modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-27 00:39:58
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 521
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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digiplay/majicMIX_realistic_v4
|
digiplay
| 2023-09-26T06:35:55Z | 665 | 6 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-29T20:14:50Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/43331/majicmix-realistic
Sample image I made generated by huggingface's API :
|
AescF/hubert-base-ls960-finetuned-common_language
|
AescF
| 2023-09-26T06:31:48Z | 161 | 1 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"base_model:facebook/hubert-base-ls960",
"base_model:finetune:facebook/hubert-base-ls960",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-25T22:50:25Z |
---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: hubert-base-ls960-finetuned-common_language-finetuned-common_language
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Common Language
type: common_language
config: full
split: test
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.8011068254234446
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert-base-ls960-finetuned-common_language-finetuned-common_language
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the Common Language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4164
- Accuracy: 0.8011
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.9713 | 1.0 | 2774 | 3.0764 | 0.1615 |
| 1.7443 | 2.0 | 5549 | 1.8279 | 0.4734 |
| 1.1304 | 3.0 | 8323 | 1.3202 | 0.6371 |
| 1.2718 | 4.0 | 11098 | 1.1571 | 0.6968 |
| 0.769 | 5.0 | 13872 | 1.2917 | 0.7127 |
| 0.2656 | 6.0 | 16647 | 1.1549 | 0.7479 |
| 0.2939 | 7.0 | 19421 | 1.2372 | 0.7736 |
| 0.1278 | 8.0 | 22196 | 1.2985 | 0.7875 |
| 0.5175 | 9.0 | 24970 | 1.3664 | 0.7986 |
| 0.0547 | 10.0 | 27740 | 1.4164 | 0.8011 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
mikeee/llama-2-7b-nyt31k
|
mikeee
| 2023-09-26T06:29:32Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-26T05:47:44Z |
---
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 0.5.0
### Trained with
- https://huggingface.co/datasets/mikeee/en-zh-nyt31k
- 500 epochs
- Instruction Template
```
### Instruction:
Translate the following text to Chinese.
### Input:
{english}
### Response:
```
|
jiang9527li/ppo-LunarLander-v2
|
jiang9527li
| 2023-09-26T06:26:59Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-26T06:26:18Z |
---
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: -55.30 +/- 36.06
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
...
```
|
shrimantasatpati/lora-trained-xl-colab
|
shrimantasatpati
| 2023-09-26T06:25:27Z | 13 | 1 |
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
| 2023-08-24T08:16:42Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of WARLI painting
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - shrimantasatpati/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 WARLI painting 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.
|
NEU-HAI/mental-flan-t5-xxl
|
NEU-HAI
| 2023-09-26T06:18:24Z | 107 | 3 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"mental",
"mental health",
"large language model",
"flan-t5",
"en",
"arxiv:2307.14385",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-21T18:11:01Z |
---
license: apache-2.0
language:
- en
tags:
- mental
- mental health
- large language model
- flan-t5
---
# Model Card for mental-flan-t5-xxl
<!-- Provide a quick summary of what the model is/does. -->
This is a fine-tuned large language model for mental health prediction via online text data.
## Model Details
### Model Description
We fine-tune a FLAN-T5-XXL model with 4 high-quality text (6 tasks in total) datasets for the mental health prediction scenario: Dreaddit, DepSeverity, SDCNL, and CCRS-Suicide.
We have a separate model, fine-tuned on Alpaca, namely Mental-Alpaca, shared [here](https://huggingface.co/NEU-HAI/mental-alpaca)
- **Developed by:** Northeastern University Human-Centered AI Lab
- **Model type:** Sequence-to-sequence Text-generation
- **Language(s) (NLP):** English
- **License:** Apache 2.0 License
- **Finetuned from model :** [FLAN-T5-XXL](https://huggingface.co/google/flan-t5-xxl)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/neuhai/Mental-LLM
- **Paper:** https://arxiv.org/abs/2307.14385
## 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
The model is intended to be used for research purposes only in English.
The model has been fine-tuned for mental health prediction via online text data. Detailed information about the fine-tuning process and prompts can be found in our [paper](https://arxiv.org/abs/2307.14385).
The use of this model should also comply with the restrictions from [FLAN-T5-XXL](https://huggingface.co/google/flan-t5-xxl)
### Out-of-Scope Use
The out-of-scope use of this model should comply with [FLAN-T5-XXL](https://huggingface.co/google/flan-t5-xxl).
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The Bias, Risks, and Limitations of this model should also comply with [FLAN-T5-XXL](https://huggingface.co/google/flan-t5-xxl).
## How to Get Started with the Model
Use the code below to get started with the model.
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5ForConditionalGeneration.from_pretrained("NEU-HAI/mental-flan-t5-xxl")
mdoel = T5Tokenizer.from_pretrained("NEU-HAI/mental-flan-t5-xxl")
```
## Training Details and Evaluation
Detailed information about our work can be found in our [paper](https://arxiv.org/abs/2307.14385).
## Citation
```
@article{xu2023leveraging,
title={Mental-LLM: Leveraging large language models for mental health prediction via online text data},
author={Xu, Xuhai and Yao, Bingshen and Dong, Yuanzhe and Gabriel, Saadia and Yu, Hong and Ghassemi, Marzyeh and Hendler, James and Dey, Anind K and Wang, Dakuo},
journal={arXiv preprint arXiv:2307.14385},
year={2023}
}
```
|
sarahnfdez/sarahsRLRepo
|
sarahnfdez
| 2023-09-26T06:13:41Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-26T02:54:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.56 +/- 13.29
name: mean_reward
verified: false
---
# **MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **MlpPolicy** 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
...
```
|
glens98/phi-1_5-finetuned-gsm8k
|
glens98
| 2023-09-26T06:07:07Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-09-26T05:10:26Z |
---
license: other
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 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
rooftopcoder/t5-small-coqa
|
rooftopcoder
| 2023-09-26T05:52:24Z | 24 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"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
| 2023-05-12T08:19:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
base_model: t5-small
model-index:
- name: t5-small-coqa
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-small-coqa
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: 1.0055
- Accuracy: 0.0777
- F1: 0.0501
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
rooftopcoder/long-t5-tglobal-base-16384-book-summary-finetuned-dialogsum
|
rooftopcoder
| 2023-09-26T05:52:16Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"longt5",
"text2text-generation",
"generated_from_trainer",
"base_model:pszemraj/long-t5-tglobal-base-16384-book-summary",
"base_model:finetune:pszemraj/long-t5-tglobal-base-16384-book-summary",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-15T16:11:04Z |
---
license: bsd-3-clause
tags:
- generated_from_trainer
metrics:
- rouge
base_model: pszemraj/long-t5-tglobal-base-16384-book-summary
model-index:
- name: long-t5-tglobal-base-16384-book-summary-finetuned-dialogsum
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. -->
# long-t5-tglobal-base-16384-book-summary-finetuned-dialogsum
This model is a fine-tuned version of [pszemraj/long-t5-tglobal-base-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 2.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 3115 | nan | 25.3388 | 5.7186 | 18.439 | 21.6766 | 53.338 |
| 0.0 | 2.0 | 6230 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
prateeky2806/bert-base-uncased-mnli-lora-epochs-2-lr-0.001
|
prateeky2806
| 2023-09-26T05:50:06Z | 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 | 2023-09-26T01:34:12Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-mnli-lora-epochs-2-lr-0.001
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-mnli-lora-epochs-2-lr-0.001
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: 0.3961
- Accuracy: 0.85
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6399 | 1.0 | 12269 | 0.4920 | 0.76 |
| 0.4959 | 2.0 | 24538 | 0.3961 | 0.85 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CyberHarem/wakana_shiki_lovelivesuperstar
|
CyberHarem
| 2023-09-26T05:43:31Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/wakana_shiki_lovelivesuperstar",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-26T05:32:36Z |
---
license: mit
datasets:
- CyberHarem/wakana_shiki_lovelivesuperstar
pipeline_tag: text-to-image
tags:
- art
---
# Lora of wakana_shiki_lovelivesuperstar
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 6600, you need to download `6600/wakana_shiki_lovelivesuperstar.pt` as the embedding and `6600/wakana_shiki_lovelivesuperstar.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6600**, with the score of 0.988. The trigger words are:
1. `wakana_shiki_lovelivesuperstar`
2. `blue_hair, short_hair, bangs, hair_between_eyes, blush, jewelry, earrings, ribbon, red_ribbon, neck_ribbon, breasts`
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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **6600** | **0.988** | [**Download**](6600/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](6600/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](6600/previews/bikini.png) | [<NSFW, click to see>](6600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) |  |  |
| 6160 | 0.911 | [Download](6160/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](6160/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](6160/previews/bikini.png) | [<NSFW, click to see>](6160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) |  |  |
| 5720 | 0.902 | [Download](5720/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](5720/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5280 | 0.916 | [Download](5280/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](5280/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4840 | 0.986 | [Download](4840/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](4840/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](4840/previews/bikini.png) | [<NSFW, click to see>](4840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4840/previews/nude.png) | [<NSFW, click to see>](4840/previews/nude2.png) |  |  |
| 4400 | 0.975 | [Download](4400/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](4400/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](4400/previews/bikini.png) | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| 3960 | 0.840 | [Download](3960/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](3960/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](3960/previews/bikini.png) | [<NSFW, click to see>](3960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) |  |  |
| 3520 | 0.852 | [Download](3520/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](3520/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](3520/previews/bikini.png) | [<NSFW, click to see>](3520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3520/previews/nude.png) | [<NSFW, click to see>](3520/previews/nude2.png) |  |  |
