern_10.png) | ![pattern_11-3720](3720/previews/pattern_11.png) | ![pattern_12-3720](3720/previews/pattern_12.png) | ![pattern_13-3720](3720/previews/pattern_13.png) | [<NSFW, click to see>](3720/previews/pattern_14.png) | ![pattern_15-3720](3720/previews/pattern_15.png) | ![pattern_16-3720](3720/previews/pattern_16.png) | ![pattern_17-3720](3720/previews/pattern_17.png) | ![pattern_18-3720](3720/previews/pattern_18.png) | ![bikini-3720](3720/previews/bikini.png) | [<NSFW, click to see>](3720/previews/bondage.png) | ![free-3720](3720/previews/free.png) | ![maid-3720](3720/previews/maid.png) | ![miko-3720](3720/previews/miko.png) | [<NSFW, click to see>](3720/previews/nude.png) | [<NSFW, click to see>](3720/previews/nude2.png) | ![suit-3720](3720/previews/suit.png) | ![yukata-3720](3720/previews/yukata.png) | | 3100 | 0.969 | [Download](3100/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-3100](3100/previews/pattern_1.png) | ![pattern_2-3100](3100/previews/pattern_2.png) | ![pattern_3-3100](3100/previews/pattern_3.png) | ![pattern_4-3100](3100/previews/pattern_4.png) | ![pattern_5-3100](3100/previews/pattern_5.png) | ![pattern_6-3100](3100/previews/pattern_6.png) | ![pattern_7-3100](3100/previews/pattern_7.png) | ![pattern_8-3100](3100/previews/pattern_8.png) | ![pattern_9-3100](3100/previews/pattern_9.png) | ![pattern_10-3100](3100/previews/pattern_10.png) | ![pattern_11-3100](3100/previews/pattern_11.png) | ![pattern_12-3100](3100/previews/pattern_12.png) | ![pattern_13-3100](3100/previews/pattern_13.png) | [<NSFW, click to see>](3100/previews/pattern_14.png) | ![pattern_15-3100](3100/previews/pattern_15.png) | ![pattern_16-3100](3100/previews/pattern_16.png) | ![pattern_17-3100](3100/previews/pattern_17.png) | ![pattern_18-3100](3100/previews/pattern_18.png) | ![bikini-3100](3100/previews/bikini.png) | [<NSFW, click to see>](3100/previews/bondage.png) | ![free-3100](3100/previews/free.png) | ![maid-3100](3100/previews/maid.png) | ![miko-3100](3100/previews/miko.png) | [<NSFW, click to see>](3100/previews/nude.png) | [<NSFW, click to see>](3100/previews/nude2.png) | ![suit-3100](3100/previews/suit.png) | ![yukata-3100](3100/previews/yukata.png) | | 2480 | 0.901 | [Download](2480/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-2480](2480/previews/pattern_1.png) | ![pattern_2-2480](2480/previews/pattern_2.png) | ![pattern_3-2480](2480/previews/pattern_3.png) | ![pattern_4-2480](2480/previews/pattern_4.png) | ![pattern_5-2480](2480/previews/pattern_5.png) | ![pattern_6-2480](2480/previews/pattern_6.png) | ![pattern_7-2480](2480/previews/pattern_7.png) | ![pattern_8-2480](2480/previews/pattern_8.png) | ![pattern_9-2480](2480/previews/pattern_9.png) | ![pattern_10-2480](2480/previews/pattern_10.png) | ![pattern_11-2480](2480/previews/pattern_11.png) | ![pattern_12-2480](2480/previews/pattern_12.png) | ![pattern_13-2480](2480/previews/pattern_13.png) | [<NSFW, click to see>](2480/previews/pattern_14.png) | ![pattern_15-2480](2480/previews/pattern_15.png) | ![pattern_16-2480](2480/previews/pattern_16.png) | ![pattern_17-2480](2480/previews/pattern_17.png) | ![pattern_18-2480](2480/previews/pattern_18.png) | ![bikini-2480](2480/previews/bikini.png) | [<NSFW, click to see>](2480/previews/bondage.png) | ![free-2480](2480/previews/free.png) | ![maid-2480](2480/previews/maid.png) | ![miko-2480](2480/previews/miko.png) | [<NSFW, click to see>](2480/previews/nude.png) | [<NSFW, click to see>](2480/previews/nude2.png) | ![suit-2480](2480/previews/suit.png) | ![yukata-2480](2480/previews/yukata.png) | | 1860 | 0.961 | [Download](1860/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-1860](1860/previews/pattern_1.png) | ![pattern_2-1860](1860/previews/pattern_2.png) | ![pattern_3-1860](1860/previews/pattern_3.png) | ![pattern_4-1860](1860/previews/pattern_4.png) | ![pattern_5-1860](1860/previews/pattern_5.png) | ![pattern_6-1860](1860/previews/pattern_6.png) | ![pattern_7-1860](1860/previews/pattern_7.png) | ![pattern_8-1860](1860/previews/pattern_8.png) | ![pattern_9-1860](1860/previews/pattern_9.png) | ![pattern_10-1860](1860/previews/pattern_10.png) | ![pattern_11-1860](1860/previews/pattern_11.png) | ![pattern_12-1860](1860/previews/pattern_12.png) | ![pattern_13-1860](1860/previews/pattern_13.png) | [<NSFW, click to see>](1860/previews/pattern_14.png) | ![pattern_15-1860](1860/previews/pattern_15.png) | ![pattern_16-1860](1860/previews/pattern_16.png) | ![pattern_17-1860](1860/previews/pattern_17.png) | ![pattern_18-1860](1860/previews/pattern_18.png) | ![bikini-1860](1860/previews/bikini.png) | [<NSFW, click to see>](1860/previews/bondage.png) | ![free-1860](1860/previews/free.png) | ![maid-1860](1860/previews/maid.png) | ![miko-1860](1860/previews/miko.png) | [<NSFW, click to see>](1860/previews/nude.png) | [<NSFW, click to see>](1860/previews/nude2.png) | ![suit-1860](1860/previews/suit.png) | ![yukata-1860](1860/previews/yukata.png) | | 1240 | 0.962 | [Download](1240/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-1240](1240/previews/pattern_1.png) | ![pattern_2-1240](1240/previews/pattern_2.png) | ![pattern_3-1240](1240/previews/pattern_3.png) | ![pattern_4-1240](1240/previews/pattern_4.png) | ![pattern_5-1240](1240/previews/pattern_5.png) | ![pattern_6-1240](1240/previews/pattern_6.png) | ![pattern_7-1240](1240/previews/pattern_7.png) | ![pattern_8-1240](1240/previews/pattern_8.png) | ![pattern_9-1240](1240/previews/pattern_9.png) | ![pattern_10-1240](1240/previews/pattern_10.png) | ![pattern_11-1240](1240/previews/pattern_11.png) | ![pattern_12-1240](1240/previews/pattern_12.png) | ![pattern_13-1240](1240/previews/pattern_13.png) | [<NSFW, click to see>](1240/previews/pattern_14.png) | ![pattern_15-1240](1240/previews/pattern_15.png) | ![pattern_16-1240](1240/previews/pattern_16.png) | ![pattern_17-1240](1240/previews/pattern_17.png) | ![pattern_18-1240](1240/previews/pattern_18.png) | ![bikini-1240](1240/previews/bikini.png) | [<NSFW, click to see>](1240/previews/bondage.png) | ![free-1240](1240/previews/free.png) | ![maid-1240](1240/previews/maid.png) | ![miko-1240](1240/previews/miko.png) | [<NSFW, click to see>](1240/previews/nude.png) | [<NSFW, click to see>](1240/previews/nude2.png) | ![suit-1240](1240/previews/suit.png) | ![yukata-1240](1240/previews/yukata.png) | | 620 | 0.877 | [Download](620/kagamihara_nadeshiko_yurucamp.zip) | ![pattern_1-620](620/previews/pattern_1.png) | ![pattern_2-620](620/previews/pattern_2.png) | ![pattern_3-620](620/previews/pattern_3.png) | ![pattern_4-620](620/previews/pattern_4.png) | ![pattern_5-620](620/previews/pattern_5.png) | ![pattern_6-620](620/previews/pattern_6.png) | ![pattern_7-620](620/previews/pattern_7.png) | ![pattern_8-620](620/previews/pattern_8.png) | ![pattern_9-620](620/previews/pattern_9.png) | ![pattern_10-620](620/previews/pattern_10.png) | ![pattern_11-620](620/previews/pattern_11.png) | ![pattern_12-620](620/previews/pattern_12.png) | ![pattern_13-620](620/previews/pattern_13.png) | [<NSFW, click to see>](620/previews/pattern_14.png) | ![pattern_15-620](620/previews/pattern_15.png) | ![pattern_16-620](620/previews/pattern_16.png) | ![pattern_17-620](620/previews/pattern_17.png) | ![pattern_18-620](620/previews/pattern_18.png) | ![bikini-620](620/previews/bikini.png) | [<NSFW, click to see>](620/previews/bondage.png) | ![free-620](620/previews/free.png) | ![maid-620](620/previews/maid.png) | ![miko-620](620/previews/miko.png) | [<NSFW, click to see>](620/previews/nude.png) | [<NSFW, click to see>](620/previews/nude2.png) | ![suit-620](620/previews/suit.png) | ![yukata-620](620/previews/yukata.png) |
zineddine/taxi-v3
zineddine
2023-09-26T17:43:44Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T17:43:41Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="zineddine/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
testing244/t5_recommendation_sports_equipment_english
testing244
2023-09-26T17:43:06Z
105
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-large", "base_model:finetune:google-t5/t5-large", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-26T17:33:59Z
--- license: apache-2.0 base_model: t5-large tags: - generated_from_trainer metrics: - rouge model-index: - name: t5_recommendation_sports_equipment_english 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_recommendation_sports_equipment_english This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4020 - Rouge1: 57.9365 - Rouge2: 47.6190 - Rougel: 57.9365 - Rougelsum: 57.9365 - Gen Len: 4.1429 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 0.96 | 6 | 7.7857 | 20.2721 | 10.3896 | 20.0454 | 20.9524 | 11.3810 | | No log | 1.92 | 12 | 3.1922 | 20.0 | 4.7619 | 20.4762 | 20.4762 | 3.1905 | | No log | 2.88 | 18 | 0.8028 | 5.5556 | 0.0 | 5.5556 | 5.5556 | 3.0 | | No log | 4.0 | 25 | 0.7207 | 32.8571 | 19.0476 | 32.9365 | 34.0476 | 3.2381 | | No log | 4.96 | 31 | 0.5217 | 50.3968 | 42.8571 | 50.0 | 50.7937 | 3.9524 | | No log | 5.92 | 37 | 0.4420 | 57.9365 | 47.6190 | 57.9365 | 57.9365 | 4.0476 | | No log | 6.88 | 43 | 0.4694 | 67.4603 | 61.9048 | 67.4603 | 67.4603 | 4.0 | | No log | 8.0 | 50 | 0.4408 | 57.9365 | 47.6190 | 57.9365 | 57.9365 | 4.1429 | | No log | 8.96 | 56 | 0.4269 | 57.9365 | 47.6190 | 57.9365 | 57.9365 | 4.1429 | | No log | 9.6 | 60 | 0.4020 | 57.9365 | 47.6190 | 57.9365 | 57.9365 | 4.1429 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.8.0 - Tokenizers 0.13.3
zineddine/q-FrozenLake-v1-4x4-noSlippery
zineddine
2023-09-26T17:39:37Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T17:39:35Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="zineddine/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
luisgasco/biomedical-roberta-finetuned-iomed_task
luisgasco
2023-09-26T17:38:07Z
116
0
transformers
[ "transformers", "pytorch", "roberta", "token-classification", "generated_from_trainer", "base_model:PlanTL-GOB-ES/roberta-base-biomedical-es", "base_model:finetune:PlanTL-GOB-ES/roberta-base-biomedical-es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-09-26T15:38:06Z
--- license: apache-2.0 base_model: PlanTL-GOB-ES/roberta-base-biomedical-es tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: biomedical-roberta-finetuned-iomed_task 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. --> # biomedical-roberta-finetuned-iomed_task This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0582 - Precision: 0.2269 - Recall: 0.4283 - F1: 0.2966 - Accuracy: 0.7695 ## 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: 2.1e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.2536 | 2.0 | 1520 | 1.2135 | 0.1082 | 0.2685 | 0.1542 | 0.7422 | | 1.0249 | 4.0 | 3040 | 1.0510 | 0.1448 | 0.3244 | 0.2002 | 0.7650 | | 0.9 | 6.0 | 4560 | 1.0098 | 0.1587 | 0.3512 | 0.2186 | 0.7694 | | 0.8002 | 8.0 | 6080 | 1.0143 | 0.1835 | 0.3795 | 0.2474 | 0.7664 | | 0.7195 | 10.0 | 7600 | 1.0173 | 0.2007 | 0.4055 | 0.2685 | 0.7691 | | 0.693 | 12.0 | 9120 | 1.0218 | 0.1991 | 0.4079 | 0.2676 | 0.7683 | | 0.6139 | 14.0 | 10640 | 1.0394 | 0.2063 | 0.4071 | 0.2738 | 0.7672 | | 0.616 | 16.0 | 12160 | 1.0376 | 0.2141 | 0.4142 | 0.2823 | 0.7695 | | 0.5911 | 18.0 | 13680 | 1.0491 | 0.2240 | 0.4268 | 0.2938 | 0.7697 | | 0.6042 | 20.0 | 15200 | 1.0582 | 0.2269 | 0.4283 | 0.2966 | 0.7695 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
johnpaulbin/toxic-gte-small-1
johnpaulbin
2023-09-26T17:34:58Z
4
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-09-26T17:34:39Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # johnpaulbin/toxic-gte-small-1 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("johnpaulbin/toxic-gte-small-1") # 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} } ```
johnathan32992/TeresaTengRVCv1
johnathan32992
2023-09-26T17:24:55Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-06-23T18:44:11Z
--- license: openrail --- # Teresa Teng / 鄧麗君 ## 24 minutes 18 seconds of data from [เติ้งลี่จวิน รำลึก 25 ปี](https://www.youtube.com/watch?v=MlLIk71h7ik&t=1007s&ab_channel=monairuektavilchai) on YouTube. Note: This model has its dataset from YouTube the audio is compressed and the audio **not** being de-echoed it is very bad at pronunciation.
hdeldar/llama-2-7b-persian-text-1k-1
hdeldar
2023-09-26T17:02:41Z
19
1
transformers
[ "transformers", "pytorch", "llama", "text-generation", "llama2", "pythorch", "en", "fa", "dataset:hdeldar/Persian-Text-llama2-1k-1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-26T16:19:46Z
--- license: apache-2.0 datasets: - hdeldar/Persian-Text-llama2-1k-1 pipeline_tag: text-generation language: - en - fa tags: - llama - llama2 - pythorch --- # 🦙🧠 Persion-Text-llama2-7b-1k-1 📝 [Article](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32) | 💻 [Colab](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing) | 📄 [Script](https://gist.github.com/mlabonne/b5718e1b229ce6553564e3f56df72c5c) <center><img src="https://i.imgur.com/1IZmjU4.png" width="300"></center> This is a [`llama-2-7b-persian-text-1k`](https://huggingface.co/hdeldar/llama-2-7b-persian-text-1k) model fine-tuned using QLoRA (4-bit precision) on the [`hdeldar/Persian-Text-llama2-1k-1`](https://huggingface.co/datasets/hdeldar/Persian-Text-llama2-1k-1) dataset, which is a subset of the [`SeyedAli/Persian-Text-QA`](https://huggingface.co/datasets/SeyedAli/Persian-Text-QA). ## 🔧 Training It was trained on a Google Colab notebook with a T4 GPU and high RAM. It is mainly designed for educational purposes, not for inference. ## 💻 Usage ``` python # pip install transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "hdeldar/llama-2-7b-persian-text-1k-1" prompt = "What is a large language model?" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( f'<s>[INST] {prompt} [/INST]', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` Output: > A large language model is trained on massive amounts of text data to understand and generate human language. The model learns by predicting the next word in a sequence based on the context of the previous words. This process allows the language model to learn patterns, rules, and relationships within the language that allow it to generate text that looks and sounds authentic and coherent. These large language models are used for many applications, such as language translation, sentiment analysis, and language generation. These models can also be used to generate text summaries of complex documents, such as legal or scientific papers, or to generate text summaries of social media posts. These models are often used in natural language processing (NLP) and machine learning applications. > The large language models are trained using a large number of parameters, often in the billions or even in the tens of billions.
auhide/chef-gpt
auhide
2023-09-26T16:50:18Z
158
1
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "bg", "base_model:auhide/chef-gpt-base", "base_model:finetune:auhide/chef-gpt-base", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-04-15T11:28:09Z
--- language: - bg license: mit inference: false pipeline_tag: text-generation base_model: auhide/chef-gpt-base model-index: - name: chef-gpt results: [] --- # chef-gpt This model is a fine-tuned version of [auhide/chef-gpt-base](https://huggingface.co/auhide/chef-gpt-base). Visit this [website](https://chef-gpt.streamlit.app/) to test it out. ## Model Description This is GPT-2 pretrained on a custom Bulgarian dataset. You can find the dataset [here](https://www.kaggle.com/datasets/auhide/bulgarian-recipes-dataset). The difference between this one and the base version is that this one can also generate recipes based on recipe name. ## Usage ```python import re # Using this library to beautifully print the long recipe string. from pprint import pprint from transformers import AutoModelForCausalLM, AutoTokenizer # Load the model and tokenizer: MODEL_ID = "auhide/chef-gpt" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) chef_gpt = AutoModelForCausalLM.from_pretrained(MODEL_ID) # Prepare the input: title = "Пиле с ориз" input_text = f"[TTL]{title}[ING]" input_ids = tokenizer(input_text, return_tensors="pt").input_ids # Generate the text: output = chef_gpt.generate(input_ids, max_length=150) recipe = tokenizer.batch_decode(output)[0] # Get the generated recipe - it is up until the 1st [SEP] token. It includes the ingredients. recipe = re.findall(r"\[ING\](.+?)\[SEP\]", recipe)[0] # Format the output text: recipe = recipe.replace("[ING]", "- ") recipe = recipe.replace("[EOL]", "\n- ") recipe = recipe.replace("[REC]", "\n\n") print("Име на рецепта/Recipe name:") print(title) print("\nРецепта/Recipe:") pprint(recipe) ``` ```bash Име на рецепта/Recipe name: Пиле с ориз Рецепта/Recipe: ('- 2 бр. пилешки бутчета\n' '- 1 кг зеле\n' '- 1 ч.ч. ориз\n' '- 1 ч.ч. доматено пюре\n' '- 1 глава лук\n' '- олио\n' '- червен пипер, черен пипер, сол, джоджен, чубрица\n' '- целина\n' '\n' 'Бутчетата се сваряват, обезкостяват и месото се накъсва. Лукът се нарязва на ' 'полумесеци е се задушава в олио. Прибавя се нарязаното на ивици зеле. Когато ' 'зелето омекне се слага оризът, а като стане прозрачен се добавят ' 'подправките. Разбърква се добре, полива се с доматеното пюре и 3 ч.ч. от ' 'бульона, в който е вряло месото. Оставя се да ври на тих огън около 20-30 ' 'минути. Ястието се прехвърля в тава и се пече на 250С докато изври водата.') ```
erkam/sg2im-128-bs-32-depth-cc
erkam
2023-09-26T16:38:48Z
3
0
diffusers
[ "diffusers", "sg-to-image", "scene-graph", "stable-diffusion", "stable-diffusion-diffusers", "lora", "base_model:stabilityai/stable-diffusion-2", "base_model:adapter:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "region:us" ]
null
2023-09-20T16:07:08Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 tags: - sg-to-image - scene-graph - stable-diffusion - stable-diffusion-diffusers - diffusers - lora inference: true --- # LoRA text2image fine-tuning - erkam/sg2im-128-bs-32-depth-cc These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the erkam/clevr-full-v5 dataset. You can find some example images in the following.
Undi95/SynthiAthena-v2
Undi95
2023-09-26T16:35:25Z
19
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-26T15:56:30Z
--- license: cc-by-nc-4.0 --- Merging of [migtissera/Synthia-13B](https://huggingface.co/migtissera/Synthia-13B) and [IkariDev/Athena-v2](https://huggingface.co/IkariDev/Athena-v2), 50/50. Made for DarkReaperBoy.
