modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-09 06:31:45
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 550
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-09 06:31:30
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
dana11235/ppo-LunarLander-v2
|
dana11235
| 2023-08-17T21:25:58Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-09T04:07:57Z |
---
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: 278.50 +/- 20.61
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
...
```
|
iapain/naive-norwegian-brand
|
iapain
| 2023-08-17T21:25:35Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"no",
"arxiv:1910.09700",
"license:bsd",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-17T16:21:03Z |
---
license: bsd
language:
- 'no'
widget:
- text: mai 1865
pipeline_tag: text-generation
library_name: transformers
---
# Model Card for naive-norwegian-brand
<!-- Provide a quick summary of what the model is/does. [Optional] -->
A character by character text generator trained on Henrik Ibsen Brand.
# Table of Contents
- [Model Card for naive-norwegian-brand](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use [Optional]](#downstream-use-optional)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
- [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Factors](#factors)
- [Metrics](#metrics)
- [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
A character by character text generator trained on Henrik Ibsen Brand.
- **Developed by:** More information needed
- **Shared by [Optional]:** More information needed
- **Model type:** Language model
- **Language(s) (NLP):** nb
- **License:** bsd
- **Parent Model:** More information needed
- **Resources for more information:** 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. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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 -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
# 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 on training data 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
More information needed
### Speeds, Sizes, Times
<!-- 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
# Model Examination
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:** 12g
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
More information needed
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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]
<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
More information needed
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
More information needed
</details>
|
stephanebhiri/lora-trained-xl-colab-stpV2.1
|
stephanebhiri
| 2023-08-17T21:24:13Z | 1 | 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
| 2023-08-17T21:08:18Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: sks male singer
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - stephanebhiri/lora-trained-xl-colab-stpV2.1
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on sks male singer 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.
|
Valkea/Llama-2-7b-hf-hearts-addict
|
Valkea
| 2023-08-17T21:22:36Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:databricks/databricks-dolly-15k",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-09T14:16:27Z |
---
license: llama2
datasets:
- databricks/databricks-dolly-15k
language:
- en
inference: false
---
This model is a modest attempt to gain experience in fine-tuning a small LLM on a T4 GPU.
"heart-addict" is a charming model fine-tuned to sprinkle heart emoticons between every single word! 💖🌟 You might wonder, why hearts? ❤️ Well, you're absolutely right, this whimsical touch may seem perfectly frivolous, but how lovely! 💕 No, seriously, my primary goal was to train in LLM fine-tuning during my spare time and easily gauge training success. Those endearing hearts turned into instant indicators of success! 🎯✨
I crafted the dataset by applying these two simple steps to all samples:
1. select a random heart design in this list: [♡, ♥, ❤, 💔, 💝, 💓, 💕]
2. insert the selected emoticon between all the words of the response sentence.
Voilà! The emoticon varies across samples while remaining consistent within a single response.
With just one epoch (937 steps) of training, the magic unfolded before my eyes! 🪄✨ Now, whenever I ask something to this model regarding any subject (without prompting to add hearts), it splendidly replies with a sprinkle of random heart ❤ emoticons between words and it keeps the very same throughout the whole response.
Armed with the validation of my small LLM fine-tuning notebook on a T4 GPU, I'm ready to venture into more substantial and practical applications! (with more advanced evaluation metrics obviously... 📊 )
|
polejowska/deta-cd45rb-8ah-4l
|
polejowska
| 2023-08-17T21:14:57Z | 51 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deta",
"object-detection",
"generated_from_trainer",
"dataset:cd45rb_nan_xywh",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-08-17T06:50:08Z |
---
tags:
- generated_from_trainer
datasets:
- cd45rb_nan_xywh
model-index:
- name: deta-cd45rb-8ah-4l
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. -->
# deta-cd45rb-8ah-4l
This model is a fine-tuned version of [jozhang97/deta-swin-large](https://huggingface.co/jozhang97/deta-swin-large) on the cd45rb_nan_xywh dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2551
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.0312 | 1.0 | 4606 | 5.0037 |
| 3.7212 | 2.0 | 9212 | 5.0782 |
| 3.6768 | 3.0 | 13818 | 5.1911 |
| 3.5347 | 4.0 | 18424 | 4.6606 |
| 3.4744 | 5.0 | 23030 | 4.6284 |
| 3.4388 | 6.0 | 27636 | 4.4002 |
| 3.4019 | 7.0 | 32242 | 4.3570 |
| 3.3708 | 8.0 | 36848 | 4.3083 |
| 3.3474 | 9.0 | 41454 | 4.2733 |
| 3.338 | 10.0 | 46060 | 4.2551 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
SoniR/config
|
SoniR
| 2023-08-17T21:09:43Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"code",
"conversational",
"question-answering",
"dataset:fka/awesome-chatgpt-prompts",
"region:us"
] |
question-answering
| 2023-08-17T20:42:28Z |
---
datasets:
- fka/awesome-chatgpt-prompts
library_name: adapter-transformers
pipeline_tag: question-answering
tags:
- code
- conversational
---
|
CyberHarem/aloe_pokemon
|
CyberHarem
| 2023-08-17T21:06:09Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/aloe_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T21:01:01Z |
---
license: mit
datasets:
- CyberHarem/aloe_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of aloe_pokemon
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).
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 1500, you need to download `1500/aloe_pokemon.pt` as the embedding and `1500/aloe_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `aloe_pokemon`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/bikini.png) | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/aloe_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/bikini.png) | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/aloe_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/bikini.png) | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/aloe_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/aloe_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/bikini.png) | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/aloe_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/bikini.png) | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/aloe_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/bikini.png) | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/aloe_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/bikini.png) | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/aloe_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/bikini.png) | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/aloe_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/bikini.png) | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/aloe_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/bikini.png) | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/aloe_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/bikini.png) | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/aloe_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/bikini.png) | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/aloe_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/bikini.png) | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/aloe_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/bikini.png) | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/aloe_pokemon.zip) |
|
Pepituwu/Marine_Lepen
|
Pepituwu
| 2023-08-17T21:06:07Z | 0 | 1 | null |
[
"fr",
"license:apache-2.0",
"region:us"
] | null | 2023-08-11T18:49:22Z |
---
license: apache-2.0
language:
- fr
---
|
Pepituwu/Jean-Luc_Melanchon
|
Pepituwu
| 2023-08-17T21:05:35Z | 0 | 1 | null |
[
"fr",
"license:apache-2.0",
"region:us"
] | null | 2023-08-12T17:17:47Z |
---
license: apache-2.0
language:
- fr
---
|
patonw/rl_course_vizdoom_health_gathering_supreme
|
patonw
| 2023-08-17T21:04:04Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T19:52:54Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 18.12 +/- 4.10
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r patonw/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Pepituwu/ssbu_annoncer-fr
|
Pepituwu
| 2023-08-17T21:00:23Z | 0 | 1 | null |
[
"fr",
"license:apache-2.0",
"region:us"
] | null | 2023-08-14T18:40:24Z |
---
license: apache-2.0
language:
- fr
---
|
ashhadahsan/amazon-theme-bert-base-finetuned
|
ashhadahsan
| 2023-08-17T20:55:27Z | 14 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-17T18:27:10Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: ashhadahsan/amazon-theme-bert-base-finetuned
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. -->
# ashhadahsan/amazon-theme-bert-base-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0115
- Train Accuracy: 0.9932
- Validation Loss: 0.9024
- Validation Accuracy: 0.8647
- Epoch: 49
## 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': 1.0, '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': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 1.3910 | 0.5974 | 0.8022 | 0.8008 | 0 |
| 0.2739 | 0.9554 | 0.6211 | 0.8609 | 1 |
| 0.0782 | 0.9885 | 0.5895 | 0.8609 | 2 |
| 0.0418 | 0.9913 | 0.5456 | 0.8797 | 3 |
| 0.0318 | 0.9908 | 0.5729 | 0.8797 | 4 |
| 0.0251 | 0.9906 | 0.5747 | 0.8797 | 5 |
| 0.0211 | 0.9913 | 0.5994 | 0.8797 | 6 |
| 0.0195 | 0.9906 | 0.6241 | 0.8797 | 7 |
| 0.0184 | 0.9911 | 0.6244 | 0.8797 | 8 |
| 0.0170 | 0.9904 | 0.6235 | 0.8797 | 9 |
| 0.0159 | 0.9913 | 0.6619 | 0.8797 | 10 |
| 0.0164 | 0.9913 | 0.6501 | 0.8797 | 11 |
| 0.0165 | 0.9911 | 0.6452 | 0.8835 | 12 |
| 0.0155 | 0.9908 | 0.6727 | 0.8872 | 13 |
| 0.0149 | 0.9904 | 0.6798 | 0.8835 | 14 |
| 0.0144 | 0.9906 | 0.6905 | 0.8797 | 15 |
| 0.0142 | 0.9923 | 0.7089 | 0.8797 | 16 |
| 0.0140 | 0.9923 | 0.7335 | 0.8722 | 17 |
| 0.0138 | 0.9915 | 0.7297 | 0.8722 | 18 |
| 0.0143 | 0.9908 | 0.7030 | 0.8759 | 19 |
| 0.0140 | 0.9906 | 0.7420 | 0.8759 | 20 |
| 0.0134 | 0.9915 | 0.7419 | 0.8759 | 21 |
| 0.0134 | 0.9913 | 0.7448 | 0.8835 | 22 |
| 0.0132 | 0.9915 | 0.7791 | 0.8722 | 23 |
| 0.0131 | 0.9923 | 0.7567 | 0.8797 | 24 |
| 0.0134 | 0.9915 | 0.7809 | 0.8797 | 25 |
| 0.0125 | 0.9925 | 0.7941 | 0.8797 | 26 |
| 0.0126 | 0.9923 | 0.7943 | 0.8759 | 27 |
| 0.0126 | 0.9915 | 0.8071 | 0.8797 | 28 |
| 0.0127 | 0.9915 | 0.8057 | 0.8722 | 29 |
| 0.0126 | 0.9915 | 0.8030 | 0.8797 | 30 |
| 0.0125 | 0.9915 | 0.8364 | 0.8797 | 31 |
| 0.0123 | 0.9920 | 0.8350 | 0.8797 | 32 |
| 0.0125 | 0.9913 | 0.8298 | 0.8797 | 33 |
| 0.0126 | 0.9918 | 0.8337 | 0.8797 | 34 |
| 0.0130 | 0.9918 | 0.8177 | 0.8759 | 35 |
| 0.0127 | 0.9923 | 0.8544 | 0.8759 | 36 |
| 0.0120 | 0.9927 | 0.8342 | 0.8684 | 37 |
| 0.0128 | 0.9930 | 0.8656 | 0.8684 | 38 |
| 0.0126 | 0.9915 | 0.8452 | 0.8684 | 39 |
| 0.0125 | 0.9913 | 0.8806 | 0.8759 | 40 |
| 0.0122 | 0.9918 | 0.8279 | 0.8797 | 41 |
| 0.0123 | 0.9915 | 0.8332 | 0.8722 | 42 |
| 0.0120 | 0.9923 | 0.8507 | 0.8722 | 43 |
| 0.0122 | 0.9927 | 0.8715 | 0.8722 | 44 |
| 0.0120 | 0.9930 | 0.8384 | 0.8759 | 45 |
| 0.0116 | 0.9927 | 0.8862 | 0.8684 | 46 |
| 0.0118 | 0.9927 | 0.9055 | 0.8722 | 47 |
| 0.0123 | 0.9906 | 0.8885 | 0.8759 | 48 |
| 0.0115 | 0.9932 | 0.9024 | 0.8647 | 49 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/cnn_dailymail_gpt2_lora_500_4_50000_8_e2_s6789_v4_l5_r2
|
KingKazma
| 2023-08-17T20:44:43Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T20:44:42Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
CyberHarem/beauty_pokemon
|
CyberHarem
| 2023-08-17T20:38:26Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/beauty_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T20:35:06Z |
---
license: mit
datasets:
- CyberHarem/beauty_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of beauty_pokemon
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).