| 3080 | 0.920 | [Download](3080/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](3080/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](3080/previews/bikini.png) | [<NSFW, click to see>](3080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3080/previews/nude.png) | [<NSFW, click to see>](3080/previews/nude2.png) |  |  |
| 2640 | 0.920 | [Download](2640/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](2640/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](2640/previews/bikini.png) | [<NSFW, click to see>](2640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2640/previews/nude.png) | [<NSFW, click to see>](2640/previews/nude2.png) |  |  |
| 2200 | 0.920 | [Download](2200/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](2200/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](2200/previews/bikini.png) | [<NSFW, click to see>](2200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2200/previews/nude.png) | [<NSFW, click to see>](2200/previews/nude2.png) |  |  |
| 1760 | 0.910 | [Download](1760/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](1760/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](1760/previews/bikini.png) | [<NSFW, click to see>](1760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1760/previews/nude.png) | [<NSFW, click to see>](1760/previews/nude2.png) |  |  |
| 1320 | 0.914 | [Download](1320/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](1320/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](1320/previews/bikini.png) | [<NSFW, click to see>](1320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1320/previews/nude.png) | [<NSFW, click to see>](1320/previews/nude2.png) |  |  |
| 880 | 0.910 | [Download](880/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](880/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](880/previews/bikini.png) | [<NSFW, click to see>](880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](880/previews/nude.png) | [<NSFW, click to see>](880/previews/nude2.png) |  |  |
| 440 | 0.894 | [Download](440/wakana_shiki_lovelivesuperstar.zip) | [<NSFW, click to see>](440/previews/pattern_1.png) |  |  |  |  |  | [<NSFW, click to see>](440/previews/bikini.png) | [<NSFW, click to see>](440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](440/previews/nude.png) | [<NSFW, click to see>](440/previews/nude2.png) |  |  |
|
Ori/lama-2-13b-peft-mh-ret-mix-v2-seed-2
|
Ori
| 2023-09-26T05:25:29Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-09-26T05:24:12Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
hellomyoh/llama2-2b-s117755-v1
|
hellomyoh
| 2023-09-26T05:08:38Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"region:us"
] | null | 2023-09-22T15:22:03Z |
---
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
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
- PEFT 0.6.0.dev0
|
BrianDsouzaAI/autotrain-even_better-91480144518
|
BrianDsouzaAI
| 2023-09-26T05:06:08Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:BrianDsouzaAI/autotrain-data-even_better",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-26T05:04:57Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- BrianDsouzaAI/autotrain-data-even_better
co2_eq_emissions:
emissions: 0.387970627555954
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 91480144518
- CO2 Emissions (in grams): 0.3880
## Validation Metrics
- Loss: 0.738
- Accuracy: 0.667
- Macro F1: 0.456
- Micro F1: 0.667
- Weighted F1: 0.648
- Macro Precision: 0.442
- Micro Precision: 0.667
- Weighted Precision: 0.632
- Macro Recall: 0.471
- Micro Recall: 0.667
- Weighted Recall: 0.667
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/BrianDsouzaAI/autotrain-even_better-91480144518
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("BrianDsouzaAI/autotrain-even_better-91480144518", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("BrianDsouzaAI/autotrain-even_better-91480144518", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
dipplestix/Reinforce-cart_pole
|
dipplestix
| 2023-09-26T05:01:46Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-26T05:01:37Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cart_pole
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
|
nc33/3label_model
|
nc33
| 2023-09-26T05:00:49Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-11T04:26:28Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: microsoft/deberta-v3-base
model-index:
- name: 3label_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 3label_model
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3920
- Accuracy: 0.8520
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6073 | 1.0 | 707 | 0.3921 | 0.8343 |
| 0.3319 | 2.0 | 1414 | 0.3920 | 0.8520 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
nc33/yes_no_qna_deberta_model
|
nc33
| 2023-09-26T04:59:40Z | 302 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:super_glue",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-30T14:50:14Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
base_model: microsoft/deberta-v3-base
model-index:
- name: yes_no_qna_deberta_model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: super_glue
type: super_glue
config: boolq
split: train
args: boolq
metrics:
- type: accuracy
value: 0.8507645259938837
name: Accuracy
---
<!-- 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. -->
# yes_no_qna_deberta_model
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5570
- Accuracy: 0.8508
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.583 | 1.0 | 590 | 0.4086 | 0.8251 |
| 0.348 | 2.0 | 1180 | 0.4170 | 0.8465 |
| 0.2183 | 3.0 | 1770 | 0.5570 | 0.8508 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
vstudent/bert-finetuned-ner
|
vstudent
| 2023-09-26T04:58:20Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"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
| 2023-09-26T03:00:26Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: vstudent/bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vstudent/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0280
- Validation Loss: 0.0538
- 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1764 | 0.0653 | 0 |
| 0.0482 | 0.0571 | 1 |
| 0.0280 | 0.0538 | 2 |
### Framework versions
- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
prateeky2806/bert-base-uncased-qqp-lora-epochs-2-lr-0.0005
|
prateeky2806
| 2023-09-26T04:50:50Z | 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 | 2023-09-26T03:20:04Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-qqp-lora-epochs-2-lr-0.0005
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-qqp-lora-epochs-2-lr-0.0005
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: 0.1329
- Accuracy: 0.95
- F1: 0.9333
## 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: 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 | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.2986 | 1.0 | 11368 | 0.1556 | 0.94 | 0.9189 |
| 0.238 | 2.0 | 22736 | 0.1329 | 0.95 | 0.9333 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters
|
luonghuuthanhnam5
| 2023-09-26T04:44:57Z | 0 | 0 |
peft
|
[
"peft",
"llama",
"region:us"
] | null | 2023-09-19T05:08:55Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
prateeky2806/bert-base-uncased-wnli-epochs-10-lr-0.0001
|
prateeky2806
| 2023-09-26T04:39:45Z | 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
| 2023-09-26T01:23:40Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-wnli-epochs-10-lr-0.0001
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: wnli
split: train
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.36
---
<!-- 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-wnli-epochs-10-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: 0.6970
- Accuracy: 0.36
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 17 | 0.7353 | 0.35 |
| No log | 2.0 | 34 | 0.7247 | 0.43 |
| No log | 3.0 | 51 | 0.7062 | 0.43 |
| No log | 4.0 | 68 | 0.6870 | 0.57 |
| No log | 5.0 | 85 | 0.6935 | 0.47 |
| No log | 6.0 | 102 | 0.6891 | 0.57 |
| No log | 7.0 | 119 | 0.6949 | 0.44 |
| No log | 8.0 | 136 | 0.6996 | 0.43 |
| No log | 9.0 | 153 | 0.6981 | 0.42 |
| No log | 10.0 | 170 | 0.6970 | 0.36 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
prateeky2806/bert-base-uncased-wnli-ia3-epochs-10-lr-5e-05
|
prateeky2806
| 2023-09-26T04:36:21Z | 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 | 2023-09-26T01:22:17Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-wnli-ia3-epochs-10-lr-5e-05
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-wnli-ia3-epochs-10-lr-5e-05
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: 0.6952
- Accuracy: 0.48
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 17 | 0.6792 | 0.57 |
| No log | 2.0 | 34 | 0.6907 | 0.57 |
| No log | 3.0 | 51 | 0.7020 | 0.43 |
| No log | 4.0 | 68 | 0.7008 | 0.44 |
| No log | 5.0 | 85 | 0.6960 | 0.48 |
| No log | 6.0 | 102 | 0.6953 | 0.47 |
| No log | 7.0 | 119 | 0.6933 | 0.5 |
| No log | 8.0 | 136 | 0.6944 | 0.5 |
| No log | 9.0 | 153 | 0.6952 | 0.48 |
| No log | 10.0 | 170 | 0.6952 | 0.48 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
prateeky2806/bert-base-uncased-wnli-lora-epochs-10-lr-1e-06
|
prateeky2806
| 2023-09-26T04:32:12Z | 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 | 2023-09-26T01:21:25Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-wnli-lora-epochs-10-lr-1e-06
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-wnli-lora-epochs-10-lr-1e-06
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: 0.6821
- Accuracy: 0.58
## 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-06
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 17 | 0.6812 | 0.57 |
| No log | 2.0 | 34 | 0.6814 | 0.57 |
| No log | 3.0 | 51 | 0.6815 | 0.57 |
| No log | 4.0 | 68 | 0.6817 | 0.57 |
| No log | 5.0 | 85 | 0.6818 | 0.57 |
| No log | 6.0 | 102 | 0.6819 | 0.57 |
| No log | 7.0 | 119 | 0.6820 | 0.57 |
| No log | 8.0 | 136 | 0.6820 | 0.58 |
| No log | 9.0 | 153 | 0.6821 | 0.58 |
| No log | 10.0 | 170 | 0.6821 | 0.58 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
prateeky2806/bert-base-uncased-rte-epochs-10-lr-1e-05
|
prateeky2806
| 2023-09-26T04:12:06Z | 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
| 2023-09-26T04:04:46Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-rte-epochs-10-lr-1e-05
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: rte
split: train
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.74
---
<!-- 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-rte-epochs-10-lr-1e-05
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: 0.6968
- Accuracy: 0.74
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 75 | 0.6999 | 0.44 |
| No log | 2.0 | 150 | 0.6216 | 0.69 |
| No log | 3.0 | 225 | 0.5941 | 0.69 |
| No log | 4.0 | 300 | 0.5779 | 0.74 |
| No log | 5.0 | 375 | 0.5871 | 0.73 |
| No log | 6.0 | 450 | 0.6203 | 0.76 |
| 0.5133 | 7.0 | 525 | 0.6944 | 0.76 |
| 0.5133 | 8.0 | 600 | 0.6647 | 0.75 |
| 0.5133 | 9.0 | 675 | 0.6803 | 0.78 |
| 0.5133 | 10.0 | 750 | 0.6968 | 0.74 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Adun/openthaigpt-1.0.0-beta-7b-ckpt-hf
|
Adun
| 2023-09-26T04:04:02Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-29T15:39:53Z |
This model exported from
### base_model = 'ChanonUtupon/openthaigpt-merge-lora-llama-2-7B-3470k'
### lora_weights = 'openthaigpt/openthaigpt-1.0.0-beta-7b-chat'
for education purpose.
## more details.
https://aiotplatform.blogspot.com/2023/09/demo-openthaigpt-1.0.0-beta-colab.html
|
guetLzy/VITS-fast-fine-tuning
|
guetLzy
| 2023-09-26T04:03:57Z | 0 | 6 | null |
[
"license:cc-by-2.0",
"region:us"
] | null | 2023-09-26T03:51:32Z |
---
license: cc-by-2.0
---
# VITS-fast-fine-tuning模型分享
1.此模型包含三个说话人，刻晴，神里绫华，钟离。
2.模型训练了500个epoch，使用C底模训练而成。
3.训练的数据为每个说话人至少500条语音。
4.本地推理建议使用官方的[推理程序](https://github.com/Plachtaa/VITS-fast-fine-tuning/releases/download/webui-v1.1/inference.rar)。
5.解压之后把模型和json文件如下放置，之后运行 inference.exe文件即可。
```
inference
├───inference.exe
├───...