Nazzyk/a2c-PandaReachDense-v2
Nazzyk
2023-09-26T16:34:00Z
1
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "arxiv:2106.13687", "model-index", "region:us" ]
reinforcement-learning
2023-03-26T00:25:40Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.08 +/- 0.49 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ``` Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
seaweed4/MNIST
seaweed4
2023-09-26T16:26:49Z
0
0
null
[ "image-classification", "en", "dataset:mnist", "region:us" ]
image-classification
2023-09-26T16:01:22Z
--- datasets: - mnist language: - en metrics: - accuracy pipeline_tag: image-classification ---
tangjs/uv-sdxl-r32-lr-4e7
tangjs
2023-09-26T16:24:23Z
2
0
diffusers
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-26T08:32:03Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - tangjs/uv-sdxl-r32-lr-4e7 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None 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) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
LarryAIDraw/missionarymotion
LarryAIDraw
2023-09-26T16:18:27Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-09-26T16:12:21Z
--- license: creativeml-openrail-m --- https://civitai.com/models/123612/missionary-pov-motion-module-for-animatediff-proof-of-concept
LarryAIDraw/yoinkoorlabsNSFWMotion_godmodeReal
LarryAIDraw
2023-09-26T16:13:20Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-09-26T00:33:11Z
--- license: creativeml-openrail-m --- https://civitai.com/models/144934/yoinkoorlabs-nsfw-motion-module-v2
mindchain/META-LLAMA-LLAMA-2-7B-HF-GGUF
mindchain
2023-09-26T16:11:49Z
0
2
null
[ "arxiv:1910.09700", "region:us" ]
null
2023-09-21T13:54:07Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Llama 2 <!-- Provide a quick summary of what the model is/does. --> Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## 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]
Lamurias/a2c-PandaReachDense-v3
Lamurias
2023-09-26T16:10:28Z
2
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T15:33:19Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.88 +/- 1.25 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```
danieljova1/football
danieljova1
2023-09-26T16:07:30Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-26T16:07:24Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 0.6.0.dev0
GHonem/blip-image-captioning-base-test_sagemaker-tops-3
GHonem
2023-09-26T16:06:49Z
60
0
transformers
[ "transformers", "pytorch", "blip", "image-text-to-text", "generated_from_trainer", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]
image-text-to-text
2023-09-26T15:40:15Z
--- license: bsd-3-clause tags: - generated_from_trainer model-index: - name: blip-image-captioning-base-test_sagemaker-tops-3 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. --> # blip-image-captioning-base-test_sagemaker-tops-3 This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - distributed_type: sagemaker_model_parallel - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
roa7n/gpt2-human_nontata_promoters-randomized_0_layers_3e-05_lr_2_e
roa7n
2023-09-26T16:05:25Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-26T16:05:22Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
OpenNLG/OpenBA-V1-Based
OpenNLG
2023-09-26T16:04:48Z
29
9
transformers
[ "transformers", "pytorch", "openba", "feature-extraction", "text-generation", "custom_code", "zh", "en", "arxiv:2309.10706", "license:apache-2.0", "region:us" ]
text-generation
2023-09-20T05:56:52Z
--- license: apache-2.0 language: - zh - en tags: - openba pipeline_tag: text-generation --- # Introduction OpenBA is an Open-Sourced 15B Bilingual Asymmetric Seq2Seq Model Pre-trained from Scratch. ## Open Source Plan We are excited to unveil two distinguished versions of our model, with another on the horizon: - [OpenBA-LM](https://huggingface.co/OpenBA/OpenBA-LM): The backbone language models was pre-trained on 340B English, Chinese, and code tokens. - [OpenBA-Flan](https://huggingface.co/OpenBA/OpenBA-Flan): We perform supervised fine-tuning on the base model with additional 40B tokens using our collected BiFlan Dataset. - OpenBA-Chat: coming soon ## Model Description - **Model type:** Language model - **Language(s) (NLP):** zh, en (We also offer the possibility for multilingual learning, by using a multilingual tokenizer.) - **License:** Apache 2.0 - **Resources for more information:** - [Paper](https://arxiv.org/abs/2309.10706) - [GitHub Repo](https://github.com/OpenNLG/OpenBA/) # Usage ## Install requirements ```bash pip install transformers torch>=2.0 sentencepiece ``` ## Demo usage ```python >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> tokenizer = AutoTokenizer.from_pretrained("OpenBA/OpenBA-LM", trust_remote_code=True) >>> model = AutoModelForSeq2SeqLM.from_pretrained("OpenBA/OpenBA-LM", trust_remote_code=True).half().cuda() >>> model = model.eval() >>> query = "<S>" + "苏州处太湖平原,沿江为高沙平原,河" + "<extra_id_0>" >>> inputs = tokenizer(query, return_tensors="pt").to("cuda") >>> outputs = model.generate(**inputs, do_sample=True, max_new_tokens=32) >>> response = tokenizer.decode(outputs[0], skip_special_tokens=True) >>> print(response) 流两侧为河淤平原,苏州平原是江苏平原主体,地势低平,土地肥沃,气候温和 ```
eugene6/Reinforce-CartPole-v1
eugene6
2023-09-26T16:04:23Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T16:04:12Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
anaReviewsWorks/DtxBlackFunciona
anaReviewsWorks
2023-09-26T15:58:46Z
0
0
null
[ "region:us" ]
null
2023-09-26T15:47:02Z
Deseja saber se o DTX Black realmente funciona? Este artigo fornecerá informações sobre a eficácia do produto, sua composição, como usá-lo e onde comprá-lo no site oficial. Como Emagrecer Rapidamente com o DTX Black Emagrecer não é uma tarefa simples, mas agora você tem um aliado nessa batalha: o DTX Black. Este suplemento permite eliminar o excesso de peso de maneira descomplicada e eficaz. Com o DTX Black, você experimentará uma maior sensação de saciedade, terá mais energia para suas atividades diárias e alcançará uma perda de peso natural e segura, sem comprometer sua saúde. Continue lendo para descobrir como o DTX Black pode ajudá-lo a derreter o tecido adiposo de forma acelerada e conquistar o corpo que deseja. DTX Black Funciona Mesmo? Uma pesquisa realizada em 2016 avaliou mulheres que utilizaram o DTX Black por três meses ou mais, comparando-as com aquelas que não usaram nenhum suplemento. O grupo que usou o DTX Black obteve resultados surpreendentes: Perda de peso de 10 kg ou mais. Redução significativa de celulite e inchaço (63% das participantes). Redução de 40% na sensação de fome. Melhora no funcionamento do intestino relatada por 90% das mulheres. Esses resultados foram posteriormente confirmados por outras pesquisas, destacando a eficácia do DTX Black na perda de gordura. O DTX Black realmente funciona! Ele pode ajudar você a perder mais de 1 kg de gordura por semana, mesmo se sua dieta não for ideal. Isso acontece porque o suplemento atua diretamente nas células de gordura, liberando ácidos graxos para serem utilizados como energia. [Clique Aqui Para Comprar DTX Black Com Desconto] https://bit.ly/DtxBlack-com https://bit.ly/DtxBlack-com https://bit.ly/DtxBlack-com
aidiffusionartist/exmachina420-realistic-v1-5
aidiffusionartist
2023-09-26T15:41:44Z
0
0
null
[ "en", "license:mit", "region:us" ]
null
2023-09-26T14:33:49Z
--- license: mit language: - en ---
airjairj/MODELLO
airjairj
2023-09-26T15:37:00Z
91
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-13T16:31:44Z
--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: MODELLO 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. --> # MODELLO This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1818 - Edit Distance: 13.598 ## 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: 18 - eval_batch_size: 18 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 18 ### Training results | Training Loss | Epoch | Step | Validation Loss | Edit Distance | |:-------------:|:-----:|:----:|:---------------:|:-------------:| | 0.7351 | 1.0 | 500 | 0.2832 | 13.844 | | 0.3224 | 2.0 | 1000 | 0.2401 | 13.85 | | 0.2788 | 3.0 | 1500 | 0.2285 | 13.795 | | 0.2595 | 4.0 | 2000 | 0.2179 | 13.805 | | 0.2469 | 5.0 | 2500 | 0.2066 | 13.687 | | 0.233 | 6.0 | 3000 | 0.1912 | 13.67 | | 0.219 | 7.0 | 3500 | 0.1874 | 13.658 | | 0.2135 | 8.0 | 4000 | 0.1895 | 13.65 | | 0.2101 | 9.0 | 4500 | 0.1883 | 13.643 | | 0.2074 | 10.0 | 5000 | 0.1836 | 13.643 | | 0.2057 | 11.0 | 5500 | 0.1825 | 13.649 | | 0.2042 | 12.0 | 6000 | 0.1834 | 13.614 | | 0.2034 | 13.0 | 6500 | 0.1828 | 13.623 | | 0.2017 | 14.0 | 7000 | 0.1820 | 13.653 | | 0.2017 | 15.0 | 7500 | 0.1824 | 13.634 | | 0.2004 | 16.0 | 8000 | 0.1822 | 13.641 | | 0.2006 | 17.0 | 8500 | 0.1817 | 13.62 | | 0.2005 | 18.0 | 9000 | 0.1818 | 13.598 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Tommert25/multibert2809_flow
Tommert25
2023-09-26T15:33:17Z
106
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-09-26T15:21:05Z
--- license: apache-2.0 base_model: bert-base-multilingual-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: multibert2809_flow 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. --> # multibert2809_flow This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4534 - Precision: 0.7055 - Recall: 0.7076 - F1: 0.7066 - Accuracy: 0.8709 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 118 | 0.5021 | 0.6550 | 0.6229 | 0.6385 | 0.8414 | | No log | 2.0 | 236 | 0.4534 | 0.7055 | 0.7076 | 0.7066 | 0.8709 | | No log | 3.0 | 354 | 0.4903 | 0.7455 | 0.7237 | 0.7345 | 0.8752 | | No log | 4.0 | 472 | 0.5158 | 0.7488 | 0.7327 | 0.7407 | 0.8755 | | 0.3074 | 5.0 | 590 | 0.5685 | 0.7502 | 0.7434 | 0.7468 | 0.8758 | | 0.3074 | 6.0 | 708 | 0.5799 | 0.7612 | 0.7530 | 0.7570 | 0.8809 | | 0.3074 | 7.0 | 826 | 0.6022 | 0.7673 | 0.7494 | 0.7582 | 0.8791 | | 0.3074 | 8.0 | 944 | 0.6054 | 0.7663 | 0.7554 | 0.7608 | 0.8840 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
roa7n/gpt2-human_nontata_promoters-randomized_0_layers_0.0003_lr_2_e
roa7n
2023-09-26T15:32:57Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-26T15:32:55Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
Takagi-san/SaProt_650M_AF2
Takagi-san
2023-09-26T15:24:16Z
172
2
transformers
[ "transformers", "pytorch", "esm", "fill-mask", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-26T12:02:40Z
--- license: mit --- We provide both huggingface version and [esm version](https://github.com/facebookresearch/esm) of SaProt (see our github <https://github.com/SaProt/SaProt>). Users can choose either one to use. ### Huggingface model The following code shows how to load the model. ``` from transformers import EsmTokenizer, EsmForMaskedLM model_path = "/your/path/to/SaProt_650M_AF2" tokenizer = EsmTokenizer.from_pretrained(model_path) model = EsmForMaskedLM.from_pretrained(model_path) #################### Example #################### device = "cuda" model.to(device) seq = "MdEvVpQpLrVyQdYaKv" tokens = tokenizer.tokenize(seq) print(tokens) inputs = tokenizer(seq, return_tensors="pt") inputs = {k: v.to(device) for k, v in inputs.items()} outputs = model(**inputs) print(outputs.logits.shape) """ ['Md', 'Ev', 'Vp', 'Qp', 'Lr', 'Vy', 'Qd', 'Ya', 'Kv'] torch.Size([1, 11, 446]) """ ``` ### esm model The esm version is also stored in the same folder, named `SaProt_650M_AF2.pt`. We provide a function to load the model. ``` from utils.esm_loader import load_esm_saprot model_path = "/your/path/to/SaProt_650M_AF2.pt" model, alphabet = load_esm_saprot(model_path) ```
traeval/tesla1500_llama2_7b-2-7b
traeval
2023-09-26T15:20:19Z
4
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-26T15:10:58Z
***** train metrics ***** epoch = 1.33 total_flos = 14124142GF train_loss = 0.7836 train_runtime = 1:27:16.97 train_samples_per_second = 0.382 train_steps_per_second = 0.095 {'train_runtime': 5236.9755, 'train_samples_per_second': 0.382, 'train_steps_per_second': 0.095, 'total_flos': 1.5165682398461952e+16, 'train_loss': 0.7835705888271332, 'epoch': 1.33}
md-nishat-008/Tri-Distil-BERT
md-nishat-008
2023-09-26T15:11:27Z
105
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "arxiv:2309.10272", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-17T00:12:17Z
--- license: apache-2.0 --- The model is pretrained on the OSCAR dataset for Bangla, English and Hindi. The base model is Distil-BERT and the intended use for this model is for the datasets that contain a mix of these languages. To Cite: @article{raihan2023mixed, title={Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi}, author={Raihan, Md Nishat and Goswami, Dhiman and Mahmud, Antara}, journal={arXiv preprint arXiv:2309.10272}, year={2023} }
VuongQuoc/checkpoints_1_microsoft_deberta_21_9
VuongQuoc
2023-09-26T15:11:04Z
1
0
transformers
[ "transformers", "pytorch", "deberta-v2", "multiple-choice", "generated_from_trainer", "base_model:VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9", "base_model:finetune:VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9", "license:mit", "endpoints_compatible", "region:us" ]
multiple-choice
2023-09-21T11:35:36Z
--- license: mit base_model: VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9 tags: - generated_from_trainer model-index: - name: checkpoints_1_microsoft_deberta_21_9 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. --> # checkpoints_1_microsoft_deberta_21_9 This model is a fine-tuned version of [VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9](https://huggingface.co/VuongQuoc/checkpoints_26_9_microsoft_deberta_21_9) 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: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0+cpu - Datasets 2.1.0 - Tokenizers 0.13.3
therealcyberlord/llama2-qlora-finetuned-medical
therealcyberlord
2023-09-26T15:10:42Z
12
5
peft
[ "peft", "llama", "llm", "llama2", "medical", "text-generation", "dataset:BI55/MedText", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
text-generation
2023-08-05T23:10:14Z
--- library_name: peft tags: - llm - llama2 - medical datasets: - BI55/MedText pipeline_tag: text-generation base_model: meta-llama/Llama-2-7b-chat-hf --- # Llama2 🦙 finetuned on medical diagnosis MedText dataset: https://huggingface.co/datasets/BI55/MedText 1412 pairs of diagnosis cases # About: The primary objective of this fine-tuning process is to equip Llama2 with the ability to assist in diagnosing various medical cases and diseases. However, it is essential to clarify that it is not designed to replace real medical professionals. Instead, its purpose is to provide helpful information to users, suggesting potential next steps based on the input data and the patterns it has learned from the MedText dataset. Finetuned on guanaco styled instructions ``` ###Human ###Assistant ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
jpostma/s-DagoBERT-TSDAE
jpostma
2023-09-26T15:04:03Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-09-03T14:39:21Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {s-DagoBERT-TSDAE} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 90 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 15, "evaluation_steps": 360, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 3e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 80, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
roa7n/gpt2-human_nontata_promoters-randomized_0_layers_0.003_lr_2_e
roa7n
2023-09-26T15:00:21Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-26T14:32:15Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
luisgasco/setfit-sentence-classifier_test_biomed_5it_b16
luisgasco
2023-09-26T14:57:20Z
6
0
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-09-26T14:56:51Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # luisgasco/setfit-sentence-classifier_test_biomed_5it_b16 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("luisgasco/setfit-sentence-classifier_test_biomed_5it_b16") # 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} } ```
LeeSolomonson/ppo-LunarLander-v2
LeeSolomonson
2023-09-26T14:56:48Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T14:56:25Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 244.29 +/- 73.77 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
rezaparseh/phi-1_5-finetuned-gsm8k
rezaparseh
2023-09-26T14:48:24Z
0
0
null
[ "generated_from_trainer", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "region:us" ]
null
2023-09-26T14:14:04Z
--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: phi-1_5-finetuned-gsm8k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1_5-finetuned-gsm8k This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
nguyenlephucvinh2011/llama2-qlora-finetunined
nguyenlephucvinh2011
2023-09-26T14:45:57Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-26T14:45:48Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 0.6.0.dev0
mehranmehr/ppo-LunarLander-v2
mehranmehr
2023-09-26T14:41:44Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T14:41:20Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 248.34 +/- 19.82 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
MightyDuckk/lora-trained-xl-colab
MightyDuckk
2023-09-26T14:35:03Z
5
2
diffusers
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-09-26T13:19:14Z
--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - MightyDuckk/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 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. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
anders0204/q-FrozenLake-v1-4x4-noSlippery
anders0204
2023-09-26T14:31:58Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T14:31:56Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="anders0204/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
MUmairAB/marian-finetuned-kde4-english-to-french
MUmairAB
2023-09-26T14:30:30Z
63
1
transformers
[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-07-11T15:22:18Z
--- license: apache-2.0 tags: - generated_from_keras_callback base_model: Helsinki-NLP/opus-mt-en-fr model-index: - name: marian-finetuned-kde4-english-to-french 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. --> # marian-finetuned-kde4-english-to-french This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6794 - Validation Loss: 0.8119 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 29555, '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 | Epoch | |:----------:|:---------------:|:-----:| | 1.0577 | 0.8929 | 0 | | 0.8023 | 0.8343 | 1 | | 0.6794 | 0.8119 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
MUmairAB/bert-based-MaskedLM
MUmairAB
2023-09-26T14:29:12Z
70
1
transformers
[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-08T14:03:10Z
--- license: apache-2.0 tags: - generated_from_keras_callback datasets: - imdb pipeline_tag: fill-mask base_model: distilbert-base-uncased model-index: - name: MUmairAB/bert-based-MaskedLM results: [] --- # MUmairAB/bert-based-MaskedLM **The model training code is available as a notebook on my [GitHub](https://github.com/MUmairAB/Masked-Language-Model-Fine-Tuning-with-HuggingFace-Transformers/tree/main)** This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [IMDB Movies Review](https://huggingface.co/datasets/imdb) dataset. It achieves the following results on the evaluation set: - Train Loss: 2.4360 - Validation Loss: 2.3284 - Epoch: 20 ## Training and validation loss during training <img src="https://huggingface.co/MUmairAB/bert-based-MaskedLM/resolve/main/Loss%20plot.png" style="height: 432px; width:567px;"/> ## Model description [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased) ``` Model: "tf_distil_bert_for_masked_lm" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= distilbert (TFDistilBertMai multiple 66362880 nLayer) vocab_transform (Dense) multiple 590592 vocab_layer_norm (LayerNorm multiple 1536 alization) vocab_projector (TFDistilBe multiple 23866170 rtLMHead) ================================================================= Total params: 66,985,530 Trainable params: 66,985,530 Non-trainable params: 0 _________________________________________________________________ ``` ## Intended uses & limitations The model was trained on IMDB movies review dataset. So, it inherits the language biases from the dataset. ## Training and evaluation data The model was trained on [IMDB Movies Review](https://huggingface.co/datasets/imdb) dataset. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -60, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.0754 | 2.7548 | 0 | | 2.7969 | 2.6209 | 1 | | 2.7214 | 2.5588 | 2 | | 2.6626 | 2.5554 | 3 | | 2.6466 | 2.4881 | 4 | | 2.6238 | 2.4775 | 5 | | 2.5696 | 2.4280 | 6 | | 2.5504 | 2.3924 | 7 | | 2.5171 | 2.3725 | 8 | | 2.5180 | 2.3142 | 9 | | 2.4443 | 2.2974 | 10 | | 2.4497 | 2.3317 | 11 | | 2.4371 | 2.3317 | 12 | | 2.4377 | 2.3237 | 13 | | 2.4369 | 2.3338 | 14 | | 2.4350 | 2.3021 | 15 | | 2.4267 | 2.3264 | 16 | | 2.4557 | 2.3280 | 17 | | 2.4461 | 2.3165 | 18 | | 2.4360 | 2.3284 | 19 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Tokenizers 0.13.3
MUmairAB/bert-ner
MUmairAB
2023-09-26T14:28:31Z
7
3
transformers
[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "named entity recognition", "bert-base finetuned", "umair akram", "en", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-07-05T15:45:06Z
--- language: - en license: apache-2.0 library_name: transformers tags: - generated_from_keras_callback - named entity recognition - bert-base finetuned - umair akram datasets: - conll2003 metrics: - seqeval pipeline_tag: token-classification base_model: bert-base-cased model-index: - name: MUmairAB/bert-ner results: [] --- # MUmairAB/bert-ner The model training notebook is available on my [GitHub Repo](https://github.com/MUmairAB/BERT-based-NER-using-HuggingFace-Transformers/tree/main). This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on [Cnoll2003](https://huggingface.co/datasets/conll2003) dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0003 - Validation Loss: 0.0880 - Epoch: 19 ## How to use this model ``` #Install the transformers library !pip install transformers #Import the pipeline from transformers import pipeline #Import the model from HuggingFace checkpoint = "MUmairAB/bert-ner" model = pipeline(task="token-classification", model=checkpoint) #Use the model raw_text = "My name is umair and i work at Swits AI in Antarctica." model(raw_text) ``` ## Model description Model: "tf_bert_for_token_classification" ``` _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= bert (TFBertMainLayer) multiple 107719680 dropout_37 (Dropout) multiple 0 classifier (Dense) multiple 6921 ================================================================= Total params: 107,726,601 Trainable params: 107,726,601 Non-trainable params: 0 _________________________________________________________________ ``` ## Intended uses & limitations This model can be used for named entity recognition tasks. It is trained on [Conll2003](https://huggingface.co/datasets/conll2003) dataset. The model can classify four types of named entities: 1. persons, 2. locations, 3. organizations, and 4. names of miscellaneous entities that do not belong to the previous three groups. ## Training and evaluation data The model is evaluated on [seqeval](https://github.com/chakki-works/seqeval) metric and the result is as follows: ``` {'LOC': {'precision': 0.9655361050328227, 'recall': 0.9608056614044638, 'f1': 0.9631650750341064, 'number': 1837}, 'MISC': {'precision': 0.8789144050104384, 'recall': 0.913232104121475, 'f1': 0.8957446808510638, 'number': 922}, 'ORG': {'precision': 0.9075144508670521, 'recall': 0.9366144668158091, 'f1': 0.9218348623853211, 'number': 1341}, 'PER': {'precision': 0.962011771000535, 'recall': 0.9761129207383279, 'f1': 0.9690110482349771, 'number': 1842}, 'overall_precision': 0.9374068554396423, 'overall_recall': 0.9527095254123191, 'overall_f1': 0.944996244053084, 'overall_accuracy': 0.9864013657502796} ``` ## 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': 17560, '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 | Epoch | |:----------:|:---------------:|:-----:| | 0.1775 | 0.0635 | 0 | | 0.0470 | 0.0559 | 1 | | 0.0278 | 0.0603 | 2 | | 0.0174 | 0.0603 | 3 | | 0.0124 | 0.0615 | 4 | | 0.0077 | 0.0722 | 5 | | 0.0060 | 0.0731 | 6 | | 0.0038 | 0.0757 | 7 | | 0.0043 | 0.0731 | 8 | | 0.0041 | 0.0735 | 9 | | 0.0019 | 0.0724 | 10 | | 0.0019 | 0.0786 | 11 | | 0.0010 | 0.0843 | 12 | | 0.0008 | 0.0814 | 13 | | 0.0011 | 0.0867 | 14 | | 0.0008 | 0.0883 | 15 | | 0.0005 | 0.0861 | 16 | | 0.0005 | 0.0869 | 17 | | 0.0003 | 0.0880 | 18 | | 0.0003 | 0.0880 | 19 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