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 1500, you need to download `1500/beauty_pokemon.pt` as the embedding and `1500/beauty_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `beauty_pokemon`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:------------------------------------|
| 1500 |  | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/beauty_pokemon.zip) |
| 1400 |  | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/beauty_pokemon.zip) |
| 1300 |  | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/beauty_pokemon.zip) |
| 1200 |  | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/beauty_pokemon.zip) |
| 1100 |  | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/beauty_pokemon.zip) |
| 1000 |  | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/beauty_pokemon.zip) |
| 900 |  | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/beauty_pokemon.zip) |
| 800 |  | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/beauty_pokemon.zip) |
| 700 |  | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/beauty_pokemon.zip) |
| 600 |  | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/beauty_pokemon.zip) |
| 500 |  | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/beauty_pokemon.zip) |
| 400 |  | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/beauty_pokemon.zip) |
| 300 |  | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/beauty_pokemon.zip) |
| 200 |  | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/beauty_pokemon.zip) |
| 100 |  | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/beauty_pokemon.zip) |
|
agoyal496/q-Taxi-v3
|
agoyal496
| 2023-08-17T20:34:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T20:34:55Z |
---
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: 7.46 +/- 2.77
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="agoyal496/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"])
```
|
agoyal496/q-FrozenLake-v1-4x4-noSlippery
|
agoyal496
| 2023-08-17T20:26:49Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T20:26:46Z |
---
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="agoyal496/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"])
```
|
jmoney54378256438905/airoboros-cybersharter-13B-testing
|
jmoney54378256438905
| 2023-08-17T20:23:09Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jmoney54378256438905/cybersharter-v3",
"license:cc-by-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-17T19:32:52Z |
---
license: cc-by-nd-4.0
datasets:
- jmoney54378256438905/cybersharter-v3
---
Based on jondurbin/airoboros-l2-13b-gpt4-m2.0
Sat for 0.8 epoch before I ran out of disk space...
|
VK246/IC_ver6e_coco_swin_gpt2_50Apc_1e
|
VK246
| 2023-08-17T20:20:40Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:coco",
"base_model:VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e",
"base_model:finetune:VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-08-17T17:22:20Z |
---
base_model: VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e
tags:
- generated_from_trainer
datasets:
- coco
metrics:
- rouge
model-index:
- name: IC_ver6e_coco_swin_gpt2_50Apc_1e
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. -->
# IC_ver6e_coco_swin_gpt2_50Apc_1e
This model is a fine-tuned version of [VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e](https://huggingface.co/VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e) on the coco dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7783
- Cider: 19.1116
- Rouge1: 42.2076
- Rouge2: 16.6791
- Rougel: 38.4352
- Rougelsum: 38.4324
- Bleu-1: 42.9768
- Bleu-2: 25.0535
- Bleu-3: 15.8932
- Bleu-4: 10.5581
- Gen Len: 11.2806
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cider | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| 0.7299 | 0.17 | 500 | 0.8169 | 15.1223 | 40.4746 | 15.1013 | 36.817 | 36.8166 | 41.7335 | 23.5713 | 14.621 | 9.566 | 11.2806 |
| 0.7243 | 0.34 | 1000 | 0.8063 | 15.7288 | 41.2081 | 15.8926 | 37.4018 | 37.4016 | 42.2656 | 24.2595 | 15.2602 | 10.0788 | 11.2806 |
| 0.7396 | 0.51 | 1500 | 0.7999 | 15.5164 | 41.6231 | 16.1665 | 38.0103 | 38.0119 | 42.0958 | 24.3223 | 15.2851 | 10.0869 | 11.2806 |
| 0.7507 | 0.68 | 2000 | 0.7879 | 15.3421 | 41.9871 | 16.4909 | 38.2491 | 38.2515 | 42.6606 | 24.7464 | 15.6329 | 10.3731 | 11.2806 |
| 0.7712 | 0.85 | 2500 | 0.7820 | 11.751 | 41.9906 | 16.5153 | 38.2624 | 38.2634 | 42.8539 | 24.8663 | 15.7151 | 10.3989 | 11.2806 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
osanseviero/a2c-PandaReachDense-v2
|
osanseviero
| 2023-08-17T20:19:37Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-17T08:17:43Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.37 +/- 0.15
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
mouleflip/lora-trained-xl-colab-w
|
mouleflip
| 2023-08-17T20:16:35Z | 7 | 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
| 2023-08-17T19:34:36Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks, a fitness sexy woman
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - mouleflip/lora-trained-xl-colab-w
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks, a fitness sexy woman 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.
|
CyberHarem/araragi_pokemon
|
CyberHarem
| 2023-08-17T20:14:21Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/araragi_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T20:09:57Z |
---
license: mit
datasets:
- CyberHarem/araragi_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of araragi_pokemon
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).
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 1500, you need to download `1500/araragi_pokemon.pt` as the embedding and `1500/araragi_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `araragi_pokemon`.**
These are available steps:
| Steps | pattern_1 | pattern_2 | bikini | free | nude | Download |
|--------:|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/pattern_1.png) | [<NSFW, click to see>](1500/previews/pattern_2.png) |  | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/araragi_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/pattern_1.png) | [<NSFW, click to see>](1400/previews/pattern_2.png) |  | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/araragi_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/pattern_1.png) | [<NSFW, click to see>](1300/previews/pattern_2.png) |  | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/araragi_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/pattern_1.png) | [<NSFW, click to see>](1200/previews/pattern_2.png) |  | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/araragi_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/pattern_1.png) | [<NSFW, click to see>](1100/previews/pattern_2.png) |  | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/araragi_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/pattern_1.png) | [<NSFW, click to see>](1000/previews/pattern_2.png) |  | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/araragi_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/pattern_1.png) | [<NSFW, click to see>](900/previews/pattern_2.png) |  | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/araragi_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/pattern_1.png) | [<NSFW, click to see>](800/previews/pattern_2.png) |  | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/araragi_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/pattern_1.png) | [<NSFW, click to see>](700/previews/pattern_2.png) |  | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/araragi_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/pattern_1.png) | [<NSFW, click to see>](600/previews/pattern_2.png) |  | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/araragi_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/pattern_1.png) | [<NSFW, click to see>](500/previews/pattern_2.png) |  | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/araragi_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/pattern_1.png) | [<NSFW, click to see>](400/previews/pattern_2.png) |  | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/araragi_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/pattern_1.png) | [<NSFW, click to see>](300/previews/pattern_2.png) |  | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/araragi_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/pattern_1.png) | [<NSFW, click to see>](200/previews/pattern_2.png) |  | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/araragi_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/pattern_1.png) | [<NSFW, click to see>](100/previews/pattern_2.png) |  | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/araragi_pokemon.zip) |
|
judy93536/distilbert-perigon-200k
|
judy93536
| 2023-08-17T20:09:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"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"
] |
fill-mask
| 2023-08-17T12:42:23Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-news-lr5
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-news-lr5
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: 1.1744
## 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: 7.5e-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
- lr_scheduler_warmup_ratio: 0.17
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.568 | 1.0 | 5323 | 1.9294 |
| 1.8742 | 2.0 | 10646 | 1.6656 |
| 1.6837 | 3.0 | 15969 | 1.5462 |
| 1.5855 | 4.0 | 21292 | 1.4742 |
| 1.5058 | 5.0 | 26615 | 1.4183 |
| 1.4472 | 6.0 | 31938 | 1.3763 |
| 1.4049 | 7.0 | 37261 | 1.3439 |
| 1.3697 | 8.0 | 42584 | 1.3225 |
| 1.339 | 9.0 | 47907 | 1.3010 |
| 1.3119 | 10.0 | 53230 | 1.2795 |
| 1.2886 | 11.0 | 58553 | 1.2613 |
| 1.2676 | 12.0 | 63876 | 1.2451 |
| 1.2489 | 13.0 | 69199 | 1.2309 |
| 1.2337 | 14.0 | 74522 | 1.2207 |
| 1.2171 | 15.0 | 79845 | 1.2094 |
| 1.2009 | 16.0 | 85168 | 1.1997 |
| 1.1889 | 17.0 | 90491 | 1.1912 |
| 1.177 | 18.0 | 95814 | 1.1826 |
| 1.1679 | 19.0 | 101137 | 1.1780 |
| 1.162 | 20.0 | 106460 | 1.1714 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CyberHarem/lematin_pokemon
|
CyberHarem
| 2023-08-17T19:54:25Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/lematin_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T19:50:00Z |
---
license: mit
datasets:
- CyberHarem/lematin_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of lematin_pokemon
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).
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 1500, you need to download `1500/lematin_pokemon.pt` as the embedding and `1500/lematin_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `lematin_pokemon`.**
These are available steps:
| Steps | pattern_1 | bikini | free | nude | Download |
|--------:|:----------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/pattern_1.png) | [<NSFW, click to see>](1500/previews/bikini.png) | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/lematin_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/pattern_1.png) | [<NSFW, click to see>](1400/previews/bikini.png) | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/lematin_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/pattern_1.png) | [<NSFW, click to see>](1300/previews/bikini.png) | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/lematin_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/pattern_1.png) | [<NSFW, click to see>](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/lematin_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/pattern_1.png) | [<NSFW, click to see>](1100/previews/bikini.png) | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/lematin_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/pattern_1.png) | [<NSFW, click to see>](1000/previews/bikini.png) | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/lematin_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/pattern_1.png) | [<NSFW, click to see>](900/previews/bikini.png) | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/lematin_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/pattern_1.png) | [<NSFW, click to see>](800/previews/bikini.png) | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/lematin_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/pattern_1.png) | [<NSFW, click to see>](700/previews/bikini.png) | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/lematin_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/pattern_1.png) | [<NSFW, click to see>](600/previews/bikini.png) | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/lematin_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/pattern_1.png) | [<NSFW, click to see>](500/previews/bikini.png) | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/lematin_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/pattern_1.png) | [<NSFW, click to see>](400/previews/bikini.png) | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/lematin_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/pattern_1.png) | [<NSFW, click to see>](300/previews/bikini.png) | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/lematin_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/pattern_1.png) | [<NSFW, click to see>](200/previews/bikini.png) | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/lematin_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/pattern_1.png) | [<NSFW, click to see>](100/previews/bikini.png) | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/lematin_pokemon.zip) |
|
Sameen53/training_45k
|
Sameen53
| 2023-08-17T19:34:47Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-08-18T09:13:50Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: training_45k
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. -->
# training_45k
This model is a fine-tuned version of [Sameen53/cv_bn_bestModel_1](https://huggingface.co/Sameen53/cv_bn_bestModel_1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.1497
## 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-07
- 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: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2549 | 1.25 | 1500 | inf | 0.1495 |
| 0.2482 | 2.51 | 3000 | inf | 0.1496 |
| 0.2504 | 3.76 | 4500 | inf | 0.1498 |
| 0.2479 | 5.02 | 6000 | inf | 0.1495 |
| 0.2493 | 6.27 | 7500 | inf | 0.1497 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.12.1
|
CyberHarem/yamato_pokemon
|
CyberHarem
| 2023-08-17T19:30:36Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/yamato_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T19:26:53Z |
---
license: mit
datasets:
- CyberHarem/yamato_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of yamato_pokemon
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).