├───finetune_speaker.json
└───G_latest.pth
```
|
alperenunlu/Reinforce-CartPole-v1
|
alperenunlu
| 2023-09-26T04:02:39Z | 0 | 2 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-26T01:28:45Z |
---
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** .
|
ajash/Amazon-lm-10k
|
ajash
| 2023-09-26T04:00:00Z | 5 | 0 |
peft
|
[
"peft",
"base_model:togethercomputer/LLaMA-2-7B-32K",
"base_model:adapter:togethercomputer/LLaMA-2-7B-32K",
"region:us"
] | null | 2023-09-08T20:57:11Z |
---
library_name: peft
base_model: togethercomputer/LLaMA-2-7B-32K
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
prateeky2806/bert-base-uncased-rte-lora-epochs-10-lr-0.0005
|
prateeky2806
| 2023-09-26T03:42:24Z | 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 | 2023-09-26T01:17:47Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-rte-lora-epochs-10-lr-0.0005
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-rte-lora-epochs-10-lr-0.0005
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: 2.0542
- Accuracy: 0.63
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 75 | 0.6901 | 0.56 |
| No log | 2.0 | 150 | 0.6403 | 0.66 |
| No log | 3.0 | 225 | 0.6422 | 0.71 |
| No log | 4.0 | 300 | 0.7092 | 0.65 |
| No log | 5.0 | 375 | 0.8987 | 0.67 |
| No log | 6.0 | 450 | 1.2375 | 0.65 |
| 0.4488 | 7.0 | 525 | 1.7709 | 0.61 |
| 0.4488 | 8.0 | 600 | 1.7151 | 0.63 |
| 0.4488 | 9.0 | 675 | 1.9559 | 0.62 |
| 0.4488 | 10.0 | 750 | 2.0542 | 0.63 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CyberHarem/heanna_sumire_lovelivesuperstar
|
CyberHarem
| 2023-09-26T03:10:08Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/heanna_sumire_lovelivesuperstar",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-26T02:51:59Z |
---
license: mit
datasets:
- CyberHarem/heanna_sumire_lovelivesuperstar
pipeline_tag: text-to-image
tags:
- art
---
# Lora of heanna_sumire_lovelivesuperstar
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 3640, you need to download `3640/heanna_sumire_lovelivesuperstar.pt` as the embedding and `3640/heanna_sumire_lovelivesuperstar.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 3640**, with the score of 0.986. The trigger words are:
1. `heanna_sumire_lovelivesuperstar`
2. `blonde_hair, bangs, green_eyes, long_hair, blunt_bangs, smile, hairband, blush, breasts, neck_ribbon`
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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.982 | [Download](7800/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.967 | [Download](7280/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.972 | [Download](6760/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.965 | [Download](6240/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.946 | [Download](5720/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.910 | [Download](5200/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.975 | [Download](4680/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.948 | [Download](4160/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| **3640** | **0.986** | [**Download**](3640/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.944 | [Download](3120/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.916 | [Download](2600/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.980 | [Download](2080/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.960 | [Download](1560/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.947 | [Download](1040/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.978 | [Download](520/heanna_sumire_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
prateeky2806/bert-base-uncased-cola-epochs-10-lr-5e-05
|
prateeky2806
| 2023-09-26T03:10:04Z | 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
| 2023-09-26T02:59:59Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-cola-epochs-10-lr-5e-05
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5435768262757358
---
<!-- 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-cola-epochs-10-lr-5e-05
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: 1.3812
- Matthews Correlation: 0.5436
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 265 | 0.5922 | 0.4074 |
| 0.4065 | 2.0 | 530 | 0.4494 | 0.6144 |
| 0.4065 | 3.0 | 795 | 0.4548 | 0.5738 |
| 0.1623 | 4.0 | 1060 | 0.6883 | 0.5687 |
| 0.1623 | 5.0 | 1325 | 0.7222 | 0.5183 |
| 0.081 | 6.0 | 1590 | 1.0246 | 0.5371 |
| 0.081 | 7.0 | 1855 | 1.1457 | 0.5145 |
| 0.0344 | 8.0 | 2120 | 1.1771 | 0.5436 |
| 0.0344 | 9.0 | 2385 | 1.3187 | 0.5485 |
| 0.0123 | 10.0 | 2650 | 1.3812 | 0.5436 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
prateeky2806/bert-base-uncased-mrpc-epochs-10-lr-5e-05
|
prateeky2806
| 2023-09-26T03:09:18Z | 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
| 2023-09-26T03:00:33Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-mrpc-epochs-10-lr-5e-05
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.83
- name: F1
type: f1
value: 0.8794326241134751
---
<!-- 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-mrpc-epochs-10-lr-5e-05
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: 1.1650
- Accuracy: 0.83
- F1: 0.8794
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 112 | 0.4455 | 0.77 | 0.8414 |
| No log | 2.0 | 224 | 0.4557 | 0.8 | 0.8611 |
| No log | 3.0 | 336 | 0.6409 | 0.8 | 0.8551 |
| No log | 4.0 | 448 | 0.6648 | 0.82 | 0.8767 |
| 0.2723 | 5.0 | 560 | 0.8845 | 0.84 | 0.8873 |
| 0.2723 | 6.0 | 672 | 0.9873 | 0.84 | 0.8841 |
| 0.2723 | 7.0 | 784 | 1.0540 | 0.83 | 0.8777 |
| 0.2723 | 8.0 | 896 | 1.0712 | 0.85 | 0.8921 |
| 0.0161 | 9.0 | 1008 | 1.1467 | 0.84 | 0.8857 |
| 0.0161 | 10.0 | 1120 | 1.1650 | 0.83 | 0.8794 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Kodamn47/distilhubert-finetuned-gtzan
|
Kodamn47
| 2023-09-26T03:04:32Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-12T18:00:38Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4487
- Accuracy: 0.88
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.74 | 1.0 | 112 | 0.6136 | 0.81 |
| 0.6137 | 2.0 | 225 | 0.7364 | 0.76 |
| 0.5996 | 3.0 | 337 | 0.5322 | 0.88 |
| 0.516 | 4.0 | 450 | 0.9805 | 0.73 |
| 0.4013 | 5.0 | 562 | 0.5349 | 0.86 |
| 0.1779 | 6.0 | 675 | 0.6328 | 0.82 |
| 0.1356 | 7.0 | 787 | 0.5007 | 0.85 |
| 0.1938 | 8.0 | 900 | 0.5199 | 0.86 |
| 0.3675 | 9.0 | 1012 | 0.4209 | 0.9 |
| 0.1299 | 9.96 | 1120 | 0.4487 | 0.88 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
soohmatthew/reddit-setfit-model-multilabel-1
|
soohmatthew
| 2023-09-26T03:04:22Z | 4 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-09-12T08:22:19Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# soohmatthew/reddit-setfit-model-multilabel-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("soohmatthew/reddit-setfit-model-multilabel-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}
}
```
|
Siddhanta19/Ctrl-backup
|
Siddhanta19
| 2023-09-26T02:39:14Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-09-26T02:39:14Z |
---
license: openrail
---
This is the model files for [ControlNet 1.1](https://github.com/lllyasviel/ControlNet-v1-1-nightly).
This model card will be filled in a more detailed way after 1.1 is officially merged into ControlNet.
|
chgenly/poca-SoccerTwos
|
chgenly
| 2023-09-26T02:38:55Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-09-26T02:38:50Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: chgenly/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kongju7/distilbert-base-uncased-finetuned-emotion
|
kongju7
| 2023-09-26T02:23:18Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-26T02:10:16Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1561
- Accuracy: 0.934
- F1: 0.9345
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2034 | 1.0 | 250 | 0.1754 | 0.9315 | 0.9323 |
| 0.1344 | 2.0 | 500 | 0.1561 | 0.934 | 0.9345 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
|
wangrongsheng/careqwen-14B-Chat-sft-multi
|
wangrongsheng
| 2023-09-26T02:18:16Z | 2 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-26T02:17:21Z |
---
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: float16
### Framework versions
- PEFT 0.4.0
|
mozci/nonstablediff
|
mozci
| 2023-09-26T02:18:13Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"dataset:mozci/tinysketch",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-17T01:24:25Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
datasets:
- mozci/tinysketch
library_name: diffusers
pipeline_tag: text-to-image
---
# LoRA text2image fine-tuning - mozci/nonstablediff
Stable diffusion fine-tuning to achieve simple sketches as the outputs. These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the mozci/tinysketch dataset. You can find some example images in the following.
|
Thangnv/t5
|
Thangnv
| 2023-09-26T02:09:05Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:NlpHUST/t5-en-vi-small",
"base_model:finetune:NlpHUST/t5-en-vi-small",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-25T14:20:11Z |
---
base_model: NlpHUST/t5-en-vi-small
tags:
- generated_from_keras_callback
model-index:
- name: Thangnv/t5
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. -->
# Thangnv/t5
This model is a fine-tuned version of [NlpHUST/t5-en-vi-small](https://huggingface.co/NlpHUST/t5-en-vi-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6838
- Train Sparse Categorical Accuracy: 0.8193
- Validation Loss: 0.6798
- Validation Sparse Categorical Accuracy: 0.8227
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.8162 | 0.7924 | 0.7433 | 0.8094 | 0 |
| 0.7413 | 0.8077 | 0.7142 | 0.8151 | 1 |
| 0.7069 | 0.8147 | 0.6927 | 0.8199 | 2 |
| 0.6838 | 0.8193 | 0.6798 | 0.8227 | 3 |
### Framework versions
- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
selinawisco/my_awesome_minds_model
|
selinawisco
| 2023-09-26T02:00:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-16T05:12:39Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: my_awesome_minds_model
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.07964601769911504
---
<!-- 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_minds_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6462
- Accuracy: 0.0796
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6467 | 0.0619 |
| No log | 1.87 | 7 | 2.6461 | 0.0796 |
| 2.6403 | 2.93 | 11 | 2.6459 | 0.0796 |
| 2.6403 | 4.0 | 15 | 2.6461 | 0.0531 |
| 2.6403 | 4.8 | 18 | 2.6460 | 0.0708 |
| 2.6378 | 5.87 | 22 | 2.6460 | 0.0708 |
| 2.6378 | 6.93 | 26 | 2.6463 | 0.0796 |
| 2.6367 | 8.0 | 30 | 2.6462 | 0.0796 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
knarasi1/ppo-Pyramids
|
knarasi1
| 2023-09-26T01:50:11Z | 9 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-26T01:48:25Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: knarasi1/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
nlp-chula/sentiment-finnlp-th
|
nlp-chula
| 2023-09-26T01:31:27Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:airesearch/wangchanberta-base-att-spm-uncased",
"base_model:finetune:airesearch/wangchanberta-base-att-spm-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-15T06:12:05Z |
---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiment-finnlp-th
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. -->
# sentiment-finnlp-th
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4313
- Accuracy: 0.7352
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7732 | 1.0 | 512 | 0.7954 | 0.6452 |
| 0.5449 | 2.0 | 1024 | 0.6498 | 0.7289 |
| 0.3744 | 3.0 | 1536 | 0.7705 | 0.7318 |
| 0.2475 | 4.0 | 2048 | 1.0368 | 0.7358 |
| 0.1818 | 5.0 | 2560 | 1.1335 | 0.7392 |
| 0.1383 | 6.0 | 3072 | 1.3119 | 0.7358 |
| 0.0896 | 7.0 | 3584 | 1.4313 | 0.7352 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dlby/llm_model_new3
|
dlby
| 2023-09-26T01:21:16Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-26T01:21:12Z |
---
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 0.6.0.dev0
|
iainmcqueen/coffee_model3
|
iainmcqueen
| 2023-09-26T01:19:19Z | 32 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-26T01:03:32Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of a cts coffee cup
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - iainmcqueen/coffee_model3
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of a cts coffee cup using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
CyberHarem/ichigaya_arisa_bangdream
|
CyberHarem
| 2023-09-26T01:12:31Z | 0 | 1 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/ichigaya_arisa_bangdream",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-14T13:05:28Z |
---
license: mit
datasets:
- CyberHarem/ichigaya_arisa_bangdream
pipeline_tag: text-to-image
tags:
- art
---
# Lora of ichigaya_arisa_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 5520, you need to download `5520/ichigaya_arisa_bangdream.pt` as the embedding and `5520/ichigaya_arisa_bangdream.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5520**, with the score of 0.941. The trigger words are:
1. `ichigaya_arisa_bangdream`
2. `blonde_hair, long_hair, bangs, hair_ornament, blush, twintails, x_hair_ornament, yellow_eyes, sidelocks, open_mouth, brown_eyes, smile, breasts`