dyaminda/pneumonia-classification
dyaminda
2023-09-26T14:28:22Z
13
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-09-24T03:27:46Z
--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: pneumonia-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. --> # pneumonia-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0288 - Accuracy: 0.9923 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1574 | 0.99 | 52 | 0.0976 | 0.9726 | | 0.0643 | 2.0 | 105 | 0.0535 | 0.9845 | | 0.0189 | 2.99 | 157 | 0.0490 | 0.9821 | | 0.0208 | 4.0 | 210 | 0.0484 | 0.9881 | | 0.0096 | 4.95 | 260 | 0.0463 | 0.9881 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
BBBBirdIsTheWord/rl_course_vizdoom_health_gathering_supreme
BBBBirdIsTheWord
2023-09-26T14:25:37Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T14:25:19Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.20 +/- 4.60 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 BBBBirdIsTheWord/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
rayhanozzy/image_classification
rayhanozzy
2023-09-26T14:14:04Z
28
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-09-17T14:13:52Z
--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: image_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.5625 --- <!-- 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. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3383 - Accuracy: 0.5625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 80 | 1.6519 | 0.3312 | | No log | 2.0 | 160 | 1.4509 | 0.4125 | | No log | 3.0 | 240 | 1.3641 | 0.5062 | | No log | 4.0 | 320 | 1.2676 | 0.5875 | | No log | 5.0 | 400 | 1.2718 | 0.5188 | | No log | 6.0 | 480 | 1.2250 | 0.5125 | | 1.2828 | 7.0 | 560 | 1.1933 | 0.55 | | 1.2828 | 8.0 | 640 | 1.1538 | 0.575 | | 1.2828 | 9.0 | 720 | 1.2479 | 0.55 | | 1.2828 | 10.0 | 800 | 1.2487 | 0.575 | | 1.2828 | 11.0 | 880 | 1.2418 | 0.5938 | | 1.2828 | 12.0 | 960 | 1.1514 | 0.6062 | | 0.5147 | 13.0 | 1040 | 1.2563 | 0.5563 | | 0.5147 | 14.0 | 1120 | 1.2933 | 0.5813 | | 0.5147 | 15.0 | 1200 | 1.2857 | 0.5813 | | 0.5147 | 16.0 | 1280 | 1.3044 | 0.575 | | 0.5147 | 17.0 | 1360 | 1.4134 | 0.5687 | | 0.5147 | 18.0 | 1440 | 1.3277 | 0.5875 | | 0.2675 | 19.0 | 1520 | 1.2963 | 0.575 | | 0.2675 | 20.0 | 1600 | 1.2049 | 0.6125 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
amitraheja82/MarketMailAIFineTuningModel
amitraheja82
2023-09-26T14:06:26Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-26T14:06:23Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 0.6.0.dev0
BBBBirdIsTheWord/LunarLander-v2_u8
BBBBirdIsTheWord
2023-09-26T14:01:53Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T13:37:01Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -176.90 +/- 0.00 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'BBBBirdIsTheWord/LunarLander-v2_u8' 'batch_size': 512 'minibatch_size': 128} ```
CyberHarem/okusawa_misaki_bangdream
CyberHarem
2023-09-26T13:58:28Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/okusawa_misaki_bangdream", "license:mit", "region:us" ]
text-to-image
2023-09-26T13:40:28Z
--- license: mit datasets: - CyberHarem/okusawa_misaki_bangdream pipeline_tag: text-to-image tags: - art --- # Lora of okusawa_misaki_bangdream This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5760, you need to download `5760/okusawa_misaki_bangdream.pt` as the embedding and `5760/okusawa_misaki_bangdream.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5760**, with the score of 0.991. The trigger words are: 1. `okusawa_misaki_bangdream` 2. `bangs, black_hair, hair_ornament, blue_eyes, hairclip, blush, smile, medium_hair, open_mouth, long_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 7200 | 0.981 | [Download](7200/okusawa_misaki_bangdream.zip) | ![pattern_1-7200](7200/previews/pattern_1.png) | ![pattern_2-7200](7200/previews/pattern_2.png) | [<NSFW, click to see>](7200/previews/pattern_3.png) | ![pattern_4-7200](7200/previews/pattern_4.png) | ![pattern_5-7200](7200/previews/pattern_5.png) | ![pattern_6-7200](7200/previews/pattern_6.png) | ![pattern_7-7200](7200/previews/pattern_7.png) | ![pattern_8-7200](7200/previews/pattern_8.png) | ![pattern_9-7200](7200/previews/pattern_9.png) | ![pattern_10-7200](7200/previews/pattern_10.png) | ![pattern_11-7200](7200/previews/pattern_11.png) | ![pattern_12-7200](7200/previews/pattern_12.png) | ![pattern_13-7200](7200/previews/pattern_13.png) | ![pattern_14-7200](7200/previews/pattern_14.png) | ![pattern_15-7200](7200/previews/pattern_15.png) | ![bikini-7200](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) | ![free-7200](7200/previews/free.png) | ![maid-7200](7200/previews/maid.png) | ![miko-7200](7200/previews/miko.png) | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) | ![suit-7200](7200/previews/suit.png) | ![yukata-7200](7200/previews/yukata.png) | | 6720 | 0.964 | [Download](6720/okusawa_misaki_bangdream.zip) | ![pattern_1-6720](6720/previews/pattern_1.png) | ![pattern_2-6720](6720/previews/pattern_2.png) | [<NSFW, click to see>](6720/previews/pattern_3.png) | ![pattern_4-6720](6720/previews/pattern_4.png) | ![pattern_5-6720](6720/previews/pattern_5.png) | ![pattern_6-6720](6720/previews/pattern_6.png) | ![pattern_7-6720](6720/previews/pattern_7.png) | ![pattern_8-6720](6720/previews/pattern_8.png) | ![pattern_9-6720](6720/previews/pattern_9.png) | ![pattern_10-6720](6720/previews/pattern_10.png) | ![pattern_11-6720](6720/previews/pattern_11.png) | ![pattern_12-6720](6720/previews/pattern_12.png) | ![pattern_13-6720](6720/previews/pattern_13.png) | ![pattern_14-6720](6720/previews/pattern_14.png) | ![pattern_15-6720](6720/previews/pattern_15.png) | ![bikini-6720](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) | ![free-6720](6720/previews/free.png) | ![maid-6720](6720/previews/maid.png) | ![miko-6720](6720/previews/miko.png) | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) | ![suit-6720](6720/previews/suit.png) | ![yukata-6720](6720/previews/yukata.png) | | 6240 | 0.973 | [Download](6240/okusawa_misaki_bangdream.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | [<NSFW, click to see>](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | ![pattern_5-6240](6240/previews/pattern_5.png) | ![pattern_6-6240](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![pattern_10-6240](6240/previews/pattern_10.png) | ![pattern_11-6240](6240/previews/pattern_11.png) | ![pattern_12-6240](6240/previews/pattern_12.png) | ![pattern_13-6240](6240/previews/pattern_13.png) | ![pattern_14-6240](6240/previews/pattern_14.png) | ![pattern_15-6240](6240/previews/pattern_15.png) | ![bikini-6240](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | **5760** | **0.991** | [**Download**](5760/okusawa_misaki_bangdream.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | [<NSFW, click to see>](5760/previews/pattern_3.png) | ![pattern_4-5760](5760/previews/pattern_4.png) | ![pattern_5-5760](5760/previews/pattern_5.png) | ![pattern_6-5760](5760/previews/pattern_6.png) | ![pattern_7-5760](5760/previews/pattern_7.png) | ![pattern_8-5760](5760/previews/pattern_8.png) | ![pattern_9-5760](5760/previews/pattern_9.png) | ![pattern_10-5760](5760/previews/pattern_10.png) | ![pattern_11-5760](5760/previews/pattern_11.png) | ![pattern_12-5760](5760/previews/pattern_12.png) | ![pattern_13-5760](5760/previews/pattern_13.png) | ![pattern_14-5760](5760/previews/pattern_14.png) | ![pattern_15-5760](5760/previews/pattern_15.png) | ![bikini-5760](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) | ![free-5760](5760/previews/free.png) | ![maid-5760](5760/previews/maid.png) | ![miko-5760](5760/previews/miko.png) | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) | ![suit-5760](5760/previews/suit.png) | ![yukata-5760](5760/previews/yukata.png) | | 5280 | 0.967 | [Download](5280/okusawa_misaki_bangdream.zip) | ![pattern_1-5280](5280/previews/pattern_1.png) | ![pattern_2-5280](5280/previews/pattern_2.png) | [<NSFW, click to see>](5280/previews/pattern_3.png) | ![pattern_4-5280](5280/previews/pattern_4.png) | ![pattern_5-5280](5280/previews/pattern_5.png) | ![pattern_6-5280](5280/previews/pattern_6.png) | ![pattern_7-5280](5280/previews/pattern_7.png) | ![pattern_8-5280](5280/previews/pattern_8.png) | ![pattern_9-5280](5280/previews/pattern_9.png) | ![pattern_10-5280](5280/previews/pattern_10.png) | ![pattern_11-5280](5280/previews/pattern_11.png) | ![pattern_12-5280](5280/previews/pattern_12.png) | ![pattern_13-5280](5280/previews/pattern_13.png) | ![pattern_14-5280](5280/previews/pattern_14.png) | ![pattern_15-5280](5280/previews/pattern_15.png) | ![bikini-5280](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) | ![free-5280](5280/previews/free.png) | ![maid-5280](5280/previews/maid.png) | ![miko-5280](5280/previews/miko.png) | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) | ![suit-5280](5280/previews/suit.png) | ![yukata-5280](5280/previews/yukata.png) | | 4800 | 0.946 | [Download](4800/okusawa_misaki_bangdream.zip) | ![pattern_1-4800](4800/previews/pattern_1.png) | ![pattern_2-4800](4800/previews/pattern_2.png) | [<NSFW, click to see>](4800/previews/pattern_3.png) | ![pattern_4-4800](4800/previews/pattern_4.png) | ![pattern_5-4800](4800/previews/pattern_5.png) | ![pattern_6-4800](4800/previews/pattern_6.png) | ![pattern_7-4800](4800/previews/pattern_7.png) | ![pattern_8-4800](4800/previews/pattern_8.png) | ![pattern_9-4800](4800/previews/pattern_9.png) | ![pattern_10-4800](4800/previews/pattern_10.png) | ![pattern_11-4800](4800/previews/pattern_11.png) | ![pattern_12-4800](4800/previews/pattern_12.png) | ![pattern_13-4800](4800/previews/pattern_13.png) | ![pattern_14-4800](4800/previews/pattern_14.png) | ![pattern_15-4800](4800/previews/pattern_15.png) | ![bikini-4800](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) | ![free-4800](4800/previews/free.png) | ![maid-4800](4800/previews/maid.png) | ![miko-4800](4800/previews/miko.png) | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) | ![suit-4800](4800/previews/suit.png) | ![yukata-4800](4800/previews/yukata.png) | | 4320 | 0.987 | [Download](4320/okusawa_misaki_bangdream.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | [<NSFW, click to see>](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | ![pattern_6-4320](4320/previews/pattern_6.png) | ![pattern_7-4320](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![pattern_9-4320](4320/previews/pattern_9.png) | ![pattern_10-4320](4320/previews/pattern_10.png) | ![pattern_11-4320](4320/previews/pattern_11.png) | ![pattern_12-4320](4320/previews/pattern_12.png) | ![pattern_13-4320](4320/previews/pattern_13.png) | ![pattern_14-4320](4320/previews/pattern_14.png) | ![pattern_15-4320](4320/previews/pattern_15.png) | ![bikini-4320](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3840 | 0.984 | [Download](3840/okusawa_misaki_bangdream.zip) | ![pattern_1-3840](3840/previews/pattern_1.png) | ![pattern_2-3840](3840/previews/pattern_2.png) | [<NSFW, click to see>](3840/previews/pattern_3.png) | ![pattern_4-3840](3840/previews/pattern_4.png) | ![pattern_5-3840](3840/previews/pattern_5.png) | ![pattern_6-3840](3840/previews/pattern_6.png) | ![pattern_7-3840](3840/previews/pattern_7.png) | ![pattern_8-3840](3840/previews/pattern_8.png) | ![pattern_9-3840](3840/previews/pattern_9.png) | ![pattern_10-3840](3840/previews/pattern_10.png) | ![pattern_11-3840](3840/previews/pattern_11.png) | ![pattern_12-3840](3840/previews/pattern_12.png) | ![pattern_13-3840](3840/previews/pattern_13.png) | ![pattern_14-3840](3840/previews/pattern_14.png) | ![pattern_15-3840](3840/previews/pattern_15.png) | ![bikini-3840](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) | ![free-3840](3840/previews/free.png) | ![maid-3840](3840/previews/maid.png) | ![miko-3840](3840/previews/miko.png) | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) | ![suit-3840](3840/previews/suit.png) | ![yukata-3840](3840/previews/yukata.png) | | 3360 | 0.956 | [Download](3360/okusawa_misaki_bangdream.zip) | ![pattern_1-3360](3360/previews/pattern_1.png) | ![pattern_2-3360](3360/previews/pattern_2.png) | [<NSFW, click to see>](3360/previews/pattern_3.png) | ![pattern_4-3360](3360/previews/pattern_4.png) | ![pattern_5-3360](3360/previews/pattern_5.png) | ![pattern_6-3360](3360/previews/pattern_6.png) | ![pattern_7-3360](3360/previews/pattern_7.png) | ![pattern_8-3360](3360/previews/pattern_8.png) | ![pattern_9-3360](3360/previews/pattern_9.png) | ![pattern_10-3360](3360/previews/pattern_10.png) | ![pattern_11-3360](3360/previews/pattern_11.png) | ![pattern_12-3360](3360/previews/pattern_12.png) | ![pattern_13-3360](3360/previews/pattern_13.png) | ![pattern_14-3360](3360/previews/pattern_14.png) | ![pattern_15-3360](3360/previews/pattern_15.png) | ![bikini-3360](3360/previews/bikini.png) | [<NSFW, click to see>](3360/previews/bondage.png) | ![free-3360](3360/previews/free.png) | ![maid-3360](3360/previews/maid.png) | ![miko-3360](3360/previews/miko.png) | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) | ![suit-3360](3360/previews/suit.png) | ![yukata-3360](3360/previews/yukata.png) | | 2880 | 0.978 | [Download](2880/okusawa_misaki_bangdream.zip) | ![pattern_1-2880](2880/previews/pattern_1.png) | ![pattern_2-2880](2880/previews/pattern_2.png) | [<NSFW, click to see>](2880/previews/pattern_3.png) | ![pattern_4-2880](2880/previews/pattern_4.png) | ![pattern_5-2880](2880/previews/pattern_5.png) | ![pattern_6-2880](2880/previews/pattern_6.png) | ![pattern_7-2880](2880/previews/pattern_7.png) | ![pattern_8-2880](2880/previews/pattern_8.png) | ![pattern_9-2880](2880/previews/pattern_9.png) | ![pattern_10-2880](2880/previews/pattern_10.png) | ![pattern_11-2880](2880/previews/pattern_11.png) | ![pattern_12-2880](2880/previews/pattern_12.png) | ![pattern_13-2880](2880/previews/pattern_13.png) | ![pattern_14-2880](2880/previews/pattern_14.png) | ![pattern_15-2880](2880/previews/pattern_15.png) | ![bikini-2880](2880/previews/bikini.png) | [<NSFW, click to see>](2880/previews/bondage.png) | ![free-2880](2880/previews/free.png) | ![maid-2880](2880/previews/maid.png) | ![miko-2880](2880/previews/miko.png) | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) | ![suit-2880](2880/previews/suit.png) | ![yukata-2880](2880/previews/yukata.png) | | 2400 | 0.979 | [Download](2400/okusawa_misaki_bangdream.zip) | ![pattern_1-2400](2400/previews/pattern_1.png) | ![pattern_2-2400](2400/previews/pattern_2.png) | [<NSFW, click to see>](2400/previews/pattern_3.png) | ![pattern_4-2400](2400/previews/pattern_4.png) | ![pattern_5-2400](2400/previews/pattern_5.png) | ![pattern_6-2400](2400/previews/pattern_6.png) | ![pattern_7-2400](2400/previews/pattern_7.png) | ![pattern_8-2400](2400/previews/pattern_8.png) | ![pattern_9-2400](2400/previews/pattern_9.png) | ![pattern_10-2400](2400/previews/pattern_10.png) | ![pattern_11-2400](2400/previews/pattern_11.png) | ![pattern_12-2400](2400/previews/pattern_12.png) | ![pattern_13-2400](2400/previews/pattern_13.png) | ![pattern_14-2400](2400/previews/pattern_14.png) | ![pattern_15-2400](2400/previews/pattern_15.png) | ![bikini-2400](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | 1920 | 0.958 | [Download](1920/okusawa_misaki_bangdream.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | [<NSFW, click to see>](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | ![pattern_10-1920](1920/previews/pattern_10.png) | ![pattern_11-1920](1920/previews/pattern_11.png) | ![pattern_12-1920](1920/previews/pattern_12.png) | ![pattern_13-1920](1920/previews/pattern_13.png) | ![pattern_14-1920](1920/previews/pattern_14.png) | ![pattern_15-1920](1920/previews/pattern_15.png) | ![bikini-1920](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) | ![free-1920](1920/previews/free.png) | ![maid-1920](1920/previews/maid.png) | ![miko-1920](1920/previews/miko.png) | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1440 | 0.969 | [Download](1440/okusawa_misaki_bangdream.zip) | ![pattern_1-1440](1440/previews/pattern_1.png) | ![pattern_2-1440](1440/previews/pattern_2.png) | [<NSFW, click to see>](1440/previews/pattern_3.png) | ![pattern_4-1440](1440/previews/pattern_4.png) | ![pattern_5-1440](1440/previews/pattern_5.png) | ![pattern_6-1440](1440/previews/pattern_6.png) | ![pattern_7-1440](1440/previews/pattern_7.png) | ![pattern_8-1440](1440/previews/pattern_8.png) | ![pattern_9-1440](1440/previews/pattern_9.png) | ![pattern_10-1440](1440/previews/pattern_10.png) | ![pattern_11-1440](1440/previews/pattern_11.png) | ![pattern_12-1440](1440/previews/pattern_12.png) | ![pattern_13-1440](1440/previews/pattern_13.png) | ![pattern_14-1440](1440/previews/pattern_14.png) | ![pattern_15-1440](1440/previews/pattern_15.png) | ![bikini-1440](1440/previews/bikini.png) | [<NSFW, click to see>](1440/previews/bondage.png) | ![free-1440](1440/previews/free.png) | ![maid-1440](1440/previews/maid.png) | ![miko-1440](1440/previews/miko.png) | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) | ![suit-1440](1440/previews/suit.png) | ![yukata-1440](1440/previews/yukata.png) | | 960 | 0.941 | [Download](960/okusawa_misaki_bangdream.zip) | ![pattern_1-960](960/previews/pattern_1.png) | ![pattern_2-960](960/previews/pattern_2.png) | [<NSFW, click to see>](960/previews/pattern_3.png) | ![pattern_4-960](960/previews/pattern_4.png) | ![pattern_5-960](960/previews/pattern_5.png) | ![pattern_6-960](960/previews/pattern_6.png) | ![pattern_7-960](960/previews/pattern_7.png) | ![pattern_8-960](960/previews/pattern_8.png) | ![pattern_9-960](960/previews/pattern_9.png) | ![pattern_10-960](960/previews/pattern_10.png) | ![pattern_11-960](960/previews/pattern_11.png) | ![pattern_12-960](960/previews/pattern_12.png) | ![pattern_13-960](960/previews/pattern_13.png) | ![pattern_14-960](960/previews/pattern_14.png) | ![pattern_15-960](960/previews/pattern_15.png) | ![bikini-960](960/previews/bikini.png) | [<NSFW, click to see>](960/previews/bondage.png) | ![free-960](960/previews/free.png) | ![maid-960](960/previews/maid.png) | ![miko-960](960/previews/miko.png) | [<NSFW, click to see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) | ![suit-960](960/previews/suit.png) | ![yukata-960](960/previews/yukata.png) | | 480 | 0.715 | [Download](480/okusawa_misaki_bangdream.zip) | ![pattern_1-480](480/previews/pattern_1.png) | ![pattern_2-480](480/previews/pattern_2.png) | [<NSFW, click to see>](480/previews/pattern_3.png) | ![pattern_4-480](480/previews/pattern_4.png) | ![pattern_5-480](480/previews/pattern_5.png) | ![pattern_6-480](480/previews/pattern_6.png) | ![pattern_7-480](480/previews/pattern_7.png) | ![pattern_8-480](480/previews/pattern_8.png) | ![pattern_9-480](480/previews/pattern_9.png) | ![pattern_10-480](480/previews/pattern_10.png) | ![pattern_11-480](480/previews/pattern_11.png) | ![pattern_12-480](480/previews/pattern_12.png) | ![pattern_13-480](480/previews/pattern_13.png) | ![pattern_14-480](480/previews/pattern_14.png) | ![pattern_15-480](480/previews/pattern_15.png) | ![bikini-480](480/previews/bikini.png) | [<NSFW, click to see>](480/previews/bondage.png) | ![free-480](480/previews/free.png) | ![maid-480](480/previews/maid.png) | ![miko-480](480/previews/miko.png) | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) | ![suit-480](480/previews/suit.png) | ![yukata-480](480/previews/yukata.png) |
CyberHarem/zhong_lanzhu_lovelivenijigasakihighschoolidolclub
CyberHarem
2023-09-26T13:50:55Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/zhong_lanzhu_lovelivenijigasakihighschoolidolclub", "license:mit", "region:us" ]
text-to-image
2023-09-26T13:39:45Z