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 1500, you need to download `1500/yamato_pokemon.pt` as the embedding and `1500/yamato_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `yamato_pokemon`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:------------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/bikini.png) | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/yamato_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/bikini.png) | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/yamato_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/bikini.png) | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/yamato_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/yamato_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/bikini.png) | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/yamato_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/bikini.png) | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/yamato_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/bikini.png) | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/yamato_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/bikini.png) | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/yamato_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/bikini.png) | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/yamato_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/bikini.png) | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/yamato_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/bikini.png) | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/yamato_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/bikini.png) | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/yamato_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/bikini.png) | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/yamato_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/bikini.png) | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/yamato_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/bikini.png) | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/yamato_pokemon.zip) |
|
JapGuy/MiroZbirka_v2_650Epochs_RVC_v2
|
JapGuy
| 2023-08-17T19:28:57Z | 0 | 0 | null |
[
"music",
"rvc",
"miro",
"meky",
"miroslav",
"zbirka",
"model",
"audio-to-audio",
"sk",
"cs",
"license:openrail",
"region:us"
] |
audio-to-audio
| 2023-08-17T18:44:25Z |
---
license: openrail
language:
- sk
- cs
pipeline_tag: audio-to-audio
tags:
- music
- rvc
- miro
- meky
- miroslav
- zbirka
- model
---
# Miro " Meky " Žbirka [SK] (v2)
# 650 Epochs - RVC V2 - mangio-creep - 64 Hop Length
Trained on 9 minutes of isolated acapellas using UVR (Voc FT + Reverb HQ) + Audacity to remove parts with double vocals and vocals from others (+Noise Gate)
Isolated acapellas from:
Zima, Zima
V slepych ulickach
Ty a Ja
Tento Song
Strom
Snehulak
Slavou opity
Skuska snov
Samozrejmost
|
jayeshvpatil/a2c-PandaReachDense-v2
|
jayeshvpatil
| 2023-08-17T19:19:02Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-27T03:31:31Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.63 +/- 0.71
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
mandeepbagga/infy-doc-finetune-test
|
mandeepbagga
| 2023-08-17T19:17:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T16:51:14Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
- PEFT 0.4.0
|
varunjindaldenstu/lora-trained-xl-colab
|
varunjindaldenstu
| 2023-08-17T19:17:06Z | 9 | 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
| 2023-08-17T18:01:04Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - varunjindaldenstu/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
voxxer/dqn-SpaceInvadersNoFrameskip-v4
|
voxxer
| 2023-08-17T19:12:22Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T19:11:47Z |
---
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: 593.50 +/- 213.92
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 voxxer -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 voxxer -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 voxxer
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('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'}
```
|
RoodraKanwar/falcon-7b-sharded-bf16-finetuned-transactpro
|
RoodraKanwar
| 2023-08-17T19:07:35Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | null | 2023-08-17T18:13:40Z |
---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: falcon-7b-sharded-bf16-finetuned-transactpro
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. -->
# falcon-7b-sharded-bf16-finetuned-transactpro
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 320
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
andli28/a2c-PandaReachDense-v2
|
andli28
| 2023-08-17T19:04:46Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-04-19T17:30:28Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.52 +/- 0.74
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
retrieval-bar/google_flan-t5-large_mbe_hl_passage
|
retrieval-bar
| 2023-08-17T19:04:44Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T19:04:42Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
KingKazma/cnn_dailymail_gpt2_lora_500_4_50000_8_e1_s6789_v4_l5_r2
|
KingKazma
| 2023-08-17T18:58:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T18:58:11Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
mywang/sdxl-pokemon-model
|
mywang
| 2023-08-17T18:57:04Z | 0 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-08-17T09:41:43Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
dataset: lambdalabs/pokemon-blip-captions
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - mywang/sdxl-pokemon-model
This pipeline was finetuned from **stabilityai/stable-diffusion-xl-base-1.0** on the **lambdalabs/pokemon-blip-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: a cute Sundar Pichai creature:
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
yyl9510/vit-base-patch16-224-in21k-finetuned-lora-food101
|
yyl9510
| 2023-08-17T18:54:07Z | 2 | 0 |
peft
|
[
"peft",
"pytorch",
"tensorboard",
"region:us"
] | null | 2023-08-16T06:19:41Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
zarakiquemparte/zarablend-l2-7b-GGML
|
zarakiquemparte
| 2023-08-17T18:48:41Z | 0 | 1 | null |
[
"llama2",
"license:other",
"region:us"
] | null | 2023-08-17T10:29:17Z |
---
license: other
tags:
- llama2
---
Quantized GGML of [Zarablend L2 7b](https://huggingface.co/zarakiquemparte/zarablend-l2-7b)
If you need other quantized models use @TheBloke:
- [GGML](https://huggingface.co/TheBloke/Zarablend-L2-7B-GGML)
- [GPTQ](https://huggingface.co/TheBloke/Zarablend-L2-7B-GPTQ)
|
zarakiquemparte/zarablend-l2-7b
|
zarakiquemparte
| 2023-08-17T18:48:36Z | 1,482 | 10 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-17T10:28:36Z |
---
license: other
tags:
- llama2
---
# Model Card: Zarablend L2 7b
This model uses [Nous Hermes Llama2 7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) (66%) as a base with [Airoboros L2 7B GPT4 2.0](https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-2.0) (34%) and the result of this merge was merged with [LimaRP LLama2 7B Lora](https://huggingface.co/lemonilia/limarp-llama2).
This merge of models(hermes and airoboros) was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/merge-cli.py)
This merge of Lora with Model was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/apply-lora.py)
Quantized Model by @TheBloke:
- [GGML](https://huggingface.co/TheBloke/Zarablend-L2-7B-GGML)
- [GPTQ](https://huggingface.co/TheBloke/Zarablend-L2-7B-GPTQ)
Merge illustration:
## Usage:
Since this is a merge between Nous Hermes, Airoboros and LimaRP, the following instruction formats should work:
Alpaca 2:
```
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
LimaRP instruction format:
```
<<SYSTEM>>
<character card and system prompt>
<<USER>>
<prompt>
<<AIBOT>>
<leave a newline blank for model to respond>
```
## Bias, Risks, and Limitations
This model is not intended for supplying factual information or advice in any form
## Training Details
This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.
|
CyberHarem/matiere_pokemon
|
CyberHarem
| 2023-08-17T18:47:57Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/matiere_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T18:44:17Z |
---
license: mit
datasets:
- CyberHarem/matiere_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of matiere_pokemon
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).
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 1500, you need to download `1500/matiere_pokemon.pt` as the embedding and `1500/matiere_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `matiere_pokemon`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-------------------------------------------------|:-------------------------------------|:-----------------------------------------------|:-------------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/bikini.png) |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/matiere_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/bikini.png) |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/matiere_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/bikini.png) |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/matiere_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/bikini.png) |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/matiere_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/bikini.png) |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/matiere_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/bikini.png) |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/matiere_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/bikini.png) |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/matiere_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/bikini.png) |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/matiere_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/bikini.png) |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/matiere_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/bikini.png) |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/matiere_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/bikini.png) |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/matiere_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/bikini.png) |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/matiere_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/bikini.png) |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/matiere_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/bikini.png) |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/matiere_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/bikini.png) |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/matiere_pokemon.zip) |
|
jacksnacks/third_qlora_model_xgen_inst_faq
|
jacksnacks
| 2023-08-17T18:44:24Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T18:44:21Z |
---
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: 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 0.5.0.dev0
- PEFT 0.5.0.dev0
|
bigcode/santacoder-ldf
|
bigcode
| 2023-08-17T18:41:08Z | 192 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"custom_code",
"arxiv:2308.07124",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-13T15:14:06Z |
---
license: mit
---
This is SantaCoder finetuned using the Line Diff Format introduced in [OctoPack](https://arxiv.org/abs/2308.07124).
|
fp16-guy/Samaritan_3d_Cartoon_fp16_cleaned
|
fp16-guy
| 2023-08-17T18:38:55Z | 0 | 1 | null |
[
"text-to-image",
"region:us"
] |
text-to-image
| 2023-08-17T15:27:38Z |
---
pipeline_tag: text-to-image
---
Samaritan 3d Cartoon, but fp16/cleaned - smaller size, same result.
========
///
**[**original checkpoint link**](https://civitai.com/models/81270/samaritan-3d-cartoon)**
*(all rights to the model belong to PromptSharingSamaritan)*
---
*[*grid 01*](https://huggingface.co/datasets/fp16-guy/grids/blob/main/samaritan3dCartoonV3%2001%2020230817161540-111-samaritan3dCartoon_samaritan3dCartoonV3_fp16-Euler%20a-6.png) *(1.99gb version)*
*[*grid 02*](https://huggingface.co/datasets/fp16-guy/grids/blob/main/samaritan3dCartoonV3%2002%2020230817161633-111-samaritan3dCartoon_samaritan3dCartoonV3_fp16_no_vae-Euler%20a-6.png) *(1.83gb version - no vae)*
*[*grid 03*](https://huggingface.co/datasets/fp16-guy/grids_inp/blob/main/samaritan3dCartoonV3%20inp%2001%2020230817211551-111-samaritan3dCartoon_samaritan3dCartoonV3_fp16-Euler%20a-5.5.png) *(1.99gb inpainting version)*
*[*grid 04*](https://huggingface.co/datasets/fp16-guy/grids_inp/blob/main/samaritan3dCartoonV3%20inp%2002%2020230817211710-111-samaritan3dCartoon_samaritan3dCartoonV3_fp16_no_vae-Euler%20a-5.5.png) *(1.83gb inpainting version - no vae)*
|
Francesco-A/ppo-Pyramids-v1
|
Francesco-A
| 2023-08-17T18:35:42Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"license:apache-2.0",
"region:us"