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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 6900 | 0.937 | [Download](6900/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](6900/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](6900/previews/bikini.png) | [<NSFW, click to see>](6900/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6900/previews/nude.png) | [<NSFW, click to see>](6900/previews/nude2.png) |  |  |
| 6440 | 0.939 | [Download](6440/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](6440/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](6440/previews/bikini.png) | [<NSFW, click to see>](6440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6440/previews/nude.png) | [<NSFW, click to see>](6440/previews/nude2.png) |  |  |
| 5980 | 0.919 | [Download](5980/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5980/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](5980/previews/bikini.png) | [<NSFW, click to see>](5980/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5980/previews/nude.png) | [<NSFW, click to see>](5980/previews/nude2.png) |  |  |
| **5520** | **0.941** | [**Download**](5520/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5520/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](5520/previews/bikini.png) | [<NSFW, click to see>](5520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5520/previews/nude.png) | [<NSFW, click to see>](5520/previews/nude2.png) |  |  |
| 5060 | 0.929 | [Download](5060/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5060/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](5060/previews/bikini.png) | [<NSFW, click to see>](5060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5060/previews/nude.png) | [<NSFW, click to see>](5060/previews/nude2.png) |  |  |
| 4600 | 0.931 | [Download](4600/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4600/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](4600/previews/bikini.png) | [<NSFW, click to see>](4600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4600/previews/nude.png) | [<NSFW, click to see>](4600/previews/nude2.png) |  |  |
| 4140 | 0.931 | [Download](4140/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4140/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](4140/previews/bikini.png) | [<NSFW, click to see>](4140/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4140/previews/nude.png) | [<NSFW, click to see>](4140/previews/nude2.png) |  |  |
| 3680 | 0.904 | [Download](3680/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3680/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](3680/previews/bikini.png) | [<NSFW, click to see>](3680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3680/previews/nude.png) | [<NSFW, click to see>](3680/previews/nude2.png) |  |  |
| 3220 | 0.913 | [Download](3220/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3220/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](3220/previews/bikini.png) | [<NSFW, click to see>](3220/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3220/previews/nude.png) | [<NSFW, click to see>](3220/previews/nude2.png) |  |  |
| 2760 | 0.931 | [Download](2760/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2760/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](2760/previews/bikini.png) | [<NSFW, click to see>](2760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2760/previews/nude.png) | [<NSFW, click to see>](2760/previews/nude2.png) |  |  |
| 2300 | 0.906 | [Download](2300/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2300/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](2300/previews/bikini.png) | [<NSFW, click to see>](2300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2300/previews/nude.png) | [<NSFW, click to see>](2300/previews/nude2.png) |  |  |
| 1840 | 0.928 | [Download](1840/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1840/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](1840/previews/bikini.png) | [<NSFW, click to see>](1840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1840/previews/nude.png) | [<NSFW, click to see>](1840/previews/nude2.png) |  |  |
| 1380 | 0.857 | [Download](1380/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1380/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](1380/previews/bikini.png) | [<NSFW, click to see>](1380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1380/previews/nude.png) | [<NSFW, click to see>](1380/previews/nude2.png) |  |  |
| 920 | 0.880 | [Download](920/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](920/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](920/previews/bikini.png) | [<NSFW, click to see>](920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](920/previews/nude.png) | [<NSFW, click to see>](920/previews/nude2.png) |  |  |
| 460 | 0.812 | [Download](460/ichigaya_arisa_bangdream.zip) |  |  |  |  |  |  | [<NSFW, click to see>](460/previews/pattern_7.png) |  |  |  | [<NSFW, click to see>](460/previews/bikini.png) | [<NSFW, click to see>](460/previews/bondage.png) |  |  |  | [<NSFW, click to see>](460/previews/nude.png) | [<NSFW, click to see>](460/previews/nude2.png) |  |  |
|
prateeky2806/bert-base-uncased-mrpc-ia3-epochs-10-lr-0.005
|
prateeky2806
| 2023-09-26T01:03:21Z | 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 | 2023-09-26T00:56:31Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-mrpc-ia3-epochs-10-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-mrpc-ia3-epochs-10-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: 0.7044
- Accuracy: 0.76
- F1: 0.8333
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 112 | 0.5477 | 0.73 | 0.7939 |
| No log | 2.0 | 224 | 0.4859 | 0.76 | 0.8356 |
| No log | 3.0 | 336 | 0.5785 | 0.74 | 0.8354 |
| No log | 4.0 | 448 | 0.5441 | 0.8 | 0.8667 |
| 0.4779 | 5.0 | 560 | 0.5720 | 0.75 | 0.8120 |
| 0.4779 | 6.0 | 672 | 0.5316 | 0.73 | 0.8029 |
| 0.4779 | 7.0 | 784 | 0.6077 | 0.76 | 0.8356 |
| 0.4779 | 8.0 | 896 | 0.6960 | 0.74 | 0.8243 |
| 0.2391 | 9.0 | 1008 | 0.6872 | 0.75 | 0.8252 |
| 0.2391 | 10.0 | 1120 | 0.7044 | 0.76 | 0.8333 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
prateeky2806/bert-base-uncased-cola-ia3-epochs-10-lr-0.005
|
prateeky2806
| 2023-09-26T00:54:18Z | 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 | 2023-09-26T00:04:54Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-cola-ia3-epochs-10-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-cola-ia3-epochs-10-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: 0.5629
- Matthews Correlation: 0.6581
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 265 | 1.1553 | 0.0 |
| 0.5185 | 2.0 | 530 | 0.4625 | 0.5371 |
| 0.5185 | 3.0 | 795 | 0.4338 | 0.5358 |
| 0.3527 | 4.0 | 1060 | 0.4328 | 0.5687 |
| 0.3527 | 5.0 | 1325 | 0.4262 | 0.6581 |
| 0.2755 | 6.0 | 1590 | 0.5488 | 0.6094 |
| 0.2755 | 7.0 | 1855 | 0.5902 | 0.5429 |
| 0.2141 | 8.0 | 2120 | 0.5433 | 0.5871 |
| 0.2141 | 9.0 | 2385 | 0.5282 | 0.6347 |
| 0.1874 | 10.0 | 2650 | 0.5629 | 0.6581 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nelson2424/mt0-small-lora-finetune-grocery-ner-v2
|
nelson2424
| 2023-09-26T00:50:18Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-25T22:42:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
jmoney54378256438905/CodeLlama-13b-Instruct-4.65bpw
|
jmoney54378256438905
| 2023-09-26T00:49:28Z | 5 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"llama-2",
"code",
"arxiv:2308.12950",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-24T15:29:04Z |
---
language:
- code
pipeline_tag: text-generation
tags:
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 13 instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) |
| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers from `main` until the next version is released:
```bash
pip install git+https://github.com/huggingface/transformers.git@main accelerate
```
Model capabilities:
- [x] Code completion.
- [x] Infilling.
- [x] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Instruct version of the 13B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).
|
rfanucchi/ppo-Huggy
|
rfanucchi
| 2023-09-26T00:41:59Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-25T22:25:07Z |
---
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: rfanucchi/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
malanevans/pixelcopter-v4
|
malanevans
| 2023-09-26T00:35:45Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-26T00:35:40Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter-v4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.30 +/- 22.98
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars
|
CyberHarem
| 2023-09-26T00:11:04Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T23:55:15Z |
---
license: mit
datasets:
- CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars
pipeline_tag: text-to-image
tags:
- art
---
# Lora of yuuki_setsuna_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 3120, you need to download `3120/yuuki_setsuna_loveliveschoolidolfestivalallstars.pt` as the embedding and `3120/yuuki_setsuna_loveliveschoolidolfestivalallstars.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 3120**, with the score of 0.978. The trigger words are:
1. `yuuki_setsuna_loveliveschoolidolfestivalallstars`
2. `black_hair, long_hair, bangs, grey_eyes, smile, sidelocks, one_side_up, breasts, blush, black_eyes, hair_ornament, medium_breasts`
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 | 0.949 | [Download](7800/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.943 | [Download](7280/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.949 | [Download](6760/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.973 | [Download](6240/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.969 | [Download](5720/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.959 | [Download](5200/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.939 | [Download](4680/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.963 | [Download](4160/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.964 | [Download](3640/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| **3120** | **0.978** | [**Download**](3120/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.954 | [Download](2600/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.968 | [Download](2080/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.953 | [Download](1560/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.940 | [Download](1040/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.873 | [Download](520/yuuki_setsuna_loveliveschoolidolfestivalallstars.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
CyberHarem/minato_yukina_bangdream
|
CyberHarem
| 2023-09-25T23:41:39Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/minato_yukina_bangdream",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T23:25:47Z |
---
license: mit
datasets:
- CyberHarem/minato_yukina_bangdream
pipeline_tag: text-to-image
tags:
- art
---
# Lora of minato_yukina_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 6600, you need to download `6600/minato_yukina_bangdream.pt` as the embedding and `6600/minato_yukina_bangdream.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6600**, with the score of 0.943. The trigger words are:
1. `minato_yukina_bangdream`
2. `long_hair, bangs, yellow_eyes, grey_hair, hair_ornament, flower, rose, jewelry, hair_flower`
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** | **0.943** | [**Download**](6600/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) |  |  |
| 6160 | 0.938 | [Download](6160/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) |  |  |
| 5720 | 0.902 | [Download](5720/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5280 | 0.916 | [Download](5280/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4840 | 0.916 | [Download](4840/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4840/previews/nude.png) | [<NSFW, click to see>](4840/previews/nude2.png) |  |  |
| 4400 | 0.913 | [Download](4400/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| 3960 | 0.902 | [Download](3960/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) |  |  |
| 3520 | 0.903 | [Download](3520/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3520/previews/nude.png) | [<NSFW, click to see>](3520/previews/nude2.png) |  |  |
| 3080 | 0.906 | [Download](3080/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3080/previews/nude.png) | [<NSFW, click to see>](3080/previews/nude2.png) |  |  |
| 2640 | 0.906 | [Download](2640/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2640/previews/nude.png) | [<NSFW, click to see>](2640/previews/nude2.png) |  |  |
| 2200 | 0.918 | [Download](2200/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2200/previews/nude.png) | [<NSFW, click to see>](2200/previews/nude2.png) |  |  |
| 1760 | 0.917 | [Download](1760/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1760/previews/nude.png) | [<NSFW, click to see>](1760/previews/nude2.png) |  |  |
| 1320 | 0.876 | [Download](1320/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1320/previews/nude.png) | [<NSFW, click to see>](1320/previews/nude2.png) |  |  |
| 880 | 0.803 | [Download](880/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](880/previews/nude.png) | [<NSFW, click to see>](880/previews/nude2.png) |  |  |