--- license: mit datasets: - CyberHarem/zhong_lanzhu_lovelivenijigasakihighschoolidolclub pipeline_tag: text-to-image tags: - art --- # Lora of zhong_lanzhu_lovelivenijigasakihighschoolidolclub This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8100, you need to download `8100/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.pt` as the embedding and `8100/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 8100**, with the score of 0.994. The trigger words are: 1. `zhong_lanzhu_lovelivenijigasakihighschoolidolclub` 2. `long_hair, pink_hair, blue_eyes, ahoge, bangs, mole, mole_under_eye, smile, breasts, sidelocks, blush, hair_bun, double_bun` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:---------------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **8100** | **0.994** | [**Download**](8100/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-8100](8100/previews/pattern_1.png) | ![pattern_2-8100](8100/previews/pattern_2.png) | ![pattern_3-8100](8100/previews/pattern_3.png) | ![pattern_4-8100](8100/previews/pattern_4.png) | ![pattern_5-8100](8100/previews/pattern_5.png) | ![bikini-8100](8100/previews/bikini.png) | [<NSFW, click to see>](8100/previews/bondage.png) | ![free-8100](8100/previews/free.png) | ![maid-8100](8100/previews/maid.png) | ![miko-8100](8100/previews/miko.png) | [<NSFW, click to see>](8100/previews/nude.png) | [<NSFW, click to see>](8100/previews/nude2.png) | ![suit-8100](8100/previews/suit.png) | ![yukata-8100](8100/previews/yukata.png) | | 7560 | 0.983 | [Download](7560/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-7560](7560/previews/pattern_1.png) | ![pattern_2-7560](7560/previews/pattern_2.png) | ![pattern_3-7560](7560/previews/pattern_3.png) | ![pattern_4-7560](7560/previews/pattern_4.png) | ![pattern_5-7560](7560/previews/pattern_5.png) | ![bikini-7560](7560/previews/bikini.png) | [<NSFW, click to see>](7560/previews/bondage.png) | ![free-7560](7560/previews/free.png) | ![maid-7560](7560/previews/maid.png) | ![miko-7560](7560/previews/miko.png) | [<NSFW, click to see>](7560/previews/nude.png) | [<NSFW, click to see>](7560/previews/nude2.png) | ![suit-7560](7560/previews/suit.png) | ![yukata-7560](7560/previews/yukata.png) | | 7020 | 0.981 | [Download](7020/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-7020](7020/previews/pattern_1.png) | ![pattern_2-7020](7020/previews/pattern_2.png) | ![pattern_3-7020](7020/previews/pattern_3.png) | ![pattern_4-7020](7020/previews/pattern_4.png) | ![pattern_5-7020](7020/previews/pattern_5.png) | ![bikini-7020](7020/previews/bikini.png) | [<NSFW, click to see>](7020/previews/bondage.png) | ![free-7020](7020/previews/free.png) | ![maid-7020](7020/previews/maid.png) | ![miko-7020](7020/previews/miko.png) | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) | ![suit-7020](7020/previews/suit.png) | ![yukata-7020](7020/previews/yukata.png) | | 6480 | 0.977 | [Download](6480/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-6480](6480/previews/pattern_1.png) | ![pattern_2-6480](6480/previews/pattern_2.png) | ![pattern_3-6480](6480/previews/pattern_3.png) | ![pattern_4-6480](6480/previews/pattern_4.png) | ![pattern_5-6480](6480/previews/pattern_5.png) | ![bikini-6480](6480/previews/bikini.png) | [<NSFW, click to see>](6480/previews/bondage.png) | ![free-6480](6480/previews/free.png) | ![maid-6480](6480/previews/maid.png) | ![miko-6480](6480/previews/miko.png) | [<NSFW, click to see>](6480/previews/nude.png) | [<NSFW, click to see>](6480/previews/nude2.png) | ![suit-6480](6480/previews/suit.png) | ![yukata-6480](6480/previews/yukata.png) | | 5940 | 0.970 | [Download](5940/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-5940](5940/previews/pattern_1.png) | ![pattern_2-5940](5940/previews/pattern_2.png) | ![pattern_3-5940](5940/previews/pattern_3.png) | ![pattern_4-5940](5940/previews/pattern_4.png) | ![pattern_5-5940](5940/previews/pattern_5.png) | ![bikini-5940](5940/previews/bikini.png) | [<NSFW, click to see>](5940/previews/bondage.png) | ![free-5940](5940/previews/free.png) | ![maid-5940](5940/previews/maid.png) | ![miko-5940](5940/previews/miko.png) | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) | ![suit-5940](5940/previews/suit.png) | ![yukata-5940](5940/previews/yukata.png) | | 5400 | 0.962 | [Download](5400/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | ![pattern_2-5400](5400/previews/pattern_2.png) | ![pattern_3-5400](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | ![pattern_5-5400](5400/previews/pattern_5.png) | ![bikini-5400](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | ![free-5400](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | 4860 | 0.965 | [Download](4860/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-4860](4860/previews/pattern_1.png) | ![pattern_2-4860](4860/previews/pattern_2.png) | ![pattern_3-4860](4860/previews/pattern_3.png) | ![pattern_4-4860](4860/previews/pattern_4.png) | ![pattern_5-4860](4860/previews/pattern_5.png) | ![bikini-4860](4860/previews/bikini.png) | [<NSFW, click to see>](4860/previews/bondage.png) | ![free-4860](4860/previews/free.png) | ![maid-4860](4860/previews/maid.png) | ![miko-4860](4860/previews/miko.png) | [<NSFW, click to see>](4860/previews/nude.png) | [<NSFW, click to see>](4860/previews/nude2.png) | ![suit-4860](4860/previews/suit.png) | ![yukata-4860](4860/previews/yukata.png) | | 4320 | 0.978 | [Download](4320/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | ![pattern_3-4320](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | ![bikini-4320](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3780 | 0.988 | [Download](3780/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-3780](3780/previews/pattern_1.png) | ![pattern_2-3780](3780/previews/pattern_2.png) | ![pattern_3-3780](3780/previews/pattern_3.png) | ![pattern_4-3780](3780/previews/pattern_4.png) | ![pattern_5-3780](3780/previews/pattern_5.png) | ![bikini-3780](3780/previews/bikini.png) | [<NSFW, click to see>](3780/previews/bondage.png) | ![free-3780](3780/previews/free.png) | ![maid-3780](3780/previews/maid.png) | ![miko-3780](3780/previews/miko.png) | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) | ![suit-3780](3780/previews/suit.png) | ![yukata-3780](3780/previews/yukata.png) | | 3240 | 0.991 | [Download](3240/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-3240](3240/previews/pattern_1.png) | ![pattern_2-3240](3240/previews/pattern_2.png) | ![pattern_3-3240](3240/previews/pattern_3.png) | ![pattern_4-3240](3240/previews/pattern_4.png) | ![pattern_5-3240](3240/previews/pattern_5.png) | ![bikini-3240](3240/previews/bikini.png) | [<NSFW, click to see>](3240/previews/bondage.png) | ![free-3240](3240/previews/free.png) | ![maid-3240](3240/previews/maid.png) | ![miko-3240](3240/previews/miko.png) | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) | ![suit-3240](3240/previews/suit.png) | ![yukata-3240](3240/previews/yukata.png) | | 2700 | 0.990 | [Download](2700/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-2700](2700/previews/pattern_1.png) | ![pattern_2-2700](2700/previews/pattern_2.png) | ![pattern_3-2700](2700/previews/pattern_3.png) | ![pattern_4-2700](2700/previews/pattern_4.png) | ![pattern_5-2700](2700/previews/pattern_5.png) | ![bikini-2700](2700/previews/bikini.png) | [<NSFW, click to see>](2700/previews/bondage.png) | ![free-2700](2700/previews/free.png) | ![maid-2700](2700/previews/maid.png) | ![miko-2700](2700/previews/miko.png) | [<NSFW, click to see>](2700/previews/nude.png) | [<NSFW, click to see>](2700/previews/nude2.png) | ![suit-2700](2700/previews/suit.png) | ![yukata-2700](2700/previews/yukata.png) | | 2160 | 0.981 | [Download](2160/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-2160](2160/previews/pattern_1.png) | ![pattern_2-2160](2160/previews/pattern_2.png) | ![pattern_3-2160](2160/previews/pattern_3.png) | ![pattern_4-2160](2160/previews/pattern_4.png) | ![pattern_5-2160](2160/previews/pattern_5.png) | ![bikini-2160](2160/previews/bikini.png) | [<NSFW, click to see>](2160/previews/bondage.png) | ![free-2160](2160/previews/free.png) | ![maid-2160](2160/previews/maid.png) | ![miko-2160](2160/previews/miko.png) | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) | ![suit-2160](2160/previews/suit.png) | ![yukata-2160](2160/previews/yukata.png) | | 1620 | 0.955 | [Download](1620/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-1620](1620/previews/pattern_1.png) | ![pattern_2-1620](1620/previews/pattern_2.png) | ![pattern_3-1620](1620/previews/pattern_3.png) | ![pattern_4-1620](1620/previews/pattern_4.png) | ![pattern_5-1620](1620/previews/pattern_5.png) | ![bikini-1620](1620/previews/bikini.png) | [<NSFW, click to see>](1620/previews/bondage.png) | ![free-1620](1620/previews/free.png) | ![maid-1620](1620/previews/maid.png) | ![miko-1620](1620/previews/miko.png) | [<NSFW, click to see>](1620/previews/nude.png) | [<NSFW, click to see>](1620/previews/nude2.png) | ![suit-1620](1620/previews/suit.png) | ![yukata-1620](1620/previews/yukata.png) | | 1080 | 0.940 | [Download](1080/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-1080](1080/previews/pattern_1.png) | ![pattern_2-1080](1080/previews/pattern_2.png) | ![pattern_3-1080](1080/previews/pattern_3.png) | ![pattern_4-1080](1080/previews/pattern_4.png) | ![pattern_5-1080](1080/previews/pattern_5.png) | ![bikini-1080](1080/previews/bikini.png) | [<NSFW, click to see>](1080/previews/bondage.png) | ![free-1080](1080/previews/free.png) | ![maid-1080](1080/previews/maid.png) | ![miko-1080](1080/previews/miko.png) | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) | ![suit-1080](1080/previews/suit.png) | ![yukata-1080](1080/previews/yukata.png) | | 540 | 0.830 | [Download](540/zhong_lanzhu_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-540](540/previews/pattern_1.png) | ![pattern_2-540](540/previews/pattern_2.png) | ![pattern_3-540](540/previews/pattern_3.png) | ![pattern_4-540](540/previews/pattern_4.png) | ![pattern_5-540](540/previews/pattern_5.png) | ![bikini-540](540/previews/bikini.png) | [<NSFW, click to see>](540/previews/bondage.png) | ![free-540](540/previews/free.png) | ![maid-540](540/previews/maid.png) | ![miko-540](540/previews/miko.png) | [<NSFW, click to see>](540/previews/nude.png) | [<NSFW, click to see>](540/previews/nude2.png) | ![suit-540](540/previews/suit.png) | ![yukata-540](540/previews/yukata.png) |
takumi12/id2pg_pattern2_triple_epoch40
takumi12
2023-09-26T13:45:39Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-26T13:45:32Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 0.6.0.dev0 - PEFT 0.6.0.dev0
HoangCuongNguyen/Flan-T5-finetuned-cti2
HoangCuongNguyen
2023-09-26T13:38:29Z
107
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-22T00:45:11Z
--- language: - en pipeline_tag: text2text-generation license: mit ---
tomaarsen/span-marker-bert-base-fewnerd-fine-super
tomaarsen
2023-09-26T13:33:51Z
545
12
span-marker
[ "span-marker", "pytorch", "safetensors", "token-classification", "ner", "named-entity-recognition", "generated_from_span_marker_trainer", "en", "dataset:DFKI-SLT/few-nerd", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:cc-by-sa-4.0", "model-index", "region:us" ]
token-classification
2023-03-31T07:28:50Z
--- language: - en license: cc-by-sa-4.0 library_name: span-marker tags: - span-marker - token-classification - ner - named-entity-recognition - generated_from_span_marker_trainer datasets: - DFKI-SLT/few-nerd metrics: - f1 - recall - precision pipeline_tag: token-classification widget: - text: Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris. example_title: Amelia Earhart - text: Leonardo di ser Piero da Vinci painted the Mona Lisa based on Italian noblewoman Lisa del Giocondo. example_title: Leonardo da Vinci base_model: bert-base-cased model-index: - name: SpanMarker w. bert-base-cased on finegrained, supervised FewNERD by Tom Aarsen results: - task: type: token-classification name: Named Entity Recognition dataset: name: finegrained, supervised FewNERD type: DFKI-SLT/few-nerd config: supervised split: test revision: 2e3e727c63604fbfa2ff4cc5055359c84fe5ef2c metrics: - type: f1 value: 0.7053 name: F1 - type: precision value: 0.7101 name: Precision - type: recall value: 0.7005 name: Recall --- # SpanMarker with bert-base-cased on FewNERD This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. ## Model Details ### Model Description - **Model Type:** SpanMarker - **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased) - **Maximum Sequence Length:** 256 tokens - **Maximum Entity Length:** 8 words - **Training Dataset:** [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) - **Language:** en - **License:** cc-by-sa-4.0 ### Model Sources - **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER) - **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf) ### Model Labels | Label | Examples | |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------| | art-broadcastprogram | "Street Cents", "Corazones", "The Gale Storm Show : Oh , Susanna" | | art-film | "Bosch", "L'Atlantide", "Shawshank Redemption" | | art-music | "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover", "Hollywood Studio Symphony" | | art-other | "Aphrodite of Milos", "Venus de Milo", "The Today Show" | | art-painting | "Production/Reproduction", "Touit", "Cofiwch Dryweryn" | | art-writtenart | "Imelda de ' Lambertazzi", "Time", "The Seven Year Itch" | | building-airport | "Luton Airport", "Newark Liberty International Airport", "Sheremetyevo International Airport" | | building-hospital | "Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center" | | building-hotel | "The Standard Hotel", "Radisson Blu Sea Plaza Hotel", "Flamingo Hotel" | | building-library | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek" | | building-other | "Communiplex", "Alpha Recording Studios", "Henry Ford Museum" | | building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" | | building-sportsfacility | "Glenn Warner Soccer Facility", "Boston Garden", "Sports Center" | | building-theater | "Pittsburgh Civic Light Opera", "Sanders Theatre", "National Paris Opera" | | event-attack/battle/war/militaryconflict | "Easter Offensive", "Vietnam War", "Jurist" | | event-disaster | "the 1912 North Mount Lyell Disaster", "1693 Sicily earthquake", "1990s North Korean famine" | | event-election | "March 1898 elections", "1982 Mitcham and Morden by-election", "Elections to the European Parliament" | | event-other | "Eastwood Scoring Stage", "Union for a Popular Movement", "Masaryk Democratic Movement" | | event-protest | "French Revolution", "Russian Revolution", "Iranian Constitutional Revolution" | | event-sportsevent | "National Champions", "World Cup", "Stanley Cup" | | location-GPE | "Mediterranean Basin", "the Republic of Croatia", "Croatian" | | location-bodiesofwater | "Atatürk Dam Lake", "Norfolk coast", "Arthur Kill" | | location-island | "Laccadives", "Staten Island", "new Samsat district" | | location-mountain | "Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge" | | location-other | "Northern City Line", "Victoria line", "Cartuther" | | location-park | "Gramercy Park", "Painted Desert Community Complex Historic District", "Shenandoah National Park" | | location-road/railway/highway/transit | "Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT" | | organization-company | "Dixy Chicken", "Texas Chicken", "Church 's Chicken" | | organization-education | "MIT", "Belfast Royal Academy and the Ulster College of Physical Education", "Barnard College" | | organization-government/governmentagency | "Congregazione dei Nobili", "Diet", "Supreme Court" | | organization-media/newspaper | "TimeOut Melbourne", "Clash", "Al Jazeera" | | organization-other | "Defence Sector C", "IAEA", "4th Army" | | organization-politicalparty | "Shimpotō", "Al Wafa ' Islamic", "Kenseitō" | | organization-religion | "Jewish", "Christian", "UPCUSA" | | organization-showorganization | "Lizzy", "Bochumer Symphoniker", "Mr. Mister" | | organization-sportsleague | "China League One", "First Division", "NHL" | | organization-sportsteam | "Tottenham", "Arsenal", "Luc Alphand Aventures" | | other-astronomything | "Zodiac", "Algol", "`` Caput Larvae ''" | | other-award | "GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger" | | other-biologything | "N-terminal lipid", "BAR", "Amphiphysin" | | other-chemicalthing | "uranium", "carbon dioxide", "sulfur" | | other-currency | "$", "Travancore Rupee", "lac crore" | | other-disease | "French Dysentery Epidemic of 1779", "hypothyroidism", "bladder cancer" | | other-educationaldegree | "Master", "Bachelor", "BSc ( Hons ) in physics" | | other-god | "El", "Fujin", "Raijin" | | other-language | "Breton-speaking", "English", "Latin" | | other-law | "Thirty Years ' Peace", "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act" | | other-livingthing | "insects", "monkeys", "patchouli" | | other-medical | "Pediatrics", "amitriptyline", "pediatrician" | | person-actor | "Ellaline Terriss", "Tchéky Karyo", "Edmund Payne" | | person-artist/author | "George Axelrod", "Gaetano Donizett", "Hicks" | | person-athlete | "Jaguar", "Neville", "Tozawa" | | person-director | "Bob Swaim", "Richard Quine", "Frank Darabont" | | person-other | "Richard Benson", "Holden", "Campbell" | | person-politician | "William", "Rivière", "Emeric" | | person-scholar | "Stedman", "Wurdack", "Stalmine" | | person-soldier | "Helmuth Weidling", "Krukenberg", "Joachim Ziegler" | | product-airplane | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS" | | product-car | "100EX", "Corvettes - GT1 C6R", "Phantom" | | product-food | "red grape", "yakiniku", "V. labrusca" | | product-game | "Airforce Delta", "Hardcore RPG", "Splinter Cell" | | product-other | "Fairbottom Bobs", "X11", "PDP-1" | | product-ship | "Congress", "Essex", "HMS `` Chinkara ''" | | product-software | "AmiPDF", "Apdf", "Wikipedia" | | product-train | "High Speed Trains", "55022", "Royal Scots Grey" | | product-weapon | "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II" | ## Uses ### Direct Use ```python from span_marker import SpanMarkerModel # Download from the 🤗 Hub model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super") # Run inference entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") ``` ### Downstream Use You can finetune this model on your own dataset. <details><summary>Click to expand</summary> ```python from span_marker import SpanMarkerModel, Trainer # Download from the 🤗 Hub model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super") # Specify a Dataset with "tokens" and "ner_tag" columns dataset = load_dataset("conll2003") # For example CoNLL2003 # Initialize a Trainer using the pretrained model & dataset trainer = Trainer( model=model, train_dataset=dataset["train"], eval_dataset=dataset["validation"], ) trainer.train() trainer.save_model("tomaarsen/span-marker-bert-base-fewnerd-fine-super-finetuned") ``` </details> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:----------------------|:----|:--------|:----| | Sentence length | 1 | 24.4945 | 267 | | Entities per sentence | 0 | 2.5832 | 88 | ### Training Hyperparameters - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.9.16 - SpanMarker: 1.3.1.dev - Transformers : 4.29.2 - PyTorch: 2.0.1+cu118 - Datasets: 2.14.3 - Tokenizers: 0.13.2
barberry-nut/wing_damselfly
barberry-nut
2023-09-26T13:30:19Z
0
0
null
[ "en", "license:ecl-2.0", "region:us" ]
null
2023-09-26T13:06:06Z
--- license: ecl-2.0 language: - en --- The detectron2 model for recognizing damselfly wings for standard and perching photos
luisgasco/setfit-sentence-classifier_test
luisgasco
2023-09-26T13:22:26Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-09-26T11:21:24Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # luisgasco/setfit-sentence-classifier_test 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("luisgasco/setfit-sentence-classifier_test") # 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} } ```
trieudemo11/llama_7b_attrb_cate_4m_18
trieudemo11
2023-09-26T13:20:32Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-26T13:20:15Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
danielpleus/PlattGPT-LLama2ChatHF
danielpleus
2023-09-26T13:16:21Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-26T13:16:18Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0
BUDDYB/we
BUDDYB
2023-09-26T13:05:47Z
0
1
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-09-26T13:05:47Z
--- license: bigscience-openrail-m ---
CyberHarem/hanazono_tae_bangdream
CyberHarem
2023-09-26T12:55:05Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/hanazono_tae_bangdream", "license:mit", "region:us" ]
text-to-image
2023-08-14T14:53:17Z
--- license: mit datasets: - CyberHarem/hanazono_tae_bangdream pipeline_tag: text-to-image tags: - art --- # Lora of hanazono_tae_bangdream This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2640, you need to download `2640/hanazono_tae_bangdream.pt` as the embedding and `2640/hanazono_tae_bangdream.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 2640**, with the score of 0.890. The trigger words are: 1. `hanazono_tae_bangdream` 2. `long_hair, green_eyes, bangs, smile, blush, black_hair, brown_hair, hair_between_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 6600 | 0.865 | [Download](6600/hanazono_tae_bangdream.zip) | ![pattern_1-6600](6600/previews/pattern_1.png) | ![pattern_2-6600](6600/previews/pattern_2.png) | ![pattern_3-6600](6600/previews/pattern_3.png) | ![pattern_4-6600](6600/previews/pattern_4.png) | ![pattern_5-6600](6600/previews/pattern_5.png) | ![pattern_6-6600](6600/previews/pattern_6.png) | ![pattern_7-6600](6600/previews/pattern_7.png) | ![pattern_8-6600](6600/previews/pattern_8.png) | ![pattern_9-6600](6600/previews/pattern_9.png) | ![pattern_10-6600](6600/previews/pattern_10.png) | ![pattern_11-6600](6600/previews/pattern_11.png) | ![pattern_12-6600](6600/previews/pattern_12.png) | ![pattern_13-6600](6600/previews/pattern_13.png) | ![bikini-6600](6600/previews/bikini.png) | [<NSFW, click to see>](6600/previews/bondage.png) | ![free-6600](6600/previews/free.png) | ![maid-6600](6600/previews/maid.png) | ![miko-6600](6600/previews/miko.png) | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) | ![suit-6600](6600/previews/suit.png) | ![yukata-6600](6600/previews/yukata.png) | | 6160 | 0.829 | [Download](6160/hanazono_tae_bangdream.zip) | ![pattern_1-6160](6160/previews/pattern_1.png) | ![pattern_2-6160](6160/previews/pattern_2.png) | ![pattern_3-6160](6160/previews/pattern_3.png) | ![pattern_4-6160](6160/previews/pattern_4.png) | ![pattern_5-6160](6160/previews/pattern_5.png) | ![pattern_6-6160](6160/previews/pattern_6.png) | ![pattern_7-6160](6160/previews/pattern_7.png) | ![pattern_8-6160](6160/previews/pattern_8.png) | ![pattern_9-6160](6160/previews/pattern_9.png) | ![pattern_10-6160](6160/previews/pattern_10.png) | ![pattern_11-6160](6160/previews/pattern_11.png) | ![pattern_12-6160](6160/previews/pattern_12.png) | ![pattern_13-6160](6160/previews/pattern_13.png) | ![bikini-6160](6160/previews/bikini.png) | [<NSFW, click to see>](6160/previews/bondage.png) | ![free-6160](6160/previews/free.png) | ![maid-6160](6160/previews/maid.png) | ![miko-6160](6160/previews/miko.png) | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) | ![suit-6160](6160/previews/suit.png) | ![yukata-6160](6160/previews/yukata.png) | | 5720 | 0.875 | [Download](5720/hanazono_tae_bangdream.zip) | ![pattern_1-5720](5720/previews/pattern_1.png) | ![pattern_2-5720](5720/previews/pattern_2.png) | ![pattern_3-5720](5720/previews/pattern_3.png) | ![pattern_4-5720](5720/previews/pattern_4.png) | ![pattern_5-5720](5720/previews/pattern_5.png) | ![pattern_6-5720](5720/previews/pattern_6.png) | ![pattern_7-5720](5720/previews/pattern_7.png) | ![pattern_8-5720](5720/previews/pattern_8.png) | ![pattern_9-5720](5720/previews/pattern_9.png) | ![pattern_10-5720](5720/previews/pattern_10.png) | ![pattern_11-5720](5720/previews/pattern_11.png) | ![pattern_12-5720](5720/previews/pattern_12.png) | ![pattern_13-5720](5720/previews/pattern_13.png) | ![bikini-5720](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) | ![free-5720](5720/previews/free.png) | ![maid-5720](5720/previews/maid.png) | ![miko-5720](5720/previews/miko.png) | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) | ![suit-5720](5720/previews/suit.png) | ![yukata-5720](5720/previews/yukata.png) | | 5280 | 0.875 | [Download](5280/hanazono_tae_bangdream.zip) | ![pattern_1-5280](5280/previews/pattern_1.png) | ![pattern_2-5280](5280/previews/pattern_2.png) | ![pattern_3-5280](5280/previews/pattern_3.png) | ![pattern_4-5280](5280/previews/pattern_4.png) | ![pattern_5-5280](5280/previews/pattern_5.png) | ![pattern_6-5280](5280/previews/pattern_6.png) | ![pattern_7-5280](5280/previews/pattern_7.png) | ![pattern_8-5280](5280/previews/pattern_8.png) | ![pattern_9-5280](5280/previews/pattern_9.png) | ![pattern_10-5280](5280/previews/pattern_10.png) | ![pattern_11-5280](5280/previews/pattern_11.png) | ![pattern_12-5280](5280/previews/pattern_12.png) | ![pattern_13-5280](5280/previews/pattern_13.png) | ![bikini-5280](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) | ![free-5280](5280/previews/free.png) | ![maid-5280](5280/previews/maid.png) | ![miko-5280](5280/previews/miko.png) | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) | ![suit-5280](5280/previews/suit.png) | ![yukata-5280](5280/previews/yukata.png) | | 4840 | 0.877 | [Download](4840/hanazono_tae_bangdream.zip) | ![pattern_1-4840](4840/previews/pattern_1.png) | ![pattern_2-4840](4840/previews/pattern_2.png) | ![pattern_3-4840](4840/previews/pattern_3.png) | ![pattern_4-4840](4840/previews/pattern_4.png) | ![pattern_5-4840](4840/previews/pattern_5.png) | ![pattern_6-4840](4840/previews/pattern_6.png) | ![pattern_7-4840](4840/previews/pattern_7.png) | ![pattern_8-4840](4840/previews/pattern_8.png) | ![pattern_9-4840](4840/previews/pattern_9.png) | ![pattern_10-4840](4840/previews/pattern_10.png) | ![pattern_11-4840](4840/previews/pattern_11.png) | ![pattern_12-4840](4840/previews/pattern_12.png) | ![pattern_13-4840](4840/previews/pattern_13.png) | ![bikini-4840](4840/previews/bikini.png) | [<NSFW, click to