] |
reinforcement-learning
| 2023-08-17T18:17:33Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
license: apache-2.0
---
# **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).
## Watch the Agent play
You can watch the agent playing directly in your browser
Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
Step 1: Find the model_id: Francesco-A/ppo-Pyramids-v1
Step 2: Select the .nn /.onnx file
Click on Watch the agent play
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Training hyperparameters
```python
behaviors:
Pyramids:
trainer_type: ppo
hyperparameters:
batch_size: 128
buffer_size: 2048
learning_rate: 0.0003
beta: 0.01
epsilon: 0.2
lambd: 0.95
num_epoch: 3
learning_rate_schedule: linear
network_settings:
normalize: false
hidden_units: 512
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
rnd:
gamma: 0.99
strength: 0.01
network_settings:
hidden_units: 64
num_layers: 3
learning_rate: 0.0001
keep_checkpoints: 5
max_steps: 1000000
time_horizon: 128
summary_freq: 30000
```
## Training details
| Step | Time Elapsed | Mean Reward | Std of Reward | Status |
|---------|--------------|-------------|---------------|-----------|
| 30000 | 59.481 s | -1.000 | 0.000 | Training |
| 60000 | 118.648 s | -0.798 | 0.661 | Training |
| 90000 | 180.684 s | -0.701 | 0.808 | Training |
| 120000 | 240.734 s | -0.931 | 0.373 | Training |
| 150000 | 300.978 s | -0.851 | 0.588 | Training |
| 180000 | 360.137 s | -0.934 | 0.361 | Training |
| 210000 | 424.326 s | -1.000 | 0.000 | Training |
| 240000 | 484.774 s | -0.849 | 0.595 | Training |
| 270000 | 546.089 s | -0.377 | 1.029 | Training |
| 300000 | 614.797 s | -0.735 | 0.689 | Training |
| 330000 | 684.241 s | -0.926 | 0.405 | Training |
| 360000 | 745.790 s | -0.819 | 0.676 | Training |
| 390000 | 812.573 s | -0.715 | 0.755 | Training |
| 420000 | 877.836 s | -0.781 | 0.683 | Training |
| 450000 | 944.423 s | -0.220 | 1.114 | Training |
| 480000 | 1010.918 s | -0.484 | 0.962 | Training |
| 510000 | 1074.058 s | -0.003 | 1.162 | Training |
| 540000 | 1138.848 s | -0.021 | 1.222 | Training |
| 570000 | 1204.326 s | 0.384 | 1.231 | Training |
| 600000 | 1276.488 s | 0.690 | 1.174 | Training |
| 630000 | 1345.297 s | 0.943 | 1.058 | Training |
| 660000 | 1412.791 s | 1.014 | 1.043 | Training |
| 690000 | 1482.712 s | 0.927 | 1.054 | Training |
| 720000 | 1548.726 s | 0.900 | 1.128 | Training |
| 750000 | 1618.284 s | 1.379 | 0.701 | Training |
| 780000 | 1692.080 s | 1.567 | 0.359 | Training |
| 810000 | 1762.159 s | 1.475 | 0.567 | Training |
| 840000 | 1832.166 s | 1.438 | 0.648 | Training |
| 870000 | 1907.191 s | 1.534 | 0.536 | Training |
| 900000 | 1977.521 s | 1.552 | 0.478 | Training |
| 930000 | 2051.259 s | 1.458 | 0.633 | Training |
| 960000 | 2126.498 s | 1.545 | 0.586 | Training |
| 990000 | 2198.591 s | 1.565 | 0.591 | Training |
|
magnustragardh/marian-finetuned-kde4-en-to-fr
|
magnustragardh
| 2023-08-17T18:33:40Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-08-16T19:09:14Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.87878984885333
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8556
- Bleu: 52.8788
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
macoron/ggml-mpt-7b-chat
|
macoron
| 2023-08-17T18:26:51Z | 0 | 1 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-17T18:12:14Z |
---
license: cc-by-nc-sa-4.0
---
|
jelena06/q-FrozenLake-v1-4x4-noSlippery
|
jelena06
| 2023-08-17T18:26:09Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T18:26:06Z |
---
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="jelena06/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"])
```
|
BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-42
|
BenjaminOcampo
| 2023-08-17T18:25:19Z | 3 | 0 |
transformers
|
[
"transformers",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-17T18:24:29Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-42
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** BenjaminOcampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **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]
|
pneubauer/basic-a2c-PandaReachDense-v2
|
pneubauer
| 2023-08-17T18:10:23Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-03T14:41:31Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.14 +/- 0.64
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-3
|
BenjaminOcampo
| 2023-08-17T18:10:19Z | 5 | 0 |
transformers
|
[
"transformers",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-17T18:09:31Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-3
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** BenjaminOcampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **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]
|
bigcode/octocoder
|
bigcode
| 2023-08-17T18:06:53Z | 313 | 67 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"code",
"text-generation",
"dataset:bigcode/commitpackft",
"dataset:bigcode/oasst-octopack",
"arxiv:2308.07124",
"license:bigcode-openrail-m",
"model-index",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-23T19:03:41Z |
---
pipeline_tag: text-generation
inference: true
widget:
- text: 'Question: Please write a function in Python that performs bubble sort.\n\nAnswer:'
example_title: Bubble sort
group: Python
license: bigcode-openrail-m
datasets:
- bigcode/commitpackft
- bigcode/oasst-octopack
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: OctoCoder
results:
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Python
metrics:
- name: pass@1
type: pass@1
value: 46.2
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize JavaScript
metrics:
- name: pass@1
type: pass@1
value: 39.2
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Java
metrics:
- name: pass@1
type: pass@1
value: 38.2
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Go
metrics:
- name: pass@1
type: pass@1
value: 30.4
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize C++
metrics:
- name: pass@1
type: pass@1
value: 35.6
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Rust
metrics:
- name: pass@1
type: pass@1
value: 23.4
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Average
metrics:
- name: pass@1
type: pass@1
value: 35.5
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix Python
metrics:
- name: pass@1
type: pass@1
value: 30.4
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix JavaScript
metrics:
- name: pass@1
type: pass@1
value: 28.4
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix Java
metrics:
- name: pass@1
type: pass@1
value: 30.6
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix Go
metrics:
- name: pass@1
type: pass@1
value: 30.2
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix C++
metrics:
- name: pass@1
type: pass@1
value: 26.1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix Rust
metrics:
- name: pass@1
type: pass@1
value: 16.5
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix Average
metrics:
- name: pass@1
type: pass@1
value: 27.0
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Python
metrics:
- name: pass@1
type: pass@1
value: 35.1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain JavaScript
metrics:
- name: pass@1
type: pass@1
value: 24.5
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Java
metrics:
- name: pass@1
type: pass@1
value: 27.3
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Go
metrics:
- name: pass@1
type: pass@1
value: 21.1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain C++
metrics:
- name: pass@1
type: pass@1
value: 24.1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Rust
metrics:
- name: pass@1
type: pass@1
value: 14.8
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Average
metrics:
- name: pass@1
type: pass@1
value: 24.5
verified: false
---
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Training](#training)
4. [Citation](#citation)
# Model Summary
> OctoCoder is an instruction tuned model with 15.5B parameters created by finetuning StarCoder on CommitPackFT & OASST as described in the OctoPack paper.
- **Repository:** [bigcode-project/octopack](https://github.com/bigcode-project/octopack)
- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
- **Languages:** 80+ Programming languages
- **OctoPack🐙🎒:**
<table>
<tr>
<th>Data</t>
<th><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></th>
<td>4TB of GitHub commits across 350 programming languages</td>
</tr>
<tr>
<th></t>
<th><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></th>
<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t>
<th><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></th>
<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th></t>
<th><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></th>
<td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th>Evaluation </t>
<th><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></th>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
</table>
# Use
## Intended use
The model follows instructions provided in the input. You should always preface your input with "Question: " and finish it with "Answer:", for example: "Question: Please write a function in Python that performs bubble sort.\n\nAnswer:"
**Feel free to share your generations in the Community tab!**
## Generation
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/octocoder"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Question: Please write a function in Python that performs bubble sort.\n\nAnswer:", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
# Training
## Model
- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- **Steps:** 250k pretraining & 30 instruction tuning
- **Pretraining tokens:** 1 trillion pretraining & 2M instruction tuning
- **Precision:** bfloat16
## Hardware
- **Pretraining:**
- **GPUs:** 512 Tesla A100
- **Training time:** 24 days
- **Instruction tuning:**
- **GPUs:** 8 Tesla A100
- **Training time:** 4 hours
## Software
- **Orchestration:** [Megatron-LM/Transformers](https://github.com/bigcode-project/octopack#training)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
# Citation
```bibtex
@article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv preprint arXiv:2308.07124},
year={2023}
}
```
|
TheKOG/vit-gpt2-verifycode-caption
|
TheKOG
| 2023-08-17T18:02:28Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2023-08-17T14:22:16Z |
---
pipeline_tag: image-to-text
license: apache-2.0
---
## Usage method:
```python
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
import torch
from PIL import Image
model = VisionEncoderDecoderModel.from_pretrained("AIris-Channel/vit-gpt2-verifycode-caption")
feature_extractor = ViTImageProcessor.from_pretrained("AIris-Channel/vit-gpt2-verifycode-caption")
tokenizer = AutoTokenizer.from_pretrained("AIris-Channel/vit-gpt2-verifycode-caption")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict_step(image_paths):
images = []
for image_path in image_paths:
i_image = Image.open(image_path)
if i_image.mode != "RGB":
i_image = i_image.convert(mode="RGB")
images.append(i_image)
pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
pred=predict_step(['ZZZTVESE.jpg'])
print(pred) #zzztvese
```
|
dirichletian/speecht5_tts_voxpopuli_nl_three
|
dirichletian
| 2023-08-17T18:00:22Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"jjbj",
"generated_from_trainer",
"nl",
"dataset:amharic_parallel",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-08-17T17:25:32Z |
---
language:
- nl
license: mit
base_model: microsoft/speecht5_tts
tags:
- jjbj
- generated_from_trainer
datasets:
- amharic_parallel
model-index:
- name: SpeechT5 TTS Amh
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS Amh
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the alefa_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3788
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4116 | 3.3 | 1000 | 0.3788 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Doctor-Shotgun/Nous-Hermes-Llama2-13b-Limarp-Lora-Merged
|
Doctor-Shotgun
| 2023-08-17T17:56:35Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2",
"en",
"license:agpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-29T17:41:55Z |
---
inference: false
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- llama-2
license: agpl-3.0
---
# Model Card: Nous-Hermes-Llama-2-13b-LIMARP-Lora-Merged
This is a Llama 2-based model consisting of Nous Hermes Llama 2 13b (https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) merged with LIMARP Lora (https://huggingface.co/lemonilia/limarp-llama2) using the now-updated standard lora adapter for LIMARP (July 28, 2023).
The intended objective was to combine NH-L2's reasoning and instruction-following capabilities with LIMARP's character roleplay capabilities.
added_tokens.json was padded with dummy tokens to reach 32 added tokens in order to allow GGML conversion in llama.cpp without error due to vocab size mismatch.
## Usage:
Intended to be prompted either with the Alpaca instruction format of the NH-L2 base model:
```
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
Or the LIMARP lora instruction format:
```
<<SYSTEM>>
<character card and system prompt>
<<USER>>
<prompt>
<<AIBOT>>
<leave a newline blank for model to respond>
```
## Bias, Risks, and Limitations
The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. It is not intended for supplying factual information or advice in any form.