| 440 | 0.769 | [Download](440/minato_yukina_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](440/previews/nude.png) | [<NSFW, click to see>](440/previews/nude2.png) |  |  |
|
CyberHarem/tang_keke_lovelivesuperstar
|
CyberHarem
| 2023-09-25T23:08:24Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/tang_keke_lovelivesuperstar",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T22:57:46Z |
---
license: mit
datasets:
- CyberHarem/tang_keke_lovelivesuperstar
pipeline_tag: text-to-image
tags:
- art
---
# Lora of tang_keke_lovelivesuperstar
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 7800, you need to download `7800/tang_keke_lovelivesuperstar.pt` as the embedding and `7800/tang_keke_lovelivesuperstar.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7800**, with the score of 0.996. The trigger words are:
1. `tang_keke_lovelivesuperstar`
2. `short_hair, bangs, blue_eyes, grey_hair, smile, blush, ribbon, neck_ribbon, red_ribbon`
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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **7800** | **0.996** | [**Download**](7800/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.991 | [Download](7280/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.867 | [Download](6760/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.880 | [Download](6240/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.983 | [Download](5720/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.918 | [Download](5200/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.983 | [Download](4680/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.974 | [Download](4160/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.874 | [Download](3640/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.986 | [Download](3120/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.958 | [Download](2600/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.934 | [Download](2080/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.948 | [Download](1560/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.941 | [Download](1040/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.878 | [Download](520/tang_keke_lovelivesuperstar.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
knarasi1/ppo-SnowballTarget
|
knarasi1
| 2023-09-25T23:04:38Z | 22 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-09-25T23:04:33Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: knarasi1/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
almaghrabima/ner_column_TQ
|
almaghrabima
| 2023-09-25T23:02:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"en",
"base_model:Gladiator/microsoft-deberta-v3-large_ner_conll2003",
"base_model:finetune:Gladiator/microsoft-deberta-v3-large_ner_conll2003",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-20T21:17:28Z |
---
license: mit
base_model: Gladiator/microsoft-deberta-v3-large_ner_conll2003
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_column_TQ
results: []
language:
- en
widget:
- india 0S0308Z8 trudeau 3000 Ravensburger Hamnoy, Lofoten of gold bestseller 620463000001
- other china lc waikiki mağazacilik hi̇zmetleri̇ ti̇c aş 630140000000 hilti 6204699090_BD 55L Toaster Oven with Double Glass
- 611020000001 italy Apparel other games 9W1964Z8 debenhams guangzhou hec fashion leather co ltd
---
<!-- 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. -->
# ner_column_TQ
This model is a fine-tuned version of [Gladiator/microsoft-deberta-v3-large_ner_conll2003](https://huggingface.co/Gladiator/microsoft-deberta-v3-large_ner_conll2003) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1949
- Precision: 0.8546
- Recall: 0.8533
- F1: 0.8540
- Accuracy: 0.9154
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 702 | 0.2342 | 0.7774 | 0.7496 | 0.7632 | 0.8833 |
| 0.369 | 2.0 | 1404 | 0.1708 | 0.8050 | 0.8048 | 0.8049 | 0.9033 |
| 0.1681 | 3.0 | 2106 | 0.1646 | 0.8007 | 0.8078 | 0.8043 | 0.9054 |
| 0.1681 | 4.0 | 2808 | 0.1469 | 0.8250 | 0.8335 | 0.8292 | 0.9133 |
| 0.14 | 5.0 | 3510 | 0.1465 | 0.8235 | 0.8345 | 0.8290 | 0.9137 |
| 0.1279 | 6.0 | 4212 | 0.1517 | 0.8165 | 0.8323 | 0.8244 | 0.9127 |
| 0.1279 | 7.0 | 4914 | 0.1474 | 0.8224 | 0.8370 | 0.8297 | 0.9138 |
| 0.1212 | 8.0 | 5616 | 0.1500 | 0.8255 | 0.8409 | 0.8331 | 0.9141 |
| 0.1165 | 9.0 | 6318 | 0.1545 | 0.8297 | 0.8390 | 0.8343 | 0.9142 |
| 0.1138 | 10.0 | 7020 | 0.1590 | 0.8342 | 0.8467 | 0.8404 | 0.9150 |
| 0.1138 | 11.0 | 7722 | 0.1588 | 0.8383 | 0.8474 | 0.8428 | 0.9156 |
| 0.1099 | 12.0 | 8424 | 0.1547 | 0.8425 | 0.8446 | 0.8435 | 0.9156 |
| 0.1071 | 13.0 | 9126 | 0.1565 | 0.8475 | 0.8471 | 0.8473 | 0.9164 |
| 0.1071 | 14.0 | 9828 | 0.1625 | 0.8440 | 0.8489 | 0.8464 | 0.9156 |
| 0.1031 | 15.0 | 10530 | 0.1680 | 0.8486 | 0.8510 | 0.8498 | 0.9160 |
| 0.0992 | 16.0 | 11232 | 0.1722 | 0.8529 | 0.8505 | 0.8517 | 0.9156 |
| 0.0992 | 17.0 | 11934 | 0.1771 | 0.8527 | 0.8529 | 0.8528 | 0.9159 |
| 0.094 | 18.0 | 12636 | 0.1862 | 0.8555 | 0.8531 | 0.8543 | 0.9159 |
| 0.0892 | 19.0 | 13338 | 0.1884 | 0.8534 | 0.8534 | 0.8534 | 0.9156 |
| 0.086 | 20.0 | 14040 | 0.1949 | 0.8546 | 0.8533 | 0.8540 | 0.9154 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
almaghrabima/ner_column_bert-base-NER
|
almaghrabima
| 2023-09-25T23:01:44Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"base_model:dslim/bert-base-NER",
"base_model:finetune:dslim/bert-base-NER",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-21T00:16:24Z |
---
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_column_bert-base-NER
results: []
language:
- en
widget:
- india 0S0308Z8 trudeau 3000 Ravensburger Hamnoy, Lofoten of gold bestseller 620463000001
- other china lc waikiki mağazacilik hi̇zmetleri̇ ti̇c aş 630140000000 hilti 6204699090_BD 55L Toaster Oven with Double Glass
- 611020000001 italy Apparel other games 9W1964Z8 debenhams guangzhou hec fashion leather co ltd
---
<!-- 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. -->
# ner_column_bert-base-NER
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1855
- Precision: 0.7651
- Recall: 0.7786
- F1: 0.7718
- Accuracy: 0.9026
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 702 | 0.7382 | 0.2576 | 0.1887 | 0.2178 | 0.7127 |
| 0.9356 | 2.0 | 1404 | 0.4405 | 0.5139 | 0.4331 | 0.4700 | 0.8157 |
| 0.5445 | 3.0 | 2106 | 0.3608 | 0.5712 | 0.5143 | 0.5413 | 0.8404 |
| 0.5445 | 4.0 | 2808 | 0.3226 | 0.6188 | 0.5840 | 0.6009 | 0.8550 |
| 0.4316 | 5.0 | 3510 | 0.2757 | 0.6788 | 0.6569 | 0.6676 | 0.8728 |
| 0.3605 | 6.0 | 4212 | 0.2828 | 0.6584 | 0.6346 | 0.6463 | 0.8697 |
| 0.3605 | 7.0 | 4914 | 0.2456 | 0.7108 | 0.6926 | 0.7015 | 0.8820 |
| 0.3153 | 8.0 | 5616 | 0.2385 | 0.7055 | 0.6986 | 0.7021 | 0.8855 |
| 0.282 | 9.0 | 6318 | 0.2345 | 0.7044 | 0.6961 | 0.7002 | 0.8853 |
| 0.2587 | 10.0 | 7020 | 0.2313 | 0.7081 | 0.7049 | 0.7065 | 0.8862 |
| 0.2587 | 11.0 | 7722 | 0.2026 | 0.7734 | 0.7537 | 0.7634 | 0.8968 |
| 0.239 | 12.0 | 8424 | 0.1980 | 0.7651 | 0.7687 | 0.7669 | 0.8991 |
| 0.2241 | 13.0 | 9126 | 0.2091 | 0.7368 | 0.7423 | 0.7395 | 0.8936 |
| 0.2241 | 14.0 | 9828 | 0.1954 | 0.7693 | 0.7684 | 0.7689 | 0.8987 |
| 0.2124 | 15.0 | 10530 | 0.1916 | 0.7668 | 0.7749 | 0.7708 | 0.9008 |
| 0.2025 | 16.0 | 11232 | 0.1841 | 0.7699 | 0.7794 | 0.7746 | 0.9024 |
| 0.2025 | 17.0 | 11934 | 0.1938 | 0.7527 | 0.7626 | 0.7576 | 0.8992 |
| 0.193 | 18.0 | 12636 | 0.1849 | 0.7705 | 0.7841 | 0.7772 | 0.9040 |
| 0.1877 | 19.0 | 13338 | 0.1927 | 0.7510 | 0.7649 | 0.7579 | 0.9005 |
| 0.1821 | 20.0 | 14040 | 0.1855 | 0.7651 | 0.7786 | 0.7718 | 0.9026 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
jh1517/q-FrozenLake-v1-4x4-noSlippery
|
jh1517
| 2023-09-25T22:50:58Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T22:50:55Z |
---
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="jh1517/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"])
```
|
silvacarl/distilbert-stock-tweet-sentiment-analysis
|
silvacarl
| 2023-09-25T22:50:15Z | 184 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-25T22:48:12Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-stock-tweet-sentiment-analysis
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-stock-tweet-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6236
- Accuracy: 0.7702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6937 | 1.0 | 1000 | 0.5964 | 0.7512 |
| 0.4743 | 2.0 | 2000 | 0.5807 | 0.7675 |
| 0.3648 | 3.0 | 3000 | 0.6236 | 0.7702 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
milaidy/aventurine
|
milaidy
| 2023-09-25T22:36:56Z | 1 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-25T22:33:45Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### aventurine 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:
|
YaTharThShaRma999/Lima_Lora
|
YaTharThShaRma999
| 2023-09-25T22:15:36Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-25T22:15:33Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
CyberHarem/wien_margarete_lovelivesuperstar
|
CyberHarem
| 2023-09-25T22:10:58Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/wien_margarete_lovelivesuperstar",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T22:02:19Z |
---
license: mit
datasets:
- CyberHarem/wien_margarete_lovelivesuperstar
pipeline_tag: text-to-image
tags:
- art
---
# Lora of wien_margarete_lovelivesuperstar
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 4080, you need to download `4080/wien_margarete_lovelivesuperstar.pt` as the embedding and `4080/wien_margarete_lovelivesuperstar.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4080**, with the score of 0.971. The trigger words are:
1. `wien_margarete_lovelivesuperstar`
2. `long_hair, bangs, green_eyes, braid, purple_hair, blunt_bangs, blush, smile, breasts, pink_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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.912 | [Download](5100/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](5100/previews/pattern_1.png) | [<NSFW, click to see>](5100/previews/pattern_2.png) | [<NSFW, click to see>](5100/previews/pattern_3.png) | [<NSFW, click to see>](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | [<NSFW, click to see>](5100/previews/free.png) |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.956 | [Download](4760/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](4760/previews/pattern_1.png) | [<NSFW, click to see>](4760/previews/pattern_2.png) | [<NSFW, click to see>](4760/previews/pattern_3.png) | [<NSFW, click to see>](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | [<NSFW, click to see>](4760/previews/free.png) |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.941 | [Download](4420/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](4420/previews/pattern_1.png) | [<NSFW, click to see>](4420/previews/pattern_2.png) | [<NSFW, click to see>](4420/previews/pattern_3.png) | [<NSFW, click to see>](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | [<NSFW, click to see>](4420/previews/free.png) |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| **4080** | **0.971** | [**Download**](4080/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](4080/previews/pattern_1.png) | [<NSFW, click to see>](4080/previews/pattern_2.png) | [<NSFW, click to see>](4080/previews/pattern_3.png) | [<NSFW, click to see>](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | [<NSFW, click to see>](4080/previews/free.png) |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.845 | [Download](3740/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](3740/previews/pattern_1.png) | [<NSFW, click to see>](3740/previews/pattern_2.png) | [<NSFW, click to see>](3740/previews/pattern_3.png) | [<NSFW, click to see>](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | [<NSFW, click to see>](3740/previews/free.png) |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.881 | [Download](3400/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](3400/previews/pattern_1.png) | [<NSFW, click to see>](3400/previews/pattern_2.png) | [<NSFW, click to see>](3400/previews/pattern_3.png) | [<NSFW, click to see>](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | [<NSFW, click to see>](3400/previews/free.png) |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.968 | [Download](3060/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](3060/previews/pattern_1.png) | [<NSFW, click to see>](3060/previews/pattern_2.png) | [<NSFW, click to see>](3060/previews/pattern_3.png) | [<NSFW, click to see>](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | [<NSFW, click to see>](3060/previews/free.png) |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.939 | [Download](2720/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](2720/previews/pattern_1.png) | [<NSFW, click to see>](2720/previews/pattern_2.png) | [<NSFW, click to see>](2720/previews/pattern_3.png) | [<NSFW, click to see>](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | [<NSFW, click to see>](2720/previews/free.png) |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.916 | [Download](2380/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](2380/previews/pattern_1.png) | [<NSFW, click to see>](2380/previews/pattern_2.png) | [<NSFW, click to see>](2380/previews/pattern_3.png) | [<NSFW, click to see>](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | [<NSFW, click to