see>](4840/previews/bondage.png) | ![free-4840](4840/previews/free.png) | ![maid-4840](4840/previews/maid.png) | ![miko-4840](4840/previews/miko.png) | [<NSFW, click to see>](4840/previews/nude.png) | [<NSFW, click to see>](4840/previews/nude2.png) | ![suit-4840](4840/previews/suit.png) | ![yukata-4840](4840/previews/yukata.png) | | 4400 | 0.876 | [Download](4400/hanazono_tae_bangdream.zip) | ![pattern_1-4400](4400/previews/pattern_1.png) | ![pattern_2-4400](4400/previews/pattern_2.png) | ![pattern_3-4400](4400/previews/pattern_3.png) | ![pattern_4-4400](4400/previews/pattern_4.png) | ![pattern_5-4400](4400/previews/pattern_5.png) | ![pattern_6-4400](4400/previews/pattern_6.png) | ![pattern_7-4400](4400/previews/pattern_7.png) | ![pattern_8-4400](4400/previews/pattern_8.png) | ![pattern_9-4400](4400/previews/pattern_9.png) | ![pattern_10-4400](4400/previews/pattern_10.png) | ![pattern_11-4400](4400/previews/pattern_11.png) | ![pattern_12-4400](4400/previews/pattern_12.png) | ![pattern_13-4400](4400/previews/pattern_13.png) | ![bikini-4400](4400/previews/bikini.png) | [<NSFW, click to see>](4400/previews/bondage.png) | ![free-4400](4400/previews/free.png) | ![maid-4400](4400/previews/maid.png) | ![miko-4400](4400/previews/miko.png) | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) | ![suit-4400](4400/previews/suit.png) | ![yukata-4400](4400/previews/yukata.png) | | 3960 | 0.811 | [Download](3960/hanazono_tae_bangdream.zip) | ![pattern_1-3960](3960/previews/pattern_1.png) | ![pattern_2-3960](3960/previews/pattern_2.png) | ![pattern_3-3960](3960/previews/pattern_3.png) | ![pattern_4-3960](3960/previews/pattern_4.png) | ![pattern_5-3960](3960/previews/pattern_5.png) | ![pattern_6-3960](3960/previews/pattern_6.png) | ![pattern_7-3960](3960/previews/pattern_7.png) | ![pattern_8-3960](3960/previews/pattern_8.png) | ![pattern_9-3960](3960/previews/pattern_9.png) | ![pattern_10-3960](3960/previews/pattern_10.png) | ![pattern_11-3960](3960/previews/pattern_11.png) | ![pattern_12-3960](3960/previews/pattern_12.png) | ![pattern_13-3960](3960/previews/pattern_13.png) | ![bikini-3960](3960/previews/bikini.png) | [<NSFW, click to see>](3960/previews/bondage.png) | ![free-3960](3960/previews/free.png) | ![maid-3960](3960/previews/maid.png) | ![miko-3960](3960/previews/miko.png) | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) | ![suit-3960](3960/previews/suit.png) | ![yukata-3960](3960/previews/yukata.png) | | 3520 | 0.863 | [Download](3520/hanazono_tae_bangdream.zip) | ![pattern_1-3520](3520/previews/pattern_1.png) | ![pattern_2-3520](3520/previews/pattern_2.png) | ![pattern_3-3520](3520/previews/pattern_3.png) | ![pattern_4-3520](3520/previews/pattern_4.png) | ![pattern_5-3520](3520/previews/pattern_5.png) | ![pattern_6-3520](3520/previews/pattern_6.png) | ![pattern_7-3520](3520/previews/pattern_7.png) | ![pattern_8-3520](3520/previews/pattern_8.png) | ![pattern_9-3520](3520/previews/pattern_9.png) | ![pattern_10-3520](3520/previews/pattern_10.png) | ![pattern_11-3520](3520/previews/pattern_11.png) | ![pattern_12-3520](3520/previews/pattern_12.png) | ![pattern_13-3520](3520/previews/pattern_13.png) | ![bikini-3520](3520/previews/bikini.png) | [<NSFW, click to see>](3520/previews/bondage.png) | ![free-3520](3520/previews/free.png) | ![maid-3520](3520/previews/maid.png) | ![miko-3520](3520/previews/miko.png) | [<NSFW, click to see>](3520/previews/nude.png) | [<NSFW, click to see>](3520/previews/nude2.png) | ![suit-3520](3520/previews/suit.png) | ![yukata-3520](3520/previews/yukata.png) | | 3080 | 0.838 | [Download](3080/hanazono_tae_bangdream.zip) | ![pattern_1-3080](3080/previews/pattern_1.png) | ![pattern_2-3080](3080/previews/pattern_2.png) | ![pattern_3-3080](3080/previews/pattern_3.png) | ![pattern_4-3080](3080/previews/pattern_4.png) | ![pattern_5-3080](3080/previews/pattern_5.png) | ![pattern_6-3080](3080/previews/pattern_6.png) | ![pattern_7-3080](3080/previews/pattern_7.png) | ![pattern_8-3080](3080/previews/pattern_8.png) | ![pattern_9-3080](3080/previews/pattern_9.png) | ![pattern_10-3080](3080/previews/pattern_10.png) | ![pattern_11-3080](3080/previews/pattern_11.png) | ![pattern_12-3080](3080/previews/pattern_12.png) | ![pattern_13-3080](3080/previews/pattern_13.png) | ![bikini-3080](3080/previews/bikini.png) | [<NSFW, click to see>](3080/previews/bondage.png) | ![free-3080](3080/previews/free.png) | ![maid-3080](3080/previews/maid.png) | ![miko-3080](3080/previews/miko.png) | [<NSFW, click to see>](3080/previews/nude.png) | [<NSFW, click to see>](3080/previews/nude2.png) | ![suit-3080](3080/previews/suit.png) | ![yukata-3080](3080/previews/yukata.png) | | **2640** | **0.890** | [**Download**](2640/hanazono_tae_bangdream.zip) | ![pattern_1-2640](2640/previews/pattern_1.png) | ![pattern_2-2640](2640/previews/pattern_2.png) | ![pattern_3-2640](2640/previews/pattern_3.png) | ![pattern_4-2640](2640/previews/pattern_4.png) | ![pattern_5-2640](2640/previews/pattern_5.png) | ![pattern_6-2640](2640/previews/pattern_6.png) | ![pattern_7-2640](2640/previews/pattern_7.png) | ![pattern_8-2640](2640/previews/pattern_8.png) | ![pattern_9-2640](2640/previews/pattern_9.png) | ![pattern_10-2640](2640/previews/pattern_10.png) | ![pattern_11-2640](2640/previews/pattern_11.png) | ![pattern_12-2640](2640/previews/pattern_12.png) | ![pattern_13-2640](2640/previews/pattern_13.png) | ![bikini-2640](2640/previews/bikini.png) | [<NSFW, click to see>](2640/previews/bondage.png) | ![free-2640](2640/previews/free.png) | ![maid-2640](2640/previews/maid.png) | ![miko-2640](2640/previews/miko.png) | [<NSFW, click to see>](2640/previews/nude.png) | [<NSFW, click to see>](2640/previews/nude2.png) | ![suit-2640](2640/previews/suit.png) | ![yukata-2640](2640/previews/yukata.png) | | 2200 | 0.892 | [Download](2200/hanazono_tae_bangdream.zip) | ![pattern_1-2200](2200/previews/pattern_1.png) | ![pattern_2-2200](2200/previews/pattern_2.png) | ![pattern_3-2200](2200/previews/pattern_3.png) | ![pattern_4-2200](2200/previews/pattern_4.png) | ![pattern_5-2200](2200/previews/pattern_5.png) | ![pattern_6-2200](2200/previews/pattern_6.png) | ![pattern_7-2200](2200/previews/pattern_7.png) | ![pattern_8-2200](2200/previews/pattern_8.png) | ![pattern_9-2200](2200/previews/pattern_9.png) | ![pattern_10-2200](2200/previews/pattern_10.png) | ![pattern_11-2200](2200/previews/pattern_11.png) | ![pattern_12-2200](2200/previews/pattern_12.png) | ![pattern_13-2200](2200/previews/pattern_13.png) | ![bikini-2200](2200/previews/bikini.png) | [<NSFW, click to see>](2200/previews/bondage.png) | ![free-2200](2200/previews/free.png) | ![maid-2200](2200/previews/maid.png) | ![miko-2200](2200/previews/miko.png) | [<NSFW, click to see>](2200/previews/nude.png) | [<NSFW, click to see>](2200/previews/nude2.png) | ![suit-2200](2200/previews/suit.png) | ![yukata-2200](2200/previews/yukata.png) | | 1760 | 0.871 | [Download](1760/hanazono_tae_bangdream.zip) | ![pattern_1-1760](1760/previews/pattern_1.png) | ![pattern_2-1760](1760/previews/pattern_2.png) | ![pattern_3-1760](1760/previews/pattern_3.png) | ![pattern_4-1760](1760/previews/pattern_4.png) | ![pattern_5-1760](1760/previews/pattern_5.png) | ![pattern_6-1760](1760/previews/pattern_6.png) | ![pattern_7-1760](1760/previews/pattern_7.png) | ![pattern_8-1760](1760/previews/pattern_8.png) | ![pattern_9-1760](1760/previews/pattern_9.png) | ![pattern_10-1760](1760/previews/pattern_10.png) | ![pattern_11-1760](1760/previews/pattern_11.png) | ![pattern_12-1760](1760/previews/pattern_12.png) | ![pattern_13-1760](1760/previews/pattern_13.png) | ![bikini-1760](1760/previews/bikini.png) | [<NSFW, click to see>](1760/previews/bondage.png) | ![free-1760](1760/previews/free.png) | ![maid-1760](1760/previews/maid.png) | ![miko-1760](1760/previews/miko.png) | [<NSFW, click to see>](1760/previews/nude.png) | [<NSFW, click to see>](1760/previews/nude2.png) | ![suit-1760](1760/previews/suit.png) | ![yukata-1760](1760/previews/yukata.png) | | 1320 | 0.820 | [Download](1320/hanazono_tae_bangdream.zip) | ![pattern_1-1320](1320/previews/pattern_1.png) | ![pattern_2-1320](1320/previews/pattern_2.png) | ![pattern_3-1320](1320/previews/pattern_3.png) | ![pattern_4-1320](1320/previews/pattern_4.png) | ![pattern_5-1320](1320/previews/pattern_5.png) | ![pattern_6-1320](1320/previews/pattern_6.png) | ![pattern_7-1320](1320/previews/pattern_7.png) | ![pattern_8-1320](1320/previews/pattern_8.png) | ![pattern_9-1320](1320/previews/pattern_9.png) | ![pattern_10-1320](1320/previews/pattern_10.png) | ![pattern_11-1320](1320/previews/pattern_11.png) | ![pattern_12-1320](1320/previews/pattern_12.png) | ![pattern_13-1320](1320/previews/pattern_13.png) | ![bikini-1320](1320/previews/bikini.png) | [<NSFW, click to see>](1320/previews/bondage.png) | ![free-1320](1320/previews/free.png) | ![maid-1320](1320/previews/maid.png) | ![miko-1320](1320/previews/miko.png) | [<NSFW, click to see>](1320/previews/nude.png) | [<NSFW, click to see>](1320/previews/nude2.png) | ![suit-1320](1320/previews/suit.png) | ![yukata-1320](1320/previews/yukata.png) | | 880 | 0.820 | [Download](880/hanazono_tae_bangdream.zip) | ![pattern_1-880](880/previews/pattern_1.png) | ![pattern_2-880](880/previews/pattern_2.png) | ![pattern_3-880](880/previews/pattern_3.png) | ![pattern_4-880](880/previews/pattern_4.png) | ![pattern_5-880](880/previews/pattern_5.png) | ![pattern_6-880](880/previews/pattern_6.png) | ![pattern_7-880](880/previews/pattern_7.png) | ![pattern_8-880](880/previews/pattern_8.png) | ![pattern_9-880](880/previews/pattern_9.png) | ![pattern_10-880](880/previews/pattern_10.png) | ![pattern_11-880](880/previews/pattern_11.png) | ![pattern_12-880](880/previews/pattern_12.png) | ![pattern_13-880](880/previews/pattern_13.png) | ![bikini-880](880/previews/bikini.png) | [<NSFW, click to see>](880/previews/bondage.png) | ![free-880](880/previews/free.png) | ![maid-880](880/previews/maid.png) | ![miko-880](880/previews/miko.png) | [<NSFW, click to see>](880/previews/nude.png) | [<NSFW, click to see>](880/previews/nude2.png) | ![suit-880](880/previews/suit.png) | ![yukata-880](880/previews/yukata.png) | | 440 | 0.791 | [Download](440/hanazono_tae_bangdream.zip) | ![pattern_1-440](440/previews/pattern_1.png) | ![pattern_2-440](440/previews/pattern_2.png) | ![pattern_3-440](440/previews/pattern_3.png) | ![pattern_4-440](440/previews/pattern_4.png) | ![pattern_5-440](440/previews/pattern_5.png) | ![pattern_6-440](440/previews/pattern_6.png) | ![pattern_7-440](440/previews/pattern_7.png) | ![pattern_8-440](440/previews/pattern_8.png) | ![pattern_9-440](440/previews/pattern_9.png) | ![pattern_10-440](440/previews/pattern_10.png) | ![pattern_11-440](440/previews/pattern_11.png) | ![pattern_12-440](440/previews/pattern_12.png) | ![pattern_13-440](440/previews/pattern_13.png) | ![bikini-440](440/previews/bikini.png) | [<NSFW, click to see>](440/previews/bondage.png) | ![free-440](440/previews/free.png) | ![maid-440](440/previews/maid.png) | ![miko-440](440/previews/miko.png) | [<NSFW, click to see>](440/previews/nude.png) | [<NSFW, click to see>](440/previews/nude2.png) | ![suit-440](440/previews/suit.png) | ![yukata-440](440/previews/yukata.png) |
fsarab/ppo-LunarLander-v2
fsarab
2023-09-26T12:55:00Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T12:54:33Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.98 +/- 19.51 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jh1517/taxi_q_learning
jh1517
2023-09-26T12:36:46Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-09-26T12:36:06Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_q_learning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.74 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="jh1517/taxi_q_learning", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
aolei/llm-chatglm2-ft
aolei
2023-09-26T12:31:30Z
6
0
transformers
[ "transformers", "pytorch", "qwen", "feature-extraction", "custom_code", "region:us" ]
feature-extraction
2023-09-20T05:53:38Z
from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aolei/llm-chatglm2-ft", trust_remote_code=True) tokenizer.padding_side='left' model = AutoModel.from_pretrained("LLaMA-Efficient-Tuning/t1_export", trust_remote_code=True).half().cuda() model = model.eval() response, history = model.chat(tokenizer, "给我一个折线图", history=[]) print(response, history)
milaidy/danielll
milaidy
2023-09-26T12:24:24Z
2
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-09-26T12:20:30Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### danielll Dreambooth model trained by milaidy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
lothritz/Lb_mBERT
lothritz
2023-09-26T12:02:35Z
162
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-07T09:08:52Z
# Lb_mBERT Lb_mBERT is a BERT-like language model for the Luxembourgish language. We used the weights of the multilingual BERT (mBERT) language model as a starting point and continued pre-training it on the MLM task using the same corpus that we used for our LuxemBERT model (https://huggingface.co/lothritz/LuxemBERT). We achieved higher performances on some downstream tasks than the original LuxemBERT, and another Luxembourgish BERT model called DA BERT (https://huggingface.co/iolariu/DA_BERT). If you would like to know more about our work, the pre-training corpus, or use our models or datasets, please check out/cite the following papers: ``` @inproceedings{lothritz-etal-2022-luxembert, title = "{L}uxem{BERT}: Simple and Practical Data Augmentation in Language Model Pre-Training for {L}uxembourgish", author = "Lothritz, Cedric and Lebichot, Bertrand and Allix, Kevin and Veiber, Lisa and Bissyande, Tegawende and Klein, Jacques and Boytsov, Andrey and Lefebvre, Cl{\'e}ment and Goujon, Anne", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.543", pages = "5080--5089", abstract = "Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks. While these models are readily available for English and other widely spoken languages, they remain scarce for low-resource languages such as Luxembourgish. In this paper, we present LuxemBERT, a BERT model for the Luxembourgish language that we create using the following approach: we augment the pre-training dataset by considering text data from a closely related language that we partially translate using a simple and straightforward method. We are then able to produce the LuxemBERT model, which we show to be effective for various NLP tasks: it outperforms a simple baseline built with the available Luxembourgish text data as well the multilingual mBERT model, which is currently the only option for transformer-based language models in Luxembourgish. Furthermore, we present datasets for various downstream NLP tasks that we created for this study and will make available to researchers on request.", } ``` ``` @inproceedings{lothritz2023comparing, title={Comparing Pre-Training Schemes for Luxembourgish BERT Models}, author={Lothritz, Cedric and Ezzini, Saad and Purschke, Christoph and Bissyande, Tegawend{\'e} Fran{\c{c}}ois D Assise and Klein, Jacques and Olariu, Isabella and Boytsov, Andrey and Lefebvre, Clement and Goujon, Anne}, booktitle={Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)}, year={2023} } ```
yuliang555/my_awesome_wnut_model
yuliang555
2023-09-26T12:00:25Z
108
0
transformers
[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-09-26T11:36:52Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.0 - name: Recall type: recall value: 0.0 - name: F1 type: f1 value: 0.0 - name: Accuracy type: accuracy value: 0.9256551665170365 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3274 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9257 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 54 | 0.3564 | 0.0 | 0.0 | 0.0 | 0.9256 | | No log | 2.0 | 108 | 0.3274 | 0.0 | 0.0 | 0.0 | 0.9257 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
lothritz/Lb_GottBERT
lothritz
2023-09-26T12:00:16Z
181
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-06T11:33:33Z
# Lb_GottBERT Lb_GottBERT is a BERT-like language model for the Luxembourgish language. We used the weights of the German GottBERT language model as a starting point and continued pre-training it on the MLM task using the same corpus that we used for our LuxemBERT model (https://huggingface.co/lothritz/LuxemBERT). We achieved higher performances on several downstream tasks than the original LuxemBERT, DA BERT (https://huggingface.co/iolariu/DA_BERT), and its "sister" model Lb_mBERT (https://huggingface.co/lothritz/Lb_mBERT). If you would like to know more about our work, the pre-training corpus, or use our models or datasets, please check out /cite the following papers: ``` @inproceedings{lothritz-etal-2022-luxembert, title = "{L}uxem{BERT}: Simple and Practical Data Augmentation in Language Model Pre-Training for {L}uxembourgish", author = "Lothritz, Cedric and Lebichot, Bertrand and Allix, Kevin and Veiber, Lisa and Bissyande, Tegawende and Klein, Jacques and Boytsov, Andrey and Lefebvre, Cl{\'e}ment and Goujon, Anne", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.543", pages = "5080--5089", abstract = "Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks. While these models are readily available for English and other widely spoken languages, they remain scarce for low-resource languages such as Luxembourgish. In this paper, we present LuxemBERT, a BERT model for the Luxembourgish language that we create using the following approach: we augment the pre-training dataset by considering text data from a closely related language that we partially translate using a simple and straightforward method. We are then able to produce the LuxemBERT model, which we show to be effective for various NLP tasks: it outperforms a simple baseline built with the available Luxembourgish text data as well the multilingual mBERT model, which is currently the only option for transformer-based language models in Luxembourgish. Furthermore, we present datasets for various downstream NLP tasks that we created for this study and will make available to researchers on request.", } ``` ``` @inproceedings{lothritz2023comparing, title={Comparing Pre-Training Schemes for Luxembourgish BERT Models}, author={Lothritz, Cedric and Ezzini, Saad and Purschke, Christoph and Bissyande, Tegawend{\'e} Fran{\c{c}}ois D Assise and Klein, Jacques and Olariu, Isabella and Boytsov, Andrey and Lefebvre, Clement and Goujon, Anne}, booktitle={Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)}, year={2023} } ```
bedus-creation/mBart-small-dataset-ii-lim-to-eng-002
bedus-creation
2023-09-26T11:50:49Z
4
0
transformers
[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-25T14:20:19Z
--- license: apache-2.0 base_model: mBart tags: - generated_from_keras_callback model-index: - name: bedus-creation/t5-small-dataset-ii-lim-to-eng-002 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. --> # bedus-creation/t5-small-dataset-ii-lim-to-eng-002 This model is a fine-tuned version of [mBart](https://huggingface.co/mBart) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2514 - Validation Loss: 0.3001 - Epoch: 99 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0068 | 0.4628 | 0 | | 0.4954 | 0.3665 | 1 | | 0.4239 | 0.3488 | 2 | | 0.3989 | 0.3300 | 3 | | 0.3810 | 0.3232 | 4 | | 0.3678 | 0.3192 | 5 | | 0.3601 | 0.3140 | 6 | | 0.3523 | 0.3110 | 7 | | 0.3461 | 0.3099 | 8 | | 0.3426 | 0.3074 | 9 | | 0.3385 | 0.3055 | 10 | | 0.3347 | 0.3019 | 11 | | 0.3316 | 0.3036 | 12 | | 0.3284 | 0.2997 | 13 | | 0.3253 | 0.2983 | 14 | | 0.3230 | 0.3004 | 15 | | 0.3204 | 0.2977 | 16 | | 0.3191 | 0.2957 | 17 | | 0.3161 | 0.2931 | 18 | | 0.3150 | 0.2925 | 19 | | 0.3131 | 0.2921 | 20 | | 0.3114 | 0.2909 | 21 | | 0.3088 | 0.2925 | 22 | | 0.3081 | 0.2922 | 23 | | 0.3071 | 0.2894 | 24 | | 0.3057 | 0.2889 | 25 | | 0.3030 | 0.2898 | 26 | | 0.3032 | 0.2884 | 27 | | 0.3018 | 0.2873 | 28 | | 0.2995 | 0.2887 | 29 | | 0.3000 | 0.2864 | 30 | | 0.2986 | 0.2868 | 31 | | 0.2981 | 0.2854 | 32 | | 0.2965 | 0.2867 | 33 | | 0.2953 | 0.2862 | 34 | | 0.2959 | 0.2848 | 35 | | 0.2941 | 0.2849 | 36 | | 0.2933 | 0.2867 | 37 | | 0.2925 | 0.2875 | 38 | | 0.2905 | 0.2843 | 39 | | 0.2911 | 0.2843 | 40 | | 0.2897 | 0.2863 | 41 | | 0.2888 | 0.2855 | 42 | | 0.2875 | 0.2852 | 43 | | 0.2884 | 0.2878 | 44 | | 0.2868 | 0.2853 | 45 | | 0.2855 | 0.2843 | 46 | | 0.2846 | 0.2852 | 47 | | 0.2844 | 0.2833 | 48 | | 0.2834 | 0.2847 | 49 | | 0.2831 | 0.2851 | 50 | | 0.2818 | 0.2839 | 51 | | 0.2821 | 0.2843 | 52 | | 0.2798 | 0.2858 | 53 | | 0.2801 | 0.2843 | 54 | | 0.2798 | 0.2851 | 55 | | 0.2785 | 0.2880 | 56 | | 0.2790 | 0.2853 | 57 | | 0.2775 | 0.2860 | 58 | | 0.2776 | 0.2848 | 59 | | 0.2766 | 0.2875 | 60 | | 0.2758 | 0.2864 | 61 | | 0.2753 | 0.2857 | 62 | | 0.2741 | 0.2899 | 63 | | 0.2731 | 0.2904 | 64 | | 0.2728 | 0.2887 | 65 | | 0.2728 | 0.2879 | 66 | | 0.2714 | 0.2877 | 67 | | 0.2715 | 0.2901 | 68 | | 0.2704 | 0.2864 | 69 | | 0.2705 | 0.2876 | 70 | | 0.2694 | 0.2925 | 71 | | 0.2683 | 0.2923 | 72 | | 0.2668 | 0.2910 | 73 | | 0.2676 | 0.2878 | 74 | | 0.2666 | 0.2928 | 75 | | 0.2656 | 0.2903 | 76 | | 0.2649 | 0.2913 | 77 | | 0.2642 | 0.2912 | 78 | | 0.2643 | 0.2944 | 79 | | 0.2636 | 0.2910 | 80 | | 0.2631 | 0.2922 | 81 | | 0.2625 | 0.2983 | 82 | | 0.2617 | 0.2945 | 83 | | 0.2609 | 0.2914 | 84 | | 0.2609 | 0.2974 | 85 | | 0.2594 | 0.2960 | 86 | | 0.2597 | 0.2977 | 87 | | 0.2589 | 0.2972 | 88 | | 0.2583 | 0.2970 | 89 | | 0.2562 | 0.2951 | 90 | | 0.2565 | 0.3004 | 91 | | 0.2556 | 0.2971 | 92 | | 0.2555 | 0.2963 | 93 | | 0.2541 | 0.2991 | 94 | | 0.2548 | 0.3000 | 95 | | 0.2540 | 0.3015 | 96 | | 0.2527 | 0.3004 | 97 | | 0.2528 | 0.3012 | 98 | | 0.2514 | 0.3001 | 99 | ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/mifune_shioriko_lovelivenijigasakihighschoolidolclub
CyberHarem
2023-09-26T11:47:22Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/mifune_shioriko_lovelivenijigasakihighschoolidolclub", "license:mit", "region:us" ]
text-to-image
2023-09-26T11:30:49Z