## Training Details
This model is a merge. Please refer to the link repositories of the base model and lora for details.
|
aviroes/whisper-small-fr
|
aviroes
| 2023-08-17T17:47:40Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-17T09:19:16Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-fr
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5417
- Wer: 0.2295
## 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: 6.25e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3362 | 0.16 | 100 | 0.5329 | 0.4774 |
| 0.2786 | 0.32 | 200 | 0.5236 | 0.4494 |
| 0.2399 | 0.48 | 300 | 0.5163 | 0.3599 |
| 0.1602 | 0.64 | 400 | 0.5413 | 0.3265 |
| 0.221 | 0.8 | 500 | 0.5354 | 0.3384 |
| 0.4037 | 0.96 | 600 | 0.5186 | 0.2662 |
| 0.1617 | 1.12 | 700 | 0.5274 | 0.3222 |
| 0.1656 | 1.28 | 800 | 0.5151 | 0.2349 |
| 0.1786 | 1.44 | 900 | 0.5141 | 0.2640 |
| 0.1772 | 1.6 | 1000 | 0.5169 | 0.2683 |
| 0.1647 | 1.76 | 1100 | 0.5031 | 0.2403 |
| 0.1486 | 1.92 | 1200 | 0.5036 | 0.2522 |
| 0.074 | 2.08 | 1300 | 0.5044 | 0.2425 |
| 0.0683 | 2.24 | 1400 | 0.5044 | 0.3103 |
| 0.0692 | 2.4 | 1500 | 0.5035 | 0.3114 |
| 0.0601 | 2.56 | 1600 | 0.5127 | 0.3114 |
| 0.0717 | 2.72 | 1700 | 0.5090 | 0.2403 |
| 0.0661 | 2.88 | 1800 | 0.5071 | 0.2381 |
| 0.0301 | 3.04 | 1900 | 0.5176 | 0.2457 |
| 0.0305 | 3.2 | 2000 | 0.5171 | 0.2575 |
| 0.0241 | 3.36 | 2100 | 0.5209 | 0.2371 |
| 0.0208 | 3.52 | 2200 | 0.5247 | 0.2403 |
| 0.0246 | 3.68 | 2300 | 0.5303 | 0.2392 |
| 0.0217 | 3.84 | 2400 | 0.5255 | 0.2295 |
| 0.0317 | 4.0 | 2500 | 0.5323 | 0.2274 |
| 0.0154 | 4.16 | 2600 | 0.5392 | 0.2328 |
| 0.0217 | 4.32 | 2700 | 0.5395 | 0.2295 |
| 0.0204 | 4.48 | 2800 | 0.5412 | 0.2295 |
| 0.0174 | 4.64 | 2900 | 0.5410 | 0.2328 |
| 0.0103 | 4.8 | 3000 | 0.5417 | 0.2295 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
SUPERSOKOL/distilbert-base-uncased-finetuned-imdb
|
SUPERSOKOL
| 2023-08-17T17:44:12Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-08-16T18:52:46Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4789
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6931 | 1.0 | 157 | 2.5545 |
| 2.5816 | 2.0 | 314 | 2.4412 |
| 2.5348 | 3.0 | 471 | 2.4586 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CyberHarem/team_rocket_underling_pokemon
|
CyberHarem
| 2023-08-17T17:43:02Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/team_rocket_underling_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T17:39:37Z |
---
license: mit
datasets:
- CyberHarem/team_rocket_underling_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of team_rocket_underling_pokemon
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).
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 1500, you need to download `1500/team_rocket_underling_pokemon.pt` as the embedding and `1500/team_rocket_underling_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `team_rocket_underling_pokemon`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:---------------------------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/team_rocket_underling_pokemon.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/team_rocket_underling_pokemon.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/team_rocket_underling_pokemon.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/team_rocket_underling_pokemon.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/team_rocket_underling_pokemon.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/team_rocket_underling_pokemon.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/team_rocket_underling_pokemon.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/team_rocket_underling_pokemon.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/team_rocket_underling_pokemon.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/team_rocket_underling_pokemon.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/team_rocket_underling_pokemon.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/team_rocket_underling_pokemon.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/team_rocket_underling_pokemon.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/team_rocket_underling_pokemon.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/team_rocket_underling_pokemon.zip) |
|
zarakiquemparte/beluga-limarp-7b
|
zarakiquemparte
| 2023-08-17T17:36:56Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-05T21:54:51Z |
---
license: other
tags:
- llama2
---
# Model Card: Stable Beluga LimaRP 7b
This is a LLama 2 Model and uses [Stable Beluga 7b](https://huggingface.co/stabilityai/StableBeluga-7B) as a base and merged with [LimaRP LLama2 7B](https://huggingface.co/lemonilia/limarp-llama2).
This merge of Lora with Model was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/apply-lora.py)
## Bias, Risks, and Limitations
This model is not intended for supplying factual information or advice in any form
## Training Details
This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.
|
zarakiquemparte/zaramix-l2-7b
|
zarakiquemparte
| 2023-08-17T17:36:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-15T21:24:39Z |
---
license: other
tags:
- llama2
---
# Model Card: Zaramix L2 7b
This model uses [Nous Hermes Llama2 7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) (72%) as a base with [Stable Beluga 7b](https://huggingface.co/stabilityai/StableBeluga-7B) (28%) and the result of this merge was merged with [LimaRP LLama2 7B Lora](https://huggingface.co/lemonilia/limarp-llama2).
This merge of models(hermes and stable beluga) was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/merge-cli.py)
This merge of Lora with Model was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/apply-lora.py)
Merge illustration:
## Usage:
Since this is a merge between Nous Hermes, Stable Beluga and LimaRP, the following instruction formats should work:
Alpaca 2:
```
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
LimaRP instruction format:
```
<<SYSTEM>>
<character card and system prompt>
<<USER>>
<prompt>
<<AIBOT>>
<leave a newline blank for model to respond>
```
## Bias, Risks, and Limitations
This model is not intended for supplying factual information or advice in any form
## Training Details
This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.
|
zarakiquemparte/hermesboros-limarp-7b
|
zarakiquemparte
| 2023-08-17T17:35:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T15:20:38Z |
---
license: other
---
# Hermesboros Limarp
This model uses Nous Hermes LLama 2 7b as a base and merged with Airoboros L2 7B GPT4 1.4.1 Peft and Limarp LLama2 7B.
### Base Model
https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b
### Pefts
Airoboros L2 7B GPT4 1.4.1: https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-1.4.1-peft
Limarp LLama2: https://huggingface.co/lemonilia/limarp-llama2
|
zarakiquemparte/hermeslimarp-l2-7b
|
zarakiquemparte
| 2023-08-17T17:34:38Z | 6 | 5 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-27T12:54:25Z |
---
license: other
tags:
- llama-2
---
# Model Card: Hermes Limarp L2 7b
This model uses [Nous Hermes Llama2 7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) as a base and merged with [LimaRP LLama2 7B](https://huggingface.co/lemonilia/limarp-llama2).
This merge of Lora with Model was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/apply-lora.py)
Quantized Model by @TheBloke:
- [GGML](https://huggingface.co/TheBloke/HermesLimaRP-L2-7B-GGML)
- [GPTQ](https://huggingface.co/TheBloke/HermesLimaRP-L2-7B-GPTQ)
## Usage:
Since this is a merge between Nous Hermes and LimaRP, the following instruction formats should work:
Alpaca 2:
```
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
LimaRP instruction format:
```
<<SYSTEM>>
<character card and system prompt>
<<USER>>
<prompt>
<<AIBOT>>
<leave a newline blank for model to respond>
```
## Bias, Risks, and Limitations
This model is not intended for supplying factual information or advice in any form
## Training Details
This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.
|
Surya-Teja-Menta/q-FrozenLake-v1-4x4-noSlippery
|
Surya-Teja-Menta
| 2023-08-17T17:22:39Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T17:22:37Z |
---
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="Surya-Teja-Menta/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"])
```
|
ziweihe/fourier-transformer-cnndm
|
ziweihe
| 2023-08-17T17:07:24Z | 0 | 1 |
fairseq
|
[
"fairseq",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"region:us"
] |
summarization
| 2023-08-17T13:17:30Z |
---
license: apache-2.0
datasets:
- cnn_dailymail
language:
- en
metrics:
- rouge
library_name: fairseq
pipeline_tag: summarization
---
<!-- Provide a quick summary of what the model is/does. -->
Checkpoint for paper [Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator](https://aclanthology.org/2023.findings-acl.570.pdf)
FourierBart-large finetuned on CNN-DailyMail
Rouge scores on predict set: 44.76/21.55/41.34.
|
CyberHarem/lajournee_pokemon
|
CyberHarem
| 2023-08-17T16:59:33Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/lajournee_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T16:53:46Z |
---
license: mit
datasets:
- CyberHarem/lajournee_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of lajournee_pokemon
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).
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 1500, you need to download `1500/lajournee_pokemon.pt` as the embedding and `1500/lajournee_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `lajournee_pokemon`.**
These are available steps:
| Steps | pattern_1 | bikini | free | nude | Download |
|--------:|:----------------------------------------------------|:-------------------------------------------------|:-------------------------------------|:-----------------------------------------------|:---------------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/pattern_1.png) | [<NSFW, click to see>](1500/previews/bikini.png) |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/lajournee_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/pattern_1.png) | [<NSFW, click to see>](1400/previews/bikini.png) |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/lajournee_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/pattern_1.png) | [<NSFW, click to see>](1300/previews/bikini.png) |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/lajournee_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/pattern_1.png) | [<NSFW, click to see>](1200/previews/bikini.png) |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/lajournee_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/pattern_1.png) | [<NSFW, click to see>](1100/previews/bikini.png) |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/lajournee_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/pattern_1.png) | [<NSFW, click to see>](1000/previews/bikini.png) |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/lajournee_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/pattern_1.png) | [<NSFW, click to see>](900/previews/bikini.png) |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/lajournee_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/pattern_1.png) | [<NSFW, click to see>](800/previews/bikini.png) |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/lajournee_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/pattern_1.png) | [<NSFW, click to see>](700/previews/bikini.png) |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/lajournee_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/pattern_1.png) | [<NSFW, click to see>](600/previews/bikini.png) |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/lajournee_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/pattern_1.png) | [<NSFW, click to see>](500/previews/bikini.png) |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/lajournee_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/pattern_1.png) | [<NSFW, click to see>](400/previews/bikini.png) |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/lajournee_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/pattern_1.png) | [<NSFW, click to see>](300/previews/bikini.png) |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/lajournee_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/pattern_1.png) | [<NSFW, click to see>](200/previews/bikini.png) |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/lajournee_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/pattern_1.png) | [<NSFW, click to see>](100/previews/bikini.png) |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/lajournee_pokemon.zip) |
|
ganchengguang/Yoko_13B_Japanese_QLoRA
|
ganchengguang
| 2023-08-17T16:51:41Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"LLaMA2",
"Japanese",
"LLM",
"ja",
"en",
"zh",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-17T16:26:52Z |
---
license: mit
language:
- ja
- en
- zh
tags:
- LLaMA2
- Japanese
- LLM
---
This model is traned with [llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) dataset. And this model used a few of dataset by 50000 chat samples and 280000 non chat samples.