see>](2380/previews/free.png) |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.919 | [Download](2040/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](2040/previews/pattern_1.png) | [<NSFW, click to see>](2040/previews/pattern_2.png) | [<NSFW, click to see>](2040/previews/pattern_3.png) | [<NSFW, click to see>](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | [<NSFW, click to see>](2040/previews/free.png) |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.963 | [Download](1700/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](1700/previews/pattern_1.png) | [<NSFW, click to see>](1700/previews/pattern_2.png) | [<NSFW, click to see>](1700/previews/pattern_3.png) | [<NSFW, click to see>](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | [<NSFW, click to see>](1700/previews/free.png) |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.947 | [Download](1360/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](1360/previews/pattern_1.png) | [<NSFW, click to see>](1360/previews/pattern_2.png) | [<NSFW, click to see>](1360/previews/pattern_3.png) | [<NSFW, click to see>](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | [<NSFW, click to see>](1360/previews/free.png) |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.901 | [Download](1020/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](1020/previews/pattern_1.png) | [<NSFW, click to see>](1020/previews/pattern_2.png) | [<NSFW, click to see>](1020/previews/pattern_3.png) | [<NSFW, click to see>](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | [<NSFW, click to see>](1020/previews/free.png) |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.926 | [Download](680/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](680/previews/pattern_1.png) | [<NSFW, click to see>](680/previews/pattern_2.png) | [<NSFW, click to see>](680/previews/pattern_3.png) | [<NSFW, click to see>](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | [<NSFW, click to see>](680/previews/free.png) |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.745 | [Download](340/wien_margarete_lovelivesuperstar.zip) | [<NSFW, click to see>](340/previews/pattern_1.png) | [<NSFW, click to see>](340/previews/pattern_2.png) | [<NSFW, click to see>](340/previews/pattern_3.png) | [<NSFW, click to see>](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | [<NSFW, click to see>](340/previews/free.png) |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
CyberHarem/mitake_ran_bangdream
|
CyberHarem
| 2023-09-25T22:07:38Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/mitake_ran_bangdream",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T21:50:24Z |
---
license: mit
datasets:
- CyberHarem/mitake_ran_bangdream
pipeline_tag: text-to-image
tags:
- art
---
# Lora of mitake_ran_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 7800, you need to download `7800/mitake_ran_bangdream.pt` as the embedding and `7800/mitake_ran_bangdream.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7800**, with the score of 0.993. The trigger words are:
1. `mitake_ran_bangdream`
2. `black_hair, red_hair, multicolored_hair, streaked_hair, short_hair, bangs, blush, bob_cut, purple_eyes, breasts, collarbone, red_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 | pattern_14 | pattern_15 | pattern_16 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **7800** | **0.993** | [**Download**](7800/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](7800/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/pattern_12.png) |  |  | [<NSFW, click to see>](7800/previews/pattern_15.png) | [<NSFW, click to see>](7800/previews/pattern_16.png) |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.993 | [Download](7280/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](7280/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/pattern_12.png) |  |  | [<NSFW, click to see>](7280/previews/pattern_15.png) | [<NSFW, click to see>](7280/previews/pattern_16.png) |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.952 | [Download](6760/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](6760/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/pattern_12.png) |  |  | [<NSFW, click to see>](6760/previews/pattern_15.png) | [<NSFW, click to see>](6760/previews/pattern_16.png) |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.993 | [Download](6240/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](6240/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/pattern_12.png) |  |  | [<NSFW, click to see>](6240/previews/pattern_15.png) | [<NSFW, click to see>](6240/previews/pattern_16.png) |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.991 | [Download](5720/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](5720/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/pattern_12.png) |  |  | [<NSFW, click to see>](5720/previews/pattern_15.png) | [<NSFW, click to see>](5720/previews/pattern_16.png) |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.950 | [Download](5200/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](5200/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/pattern_12.png) |  |  | [<NSFW, click to see>](5200/previews/pattern_15.png) | [<NSFW, click to see>](5200/previews/pattern_16.png) |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.993 | [Download](4680/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](4680/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/pattern_12.png) |  |  | [<NSFW, click to see>](4680/previews/pattern_15.png) | [<NSFW, click to see>](4680/previews/pattern_16.png) |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.950 | [Download](4160/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](4160/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/pattern_12.png) |  |  | [<NSFW, click to see>](4160/previews/pattern_15.png) | [<NSFW, click to see>](4160/previews/pattern_16.png) |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.991 | [Download](3640/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](3640/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/pattern_12.png) |  |  | [<NSFW, click to see>](3640/previews/pattern_15.png) | [<NSFW, click to see>](3640/previews/pattern_16.png) |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.991 | [Download](3120/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](3120/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/pattern_12.png) |  |  | [<NSFW, click to see>](3120/previews/pattern_15.png) | [<NSFW, click to see>](3120/previews/pattern_16.png) |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.989 | [Download](2600/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](2600/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/pattern_12.png) |  |  | [<NSFW, click to see>](2600/previews/pattern_15.png) | [<NSFW, click to see>](2600/previews/pattern_16.png) |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.989 | [Download](2080/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](2080/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/pattern_12.png) |  |  | [<NSFW, click to see>](2080/previews/pattern_15.png) | [<NSFW, click to see>](2080/previews/pattern_16.png) |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.992 | [Download](1560/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](1560/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/pattern_12.png) |  |  | [<NSFW, click to see>](1560/previews/pattern_15.png) | [<NSFW, click to see>](1560/previews/pattern_16.png) |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.991 | [Download](1040/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](1040/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/pattern_12.png) |  |  | [<NSFW, click to see>](1040/previews/pattern_15.png) | [<NSFW, click to see>](1040/previews/pattern_16.png) |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.990 | [Download](520/mitake_ran_bangdream.zip) |  |  |  |  | [<NSFW, click to see>](520/previews/pattern_5.png) |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/pattern_12.png) |  |  | [<NSFW, click to see>](520/previews/pattern_15.png) | [<NSFW, click to see>](520/previews/pattern_16.png) |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
Umong/wav2vec2-xls-r-300m-bengali
|
Umong
| 2023-09-25T22:01:23Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:Umong/wav2vec2-xls-r-300m-bengali",
"base_model:finetune:Umong/wav2vec2-xls-r-300m-bengali",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-23T23:24:49Z |
---
base_model: Umong/wav2vec2-xls-r-300m-bengali
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-bengali
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. -->
# wav2vec2-xls-r-300m-bengali
This model is a fine-tuned version of [Umong/wav2vec2-xls-r-300m-bengali](https://huggingface.co/Umong/wav2vec2-xls-r-300m-bengali) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1636
- Wer: 0.0883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.3076 | 0.16 | 400 | 1.5883 | 0.9394 |
| 0.8841 | 0.33 | 800 | 0.5188 | 0.5337 |
| 0.5896 | 0.49 | 1200 | 0.4029 | 0.4340 |
| 0.4964 | 0.66 | 1600 | 0.3429 | 0.3766 |
| 0.4553 | 0.82 | 2000 | 0.3196 | 0.3642 |
| 0.4222 | 0.99 | 2400 | 0.3004 | 0.3436 |
| 0.3709 | 1.15 | 2800 | 0.2812 | 0.3225 |
| 0.352 | 1.32 | 3200 | 0.2753 | 0.3124 |
| 0.3283 | 1.48 | 3600 | 0.2616 | 0.2979 |
| 0.3235 | 1.65 | 4000 | 0.2573 | 0.2944 |
| 0.3129 | 1.81 | 4400 | 0.2458 | 0.2809 |
| 0.306 | 1.98 | 4800 | 0.2344 | 0.2771 |
| 0.2701 | 2.14 | 5200 | 0.2318 | 0.2661 |
| 0.2653 | 2.31 | 5600 | 0.2253 | 0.2629 |
| 0.2626 | 2.47 | 6000 | 0.2186 | 0.2542 |
| 0.2541 | 2.63 | 6400 | 0.2074 | 0.2474 |
| 0.2235 | 2.8 | 6800 | 0.2102 | 0.2442 |
| 0.2185 | 2.96 | 7200 | 0.2019 | 0.2327 |
| 0.2061 | 3.13 | 7600 | 0.1994 | 0.2308 |
| 0.2011 | 3.29 | 8000 | 0.1942 | 0.2260 |
| 0.1986 | 3.46 | 8400 | 0.1867 | 0.2187 |
| 0.197 | 3.62 | 8800 | 0.1825 | 0.2177 |
| 0.1931 | 3.79 | 9200 | 0.1856 | 0.2153 |
| 0.1879 | 3.95 | 9600 | 0.1777 | 0.2088 |
| 0.1599 | 4.12 | 10000 | 0.1781 | 0.0968 |
| 0.153 | 4.28 | 10400 | 0.1738 | 0.0944 |
| 0.1475 | 4.45 | 10800 | 0.1713 | 0.0905 |
| 0.1448 | 4.61 | 11200 | 0.1683 | 0.0907 |
| 0.1445 | 4.78 | 11600 | 0.1649 | 0.0897 |
| 0.1423 | 4.94 | 12000 | 0.1636 | 0.0883 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
AfricaOne/africa_one_llama2-CodeInstr-finetuned-model
|
AfricaOne
| 2023-09-25T21:49:26Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-25T21:49:09Z |
---
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
|
ayymen/crnn_mobilenet_v3_large_gen_hw
|
ayymen
| 2023-09-25T21:46:36Z | 56 | 2 |
transformers
|
[
"transformers",
"pytorch",
"OCR",
"image-to-text",
"zgh",
"ber",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2023-09-25T20:06:39Z |
---
language:
- zgh
- ber
tags:
- OCR
pipeline_tag: image-to-text
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
### Run Configuration
{
"arch": "crnn_mobilenet_v3_large",
"train_path": "train",
"val_path": "val",
"train_samples": 1000,
"val_samples": 20,
"font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf",
"min_chars": 1,
"max_chars": 12,
"name": "crnn_mobilenet_v3_large_gen_hw",
"epochs": 3,
"batch_size": 64,
"device": null,
"input_size": 32,
"lr": 0.001,
"weight_decay": 0,
"workers": 2,
"resume": "crnn_mobilenet_v3_large_printed.pt",
"vocab": "tamazight",
"test_only": false,
"show_samples": false,
"wb": true,
"push_to_hub": true,
"pretrained": false,
"sched": "cosine",
"amp": false,
"find_lr": false
}
|
mcasomm/ataritest1
|
mcasomm
| 2023-09-25T21:46:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T21:25:09Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 686.50 +/- 267.54
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mcasomm -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mcasomm -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mcasomm
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
halo-69/Bloom_3b_squad
|
halo-69
| 2023-09-25T21:44:32Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bloom",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:bigscience/bloom-3b",
"base_model:finetune:bigscience/bloom-3b",
"license:bigscience-bloom-rail-1.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-14T04:13:28Z |
---
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloom-3b
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: Bloom_3b_squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bloom_3b_squad
This model is a fine-tuned version of [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7859
## 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: 48
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.0058 | 1.0 | 1643 | 2.7510 |
| 2.7801 | 2.0 | 3286 | 2.7497 |
| 2.7284 | 3.0 | 4929 | 2.7536 |
| 2.7001 | 4.0 | 6572 | 2.7601 |
| 2.6811 | 5.0 | 8215 | 2.7669 |
| 2.6811 | 6.0 | 9858 | 2.7722 |
| 2.6639 | 7.0 | 11501 | 2.7780 |
| 2.6492 | 8.0 | 13144 | 2.7817 |
| 2.6414 | 9.0 | 14787 | 2.7841 |
| 2.6354 | 10.0 | 16430 | 2.7859 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
zrovig/ppo-LunarLander-v2
|
zrovig
| 2023-09-25T21:32:07Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T21:31:42Z |
---
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: 244.93 +/- 17.83
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
...