--- license: mit datasets: - CyberHarem/mifune_shioriko_lovelivenijigasakihighschoolidolclub pipeline_tag: text-to-image tags: - art --- # Lora of mifune_shioriko_lovelivenijigasakihighschoolidolclub This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5720, you need to download `5720/mifune_shioriko_lovelivenijigasakihighschoolidolclub.pt` as the embedding and `5720/mifune_shioriko_lovelivenijigasakihighschoolidolclub.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5720**, with the score of 0.994. The trigger words are: 1. `mifune_shioriko_lovelivenijigasakihighschoolidolclub` 2. `bangs, short_hair, black_hair, red_eyes, ribbon, dark_green_hair, fang, orange_eyes, swept_bangs, hair_ribbon, blush, hair_ornament` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 7800 | 0.987 | [Download](7800/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-7800](7800/previews/pattern_1.png) | ![pattern_2-7800](7800/previews/pattern_2.png) | ![pattern_3-7800](7800/previews/pattern_3.png) | ![pattern_4-7800](7800/previews/pattern_4.png) | ![pattern_5-7800](7800/previews/pattern_5.png) | ![pattern_6-7800](7800/previews/pattern_6.png) | ![pattern_7-7800](7800/previews/pattern_7.png) | ![pattern_8-7800](7800/previews/pattern_8.png) | ![pattern_9-7800](7800/previews/pattern_9.png) | ![pattern_10-7800](7800/previews/pattern_10.png) | ![pattern_11-7800](7800/previews/pattern_11.png) | ![pattern_12-7800](7800/previews/pattern_12.png) | ![pattern_13-7800](7800/previews/pattern_13.png) | [<NSFW, click to see>](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) | ![free-7800](7800/previews/free.png) | ![maid-7800](7800/previews/maid.png) | ![miko-7800](7800/previews/miko.png) | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) | ![suit-7800](7800/previews/suit.png) | ![yukata-7800](7800/previews/yukata.png) | | 7280 | 0.994 | [Download](7280/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-7280](7280/previews/pattern_1.png) | ![pattern_2-7280](7280/previews/pattern_2.png) | ![pattern_3-7280](7280/previews/pattern_3.png) | ![pattern_4-7280](7280/previews/pattern_4.png) | ![pattern_5-7280](7280/previews/pattern_5.png) | ![pattern_6-7280](7280/previews/pattern_6.png) | ![pattern_7-7280](7280/previews/pattern_7.png) | ![pattern_8-7280](7280/previews/pattern_8.png) | ![pattern_9-7280](7280/previews/pattern_9.png) | ![pattern_10-7280](7280/previews/pattern_10.png) | ![pattern_11-7280](7280/previews/pattern_11.png) | ![pattern_12-7280](7280/previews/pattern_12.png) | ![pattern_13-7280](7280/previews/pattern_13.png) | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) | ![free-7280](7280/previews/free.png) | ![maid-7280](7280/previews/maid.png) | ![miko-7280](7280/previews/miko.png) | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) | ![suit-7280](7280/previews/suit.png) | ![yukata-7280](7280/previews/yukata.png) | | 6760 | 0.992 | [Download](6760/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-6760](6760/previews/pattern_1.png) | ![pattern_2-6760](6760/previews/pattern_2.png) | ![pattern_3-6760](6760/previews/pattern_3.png) | ![pattern_4-6760](6760/previews/pattern_4.png) | ![pattern_5-6760](6760/previews/pattern_5.png) | ![pattern_6-6760](6760/previews/pattern_6.png) | ![pattern_7-6760](6760/previews/pattern_7.png) | ![pattern_8-6760](6760/previews/pattern_8.png) | ![pattern_9-6760](6760/previews/pattern_9.png) | ![pattern_10-6760](6760/previews/pattern_10.png) | ![pattern_11-6760](6760/previews/pattern_11.png) | ![pattern_12-6760](6760/previews/pattern_12.png) | ![pattern_13-6760](6760/previews/pattern_13.png) | [<NSFW, click to see>](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) | ![free-6760](6760/previews/free.png) | ![maid-6760](6760/previews/maid.png) | ![miko-6760](6760/previews/miko.png) | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) | ![suit-6760](6760/previews/suit.png) | ![yukata-6760](6760/previews/yukata.png) | | 6240 | 0.991 | [Download](6240/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | ![pattern_3-6240](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | ![pattern_5-6240](6240/previews/pattern_5.png) | ![pattern_6-6240](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![pattern_10-6240](6240/previews/pattern_10.png) | ![pattern_11-6240](6240/previews/pattern_11.png) | ![pattern_12-6240](6240/previews/pattern_12.png) | ![pattern_13-6240](6240/previews/pattern_13.png) | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | **5720** | **0.994** | [**Download**](5720/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-5720](5720/previews/pattern_1.png) | ![pattern_2-5720](5720/previews/pattern_2.png) | ![pattern_3-5720](5720/previews/pattern_3.png) | ![pattern_4-5720](5720/previews/pattern_4.png) | ![pattern_5-5720](5720/previews/pattern_5.png) | ![pattern_6-5720](5720/previews/pattern_6.png) | ![pattern_7-5720](5720/previews/pattern_7.png) | ![pattern_8-5720](5720/previews/pattern_8.png) | ![pattern_9-5720](5720/previews/pattern_9.png) | ![pattern_10-5720](5720/previews/pattern_10.png) | ![pattern_11-5720](5720/previews/pattern_11.png) | ![pattern_12-5720](5720/previews/pattern_12.png) | ![pattern_13-5720](5720/previews/pattern_13.png) | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) | ![free-5720](5720/previews/free.png) | ![maid-5720](5720/previews/maid.png) | ![miko-5720](5720/previews/miko.png) | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) | ![suit-5720](5720/previews/suit.png) | ![yukata-5720](5720/previews/yukata.png) | | 5200 | 0.990 | [Download](5200/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-5200](5200/previews/pattern_1.png) | ![pattern_2-5200](5200/previews/pattern_2.png) | ![pattern_3-5200](5200/previews/pattern_3.png) | ![pattern_4-5200](5200/previews/pattern_4.png) | ![pattern_5-5200](5200/previews/pattern_5.png) | ![pattern_6-5200](5200/previews/pattern_6.png) | ![pattern_7-5200](5200/previews/pattern_7.png) | ![pattern_8-5200](5200/previews/pattern_8.png) | ![pattern_9-5200](5200/previews/pattern_9.png) | ![pattern_10-5200](5200/previews/pattern_10.png) | ![pattern_11-5200](5200/previews/pattern_11.png) | ![pattern_12-5200](5200/previews/pattern_12.png) | ![pattern_13-5200](5200/previews/pattern_13.png) | [<NSFW, click to see>](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) | ![free-5200](5200/previews/free.png) | ![maid-5200](5200/previews/maid.png) | ![miko-5200](5200/previews/miko.png) | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) | ![suit-5200](5200/previews/suit.png) | ![yukata-5200](5200/previews/yukata.png) | | 4680 | 0.994 | [Download](4680/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-4680](4680/previews/pattern_1.png) | ![pattern_2-4680](4680/previews/pattern_2.png) | ![pattern_3-4680](4680/previews/pattern_3.png) | ![pattern_4-4680](4680/previews/pattern_4.png) | ![pattern_5-4680](4680/previews/pattern_5.png) | ![pattern_6-4680](4680/previews/pattern_6.png) | ![pattern_7-4680](4680/previews/pattern_7.png) | ![pattern_8-4680](4680/previews/pattern_8.png) | ![pattern_9-4680](4680/previews/pattern_9.png) | ![pattern_10-4680](4680/previews/pattern_10.png) | ![pattern_11-4680](4680/previews/pattern_11.png) | ![pattern_12-4680](4680/previews/pattern_12.png) | ![pattern_13-4680](4680/previews/pattern_13.png) | [<NSFW, click to see>](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) | ![free-4680](4680/previews/free.png) | ![maid-4680](4680/previews/maid.png) | ![miko-4680](4680/previews/miko.png) | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) | ![suit-4680](4680/previews/suit.png) | ![yukata-4680](4680/previews/yukata.png) | | 4160 | 0.990 | [Download](4160/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-4160](4160/previews/pattern_1.png) | ![pattern_2-4160](4160/previews/pattern_2.png) | ![pattern_3-4160](4160/previews/pattern_3.png) | ![pattern_4-4160](4160/previews/pattern_4.png) | ![pattern_5-4160](4160/previews/pattern_5.png) | ![pattern_6-4160](4160/previews/pattern_6.png) | ![pattern_7-4160](4160/previews/pattern_7.png) | ![pattern_8-4160](4160/previews/pattern_8.png) | ![pattern_9-4160](4160/previews/pattern_9.png) | ![pattern_10-4160](4160/previews/pattern_10.png) | ![pattern_11-4160](4160/previews/pattern_11.png) | ![pattern_12-4160](4160/previews/pattern_12.png) | ![pattern_13-4160](4160/previews/pattern_13.png) | [<NSFW, click to see>](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) | ![free-4160](4160/previews/free.png) | ![maid-4160](4160/previews/maid.png) | ![miko-4160](4160/previews/miko.png) | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) | ![suit-4160](4160/previews/suit.png) | ![yukata-4160](4160/previews/yukata.png) | | 3640 | 0.993 | [Download](3640/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-3640](3640/previews/pattern_1.png) | ![pattern_2-3640](3640/previews/pattern_2.png) | ![pattern_3-3640](3640/previews/pattern_3.png) | ![pattern_4-3640](3640/previews/pattern_4.png) | ![pattern_5-3640](3640/previews/pattern_5.png) | ![pattern_6-3640](3640/previews/pattern_6.png) | ![pattern_7-3640](3640/previews/pattern_7.png) | ![pattern_8-3640](3640/previews/pattern_8.png) | ![pattern_9-3640](3640/previews/pattern_9.png) | ![pattern_10-3640](3640/previews/pattern_10.png) | ![pattern_11-3640](3640/previews/pattern_11.png) | ![pattern_12-3640](3640/previews/pattern_12.png) | ![pattern_13-3640](3640/previews/pattern_13.png) | [<NSFW, click to see>](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) | ![free-3640](3640/previews/free.png) | ![maid-3640](3640/previews/maid.png) | ![miko-3640](3640/previews/miko.png) | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) | ![suit-3640](3640/previews/suit.png) | ![yukata-3640](3640/previews/yukata.png) | | 3120 | 0.991 | [Download](3120/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-3120](3120/previews/pattern_1.png) | ![pattern_2-3120](3120/previews/pattern_2.png) | ![pattern_3-3120](3120/previews/pattern_3.png) | ![pattern_4-3120](3120/previews/pattern_4.png) | ![pattern_5-3120](3120/previews/pattern_5.png) | ![pattern_6-3120](3120/previews/pattern_6.png) | ![pattern_7-3120](3120/previews/pattern_7.png) | ![pattern_8-3120](3120/previews/pattern_8.png) | ![pattern_9-3120](3120/previews/pattern_9.png) | ![pattern_10-3120](3120/previews/pattern_10.png) | ![pattern_11-3120](3120/previews/pattern_11.png) | ![pattern_12-3120](3120/previews/pattern_12.png) | ![pattern_13-3120](3120/previews/pattern_13.png) | [<NSFW, click to see>](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) | ![free-3120](3120/previews/free.png) | ![maid-3120](3120/previews/maid.png) | ![miko-3120](3120/previews/miko.png) | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) | ![suit-3120](3120/previews/suit.png) | ![yukata-3120](3120/previews/yukata.png) | | 2600 | 0.978 | [Download](2600/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-2600](2600/previews/pattern_1.png) | ![pattern_2-2600](2600/previews/pattern_2.png) | ![pattern_3-2600](2600/previews/pattern_3.png) | ![pattern_4-2600](2600/previews/pattern_4.png) | ![pattern_5-2600](2600/previews/pattern_5.png) | ![pattern_6-2600](2600/previews/pattern_6.png) | ![pattern_7-2600](2600/previews/pattern_7.png) | ![pattern_8-2600](2600/previews/pattern_8.png) | ![pattern_9-2600](2600/previews/pattern_9.png) | ![pattern_10-2600](2600/previews/pattern_10.png) | ![pattern_11-2600](2600/previews/pattern_11.png) | ![pattern_12-2600](2600/previews/pattern_12.png) | ![pattern_13-2600](2600/previews/pattern_13.png) | [<NSFW, click to see>](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) | ![free-2600](2600/previews/free.png) | ![maid-2600](2600/previews/maid.png) | ![miko-2600](2600/previews/miko.png) | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) | ![suit-2600](2600/previews/suit.png) | ![yukata-2600](2600/previews/yukata.png) | | 2080 | 0.988 | [Download](2080/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-2080](2080/previews/pattern_1.png) | ![pattern_2-2080](2080/previews/pattern_2.png) | ![pattern_3-2080](2080/previews/pattern_3.png) | ![pattern_4-2080](2080/previews/pattern_4.png) | ![pattern_5-2080](2080/previews/pattern_5.png) | ![pattern_6-2080](2080/previews/pattern_6.png) | ![pattern_7-2080](2080/previews/pattern_7.png) | ![pattern_8-2080](2080/previews/pattern_8.png) | ![pattern_9-2080](2080/previews/pattern_9.png) | ![pattern_10-2080](2080/previews/pattern_10.png) | ![pattern_11-2080](2080/previews/pattern_11.png) | ![pattern_12-2080](2080/previews/pattern_12.png) | ![pattern_13-2080](2080/previews/pattern_13.png) | [<NSFW, click to see>](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) | ![free-2080](2080/previews/free.png) | ![maid-2080](2080/previews/maid.png) | ![miko-2080](2080/previews/miko.png) | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) | ![suit-2080](2080/previews/suit.png) | ![yukata-2080](2080/previews/yukata.png) | | 1560 | 0.989 | [Download](1560/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-1560](1560/previews/pattern_1.png) | ![pattern_2-1560](1560/previews/pattern_2.png) | ![pattern_3-1560](1560/previews/pattern_3.png) | ![pattern_4-1560](1560/previews/pattern_4.png) | ![pattern_5-1560](1560/previews/pattern_5.png) | ![pattern_6-1560](1560/previews/pattern_6.png) | ![pattern_7-1560](1560/previews/pattern_7.png) | ![pattern_8-1560](1560/previews/pattern_8.png) | ![pattern_9-1560](1560/previews/pattern_9.png) | ![pattern_10-1560](1560/previews/pattern_10.png) | ![pattern_11-1560](1560/previews/pattern_11.png) | ![pattern_12-1560](1560/previews/pattern_12.png) | ![pattern_13-1560](1560/previews/pattern_13.png) | [<NSFW, click to see>](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) | ![free-1560](1560/previews/free.png) | ![maid-1560](1560/previews/maid.png) | ![miko-1560](1560/previews/miko.png) | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) | ![suit-1560](1560/previews/suit.png) | ![yukata-1560](1560/previews/yukata.png) | | 1040 | 0.991 | [Download](1040/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-1040](1040/previews/pattern_1.png) | ![pattern_2-1040](1040/previews/pattern_2.png) | ![pattern_3-1040](1040/previews/pattern_3.png) | ![pattern_4-1040](1040/previews/pattern_4.png) | ![pattern_5-1040](1040/previews/pattern_5.png) | ![pattern_6-1040](1040/previews/pattern_6.png) | ![pattern_7-1040](1040/previews/pattern_7.png) | ![pattern_8-1040](1040/previews/pattern_8.png) | ![pattern_9-1040](1040/previews/pattern_9.png) | ![pattern_10-1040](1040/previews/pattern_10.png) | ![pattern_11-1040](1040/previews/pattern_11.png) | ![pattern_12-1040](1040/previews/pattern_12.png) | ![pattern_13-1040](1040/previews/pattern_13.png) | [<NSFW, click to see>](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) | ![free-1040](1040/previews/free.png) | ![maid-1040](1040/previews/maid.png) | ![miko-1040](1040/previews/miko.png) | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) | ![suit-1040](1040/previews/suit.png) | ![yukata-1040](1040/previews/yukata.png) | | 520 | 0.918 | [Download](520/mifune_shioriko_lovelivenijigasakihighschoolidolclub.zip) | ![pattern_1-520](520/previews/pattern_1.png) | ![pattern_2-520](520/previews/pattern_2.png) | ![pattern_3-520](520/previews/pattern_3.png) | ![pattern_4-520](520/previews/pattern_4.png) | ![pattern_5-520](520/previews/pattern_5.png) | ![pattern_6-520](520/previews/pattern_6.png) | ![pattern_7-520](520/previews/pattern_7.png) | ![pattern_8-520](520/previews/pattern_8.png) | ![pattern_9-520](520/previews/pattern_9.png) | ![pattern_10-520](520/previews/pattern_10.png) | ![pattern_11-520](520/previews/pattern_11.png) | ![pattern_12-520](520/previews/pattern_12.png) | ![pattern_13-520](520/previews/pattern_13.png) | [<NSFW, click to see>](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) | ![free-520](520/previews/free.png) | ![maid-520](520/previews/maid.png) | ![miko-520](520/previews/miko.png) | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) | ![suit-520](520/previews/suit.png) | ![yukata-520](520/previews/yukata.png) |
diffusers/consistency_models
diffusers
2023-09-26T11:46:08Z
0
0
diffusers
[ "diffusers", "region:us" ]
null
2023-07-05T14:58:05Z
--- duplicated_from: ayushtues/consistency_models ---
openai/diffusers-cd_imagenet64_lpips
openai
2023-09-26T11:45:49Z
56
1
diffusers
[ "diffusers", "safetensors", "generative model", "unconditional image generation", "consistency-model", "arxiv:2303.01469", "arxiv:2206.00364", "arxiv:1506.03365", "arxiv:1512.00567", "license:mit", "diffusers:ConsistencyModelPipeline", "region:us" ]
null
2023-07-05T13:28:56Z
--- license: mit tags: - generative model - unconditional image generation - consistency-model --- **Disclaimer**: This model was added by the amazing community contributors [dg845](https://huggingface.co/dg845) and [ayushtues](https://huggingface.co/ayushtues)❤️ Consistency models are a new class of generative models introduced in ["Consistency Models"](https://arxiv.org/abs/2303.01469) ([paper](https://arxiv.org/pdf/2303.01469.pdf), [code](https://github.com/openai/consistency_models)) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. From the paper abstract: > Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64 x 64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64 x 64 and LSUN 256 x 256. Intuitively, a consistency model can be thought of as a model which, when evaluated on a noisy image and timestep, returns an output image sample similar to that which would be returned by running a sampling algorithm on a diffusion model. Consistency models can be parameterized by any neural network whose input has the same dimensionality as its output, such as a U-Net. More precisely, given a teacher diffusion model and fixed sampler, we can train ("distill") a consistency model such that when it is given a noisy image and its corresponding timestep, the output sample of the consistency model will be close to the output that would result by using the sampler on the diffusion model to produce a sample, starting at the same noisy image and timestep. The authors call this procedure "consistency distillation (CD)". Consistency models can also be trained from scratch to generate clean images from a noisy image and timestep, which the authors call "consistency training (CT)". This model is a `diffusers`-compatible version of the [cd_imagenet64_lpips.pt](https://github.com/openai/consistency_models#pre-trained-models) checkpont from the [original code and model release](https://github.com/openai/consistency_models). This model was distilled (via consistency distillation (CD)) from an [EDM model](https://arxiv.org/pdf/2206.00364.pdf) trained on the ImageNet 64x64 dataset, using [LPIPS](https://richzhang.github.io/PerceptualSimilarity/) as the measure of closeness. See the [original model card](https://github.com/openai/consistency_models/blob/main/model-card.md) for more information. ## Download The original PyTorch model checkpoint can be downloaded from the [original code and model release](https://github.com/openai/consistency_models#pre-trained-models). The `diffusers` pipeline for the `cd-imagenet64-lpips` model can be downloaded as follows: ```python from diffusers import ConsistencyModelPipeline pipe = ConsistencyModelPipeline.from_pretrained("openai/diffusers-cd_imagenet64_lpips") ``` ## Usage The original model checkpoint can be used with the [original consistency models codebase](https://github.com/openai/consistency_models). Here is an example of using the `cd_imagenet64_lpips` checkpoint with `diffusers`: ```python import torch from diffusers import ConsistencyModelPipeline device = "cuda" # Load the cd_imagenet64_lpips checkpoint. model_id_or_path = "openai/diffusers-cd_imagenet64_lpips" pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) pipe.to(device) # Onestep Sampling image = pipe(num_inference_steps=1).images[0] image.save("cd_imagenet64_lpips_onestep_sample.png") # Onestep sampling, class-conditional image generation # ImageNet-64 class label 145 corresponds to king penguins image = pipe(num_inference_steps=1, class_labels=145).images[0] image.save("cd_imagenet64_lpips_onestep_sample_penguin.png") # Multistep sampling, class-conditional image generation # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo: # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L74 image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0] image.save("cd_imagenet64_lpips_multistep_sample_penguin.png") ``` ## Model Details - **Model type:** Consistency model unconditional image generation model, distilled from a diffusion model - **Dataset:** ImageNet 64x64 - **License:** MIT - **Model Description:** This model performs unconditional image generation. Its main component is a U-Net, which parameterizes the consistency model. This model was distilled by the Consistency Model authors from an EDM diffusion model, also originally trained by the authors. - **Resources for more information:**: [Paper](https://arxiv.org/abs/2303.01469), [GitHub Repository](https://github.com/openai/consistency_models), [Original Model Card](/openai/consistency_models/blob/main/model-card.md) ## Datasets _Note: This section is taken from the ["Datasets" section of the original model card](https://github.com/openai/consistency_models/blob/main/model-card.md#datasets)_. The models that we are making available have been trained on the [ILSVRC 2012 subset of ImageNet](http://www.image-net.org/challenges/LSVRC/2012/) or on individual categories from [LSUN](https://arxiv.org/abs/1506.03365). Here we outline the characteristics of these datasets that influence the behavior of the models: **ILSVRC 2012 subset of ImageNet**: This dataset was curated in 2012 and has around a million pictures, each of which belongs to one of 1,000 categories. A significant number of the categories in this dataset are animals, plants, and other naturally occurring objects. Although many photographs include humans, these humans are typically not represented by the class label (for example, the category "Tench, tinca tinca" includes many photographs of individuals holding fish). **LSUN**: This dataset was collected in 2015 by a combination of human labeling via Amazon Mechanical Turk and automated data labeling. Both classes that we consider have more than a million images. The dataset creators discovered that when assessed by trained experts, the label accuracy was approximately 90% throughout the entire LSUN dataset. The pictures are gathered from the internet, and those in the cat class often follow a "meme" format. Occasionally, people, including faces, appear in these photographs. ## Performance _Note: This section is taken from the ["Performance" section of the original model card](https://github.com/openai/consistency_models/blob/main/model-card.md#performance)_. These models are intended to generate samples consistent with their training distributions. This has been measured in terms of FID, Inception Score, Precision, and Recall. These metrics all rely on the representations of a [pre-trained Inception-V3 model](https://arxiv.org/abs/1512.00567), which was trained on ImageNet, and so is likely to focus more on the ImageNet classes (such as animals) than on other visual features (such as human faces). ## Intended Use _Note: This section is taken from the ["Intended Use" section of the original model card](https://github.com/openai/consistency_models/blob/main/model-card.md#intended-use)_. These models are intended to be used for research purposes only. In particular, they can be used as a baseline for generative modeling research, or as a starting point for advancing such research. These models are not intended to be commercially deployed. Additionally, they are not intended to be used to create propaganda or offensive imagery. ## Limitations _Note: This section is taken from the ["Limitations" section of the original model card](https://github.com/openai/consistency_models/blob/main/model-card.md#limitations)_. These models sometimes produce highly unrealistic outputs, particularly when generating images containing human faces. This may stem from ImageNet's emphasis on non-human objects. In consistency distillation and training, minimizing LPIPS results in better sample quality, as evidenced by improved FID and Inception scores. However, it also carries the risk of overestimating model performance, because LPIPS uses a VGG network pre-trained on ImageNet, while FID and Inception scores also rely on convolutional neural networks (the Inception network in particular) pre-trained on the same ImageNet dataset. Although these two convolutional neural networks do not share the same architecture and we extract latents from them in substantially different ways, knowledge leakage is still plausible which can undermine the fidelity of FID and Inception scores. Because ImageNet and LSUN contain images from the internet, they include photos of real people, and the model may have memorized some of the information contained in these photos. However, these images are already publicly available, and existing generative models trained on ImageNet have not demonstrated significant leakage of this information.