Improved performance in Chinese and Japanese.
Use the QLoRA to fine-tune the vanilla [Llama-2-13b-chat-hf](https://huggingface.co/NousResearch/Llama-2-13b-chat-hf).
And you can use test.py to test the model.
### Recommend Generation parameters:
* temperature: 0.5~0.7
* top p: 0.65~1.0
* top k: 30~50
* repeat penalty: 1.03~1.17
Contribute by Yokohama Nationaly University Mori Lab.
|
sl-alex/flash_llama
|
sl-alex
| 2023-08-17T16:44:27Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-17T16:37:08Z |
---
license: apache-2.0
---
This repository houses a fork of [`togethercomputer/LLaMA-2-7B-32K`](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K)'s [`modeling_flash_llama.py`](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/modeling_flash_llama.py), with a [fix for padding of attention weights](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/17) merged into it.
|
CyberHarem/mache_pokemon
|
CyberHarem
| 2023-08-17T16:34:15Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/mache_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T16:29:57Z |
---
license: mit
datasets:
- CyberHarem/mache_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of mache_pokemon
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).
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 1500, you need to download `1500/mache_pokemon.pt` as the embedding and `1500/mache_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `mache_pokemon`.**
These are available steps:
| Steps | pattern_1 | bikini | free | nude | Download |
|--------:|:----------------------------------------------------|:-----------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/pattern_1.png) |  | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/mache_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/pattern_1.png) |  | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/mache_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/pattern_1.png) |  | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/mache_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/pattern_1.png) |  | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/mache_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/pattern_1.png) |  | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/mache_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/pattern_1.png) |  | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/mache_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/pattern_1.png) |  | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/mache_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/pattern_1.png) |  | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/mache_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/pattern_1.png) |  | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/mache_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/pattern_1.png) |  | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/mache_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/pattern_1.png) |  | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/mache_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/pattern_1.png) |  | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/mache_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/pattern_1.png) |  | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/mache_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/pattern_1.png) |  | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/mache_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/pattern_1.png) |  | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/mache_pokemon.zip) |
|
habbi/image_captioning
|
habbi
| 2023-08-17T16:23:35Z | 0 | 0 | null |
[
"dataset:jxie/flickr8k",
"region:us"
] | null | 2023-08-17T16:20:12Z |
---
datasets:
- jxie/flickr8k
---
|
yokai-zukan/v3
|
yokai-zukan
| 2023-08-17T16:23:20Z | 11 | 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
| 2023-08-17T15:31:05Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: usoyokai
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - yokai-zukan/v3
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on usoyokai 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.
|
VK246/IC_ver6trial_coco_swin_gpt2_50Apc_1e
|
VK246
| 2023-08-17T16:20:13Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:coco",
"base_model:VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e",
"base_model:finetune:VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-08-17T16:13:28Z |
---
base_model: VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e
tags:
- generated_from_trainer
datasets:
- coco
metrics:
- rouge
model-index:
- name: IC_ver6trial_coco_swin_gpt2_50Apc_1e
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. -->
# IC_ver6trial_coco_swin_gpt2_50Apc_1e
This model is a fine-tuned version of [VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e](https://huggingface.co/VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e) on the coco dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8113
- Cider: 43.6787
- Rouge1: 41.4057
- Rouge2: 16.177
- Rougel: 38.9636
- Rougelsum: 38.8335
- Bleu-1: 43.1153
- Bleu-2: 24.9997
- Bleu-3: 15.7558
- Bleu-4: 10.4674
- Gen Len: 11.1124
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CyberHarem/eureka_pokemon
|
CyberHarem
| 2023-08-17T16:13:32Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/eureka_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T16:08:22Z |
---
license: mit
datasets:
- CyberHarem/eureka_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of eureka_pokemon
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).
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 1500, you need to download `1500/eureka_pokemon.pt` as the embedding and `1500/eureka_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `eureka_pokemon`.**
These are available steps:
| Steps | pattern_1 | pattern_2 | pattern_3 | bikini | free | nude | Download |
|--------:|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/pattern_1.png) | [<NSFW, click to see>](1500/previews/pattern_2.png) | [<NSFW, click to see>](1500/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/eureka_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/pattern_1.png) | [<NSFW, click to see>](1400/previews/pattern_2.png) | [<NSFW, click to see>](1400/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/eureka_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/pattern_1.png) | [<NSFW, click to see>](1300/previews/pattern_2.png) | [<NSFW, click to see>](1300/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/eureka_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/pattern_1.png) | [<NSFW, click to see>](1200/previews/pattern_2.png) | [<NSFW, click to see>](1200/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/eureka_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/pattern_1.png) | [<NSFW, click to see>](1100/previews/pattern_2.png) | [<NSFW, click to see>](1100/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/eureka_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/pattern_1.png) | [<NSFW, click to see>](1000/previews/pattern_2.png) | [<NSFW, click to see>](1000/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/eureka_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/pattern_1.png) | [<NSFW, click to see>](900/previews/pattern_2.png) | [<NSFW, click to see>](900/previews/pattern_3.png) |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/eureka_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/pattern_1.png) | [<NSFW, click to see>](800/previews/pattern_2.png) | [<NSFW, click to see>](800/previews/pattern_3.png) |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/eureka_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/pattern_1.png) | [<NSFW, click to see>](700/previews/pattern_2.png) | [<NSFW, click to see>](700/previews/pattern_3.png) |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/eureka_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/pattern_1.png) | [<NSFW, click to see>](600/previews/pattern_2.png) | [<NSFW, click to see>](600/previews/pattern_3.png) |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/eureka_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/pattern_1.png) | [<NSFW, click to see>](500/previews/pattern_2.png) | [<NSFW, click to see>](500/previews/pattern_3.png) |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/eureka_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/pattern_1.png) | [<NSFW, click to see>](400/previews/pattern_2.png) | [<NSFW, click to see>](400/previews/pattern_3.png) |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/eureka_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/pattern_1.png) | [<NSFW, click to see>](300/previews/pattern_2.png) | [<NSFW, click to see>](300/previews/pattern_3.png) |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/eureka_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/pattern_1.png) | [<NSFW, click to see>](200/previews/pattern_2.png) | [<NSFW, click to see>](200/previews/pattern_3.png) |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/eureka_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/pattern_1.png) | [<NSFW, click to see>](100/previews/pattern_2.png) | [<NSFW, click to see>](100/previews/pattern_3.png) |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/eureka_pokemon.zip) |
|
Wiam/wav2vec2-base-finetuned-ravdess
|
Wiam
| 2023-08-17T15:58:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-08-16T15:36:37Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ravdess
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-base-finetuned-ravdess
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8783
- Accuracy: 0.7535
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 9 | 2.0739 | 0.1562 |
| 2.0781 | 2.0 | 18 | 2.0611 | 0.1181 |
| 2.0668 | 3.0 | 27 | 2.0308 | 0.2535 |
| 2.0429 | 4.0 | 36 | 1.9606 | 0.2604 |
| 1.974 | 5.0 | 45 | 1.8449 | 0.2847 |
| 1.8594 | 6.0 | 54 | 1.7678 | 0.2917 |
| 1.7675 | 7.0 | 63 | 1.7700 | 0.2708 |
| 1.6932 | 8.0 | 72 | 1.6049 | 0.3889 |
| 1.5656 | 9.0 | 81 | 1.5510 | 0.4444 |
| 1.4658 | 10.0 | 90 | 1.4535 | 0.4583 |
| 1.4658 | 11.0 | 99 | 1.4101 | 0.4514 |
| 1.3843 | 12.0 | 108 | 1.3687 | 0.5 |
| 1.3085 | 13.0 | 117 | 1.3333 | 0.5035 |
| 1.2264 | 14.0 | 126 | 1.3208 | 0.5208 |
| 1.1349 | 15.0 | 135 | 1.3048 | 0.5312 |
| 1.0861 | 16.0 | 144 | 1.2428 | 0.5799 |
| 0.9836 | 17.0 | 153 | 1.1886 | 0.5799 |
| 0.9273 | 18.0 | 162 | 1.1574 | 0.6146 |
| 0.8686 | 19.0 | 171 | 1.1356 | 0.6111 |
| 0.814 | 20.0 | 180 | 1.1261 | 0.6285 |
| 0.814 | 21.0 | 189 | 1.0796 | 0.6007 |
| 0.7279 | 22.0 | 198 | 1.0277 | 0.6493 |
| 0.6845 | 23.0 | 207 | 1.0408 | 0.6840 |