```
|
anuragrawal/flan-t5-base-YT-transcript-sum
|
anuragrawal
| 2023-09-25T21:22:30Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-25T19:44:22Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-base-YT-transcript-sum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-YT-transcript-sum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4111
- Rouge1: 25.4013
- Rouge2: 12.4728
- Rougel: 21.5206
- Rougelsum: 23.6322
- 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 216 | 1.5817 | 23.8566 | 11.0314 | 20.1664 | 22.2953 | 18.9954 |
| No log | 2.0 | 432 | 1.4907 | 24.2446 | 11.6603 | 20.6712 | 22.4196 | 18.9861 |
| 1.7643 | 3.0 | 648 | 1.4510 | 25.4355 | 12.9236 | 21.584 | 23.7272 | 19.0 |
| 1.7643 | 4.0 | 864 | 1.4312 | 24.8929 | 12.5927 | 21.3295 | 23.3504 | 19.0 |
| 1.4359 | 5.0 | 1080 | 1.4145 | 25.242 | 12.9269 | 21.6351 | 23.6509 | 19.0 |
| 1.4359 | 6.0 | 1296 | 1.4111 | 25.4013 | 12.4728 | 21.5206 | 23.6322 | 19.0 |
| 1.2819 | 7.0 | 1512 | 1.4135 | 25.6542 | 13.103 | 22.2059 | 23.9474 | 19.0 |
| 1.2819 | 8.0 | 1728 | 1.4145 | 26.0783 | 13.7584 | 22.343 | 24.3255 | 19.0 |
| 1.2819 | 9.0 | 1944 | 1.4163 | 25.4385 | 13.1278 | 21.7173 | 23.8295 | 18.9861 |
| 1.1688 | 10.0 | 2160 | 1.4208 | 25.7625 | 13.5586 | 22.2246 | 24.2042 | 19.0 |
| 1.1688 | 11.0 | 2376 | 1.4165 | 25.5482 | 13.1163 | 21.9475 | 23.8181 | 18.9907 |
| 1.0951 | 12.0 | 2592 | 1.4215 | 25.7614 | 13.5565 | 22.1965 | 24.0657 | 19.0 |
| 1.0951 | 13.0 | 2808 | 1.4285 | 26.3345 | 14.2027 | 22.7422 | 24.6261 | 18.9907 |
| 1.0549 | 14.0 | 3024 | 1.4277 | 25.8835 | 13.8044 | 22.3845 | 24.269 | 19.0 |
| 1.0549 | 15.0 | 3240 | 1.4321 | 25.8292 | 13.7231 | 22.3506 | 24.3188 | 19.0 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
itxseraphina/cartpole_env
|
itxseraphina
| 2023-09-25T21:07:46Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T18:40:37Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole_env
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
|
anupamtripathi/model_2
|
anupamtripathi
| 2023-09-25T21:04:02Z | 1 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-05T08:24:08Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a Oculus device
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
ALEXISLG/Modelo2
|
ALEXISLG
| 2023-09-25T20:54:13Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-25T20:48:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: Modelo2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8284313725490197
- name: F1
type: f1
value: 0.866920152091255
---
<!-- 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. -->
# Modelo2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5825
- Accuracy: 0.8284
- F1: 0.8669
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5082 | 1.09 | 500 | 0.6767 | 0.8113 | 0.8710 |
| 0.3554 | 2.18 | 1000 | 0.5825 | 0.8284 | 0.8669 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Schadom/ppo-LunarLander-v2
|
Schadom
| 2023-09-25T20:50:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T20:49:57Z |
---
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: 276.32 +/- 19.37
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
...
```
|
LarryAIDraw/eleonora_viltaria_v1
|
LarryAIDraw
| 2023-09-25T20:41:53Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-25T20:38:30Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/151307/eleonora-viltaria-lord-marksman-and-vanadis
|
LarryAIDraw/glimmer_v1_2
|
LarryAIDraw
| 2023-09-25T20:41:02Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-25T20:33:50Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/151194/glimmer-from-xenoblade-chronicles-3-future-redeemed
|
CyberHarem/arashi_chisato_lovelivesuperstar
|
CyberHarem
| 2023-09-25T20:40:38Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/arashi_chisato_lovelivesuperstar",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T20:26:06Z |
---
license: mit
datasets:
- CyberHarem/arashi_chisato_lovelivesuperstar
pipeline_tag: text-to-image
tags:
- art
---
# Lora of arashi_chisato_lovelivesuperstar
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 7280, you need to download `7280/arashi_chisato_lovelivesuperstar.pt` as the embedding and `7280/arashi_chisato_lovelivesuperstar.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7280**, with the score of 0.994. The trigger words are:
1. `arashi_chisato_lovelivesuperstar`
2. `bangs, white_hair, hair_bun, double_bun, red_eyes, smile, blush, twintails, long_hair, blunt_bangs, open_mouth`
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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.993 | [Download](7800/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| **7280** | **0.994** | [**Download**](7280/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.991 | [Download](6760/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.993 | [Download](6240/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.991 | [Download](5720/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.990 | [Download](5200/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.986 | [Download](4680/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.991 | [Download](4160/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.990 | [Download](3640/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.985 | [Download](3120/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.983 | [Download](2600/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.975 | [Download](2080/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.973 | [Download](1560/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.942 | [Download](1040/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.972 | [Download](520/arashi_chisato_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
LarryAIDraw/tifaV2
|
LarryAIDraw
| 2023-09-25T20:36:54Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-25T20:34:59Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/151528/ver2tifania-westwood-ver2the-familiar-of-zero
|
LarryAIDraw/pavonis-ag-richy-v1
|
LarryAIDraw
| 2023-09-25T20:36:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-25T20:33:22Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/151215/pavonis-artery-gear-fusion-lora
|
mrm8488/m-e5-large_bs64_10_all_languages
|
mrm8488
| 2023-09-25T20:29:11Z | 4 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-09-25T18:38:21Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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)
```
## 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 899 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 899,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
jeanai4/falcon-7b-instruct-ft-adapters
|
jeanai4
| 2023-09-25T20:16:15Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-25T20:16:13Z |
---
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
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 0.6.0.dev0
- PEFT 0.6.0.dev0
|
hangsiin/blip2-test2
|
hangsiin
| 2023-09-25T20:10:27Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-20T05:44:08Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
|
DrSylvainPronovost/q-Taxi-v3
|
DrSylvainPronovost
| 2023-09-25T20:07:27Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T20:07:25Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
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="DrSylvainPronovost/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mchen-hf-2023/dqn-SpaceInvadersNoFrameskip-v4
|
mchen-hf-2023
| 2023-09-25T20:04:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T20:03:26Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 683.50 +/- 223.84
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mchen-hf-2023 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mchen-hf-2023 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mchen-hf-2023
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
roa7n/gpt2-human_nontata_promoters-randomized_0_layers
|
roa7n
| 2023-09-25T19:59:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-25T19:59:16Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
osangni/sdxlroks
|
osangni
| 2023-09-25T19:53:31Z | 8 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-08-21T11:51:30Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a rktmsardr person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
CyberHarem/tsurumaki_kokoro_bangdream
|
CyberHarem
| 2023-09-25T19:51:10Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/tsurumaki_kokoro_bangdream",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T19:32:18Z |
---
license: mit
datasets:
- CyberHarem/tsurumaki_kokoro_bangdream
pipeline_tag: text-to-image
tags:
- art
---
# Lora of tsurumaki_kokoro_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 2880, you need to download `2880/tsurumaki_kokoro_bangdream.pt` as the embedding and `2880/tsurumaki_kokoro_bangdream.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 2880**, with the score of 0.970. The trigger words are:
1. `tsurumaki_kokoro_bangdream`
2. `blonde_hair, bangs, long_hair, yellow_eyes, smile, blush, open_mouth, :d`
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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7200 | 0.914 | [Download](7200/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) |  |  |
| 6720 | 0.921 | [Download](6720/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) |  |  |
| 6240 | 0.940 | [Download](6240/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5760 | 0.959 | [Download](5760/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) |  |  |
| 5280 | 0.952 | [Download](5280/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4800 | 0.928 | [Download](4800/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4320 | 0.968 | [Download](4320/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) |  |  |
| 3840 | 0.951 | [Download](3840/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) |  |  |
| 3360 | 0.936 | [Download](3360/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3360/previews/bikini.png) | [<NSFW, click to see>](3360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) |  |  |
| **2880** | **0.970** | [**Download**](2880/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2880/previews/bikini.png) | [<NSFW, click to see>](2880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) |  |  |
| 2400 | 0.927 | [Download](2400/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 1920 | 0.924 | [Download](1920/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) |  |  |
| 1440 | 0.914 | [Download](1440/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1440/previews/bikini.png) | [<NSFW, click to see>](1440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) |  |  |
| 960 | 0.880 | [Download](960/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](960/previews/bikini.png) | [<NSFW, click to see>](960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) |  |  |
| 480 | 0.856 | [Download](480/tsurumaki_kokoro_bangdream.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](480/previews/bikini.png) | [<NSFW, click to see>](480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) |  |  |
|
drew1horn/MetaBot
|
drew1horn
| 2023-09-25T19:40:21Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-09-25T18:44:44Z |
---
# 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
{}
---
# Model Card for Model ID
<!--
My first huggingface project
A Meta Search Chatbot
Get input from user,
send it to multiple chatbots/search engines
HuggingChat suggested starting with
Wikipedia and Wolfram
We will add many more
Combine the results.