colab086/mid
colab086
2023-09-26T11:28:48Z
0
0
null
[ "en", "license:openrail", "region:us" ]
null
2023-09-26T11:24:24Z
--- license: openrail language: - en ---
IlyaGusev/saiga2_13b_gguf
IlyaGusev
2023-09-26T11:27:58Z
272
47
null
[ "gguf", "conversational", "ru", "dataset:IlyaGusev/ru_turbo_alpaca", "dataset:IlyaGusev/ru_turbo_saiga", "dataset:IlyaGusev/ru_sharegpt_cleaned", "dataset:IlyaGusev/oasst1_ru_main_branch", "dataset:IlyaGusev/ru_turbo_alpaca_evol_instruct", "dataset:lksy/ru_instruct_gpt4", "license:llama2", "region:us" ]
text-generation
2023-07-26T01:09:47Z
--- datasets: - IlyaGusev/ru_turbo_alpaca - IlyaGusev/ru_turbo_saiga - IlyaGusev/ru_sharegpt_cleaned - IlyaGusev/oasst1_ru_main_branch - IlyaGusev/ru_turbo_alpaca_evol_instruct - lksy/ru_instruct_gpt4 language: - ru inference: false pipeline_tag: conversational license: llama2 --- Llama.cpp compatible versions of an original [13B model](https://huggingface.co/IlyaGusev/saiga2_13b_lora). Download one of the versions, for example `model-q4_K.gguf`. ``` wget https://huggingface.co/IlyaGusev/saiga2_13b_gguf/resolve/main/model-q4_K.gguf ``` Download [interact_llamacpp.py](https://raw.githubusercontent.com/IlyaGusev/rulm/master/self_instruct/src/interact_llamacpp.py) ``` wget https://raw.githubusercontent.com/IlyaGusev/rulm/master/self_instruct/src/interact_llamacpp.py ``` How to run: ``` pip install llama-cpp-python fire python3 interact_llamacpp.py model-q4_K.gguf ``` System requirements: * 18GB RAM for q8_K * 10GB RAM for q4_K
mindchain/ops
mindchain
2023-09-26T11:26:50Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-26T10:52:04Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: gptq - bits: 4 - tokenizer: None - dataset: None - group_size: 128 - damp_percent: 0.01 - desc_act: False - sym: True - true_sequential: True - use_cuda_fp16: False - model_seqlen: None - block_name_to_quantize: None - module_name_preceding_first_block: None - batch_size: 1 - pad_token_id: None - disable_exllama: True ### Framework versions - PEFT 0.5.0
milaidy/dcaa
milaidy
2023-09-26T11:19:34Z
1
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-09-26T11:15:05Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### dcaa Dreambooth model trained by milaidy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
s3nh/R136a1-MythoMax-L2-13B-exl2-GGUF
s3nh
2023-09-26T11:14:48Z
0
1
transformers
[ "transformers", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us" ]
text-generation
2023-09-26T11:14:48Z
--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### Perplexity params Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16 7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066 13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543 ### inference TODO # Original model card
ldos/text_shortening_model_v56
ldos
2023-09-26T11:12:06Z
116
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-26T09:38:18Z
--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v56 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. --> # text_shortening_model_v56 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2446 - Rouge1: 0.3315 - Rouge2: 0.1705 - Rougel: 0.302 - Rougelsum: 0.302 - Bert precision: 0.8254 - Bert recall: 0.8322 - Average word count: 7.3374 - Max word count: 18 - Min word count: 2 - Average token count: 11.3745 - % shortened texts with length > 12: 4.7763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 3.2947 | 1.0 | 288 | 2.7198 | 0.2581 | 0.1248 | 0.2329 | 0.2328 | 0.7592 | 0.7746 | 8.0751 | 18 | 0 | 13.4678 | 12.5095 | | 2.8745 | 2.0 | 576 | 2.5497 | 0.2967 | 0.148 | 0.2692 | 0.269 | 0.8107 | 0.8193 | 7.7149 | 18 | 0 | 11.8552 | 8.3397 | | 2.7549 | 3.0 | 864 | 2.4721 | 0.31 | 0.1548 | 0.2806 | 0.2805 | 0.8158 | 0.8247 | 7.7263 | 18 | 0 | 11.7786 | 6.975 | | 2.6785 | 4.0 | 1152 | 2.4212 | 0.3135 | 0.1582 | 0.2834 | 0.2837 | 0.8185 | 0.8264 | 7.5815 | 18 | 0 | 11.6005 | 6.3685 | | 2.6289 | 5.0 | 1440 | 2.3872 | 0.3188 | 0.1622 | 0.2879 | 0.2882 | 0.8196 | 0.8278 | 7.602 | 18 | 0 | 11.6497 | 6.5959 | | 2.587 | 6.0 | 1728 | 2.3611 | 0.3224 | 0.1633 | 0.2909 | 0.2911 | 0.8202 | 0.8291 | 7.6232 | 18 | 0 | 11.6694 | 6.5959 | | 2.5615 | 7.0 | 2016 | 2.3401 | 0.3284 | 0.168 | 0.297 | 0.2972 | 0.8222 | 0.8303 | 7.4936 | 18 | 0 | 11.5299 | 5.8378 | | 2.5354 | 8.0 | 2304 | 2.3223 | 0.3299 | 0.1703 | 0.299 | 0.299 | 0.8228 | 0.831 | 7.5171 | 18 | 0 | 11.5519 | 5.9136 | | 2.5074 | 9.0 | 2592 | 2.3069 | 0.3314 | 0.1702 | 0.2999 | 0.3 | 0.8237 | 0.832 | 7.5383 | 18 | 2 | 11.5595 | 5.8378 | | 2.4868 | 10.0 | 2880 | 2.2944 | 0.3317 | 0.1713 | 0.3014 | 0.3013 | 0.8246 | 0.8317 | 7.4193 | 18 | 2 | 11.4519 | 5.5345 | | 2.4773 | 11.0 | 3168 | 2.2830 | 0.3322 | 0.1705 | 0.3013 | 0.3013 | 0.8247 | 0.8319 | 7.3904 | 18 | 2 | 11.4238 | 5.0038 | | 2.4571 | 12.0 | 3456 | 2.2738 | 0.3288 | 0.1685 | 0.2987 | 0.2987 | 0.8242 | 0.831 | 7.3343 | 18 | 2 | 11.3715 | 4.5489 | | 2.4494 | 13.0 | 3744 | 2.2672 | 0.3322 | 0.1705 | 0.3013 | 0.3014 | 0.8251 | 0.8319 | 7.3351 | 18 | 2 | 11.3798 | 4.5489 | | 2.4401 | 14.0 | 4032 | 2.2611 | 0.33 | 0.1692 | 0.3004 | 0.3005 | 0.8246 | 0.8315 | 7.3639 | 18 | 2 | 11.4139 | 4.8522 | | 2.431 | 15.0 | 4320 | 2.2564 | 0.3303 | 0.1698 | 0.3004 | 0.3004 | 0.8248 | 0.8317 | 7.3745 | 18 | 2 | 11.4238 | 5.0796 | | 2.4253 | 16.0 | 4608 | 2.2522 | 0.3308 | 0.1704 | 0.3016 | 0.3014 | 0.8252 | 0.8319 | 7.3328 | 18 | 2 | 11.3791 | 4.8522 | | 2.4111 | 17.0 | 4896 | 2.2490 | 0.3313 | 0.1705 | 0.3017 | 0.3017 | 0.8254 | 0.8319 | 7.3222 | 18 | 2 | 11.3563 | 4.8522 | | 2.4125 | 18.0 | 5184 | 2.2464 | 0.3313 | 0.1702 | 0.3017 | 0.3017 | 0.8254 | 0.8321 | 7.3328 | 18 | 2 | 11.3654 | 4.8522 | | 2.4061 | 19.0 | 5472 | 2.2450 | 0.3313 | 0.1701 | 0.3017 | 0.3018 | 0.8254 | 0.8321 | 7.3359 | 18 | 2 | 11.3723 | 4.7763 | | 2.4129 | 20.0 | 5760 | 2.2446 | 0.3315 | 0.1705 | 0.302 | 0.302 | 0.8254 | 0.8322 | 7.3374 | 18 | 2 | 11.3745 | 4.7763 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
learn3r/longt5_xl_summ_screen_bp_only_30
learn3r
2023-09-26T11:07:19Z
8
0
transformers
[ "transformers", "pytorch", "longt5", "text2text-generation", "generated_from_trainer", "dataset:learn3r/summ_screen_fd_bp", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-22T21:21:08Z
--- base_model: /exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen_bp_only/checkpoint-210 tags: - generated_from_trainer datasets: - learn3r/summ_screen_fd_bp metrics: - rouge model-index: - name: longt5_xl_summ_screen_bp_only_30 results: - task: name: Summarization type: summarization dataset: name: learn3r/summ_screen_fd_bp type: learn3r/summ_screen_fd_bp metrics: - name: Rouge1 type: rouge value: 40.4388 --- <!-- 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. --> # longt5_xl_summ_screen_bp_only_30 This model is a fine-tuned version of [/exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen_bp_only/checkpoint-210](https://huggingface.co//exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen_bp_only/checkpoint-210) on the learn3r/summ_screen_fd_bp dataset. It achieves the following results on the evaluation set: - Loss: 2.2376 - Rouge1: 40.4388 - Rouge2: 16.4662 - Rougel: 28.0771 - Rougelsum: 38.3405 - Gen Len: 246.7396 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 0.324 | 0.97 | 14 | 2.2376 | 40.4388 | 16.4662 | 28.0771 | 38.3405 | 246.7396 | | 0.2707 | 1.95 | 28 | 2.3204 | 40.2873 | 16.7641 | 27.3895 | 38.2689 | 307.3787 | | 0.2217 | 2.99 | 43 | 2.5281 | 31.9916 | 13.8136 | 22.1895 | 30.623 | 501.9320 | | 0.1776 | 3.97 | 57 | 2.7530 | 31.7535 | 13.8852 | 22.8653 | 30.3796 | 489.6183 | | 0.1424 | 4.94 | 71 | 2.6578 | 32.117 | 14.2141 | 22.3733 | 30.8328 | 502.1124 | | 0.1449 | 5.98 | 86 | 2.5508 | 35.3448 | 13.8478 | 24.9044 | 33.6108 | 357.3136 | | 0.1191 | 6.96 | 100 | 3.1622 | 37.2189 | 16.0076 | 25.7011 | 35.294 | 408.8669 | | 0.0879 | 8.0 | 115 | 2.8510 | 39.8825 | 16.8073 | 27.2428 | 37.9568 | 318.2278 | | 0.0899 | 8.97 | 129 | 2.9138 | 31.7139 | 13.7066 | 21.8844 | 30.5075 | 500.4053 | | 0.0656 | 9.95 | 143 | 3.1616 | 33.055 | 14.5841 | 22.5883 | 31.7565 | 488.1686 | | 0.0542 | 10.99 | 158 | 3.3630 | 43.7514 | 18.9011 | 29.9017 | 41.6887 | 198.8077 | | 0.0557 | 11.97 | 172 | 3.3826 | 42.3089 | 18.2735 | 29.0356 | 40.4154 | 270.9675 | | 0.0542 | 12.94 | 186 | 3.4408 | 40.7691 | 16.529 | 28.3999 | 38.9723 | 186.7308 | | 0.0596 | 13.98 | 201 | 3.5253 | 37.0037 | 15.9098 | 25.2808 | 35.3868 | 398.4704 | | 0.0385 | 14.61 | 210 | 3.4990 | 32.5815 | 14.2951 | 22.4501 | 31.2928 | 499.3107 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
RogerB/KinyaBERT-small-pretrained-kinyarwanda
RogerB
2023-09-26T11:02:39Z
124
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:jean-paul/KinyaBERT-small", "base_model:finetune:jean-paul/KinyaBERT-small", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-26T10:51:04Z
--- base_model: jean-paul/KinyaBERT-small tags: - generated_from_trainer model-index: - name: KinyaBERT-small-pretrained-kinyarwanda 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. --> # KinyaBERT-small-pretrained-kinyarwanda This model is a fine-tuned version of [jean-paul/KinyaBERT-small](https://huggingface.co/jean-paul/KinyaBERT-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0553 ## 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: 10 - eval_batch_size: 10 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.5078 | 1.0 | 2200 | 3.2187 | | 3.278 | 2.0 | 4400 | 3.0892 | | 3.1825 | 3.0 | 6600 | 3.0563 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
pe4enov/saiga_7b_lora_8bit
pe4enov
2023-09-26T11:01:41Z
1
0
peft
[ "peft", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "region:us" ]
null
2023-07-24T09:28:29Z
--- library_name: peft base_model: huggyllama/llama-7b --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
CyberHarem/nakasu_kasumi_loveliveschoolidolfestivalallstars
CyberHarem
2023-09-26T10:42:46Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/nakasu_kasumi_loveliveschoolidolfestivalallstars", "license:mit", "region:us" ]
text-to-image
2023-09-26T10:22:41Z
--- license: mit datasets: - CyberHarem/nakasu_kasumi_loveliveschoolidolfestivalallstars pipeline_tag: text-to-image tags: - art --- # Lora of nakasu_kasumi_loveliveschoolidolfestivalallstars This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4160, you need to download `4160/nakasu_kasumi_loveliveschoolidolfestivalallstars.pt` as the embedding and `4160/nakasu_kasumi_loveliveschoolidolfestivalallstars.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4160**, with the score of 0.968. The trigger words are: 1. `nakasu_kasumi_loveliveschoolidolfestivalallstars` 2. `short_hair, bangs, red_eyes, brown_hair, blush, smile, bob_cut, hair_ornament, bow, asymmetrical_hair, grey_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | pattern_19 | pattern_20 | pattern_21 | pattern_22 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 7800 | 0.957 | [Download](7800/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-7800](7800/previews/pattern_1.png) | ![pattern_2-7800](7800/previews/pattern_2.png) | ![pattern_3-7800](7800/previews/pattern_3.png) | ![pattern_4-7800](7800/previews/pattern_4.png) | ![pattern_5-7800](7800/previews/pattern_5.png) | ![pattern_6-7800](7800/previews/pattern_6.png) | ![pattern_7-7800](7800/previews/pattern_7.png) | ![pattern_8-7800](7800/previews/pattern_8.png) | ![pattern_9-7800](7800/previews/pattern_9.png) | ![pattern_10-7800](7800/previews/pattern_10.png) | ![pattern_11-7800](7800/previews/pattern_11.png) | ![pattern_12-7800](7800/previews/pattern_12.png) | ![pattern_13-7800](7800/previews/pattern_13.png) | ![pattern_14-7800](7800/previews/pattern_14.png) | ![pattern_15-7800](7800/previews/pattern_15.png) | ![pattern_16-7800](7800/previews/pattern_16.png) | ![pattern_17-7800](7800/previews/pattern_17.png) | ![pattern_18-7800](7800/previews/pattern_18.png) | ![pattern_19-7800](7800/previews/pattern_19.png) | ![pattern_20-7800](7800/previews/pattern_20.png) | ![pattern_21-7800](7800/previews/pattern_21.png) | ![pattern_22-7800](7800/previews/pattern_22.png) | [<NSFW, click to see>](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) | ![free-7800](7800/previews/free.png) | ![maid-7800](7800/previews/maid.png) | ![miko-7800](7800/previews/miko.png) | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) | ![suit-7800](7800/previews/suit.png) | ![yukata-7800](7800/previews/yukata.png) | | 7280 | 0.968 | [Download](7280/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-7280](7280/previews/pattern_1.png) | ![pattern_2-7280](7280/previews/pattern_2.png) | ![pattern_3-7280](7280/previews/pattern_3.png) | ![pattern_4-7280](7280/previews/pattern_4.png) | ![pattern_5-7280](7280/previews/pattern_5.png) | ![pattern_6-7280](7280/previews/pattern_6.png) | ![pattern_7-7280](7280/previews/pattern_7.png) | ![pattern_8-7280](7280/previews/pattern_8.png) | ![pattern_9-7280](7280/previews/pattern_9.png) | ![pattern_10-7280](7280/previews/pattern_10.png) | ![pattern_11-7280](7280/previews/pattern_11.png) | ![pattern_12-7280](7280/previews/pattern_12.png) | ![pattern_13-7280](7280/previews/pattern_13.png) | ![pattern_14-7280](7280/previews/pattern_14.png) | ![pattern_15-7280](7280/previews/pattern_15.png) | ![pattern_16-7280](7280/previews/pattern_16.png) | ![pattern_17-7280](7280/previews/pattern_17.png) | ![pattern_18-7280](7280/previews/pattern_18.png) | ![pattern_19-7280](7280/previews/pattern_19.png) | ![pattern_20-7280](7280/previews/pattern_20.png) | ![pattern_21-7280](7280/previews/pattern_21.png) | ![pattern_22-7280](7280/previews/pattern_22.png) | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) | ![free-7280](7280/previews/free.png) | ![maid-7280](7280/previews/maid.png) | ![miko-7280](7280/previews/miko.png) | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) | ![suit-7280](7280/previews/suit.png) | ![yukata-7280](7280/previews/yukata.png) | | 6760 | 0.961 | [Download](6760/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-6760](6760/previews/pattern_1.png) | ![pattern_2-6760](6760/previews/pattern_2.png) | ![pattern_3-6760](6760/previews/pattern_3.png) | ![pattern_4-6760](6760/previews/pattern_4.png) | ![pattern_5-6760](6760/previews/pattern_5.png) | ![pattern_6-6760](6760/previews/pattern_6.png) | ![pattern_7-6760](6760/previews/pattern_7.png) | ![pattern_8-6760](6760/previews/pattern_8.png) | ![pattern_9-6760](6760/previews/pattern_9.png) | ![pattern_10-6760](6760/previews/pattern_10.png) | ![pattern_11-6760](6760/previews/pattern_11.png) | ![pattern_12-6760](6760/previews/pattern_12.png) | ![pattern_13-6760](6760/previews/pattern_13.png) | ![pattern_14-6760](6760/previews/pattern_14.png) | ![pattern_15-6760](6760/previews/pattern_15.png) | ![pattern_16-6760](6760/previews/pattern_16.png) | ![pattern_17-6760](6760/previews/pattern_17.png) | ![pattern_18-6760](6760/previews/pattern_18.png) | ![pattern_19-6760](6760/previews/pattern_19.png) | ![pattern_20-6760](6760/previews/pattern_20.png) | ![pattern_21-6760](6760/previews/pattern_21.png) | ![pattern_22-6760](6760/previews/pattern_22.png) | [<NSFW, click to see>](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) | ![free-6760](6760/previews/free.png) | ![maid-6760](6760/previews/maid.png) | ![miko-6760](6760/previews/miko.png) | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) | ![suit-6760](6760/previews/suit.png) | ![yukata-6760](6760/previews/yukata.png) | | 6240 | 0.952 | [Download](6240/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | ![pattern_3-6240](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | ![pattern_5-6240](6240/previews/pattern_5.png) | ![pattern_6-6240](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![pattern_10-6240](6240/previews/pattern_10.png) | ![pattern_11-6240](6240/previews/pattern_11.png) | ![pattern_12-6240](6240/previews/pattern_12.png) | ![pattern_13-6240](6240/previews/pattern_13.png) | ![pattern_14-6240](6240/previews/pattern_14.png) | ![pattern_15-6240](6240/previews/pattern_15.png) | ![pattern_16-6240](6240/previews/pattern_16.png) | ![pattern_17-6240](6240/previews/pattern_17.png) | ![pattern_18-6240](6240/previews/pattern_18.png) | ![pattern_19-6240](6240/previews/pattern_19.png) | ![pattern_20-6240](6240/previews/pattern_20.png) | ![pattern_21-6240](6240/previews/pattern_21.png) | ![pattern_22-6240](6240/previews/pattern_22.png) | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | 5720 | 0.958 | [Download](5720/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-5720](5720/previews/pattern_1.png) | ![pattern_2-5720](5720/previews/pattern_2.png) | ![pattern_3-5720](5720/previews/pattern_3.png) | ![pattern_4-5720](5720/previews/pattern_4.png) | ![pattern_5-5720](5720/previews/pattern_5.png) | ![pattern_6-5720](5720/previews/pattern_6.png) | ![pattern_7-5720](5720/previews/pattern_7.png) | ![pattern_8-5720](5720/previews/pattern_8.png) | ![pattern_9-5720](5720/previews/pattern_9.png) | ![pattern_10-5720](5720/previews/pattern_10.png) | ![pattern_11-5720](5720/previews/pattern_11.png) | ![pattern_12-5720](5720/previews/pattern_12.png) | ![pattern_13-5720](5720/previews/pattern_13.png) | ![pattern_14-5720](5720/previews/pattern_14.png) | ![pattern_15-5720](5720/previews/pattern_15.png) | ![pattern_16-5720](5720/previews/pattern_16.png) | ![pattern_17-5720](5720/previews/pattern_17.png) | ![pattern_18-5720](5720/previews/pattern_18.png) | ![pattern_19-5720](5720/previews/pattern_19.png) | ![pattern_20-5720](5720/previews/pattern_20.png) | ![pattern_21-5720](5720/previews/pattern_21.png) | ![pattern_22-5720](5720/previews/pattern_22.png) | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) | ![free-5720](5720/previews/free.png) | ![maid-5720](5720/previews/maid.png) | ![miko-5720](5720/previews/miko.png) | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) | ![suit-5720](5720/previews/suit.png) | ![yukata-5720](5720/previews/yukata.png) | | 5200 | 0.963 | [Download](5200/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-5200](5200/previews/pattern_1.png) | ![pattern_2-5200](5200/previews/pattern_2.png) | ![pattern_3-5200](5200/previews/pattern_3.png) | ![pattern_4-5200](5200/previews/pattern_4.png) | ![pattern_5-5200](5200/previews/pattern_5.png) | ![pattern_6-5200](5200/previews/pattern_6.png) | ![pattern_7-5200](5200/previews/pattern_7.png) | ![pattern_8-5200](5200/previews/pattern_8.png) | ![pattern_9-5200](5200/previews/pattern_9.png) | ![pattern_10-5200](5200/previews/pattern_10.png) | ![pattern_11-5200](5200/previews/pattern_11.png) | ![pattern_12-5200](5200/previews/pattern_12.png) | ![pattern_13-5200](5200/previews/pattern_13.png) | ![pattern_14-5200](5200/previews/pattern_14.png) | ![pattern_15-5200](5200/previews/pattern_15.png) | ![pattern_16-5200](5200/previews/pattern_16.png) | ![pattern_17-5200](5200/previews/pattern_17.png) | ![pattern_18-5200](5200/previews/pattern_18.png) | ![pattern_19-5200](5200/previews/pattern_19.png) | ![pattern_20-5200](5200/previews/pattern_20.png) | ![pattern_21-5200](5200/previews/pattern_21.png) | ![pattern_22-5200](5200/previews/pattern_22.png) | [<NSFW, click to see>](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) | ![free-5200](5200/previews/free.png) | ![maid-5200](5200/previews/maid.png) | ![miko-5200](5200/previews/miko.png) | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) | ![suit-5200](5200/previews/suit.png) | ![yukata-5200](5200/previews/yukata.png) | | 4680 | 0.953 | [Download](4680/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-4680](4680/previews/pattern_1.png) | ![pattern_2-4680](4680/previews/pattern_2.png) | ![pattern_3-4680](4680/previews/pattern_3.png) | ![pattern_4-4680](4680/previews/pattern_4.png) | ![pattern_5-4680](4680/previews/pattern_5.png) | ![pattern_6-4680](4680/previews/pattern_6.png) | ![pattern_7-4680](4680/previews/pattern_7.png) | ![pattern_8-4680](4680/previews/pattern_8.png) | ![pattern_9-4680](4680/previews/pattern_9.png) | ![pattern_10-4680](4680/previews/pattern_10.png) | ![pattern_11-4680](4680/previews/pattern_11.png) | ![pattern_12-4680](4680/previews/pattern_12.png) | ![pattern_13-4680](4680/previews/pattern_13.png) | ![pattern_14-4680](4680/previews/pattern_14.png) | ![pattern_15-4680](4680/previews/pattern_15.png) | ![pattern_16-4680](4680/previews/pattern_16.png) | ![pattern_17-4680](4680/previews/pattern_17.png) | ![pattern_18-4680](4680/previews/pattern_18.png) | ![pattern_19-4680](4680/previews/pattern_19.png) | ![pattern_20-4680](4680/previews/pattern_20.png) | ![pattern_21-4680](4680/previews/pattern_21.png) | ![pattern_22-4680](4680/previews/pattern_22.png) | [<NSFW, click to see>](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) | ![free-4680](4680/previews/free.png) | ![maid-4680](4680/previews/maid.png) | ![miko-4680](4680/previews/miko.png) | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) | ![suit-4680](4680/previews/suit.png) | ![yukata-4680](4680/previews/yukata.png) | | **4160** | **0.968** | [**Download**](4160/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-4160](4160/previews/pattern_1.png) | ![pattern_2-4160](4160/previews/pattern_2.png) | ![pattern_3-4160](4160/previews/pattern_3.png) | ![pattern_4-4160](4160/previews/pattern_4.png) | ![pattern_5-4160](4160/previews/pattern_5.png) | ![pattern_6-4160](4160/previews/pattern_6.png) | ![pattern_7-4160](4160/previews/pattern_7.png) | ![pattern_8-4160](4160/previews/pattern_8.png) | ![pattern_9-4160](4160/previews/pattern_9.png) | ![pattern_10-4160](4160/previews/pattern_10.png) | ![pattern_11-4160](4160/previews/pattern_11.png) | ![pattern_12-4160](4160/previews/pattern_12.png) | ![pattern_13-4160](4160/previews/pattern_13.png) | ![pattern_14-4160](4160/previews/pattern_14.png) | ![pattern_15-4160](4160/previews/pattern_15.png) | ![pattern_16-4160](4160/previews/pattern_16.png) | ![pattern_17-4160](4160/previews/pattern_17.png) | ![pattern_18-4160](4160/previews/pattern_18.png) | ![pattern_19-4160](4160/previews/pattern_19.png) | ![pattern_20-4160](4160/previews/pattern_20.png) | ![pattern_21-4160](4160/previews/pattern_21.png) | ![pattern_22-4160](4160/previews/pattern_22.png) | [<NSFW, click to see>](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) | ![free-4160](4160/previews/free.png) | ![maid-4160](4160/previews/maid.png) | ![miko-4160](4160/previews/miko.png) | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) | ![suit-4160](4160/previews/suit.png) | ![yukata-4160](4160/previews/yukata.png) | | 3640 | 0.962 | [Download](3640/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-3640](3640/previews/pattern_1.png) | ![pattern_2-3640](3640/previews/pattern_2.png) | ![pattern_3-3640](3640/previews/pattern_3.png) | ![pattern_4-3640](3640/previews/pattern_4.png) | ![pattern_5-3640](3640/previews/pattern_5.png) | ![pattern_6-3640](3640/previews/pattern_6.png) | ![pattern_7-3640](3640/previews/pattern_7.png) | ![pattern_8-3640](3640/previews/pattern_8.png) | ![pattern_9-3640](3640/previews/pattern_9.png) | ![pattern_10-3640](3640/previews/pattern_10.png) | ![pattern_11-3640](3640/previews/pattern_11.png) | ![pattern_12-3640](3640/previews/pattern_12.png) | ![pattern_13-3640](3640/previews/pattern_13.png) | ![pattern_14-3640](3640/previews/pattern_14.png) | ![pattern_15-3640](3640/previews/pattern_15.png) | ![pattern_16-3640](3640/previews/pattern_16.png) | ![pattern_17-3640](3640/previews/pattern_17.png) | ![pattern_18-3640](3640/previews/pattern_18.png) | ![pattern_19-3640](3640/previews/pattern_19.png) | ![pattern_20-3640](3640/previews/pattern_20.png) | ![pattern_21-3640](3640/previews/pattern_21.png) | ![pattern_22-3640](3640/previews/pattern_22.png) | [<NSFW, click to see>](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) | ![free-3640](3640/previews/free.png) | ![maid-3640](3640/previews/maid.png) | ![miko-3640](3640/previews/miko.png) | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) | ![suit-3640](3640/previews/suit.png) | ![yukata-3640](3640/previews/yukata.png) | | 3120 | 0.951 | [Download](3120/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-3120](3120/previews/pattern_1.png) | ![pattern_2-3120](3120/previews/pattern_2.png) | ![pattern_3-3120](3120/previews/pattern_3.png) | ![pattern_4-3120](3120/previews/pattern_4.png) | ![pattern_5-3120](3120/previews/pattern_5.png) | ![pattern_6-3120](3120/previews/pattern_6.png) | ![pattern_7-3120](3120/previews/pattern_7.png) | ![pattern_8-3120](3120/previews/pattern_8.png) | ![pattern_9-3120](3120/previews/pattern_9.png) | ![pattern_10-3120](3120/previews/pattern_10.png) | ![pattern_11-3120](3120/previews/pattern_11.png) | ![pattern_12-3120](3120/previews/pattern_12.png) | ![pattern_13-3120](3120/previews/pattern_13.png) | ![pattern_14-3120](3120/previews/pattern_14.png) | ![pattern_15-3120](3120/previews/pattern_15.png) | ![pattern_16-3120](3120/previews/pattern_16.png) | ![pattern_17-3120](3120/previews/pattern_17.png) | ![pattern_18-3120](3120/previews/pattern_18.png) | ![pattern_19-3120](3120/previews/pattern_19.png) | ![pattern_20-3120](3120/previews/pattern_20.png) | ![pattern_21-3120](3120/previews/pattern_21.png) | ![pattern_22-3120](3120/previews/pattern_22.png) | [<NSFW, click to see>](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) | ![free-3120](3120/previews/free.png) | ![maid-3120](3120/previews/maid.png) | ![miko-3120](3120/previews/miko.png) | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) | ![suit-3120](3120/previews/suit.png) | ![yukata-3120](3120/previews/yukata.png) | | 2600 | 0.960 | [Download](2600/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-2600](2600/previews/pattern_1.png) | ![pattern_2-2600](2600/previews/pattern_2.png) | ![pattern_3-2600](2600/previews/pattern_3.png) | ![pattern_4-2600](2600/previews/pattern_4.png) | ![pattern_5-2600](2600/previews/pattern_5.png) | ![pattern_6-2600](2600/previews/pattern_6.png) | ![pattern_7-2600](2600/previews/pattern_7.png) | ![pattern_8-2600](2600/previews/pattern_8.png) | ![pattern_9-2600](2600/previews/pattern_9.png) | ![pattern_10-2600](2600/previews/pattern_10.png) | ![pattern_11-2600](2600/previews/pattern_11.png) | ![pattern_12-2600](2600/previews/pattern_12.png) | ![pattern_13-2600](2600/previews/pattern_13.png) | ![pattern_14-2600](2600/previews/pattern_14.png) | ![pattern_15-2600](2600/previews/pattern_15.png) | ![pattern_16-2600](2600/previews/pattern_16.png) | ![pattern_17-2600](2600/previews/pattern_17.png) | ![pattern_18-2600](2600/previews/pattern_18.png) | ![pattern_19-2600](2600/previews/pattern_19.png) | ![pattern_20-2600](2600/previews/pattern_20.png) | ![pattern_21-2600](2600/previews/pattern_21.png) | ![pattern_22-2600](2600/previews/pattern_22.png) | [<NSFW, click to see>](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) | ![free-2600](2600/previews/free.png) | ![maid-2600](2600/previews/maid.png) | ![miko-2600](2600/previews/miko.png) | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) | ![suit-2600](2600/previews/suit.png) | ![yukata-2600](2600/previews/yukata.png) | | 2080 | 0.951 | [Download](2080/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-2080](2080/previews/pattern_1.png) | ![pattern_2-2080](2080/previews/pattern_2.png) | ![pattern_3-2080](2080/previews/pattern_3.png) | ![pattern_4-2080](2080/previews/pattern_4.png) | ![pattern_5-2080](2080/previews/pattern_5.png) | ![pattern_6-2080](2080/previews/pattern_6.png) | ![pattern_7-2080](2080/previews/pattern_7.png) | ![pattern_8-2080](2080/previews/pattern_8.png) | ![pattern_9-2080](2080/previews/pattern_9.png) | ![pattern_10-2080](2080/previews/pattern_10.png) | ![pattern_11-2080](2080/previews/pattern_11.png) | ![pattern_12-2080](2080/previews/pattern_12.png) | ![pattern_13-2080](2080/previews/pattern_13.png) | ![pattern_14-2080](2080/previews/pattern_14.png) | ![pattern_15-2080](2080/previews/pattern_15.png) | ![pattern_16-2080](2080/previews/pattern_16.png) | ![pattern_17-2080](2080/previews/pattern_17.png) | ![pattern_18-2080](2080/previews/pattern_18.png) | ![pattern_19-2080](2080/previews/pattern_19.png) | ![pattern_20-2080](2080/previews/pattern_20.png) | ![pattern_21-2080](2080/previews/pattern_21.png) | ![pattern_22-2080](2080/previews/pattern_22.png) | [<NSFW, click to see>](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) | ![free-2080](2080/previews/free.png) | ![maid-2080](2080/previews/maid.png) | ![miko-2080](2080/previews/miko.png) | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) | ![suit-2080](2080/previews/suit.png) | ![yukata-2080](2080/previews/yukata.png) | | 1560 | 0.913 | [Download](1560/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-1560](1560/previews/pattern_1.png) | ![pattern_2-1560](1560/previews/pattern_2.png) | ![pattern_3-1560](1560/previews/pattern_3.png) | ![pattern_4-1560](1560/previews/pattern_4.png) | ![pattern_5-1560](1560/previews/pattern_5.png) | ![pattern_6-1560](1560/previews/pattern_6.png) | ![pattern_7-1560](1560/previews/pattern_7.png) | ![pattern_8-1560](1560/previews/pattern_8.png) | ![pattern_9-1560](1560/previews/pattern_9.png) | ![pattern_10-1560](1560/previews/pattern_10.png) | ![pattern_11-1560](1560/previews/pattern_11.png) | ![pattern_12-1560](1560/previews/pattern_12.png) | ![pattern_13-1560](1560/previews/pattern_13.png) | ![pattern_14-1560](1560/previews/pattern_14.png) | ![pattern_15-1560](1560/previews/pattern_15.png) | ![pattern_16-1560](1560/previews/pattern_16.png) | ![pattern_17-1560](1560/previews/pattern_17.png) | ![pattern_18-1560](1560/previews/pattern_18.png) | ![pattern_19-1560](1560/previews/pattern_19.png) | ![pattern_20-1560](1560/previews/pattern_20.png) | ![pattern_21-1560](1560/previews/pattern_21.png) | ![pattern_22-1560](1560/previews/pattern_22.png) | [<NSFW, click to see>](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) | ![free-1560](1560/previews/free.png) | ![maid-1560](1560/previews/maid.png) | ![miko-1560](1560/previews/miko.png) | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) | ![suit-1560](1560/previews/suit.png) | ![yukata-1560](1560/previews/yukata.png) | | 1040 | 0.934 | [Download](1040/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-1040](1040/previews/pattern_1.png) | ![pattern_2-1040](1040/previews/pattern_2.png) | ![pattern_3-1040](1040/previews/pattern_3.png) | ![pattern_4-1040](1040/previews/pattern_4.png) | ![pattern_5-1040](1040/previews/pattern_5.png) | ![pattern_6-1040](1040/previews/pattern_6.png) | ![pattern_7-1040](1040/previews/pattern_7.png) | ![pattern_8-1040](1040/previews/pattern_8.png) | ![pattern_9-1040](1040/previews/pattern_9.png) | ![pattern_10-1040](1040/previews/pattern_10.png) | ![pattern_11-1040](1040/previews/pattern_11.png) | ![pattern_12-1040](1040/previews/pattern_12.png) | ![pattern_13-1040](1040/previews/pattern_13.png) | ![pattern_14-1040](1040/previews/pattern_14.png) | ![pattern_15-1040](1040/previews/pattern_15.png) | ![pattern_16-1040](1040/previews/pattern_16.png) | ![pattern_17-1040](1040/previews/pattern_17.png) | ![pattern_18-1040](1040/previews/pattern_18.png) | ![pattern_19-1040](1040/previews/pattern_19.png) | ![pattern_20-1040](1040/previews/pattern_20.png) | ![pattern_21-1040](1040/previews/pattern_21.png) | ![pattern_22-1040](1040/previews/pattern_22.png) | [<NSFW, click to see>](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) | ![free-1040](1040/previews/free.png) | ![maid-1040](1040/previews/maid.png) | ![miko-1040](1040/previews/miko.png) | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) | ![suit-1040](1040/previews/suit.png) | ![yukata-1040](1040/previews/yukata.png) | | 520 | 0.880 | [Download](520/nakasu_kasumi_loveliveschoolidolfestivalallstars.zip) | ![pattern_1-520](520/previews/pattern_1.png) | ![pattern_2-520](520/previews/pattern_2.png) | ![pattern_3-520](520/previews/pattern_3.png) | ![pattern_4-520](520/previews/pattern_4.png) | ![pattern_5-520](520/previews/pattern_5.png) | ![pattern_6-520](520/previews/pattern_6.png) | ![pattern_7-520](520/previews/pattern_7.png) | ![pattern_8-520](520/previews/pattern_8.png) | ![pattern_9-520](520/previews/pattern_9.png) | ![pattern_10-520](520/previews/pattern_10.png) | ![pattern_11-520](520/previews/pattern_11.png) | ![pattern_12-520](520/previews/pattern_12.png) | ![pattern_13-520](520/previews/pattern_13.png) | ![pattern_14-520](520/previews/pattern_14.png) | ![pattern_15-520](520/previews/pattern_15.png) | ![pattern_16-520](520/previews/pattern_16.png) | ![pattern_17-520](520/previews/pattern_17.png) | ![pattern_18-520](520/previews/pattern_18.png) | ![pattern_19-520](520/previews/pattern_19.png) | ![pattern_20-520](520/previews/pattern_20.png) | ![pattern_21-520](520/previews/pattern_21.png) | ![pattern_22-520](520/previews/pattern_22.png) | [<NSFW, click to see>](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) | ![free-520](520/previews/free.png) | ![maid-520](520/previews/maid.png) | ![miko-520](520/previews/miko.png) | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) | ![suit-520](520/previews/suit.png) | ![yukata-520](520/previews/yukata.png) |
RogerB/kinyaRoberta-large-pretrained-kinyarwanda
RogerB
2023-09-26T10:24:59Z
133
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:jean-paul/kinyaRoberta-large", "base_model:finetune:jean-paul/kinyaRoberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-26T10:06:06Z
--- base_model: jean-paul/kinyaRoberta-large tags: - generated_from_trainer model-index: - name: kinyaRoberta-large-pretrained-kinyarwanda 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. --> # kinyaRoberta-large-pretrained-kinyarwanda This model is a fine-tuned version of [jean-paul/kinyaRoberta-large](https://huggingface.co/jean-paul/kinyaRoberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0243 ## 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: 10 - eval_batch_size: 10 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.5219 | 1.0 | 2200 | 3.1955 | | 3.228 | 2.0 | 4400 | 3.0451 | | 3.1224 | 3.0 | 6600 | 3.0429 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
cjdshr/my_awesome_billsum_model
cjdshr
2023-09-26T10:24:22Z
103
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-12T08:03:45Z
--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.14 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5783 - Rouge1: 0.14 - Rouge2: 0.0488 - Rougel: 0.1161 - Rougelsum: 0.1159 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8730 | 0.1268 | 0.0358 | 0.1053 | 0.1052 | 19.0 | | No log | 2.0 | 124 | 2.6594 | 0.1352 | 0.0479 | 0.1123 | 0.1125 | 19.0 | | No log | 3.0 | 186 | 2.5966 | 0.1369 | 0.0471 | 0.1139 | 0.1138 | 19.0 | | No log | 4.0 | 248 | 2.5783 | 0.14 | 0.0488 | 0.1161 | 0.1159 | 19.0 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
s3nh/FreedomIntelligence-AceGPT-13B-chat-GGUF
s3nh
2023-09-26T10:21:29Z
7
1
transformers
[ "transformers", "gguf", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us" ]
text-generation
2023-09-26T10:06:46Z
--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/FreedomIntelligence/AceGPT-13B-chat). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### Perplexity params Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16 7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066 13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543 ### inference TODO # Original model card
kmaksatk/controlnet_80k_data_blip_2
kmaksatk
2023-09-26T10:19:41Z
1
0
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-26T07:37:02Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-kmaksatk/controlnet_80k_data_blip_2 These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. prompt: Man doing a cartwheel in blue suit ![images_0)](./images_0.png) prompt: Man doing a cartwheel in blue suit ![images_1)](./images_1.png) prompt: Man doing a cartwheel in blue suit ![images_2)](./images_2.png)
hasnain3142/phi-1_5-finetuned-gsm8k
hasnain3142
2023-09-26T10:17:45Z
0
0
null
[ "generated_from_trainer", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "region:us" ]
null
2023-09-26T09:57:37Z
--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: phi-1_5-finetuned-gsm8k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1_5-finetuned-gsm8k This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
soczyste-milfy/cycate
soczyste-milfy
2023-09-26T10:12:16Z
0
0
null
[ "region:us" ]
null
2023-09-26T10:09:25Z
# Soczyste milfy są Wspaniałe ## Wstęp Z biegiem lat zauważa się rosnący nacisk na młodość i urodę jako naczelną wartość w społeczeństwie. Jednak wiele osób zaczyna dostrzegać, że dojrzałość niesie ze sobą własny, niepowtarzalny urok i mądrość. W tym artykule postaramy się rozwiać mity dotyczące wieku i podkreślić, dlaczego <a href="https://unsee.pl/chetne-milfy">soczyste milfy</a> są wspaniałe na wiele różnych płaszczyzn. ## Doświadczenie życiowe Z wiekiem przychodzi doświadczenie, które jest nieocenione w różnych aspektach życia. Soczyste milfy często mają bogatą historię, pełną różnorodnych doświadczeń, która sprawia, że są ciekawymi osobami, mającymi wiele do zaoferowania w rozmowach i relacjach. ## Pewność siebie Latami pracy nad sobą i zdobytymi doświadczeniami dojrzałe kobiety zdobywają pewność siebie, której często brakuje młodszym osobom. Ta pewność siebie przejawia się nie tylko w zachowaniu, ale również w umiejętności podejmowania decyzji, zarządzania czasem i określania własnych priorytetów. ## Stabilność emocjonalna Wraz z doświadczeniem życiowym i pewnością siebie przychodzi również stabilność emocjonalna. Soczyste milfy są często bardziej zrównoważone emocjonalnie, co sprawia, że są świetnym wsparciem dla partnera, dzieci czy przyjaciół. ## Mądrość Nie da się ukryć, że dojrzałość często niesie ze sobą mądrość. Doświadczenia, zarówno dobre, jak i złe, uczą i kształtują charakter. Mądrość to nie tylko wiedza, ale również umiejętność jej zastosowania w praktyce, co jest nieocenione w trudnych sytuacjach życiowych. ## Zrozumienie własnych potrzeb W młodości często zdarza się nam, że nie do końca rozumiemy, czego chcemy od życia. Soczyste milfy mają już jasno sprecyzowane potrzeby i cele, co sprawia, że są one bardziej spełnione i zadowolone z życia. ## Podsumowanie Dojrzałe kobiety są wspaniałe na wiele różnych sposobów. Ich doświadczenie życiowe, pewność siebie, stabilność emocjonalna i mądrość czynią je niezwykle cennymi i inspirującymi osobami. Odejście od stereotypów dotyczących wieku i uznania wartości, jakie niesie ze sobą dojrzałość, to krok w stronę głębszego i bardziej satysfakcjonującego życia dla nas wszystkich.
Mkmworld/all-classification
Mkmworld
2023-09-26T10:07:00Z
0
0
keras
[ "keras", "tf-keras", "region:us" ]
null
2023-09-26T10:05:19Z
--- library_name: keras --- ## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | learning_rate | 9.999999747378752e-05 | | decay | 1e-05 | | beta_1 | 0.8999999761581421 | | beta_2 | 0.9990000128746033 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
prateeky2806/bert-base-uncased-qnli-ia3-epochs-2-lr-0.005
prateeky2806
2023-09-26T10:05:51Z
0
0
null
[ "safetensors", "generated_from_trainer", "dataset:glue", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]
null
2023-09-26T01:37:04Z
--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-base-uncased-qnli-ia3-epochs-2-lr-0.005 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-base-uncased-qnli-ia3-epochs-2-lr-0.005 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3135 - Accuracy: 0.88 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3738 | 1.0 | 3271 | 0.3193 | 0.88 | | 0.3316 | 2.0 | 6542 | 0.3135 | 0.88 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
Dibyasha2023/sd-class-butterflies-32
Dibyasha2023
2023-09-26T10:01:31Z
45
0
diffusers
[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2023-09-26T10:01:19Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Dibyasha2023/sd-class-butterflies-32') image = pipeline().images[0] image ```