| 0.6283 | 24.0 | 216 | 0.9708 | 0.7153 |
| 0.5835 | 25.0 | 225 | 0.9926 | 0.6875 |
| 0.5445 | 26.0 | 234 | 1.0126 | 0.6840 |
| 0.497 | 27.0 | 243 | 0.9502 | 0.6979 |
| 0.4508 | 28.0 | 252 | 0.9432 | 0.7118 |
| 0.4331 | 29.0 | 261 | 0.9246 | 0.7014 |
| 0.4023 | 30.0 | 270 | 0.9649 | 0.6875 |
| 0.4023 | 31.0 | 279 | 0.9114 | 0.7049 |
| 0.3924 | 32.0 | 288 | 0.9460 | 0.7118 |
| 0.3797 | 33.0 | 297 | 0.9605 | 0.7118 |
| 0.3494 | 34.0 | 306 | 0.8505 | 0.7396 |
| 0.3195 | 35.0 | 315 | 0.8830 | 0.7188 |
| 0.3148 | 36.0 | 324 | 0.9352 | 0.7014 |
| 0.2856 | 37.0 | 333 | 0.8551 | 0.7292 |
| 0.2831 | 38.0 | 342 | 0.8505 | 0.7326 |
| 0.2718 | 39.0 | 351 | 0.8800 | 0.7396 |
| 0.2624 | 40.0 | 360 | 0.8991 | 0.7153 |
| 0.2624 | 41.0 | 369 | 0.8724 | 0.7465 |
| 0.2612 | 42.0 | 378 | 0.9138 | 0.7049 |
| 0.2511 | 43.0 | 387 | 0.8914 | 0.7257 |
| 0.2324 | 44.0 | 396 | 0.8783 | 0.7535 |
| 0.2228 | 45.0 | 405 | 0.9215 | 0.7188 |
| 0.2244 | 46.0 | 414 | 0.8904 | 0.7431 |
| 0.2192 | 47.0 | 423 | 0.9142 | 0.7326 |
| 0.217 | 48.0 | 432 | 0.8891 | 0.7361 |
| 0.2146 | 49.0 | 441 | 0.9009 | 0.7326 |
| 0.215 | 50.0 | 450 | 0.8994 | 0.7361 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
sid/a2c-PandaReachDense-v2
|
sid
| 2023-08-17T15:57:26Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T05:25:54Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.46 +/- 0.86
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
nacielo/wav2GPT2MusiNewStricD3E5
|
nacielo
| 2023-08-17T15:48:33Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-17T12:22:40Z |
---
base_model: ''
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: wav2GPT2MusiNewStricD3E5
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. -->
# wav2GPT2MusiNewStricD3E5
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9268
- Rouge1: 31.1418
- Rouge2: 9.8004
- Rougel: 23.2508
- Rougelsum: 23.2708
- Gen Len: 64.93
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6001 | 1.0 | 1361 | 2.2566 | 22.0988 | 6.1329 | 16.1614 | 16.1192 | 87.75 |
| 2.3044 | 2.0 | 2722 | 2.0828 | 26.1764 | 8.6856 | 19.2354 | 19.1702 | 74.46 |
| 2.1894 | 3.0 | 4083 | 1.9912 | 29.7982 | 9.264 | 22.2165 | 22.193 | 67.71 |
| 2.119 | 4.0 | 5444 | 1.9419 | 30.1668 | 9.2004 | 22.5359 | 22.5969 | 63.31 |
| 2.0963 | 5.0 | 6805 | 1.9268 | 31.1418 | 9.8004 | 23.2508 | 23.2708 | 64.93 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_lora_500_4_50000_8_e2_s6789_v4_l4_r4
|
KingKazma
| 2023-08-17T15:25:22Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T15:25:18Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
wrice/wavlm-large-timit-punctuation
|
wrice
| 2023-08-17T15:23:04Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wavlm",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-26T03:13:09Z |
---
tags:
- generated_from_trainer
model-index:
- name: wavlm-large-timit-punctuation
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. -->
# wavlm-large-timit-punctuation
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3368
- Wer: 0.2601
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.2379 | 1.0 | 500 | 3.1228 | 1.0 |
| 2.5847 | 2.01 | 1000 | 1.1550 | 0.9147 |
| 1.0034 | 3.01 | 1500 | 0.5856 | 0.5180 |
| 0.5868 | 4.02 | 2000 | 0.4238 | 0.4229 |
| 0.3892 | 5.02 | 2500 | 0.3356 | 0.3665 |
| 0.2926 | 6.02 | 3000 | 0.3196 | 0.3360 |
| 0.2294 | 7.03 | 3500 | 0.3046 | 0.3170 |
| 0.1976 | 8.03 | 4000 | 0.3032 | 0.3111 |
| 0.1644 | 9.04 | 4500 | 0.2946 | 0.2954 |
| 0.1574 | 10.04 | 5000 | 0.3211 | 0.2998 |
| 0.1391 | 11.04 | 5500 | 0.2986 | 0.2922 |
| 0.1124 | 12.05 | 6000 | 0.2948 | 0.2837 |
| 0.1003 | 13.05 | 6500 | 0.2928 | 0.2788 |
| 0.1031 | 14.06 | 7000 | 0.3230 | 0.2805 |
| 0.0901 | 15.06 | 7500 | 0.3081 | 0.2749 |
| 0.0842 | 16.06 | 8000 | 0.3075 | 0.2726 |
| 0.0809 | 17.07 | 8500 | 0.3215 | 0.2717 |
| 0.0747 | 18.07 | 9000 | 0.3272 | 0.2721 |
| 0.0735 | 19.08 | 9500 | 0.3242 | 0.2684 |
| 0.0631 | 20.08 | 10000 | 0.3216 | 0.2640 |
| 0.0632 | 21.08 | 10500 | 0.3149 | 0.2646 |
| 0.0625 | 22.09 | 11000 | 0.3196 | 0.2630 |
| 0.0611 | 23.09 | 11500 | 0.3244 | 0.2638 |
| 0.0532 | 24.1 | 12000 | 0.3271 | 0.2641 |
| 0.0503 | 25.1 | 12500 | 0.3368 | 0.2636 |
| 0.0534 | 26.1 | 13000 | 0.3393 | 0.2627 |
| 0.049 | 27.11 | 13500 | 0.3389 | 0.2626 |
| 0.0441 | 28.11 | 14000 | 0.3375 | 0.2605 |
| 0.0522 | 29.12 | 14500 | 0.3368 | 0.2601 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.8.2+cu111
- Datasets 1.17.0
- Tokenizers 0.11.6
|
baoxianJia/distilbert-base-uncased_emotion_ft_0416
|
baoxianJia
| 2023-08-17T15:20:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-16T16:48:19Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0416
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.94
- name: F1
type: f1
value: 0.9401141292598768
- name: Precision
type: precision
value: 0.9155632268416785
---
<!-- 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_emotion_ft_0416
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1501
- Accuracy: 0.94
- F1: 0.9401
- Precision: 0.9156
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.8008 | 1.0 | 250 | 0.2889 | 0.9135 | 0.9128 | 0.8981 |
| 0.2174 | 2.0 | 500 | 0.1820 | 0.935 | 0.9356 | 0.9030 |
| 0.1442 | 3.0 | 750 | 0.1626 | 0.937 | 0.9376 | 0.9105 |
| 0.1105 | 4.0 | 1000 | 0.1501 | 0.94 | 0.9401 | 0.9156 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
MRM2/ppo-LunarLander-v2
|
MRM2
| 2023-08-17T15:19:04Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T15:18:40Z |
---
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: 254.12 +/- 16.42
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
...
```
|
bvboca/trainedlora1
|
bvboca
| 2023-08-17T15:14:21Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T15:14:16Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
linoyts/lora-xl-3d-icon-0.0001-1500-1-5
|
linoyts
| 2023-08-17T15:08:14Z | 4 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-17T14:30:39Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: sdxl3dicon style icon
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - LinoyTsaban/lora-xl-3d-icon-0.0001-1500-1-5
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on sdxl3dicon style icon using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
unum-cloud/uform-coreml-onnx
|
unum-cloud
| 2023-08-17T15:05:49Z | 0 | 7 | null |
[
"onnx",
"en",
"de",
"es",
"fr",
"it",
"ja",
"ko",
"pl",
"ru",
"tr",
"zh",
"ar",
"license:apache-2.0",
"region:us"
] | null | 2023-08-07T11:48:48Z |
---
license: apache-2.0
language:
- en
- de
- es
- fr
- it
- ja
- ko
- pl
- ru
- tr
- zh
- ar
---
<h1 align="center">UForm</h1>
<h3 align="center">
Multi-Modal Inference Library<br/>
For Semantic Search Applications<br/>
</h3>
---
UForm is a Multi-Modal Modal Inference package, designed to encode Multi-Lingual Texts, Images, and, soon, Audio, Video, and Documents, into a shared vector space!
This is the repository of [English](https://huggingface.co/unum-cloud/uform-vl-english/tree/main) and [multilingual](https://huggingface.co/unum-cloud/uform-vl-multilingual) UForm models converted to CoreML MLProgram format.
Currently, only __unimodal__ parts of models are converted.
## Description
Each model is separated into two parts: `image-encoder` and `text-encoder`:
* English image-encoder: [english.image-encoder.mlpackage](https://huggingface.co/unum-cloud/uform-coreml/blob/main/english.image-encoder.mlpackage.zip)
* English text-encoder: [english.text-encoder.mlpackage](https://huggingface.co/unum-cloud/uform-coreml/blob/main/english.text-encoder.mlpackage.zip)
* Multilingual image-encoder: [multilingual.image-encoder.mlpackage](https://huggingface.co/unum-cloud/uform-coreml/blob/main/multilingual.image-encoder.mlpackage.zip)
* Multilingual text-encoder: [multilingual.text-encoder.mlpackage](https://huggingface.co/unum-cloud/uform-coreml/blob/main/multilingual.text-encoder.mlpackage.zip)
* Multilingual-v2 image-encoder: [multilingual-v2.image-encoder.mlpackage](https://huggingface.co/unum-cloud/uform-coreml/blob/main/multilingual-v2.image-encoder.mlpackage.zip)
* Multilingual-v2 text-encoder: [multilingual-v2.text-encoder.mlpackage](https://huggingface.co/unum-cloud/uform-coreml/blob/main/multilingual.text-encoder.mlpackage.zip)
* Onnx Multilingual image-encoder: [multilingual.image-encoder.onnx](https://huggingface.co/unum-cloud/uform-coreml/blob/main/multilingual.image-encoder.onnx)
* Onnx Multilingual text-encoder: [multilingual.text-encoder.onnx](https://huggingface.co/unum-cloud/uform-coreml/blob/main/multilingual.text-encoder.onnx)
Each checkpoint is a zip archive with an MLProgram of the corresponding encoder.
Text encoders have the following input fields:
* `input_ids`: int32
* `attention_mask`: int32
and support flexible batch size.
Image encoders has a single input field `image`: float32 and support only batch of single image (due to CoreML bug).
Both encoders return:
* `features`: float32
* `embeddings`: float32
If you want to convert a model with other parameters (i.e fp16 precision or other batch size range), you can use [convert.py](https://huggingface.co/unum-cloud/uform-coreml/blob/main/convert_model.py).
|
h3lmi/mpnet_maxpool2
|
h3lmi
| 2023-08-17T14:59:22Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-17T10:39:08Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 2365 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 709,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Vedmani/output
|
Vedmani
| 2023-08-17T14:54:39Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"region:us"
] | null | 2023-08-17T12:22:51Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2749
- Accuracy: 0.9364
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2399 | 1.0 | 2500 | 0.2539 | 0.9037 |
| 0.2454 | 2.0 | 5000 | 0.2753 | 0.9064 |
| 0.2251 | 3.0 | 7500 | 0.2436 | 0.9167 |
| 0.1996 | 4.0 | 10000 | 0.2271 | 0.9246 |
| 0.1845 | 5.0 | 12500 | 0.2116 | 0.9269 |
| 0.205 | 6.0 | 15000 | 0.1946 | 0.9312 |
| 0.1352 | 7.0 | 17500 | 0.2233 | 0.9328 |
| 0.1306 | 8.0 | 20000 | 0.2257 | 0.936 |
| 0.0849 | 9.0 | 22500 | 0.2582 | 0.9372 |
| 0.0609 | 10.0 | 25000 | 0.2749 | 0.9364 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
lrthomps/a2c-PandaReachDense-v2
|
lrthomps
| 2023-08-17T14:52:20Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-16T19:27:52Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.38 +/- 0.85
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
CyberHarem/izumi_pokemon
|
CyberHarem
| 2023-08-17T14:48:10Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/izumi_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T14:44:49Z |
---
license: mit
datasets:
- CyberHarem/izumi_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of izumi_pokemon
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).