Test and Debug this much first!
-->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [drew1horn]
- **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]
|
CyberHarem/shibuya_kanon_lovelivesuperstar
|
CyberHarem
| 2023-09-25T19:39:42Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/shibuya_kanon_lovelivesuperstar",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-25T19:26:21Z |
---
license: mit
datasets:
- CyberHarem/shibuya_kanon_lovelivesuperstar
pipeline_tag: text-to-image
tags:
- art
---
# Lora of shibuya_kanon_lovelivesuperstar
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/shibuya_kanon_lovelivesuperstar.pt` as the embedding and `4160/shibuya_kanon_lovelivesuperstar.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 0.999. The trigger words are:
1. `shibuya_kanon_lovelivesuperstar`
2. `orange_hair, bangs, purple_eyes, long_hair, smile, blush, ribbon, open_mouth, neck_ribbon, red_ribbon, medium_hair, shiny_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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.998 | [Download](7800/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.999 | [Download](7280/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.997 | [Download](6760/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.994 | [Download](6240/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.996 | [Download](5720/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.996 | [Download](5200/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.997 | [Download](4680/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| **4160** | **0.999** | [**Download**](4160/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.997 | [Download](3640/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.997 | [Download](3120/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.937 | [Download](2600/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.998 | [Download](2080/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.997 | [Download](1560/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.998 | [Download](1040/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.998 | [Download](520/shibuya_kanon_lovelivesuperstar.zip) |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
anuragrawal/bart-base-cnn-YT-transcript-sum
|
anuragrawal
| 2023-09-25T19:31:11Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:ainize/bart-base-cnn",
"base_model:finetune:ainize/bart-base-cnn",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-25T18:57:04Z |
---
license: apache-2.0
base_model: ainize/bart-base-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-cnn-YT-transcript-sum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-cnn-YT-transcript-sum
This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4969
- Rouge1: 27.1516
- Rouge2: 14.6227
- Rougel: 23.3968
- Rougelsum: 25.4786
- Gen Len: 19.9954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 216 | 1.5374 | 24.7307 | 11.5124 | 20.6823 | 22.9189 | 19.9630 |
| No log | 2.0 | 432 | 1.4976 | 26.825 | 14.0512 | 23.2078 | 25.2044 | 19.9583 |
| 1.5449 | 3.0 | 648 | 1.4969 | 27.1516 | 14.6227 | 23.3968 | 25.4786 | 19.9954 |
| 1.5449 | 4.0 | 864 | 1.5345 | 27.2526 | 15.0873 | 23.8556 | 25.7798 | 19.9861 |
| 0.9 | 5.0 | 1080 | 1.5962 | 26.8267 | 14.7267 | 23.2263 | 25.2149 | 19.9676 |
| 0.9 | 6.0 | 1296 | 1.6378 | 26.8444 | 14.8753 | 23.254 | 25.2943 | 19.9815 |
| 0.5749 | 7.0 | 1512 | 1.6819 | 27.1776 | 14.898 | 23.2454 | 25.4298 | 19.9583 |
| 0.5749 | 8.0 | 1728 | 1.7360 | 26.9518 | 15.308 | 23.6574 | 25.2991 | 19.9769 |
| 0.5749 | 9.0 | 1944 | 1.7796 | 27.9253 | 15.7998 | 24.4827 | 26.4424 | 19.9769 |
| 0.3668 | 10.0 | 2160 | 1.8078 | 26.9211 | 15.0903 | 23.4484 | 25.4369 | 19.9815 |
| 0.3668 | 11.0 | 2376 | 1.8405 | 27.4434 | 15.3608 | 23.903 | 25.8117 | 19.9861 |
| 0.255 | 12.0 | 2592 | 1.8447 | 27.7175 | 15.7173 | 24.2096 | 26.0946 | 19.9815 |
| 0.255 | 13.0 | 2808 | 1.8834 | 27.2409 | 15.3865 | 23.7314 | 25.7682 | 19.9815 |
| 0.192 | 14.0 | 3024 | 1.8796 | 27.2939 | 15.5502 | 23.8294 | 25.7409 | 19.9815 |
| 0.192 | 15.0 | 3240 | 1.8851 | 27.6741 | 15.771 | 24.1976 | 26.1196 | 19.9722 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ProbeCrew/Probe-Model
|
ProbeCrew
| 2023-09-25T19:15:21Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
] | null | 2023-09-25T19:14:19Z |
---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- bleurt
library_name: adapter-transformers
---
|
vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman-finetuned-hangman
|
vaibhav9
| 2023-09-25T19:06:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman",
"base_model:finetune:vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-25T16:54:32Z |
---
license: apache-2.0
base_model: vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-4_H-256_A-4-finetuned-hangman-finetuned-hangman
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_uncased_L-4_H-256_A-4-finetuned-hangman-finetuned-hangman
This model is a fine-tuned version of [vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman](https://huggingface.co/vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1734 | 1.0 | 22730 | 2.6784 |
| 2.1693 | 2.0 | 45460 | 2.6760 |
| 2.1692 | 3.0 | 68190 | 2.6879 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
am-infoweb/QA_SYNTH_25_SEPT_WITH_FINETUNE_1.0
|
am-infoweb
| 2023-09-25T18:52:44Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-25T16:45:00Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: QA_SYNTH_25_SEPT_WITH_FINETUNE_1.0
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. -->
# QA_SYNTH_25_SEPT_WITH_FINETUNE_1.0
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0007
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3279 | 1.0 | 9675 | 0.1663 |
| 0.0487 | 2.0 | 19350 | 0.0298 |
| 0.0388 | 3.0 | 29025 | 0.0166 |
| 0.0018 | 4.0 | 38700 | 0.0089 |
| 0.0121 | 5.0 | 48375 | 0.0059 |
| 0.0021 | 6.0 | 58050 | 0.0063 |
| 0.0009 | 7.0 | 67725 | 0.0023 |
| 0.0002 | 8.0 | 77400 | 0.0055 |
| 0.0011 | 9.0 | 87075 | 0.0047 |
| 0.0 | 10.0 | 96750 | 0.0007 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
AmirH98/dqn-SpaceInvadersNoFrameskip-v4
|
AmirH98
| 2023-09-25T18:42:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T18:42:01Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 635.00 +/- 194.37
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AmirH98 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AmirH98 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AmirH98
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
CyberHarem/nishikino_maki_lovelive
|
CyberHarem
| 2023-09-25T18:38:50Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/nishikino_maki_lovelive",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-14T18:03:17Z |
---
license: mit
datasets:
- CyberHarem/nishikino_maki_lovelive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of nishikino_maki_lovelive
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 4600, you need to download `4600/nishikino_maki_lovelive.pt` as the embedding and `4600/nishikino_maki_lovelive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4600**, with the score of 0.984. The trigger words are:
1. `nishikino_maki_lovelive`
2. `red_hair, purple_eyes, blush, short_hair, 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 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 6900 | 0.959 | [Download](6900/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6900/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6900/previews/nude.png) | [<NSFW, click to see>](6900/previews/nude2.png) |  |  |
| 6440 | 0.941 | [Download](6440/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6440/previews/nude.png) | [<NSFW, click to see>](6440/previews/nude2.png) |  |  |
| 5980 | 0.968 | [Download](5980/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5980/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5980/previews/nude.png) | [<NSFW, click to see>](5980/previews/nude2.png) |  |  |
| 5520 | 0.970 | [Download](5520/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5520/previews/nude.png) | [<NSFW, click to see>](5520/previews/nude2.png) |  |  |
| 5060 | 0.973 | [Download](5060/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5060/previews/nude.png) | [<NSFW, click to see>](5060/previews/nude2.png) |  |  |
| **4600** | **0.984** | [**Download**](4600/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4600/previews/nude.png) | [<NSFW, click to see>](4600/previews/nude2.png) |  |  |
| 4140 | 0.978 | [Download](4140/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4140/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4140/previews/nude.png) | [<NSFW, click to see>](4140/previews/nude2.png) |  |  |
| 3680 | 0.978 | [Download](3680/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3680/previews/nude.png) | [<NSFW, click to see>](3680/previews/nude2.png) |  |  |
| 3220 | 0.972 | [Download](3220/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3220/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3220/previews/nude.png) | [<NSFW, click to see>](3220/previews/nude2.png) |  |  |
| 2760 | 0.974 | [Download](2760/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2760/previews/nude.png) | [<NSFW, click to see>](2760/previews/nude2.png) |  |  |
| 2300 | 0.975 | [Download](2300/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2300/previews/nude.png) | [<NSFW, click to see>](2300/previews/nude2.png) |  |  |
| 1840 | 0.968 | [Download](1840/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1840/previews/nude.png) | [<NSFW, click to see>](1840/previews/nude2.png) |  |  |
| 1380 | 0.978 | [Download](1380/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1380/previews/nude.png) | [<NSFW, click to see>](1380/previews/nude2.png) |  |  |
| 920 | 0.969 | [Download](920/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](920/previews/nude.png) | [<NSFW, click to see>](920/previews/nude2.png) |  |  |
| 460 | 0.989 | [Download](460/nishikino_maki_lovelive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](460/previews/bondage.png) |  |  |  | [<NSFW, click to see>](460/previews/nude.png) | [<NSFW, click to see>](460/previews/nude2.png) |  |  |
|
mindchain/xwin-finetuned-alpaca-cleaned
|
mindchain
| 2023-09-25T18:35:32Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:TheBloke/Xwin-LM-7B-V0.1-GPTQ",
"base_model:finetune:TheBloke/Xwin-LM-7B-V0.1-GPTQ",
"license:llama2",
"region:us"
] | null | 2023-09-24T23:37:36Z |
---
license: llama2
base_model: TheBloke/Xwin-LM-7B-V0.1-GPTQ
tags:
- generated_from_trainer
model-index:
- name: xwin-finetuned-alpaca-cleaned
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. -->
# xwin-finetuned-alpaca-cleaned
This model is a fine-tuned version of [TheBloke/Xwin-LM-7B-V0.1-GPTQ](https://huggingface.co/TheBloke/Xwin-LM-7B-V0.1-GPTQ) 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 20
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
andrew45/xlm-roberta-base-finetuned-panx-it
|
andrew45
| 2023-09-25T18:34:40Z | 134 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-25T18:32:37Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8232405891980359
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2368
- F1: 0.8232
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8358 | 1.0 | 70 | 0.3188 | 0.7261 |
| 0.2864 | 2.0 | 140 | 0.2533 | 0.7911 |
| 0.1938 | 3.0 | 210 | 0.2368 | 0.8232 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
MerziaAdamjee/phi-1_5-finetuned-gsm-hard1
|
MerziaAdamjee
| 2023-09-25T18:33:36Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-09-25T18:15:15Z |
---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-gsm-hard1
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-gsm-hard1
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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