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 1500, you need to download `1500/izumi_pokemon.pt` as the embedding and `1500/izumi_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `izumi_pokemon`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/bikini.png) | [<NSFW, click to see>](1500/previews/free.png) | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/izumi_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/bikini.png) | [<NSFW, click to see>](1400/previews/free.png) | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/izumi_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/bikini.png) | [<NSFW, click to see>](1300/previews/free.png) | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/izumi_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/free.png) | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/izumi_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/bikini.png) | [<NSFW, click to see>](1100/previews/free.png) | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/izumi_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/bikini.png) | [<NSFW, click to see>](1000/previews/free.png) | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/izumi_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/bikini.png) | [<NSFW, click to see>](900/previews/free.png) | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/izumi_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/bikini.png) | [<NSFW, click to see>](800/previews/free.png) | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/izumi_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/bikini.png) | [<NSFW, click to see>](700/previews/free.png) | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/izumi_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/bikini.png) | [<NSFW, click to see>](600/previews/free.png) | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/izumi_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/bikini.png) | [<NSFW, click to see>](500/previews/free.png) | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/izumi_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/bikini.png) | [<NSFW, click to see>](400/previews/free.png) | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/izumi_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/bikini.png) | [<NSFW, click to see>](300/previews/free.png) | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/izumi_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/bikini.png) | [<NSFW, click to see>](200/previews/free.png) | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/izumi_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/bikini.png) | [<NSFW, click to see>](100/previews/free.png) | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/izumi_pokemon.zip) |
|
Marco-Cheung/speecht5_finetuned_voxpopuli_de
|
Marco-Cheung
| 2023-08-17T14:46:13Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-17T08:02:16Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_de
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4657
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5308 | 1.66 | 1000 | 0.4861 |
| 0.5124 | 3.33 | 2000 | 0.4732 |
| 0.5076 | 4.99 | 3000 | 0.4674 |
| 0.5051 | 6.65 | 4000 | 0.4657 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Inespinoza/ppo-Huggy
|
Inespinoza
| 2023-08-17T14:42:38Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-17T14:41:48Z |
---
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: Inespinoza/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DarkRodry/Reinforce-cartpole-v1
|
DarkRodry
| 2023-08-17T14:42:28Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T14:42:20Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Ignatt/sdxl-db-nachito
|
Ignatt
| 2023-08-17T14:42:11Z | 1 | 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
| 2023-08-17T14:42:08Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of nachito
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
HangenYuu/xlm-roberta-large-finetuned-hate-implicit
|
HangenYuu
| 2023-08-17T14:33:17Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:joeddav/xlm-roberta-large-xnli",
"base_model:finetune:joeddav/xlm-roberta-large-xnli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-17T10:33:54Z |
---
license: mit
base_model: joeddav/xlm-roberta-large-xnli
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large-finetuned-hate-implicit
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. -->
# xlm-roberta-large-finetuned-hate-implicit
This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6051
- eval_accuracy: 0.7768
- eval_f1: 0.7721
- eval_runtime: 107.6127
- eval_samples_per_second: 39.921
- eval_steps_per_second: 0.316
- epoch: 3.98
- step: 537
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Nithin427/llama2-qlora-finetunined-french
|
Nithin427
| 2023-08-17T14:32:56Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-17T14:32:49Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
vikneshk/finetune_small_imdb_sentiment
|
vikneshk
| 2023-08-17T14:24:22Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-17T14:03:25Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetune_small_imdb_sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9083333333333333
- name: F1
type: f1
value: 0.9084249084249084
---
<!-- 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. -->
# finetune_small_imdb_sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2615
- Accuracy: 0.9083
- F1: 0.9084
## 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
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CyberHarem/joy_pokemon
|
CyberHarem
| 2023-08-17T14:22:33Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/joy_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T14:18:49Z |
---
license: mit
datasets:
- CyberHarem/joy_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of joy_pokemon
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).
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 1500, you need to download `1500/joy_pokemon.pt` as the embedding and `1500/joy_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `joy_pokemon`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:---------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/joy_pokemon.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/joy_pokemon.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/joy_pokemon.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/joy_pokemon.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/joy_pokemon.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/joy_pokemon.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/joy_pokemon.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/joy_pokemon.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/joy_pokemon.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/joy_pokemon.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/joy_pokemon.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/joy_pokemon.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/joy_pokemon.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/joy_pokemon.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/joy_pokemon.zip) |
|
remg1997/xl-1.0
|
remg1997
| 2023-08-17T14:15:00Z | 24 | 1 |
diffusers
|
[
"diffusers",
"onnx",
"safetensors",
"text-to-image",
"stable-diffusion",
"arxiv:2307.01952",
"arxiv:2211.01324",
"arxiv:2108.01073",
"arxiv:2112.10752",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-08-17T14:14:59Z |
---
license: openrail++
tags:
- text-to-image
- stable-diffusion
duplicated_from: stabilityai/stable-diffusion-xl-base-1.0
---
# SD-XL 1.0-base Model Card
## Model
[SDXL](https://arxiv.org/abs/2307.01952) consists of an [ensemble of experts](https://arxiv.org/abs/2211.01324) pipeline for latent diffusion:
In a first step, the base model is used to generate (noisy) latents,
which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps.
Note that the base model can be used as a standalone module.
Alternatively, we can use a two-stage pipeline as follows:
First, the base model is used to generate latents of the desired output size.
In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img")
to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations.
Source code is available at https://github.com/Stability-AI/generative-models .
### Model Description
- **Developed by:** Stability AI
- **Model type:** Diffusion-based text-to-image generative model
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)).
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/Stability-AI/generative-models) and the [SDXL report on arXiv](https://arxiv.org/abs/2307.01952).
### Model Sources
For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time.
[Clipdrop](https://clipdrop.co/stable-diffusion) provides free SDXL inference.
- **Repository:** https://github.com/Stability-AI/generative-models
- **Demo:** https://clipdrop.co/stable-diffusion
## Evaluation
The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1.
The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance.
### 🧨 Diffusers
Make sure to upgrade diffusers to >= 0.19.0:
```
pip install diffusers --upgrade
```
In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark:
```
pip install invisible_watermark transformers accelerate safetensors
```
To just use the base model, you can run:
```py
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe.to("cuda")
# if using torch < 2.0
# pipe.enable_xformers_memory_efficient_attention()
prompt = "An astronaut riding a green horse"
images = pipe(prompt=prompt).images[0]
```
To use the whole base + refiner pipeline as an ensemble of experts you can run:
```py
from diffusers import DiffusionPipeline
import torch
# load both base & refiner
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
base.to("cuda")
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
)
refiner.to("cuda")
# Define how many steps and what % of steps to be run on each experts (80/20) here
n_steps = 40
high_noise_frac = 0.8
prompt = "A majestic lion jumping from a big stone at night"
# run both experts
image = base(
prompt=prompt,
num_inference_steps=n_steps,
denoising_end=high_noise_frac,
output_type="latent",
).images
image = refiner(
prompt=prompt,
num_inference_steps=n_steps,
denoising_start=high_noise_frac,
image=image,
).images[0]
```
When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
```py
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
```
If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
instead of `.to("cuda")`:
```diff
- pipe.to("cuda")
+ pipe.enable_model_cpu_offload()
```
For more information on how to use Stable Diffusion XL with `diffusers`, please have a look at [the Stable Diffusion XL Docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl).
### Optimum
[Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with both [OpenVINO](https://docs.openvino.ai/latest/index.html) and [ONNX Runtime](https://onnxruntime.ai/).
#### OpenVINO
To install Optimum with the dependencies required for OpenVINO :
```bash
pip install optimum[openvino]
```
To load an OpenVINO model and run inference with OpenVINO Runtime, you need to replace `StableDiffusionXLPipeline` with Optimum `OVStableDiffusionXLPipeline`. In case you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, you can set `export=True`.
```diff
- from diffusers import StableDiffusionPipeline
+ from optimum.intel import OVStableDiffusionPipeline
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
- pipeline = StableDiffusionPipeline.from_pretrained(model_id)
+ pipeline = OVStableDiffusionPipeline.from_pretrained(model_id)
prompt = "A majestic lion jumping from a big stone at night"
image = pipeline(prompt).images[0]
```
You can find more examples (such as static reshaping and model compilation) in optimum [documentation](https://huggingface.co/docs/optimum/main/en/intel/inference#stable-diffusion-xl).
#### ONNX
To install Optimum with the dependencies required for ONNX Runtime inference :
```bash
pip install optimum[onnxruntime]
```
To load an ONNX model and run inference with ONNX Runtime, you need to replace `StableDiffusionXLPipeline` with Optimum `ORTStableDiffusionXLPipeline`. In case you want to load a PyTorch model and convert it to the ONNX format on-the-fly, you can set `export=True`.
```diff
- from diffusers import StableDiffusionPipeline
+ from optimum.onnxruntime import ORTStableDiffusionPipeline
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
- pipeline = StableDiffusionPipeline.from_pretrained(model_id)
+ pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)
prompt = "A majestic lion jumping from a big stone at night"
image = pipeline(prompt).images[0]
```
You can find more examples in optimum [documentation](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models#stable-diffusion-xl).
## Uses
### Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
Excluded uses are described below.
### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
|
CyberHarem/viola_pokemon
|
CyberHarem
| 2023-08-17T14:03:05Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/viola_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T13:56:27Z |
---
license: mit
datasets:
- CyberHarem/viola_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of viola_pokemon
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).
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 1500, you need to download `1500/viola_pokemon.pt` as the embedding and `1500/viola_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `viola_pokemon`.**
These are available steps:
| Steps | pattern_1 | pattern_2 | pattern_3 | bikini | free | nude | Download |
|--------:|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:-----------------------------------|
| 1500 |  | [<NSFW, click to see>](1500/previews/pattern_2.png) | [<NSFW, click to see>](1500/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/viola_pokemon.zip) |
| 1400 |  | [<NSFW, click to see>](1400/previews/pattern_2.png) | [<NSFW, click to see>](1400/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/viola_pokemon.zip) |
| 1300 |  | [<NSFW, click to see>](1300/previews/pattern_2.png) | [<NSFW, click to see>](1300/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/viola_pokemon.zip) |
| 1200 |  | [<NSFW, click to see>](1200/previews/pattern_2.png) | [<NSFW, click to see>](1200/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/viola_pokemon.zip) |
| 1100 |  | [<NSFW, click to see>](1100/previews/pattern_2.png) | [<NSFW, click to see>](1100/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/viola_pokemon.zip) |
| 1000 |  | [<NSFW, click to see>](1000/previews/pattern_2.png) | [<NSFW, click to see>](1000/previews/pattern_3.png) |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/viola_pokemon.zip) |
| 900 |  | [<NSFW, click to see>](900/previews/pattern_2.png) | [<NSFW, click to see>](900/previews/pattern_3.png) |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/viola_pokemon.zip) |
| 800 |  | [<NSFW, click to see>](800/previews/pattern_2.png) | [<NSFW, click to see>](800/previews/pattern_3.png) |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/viola_pokemon.zip) |
| 700 |  | [<NSFW, click to see>](700/previews/pattern_2.png) | [<NSFW, click to see>](700/previews/pattern_3.png) |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/viola_pokemon.zip) |
| 600 |  | [<NSFW, click to see>](600/previews/pattern_2.png) | [<NSFW, click to see>](600/previews/pattern_3.png) |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/viola_pokemon.zip) |
| 500 |  | [<NSFW, click to see>](500/previews/pattern_2.png) | [<NSFW, click to see>](500/previews/pattern_3.png) |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/viola_pokemon.zip) |
| 400 |  | [<NSFW, click to see>](400/previews/pattern_2.png) | [<NSFW, click to see>](400/previews/pattern_3.png) |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/viola_pokemon.zip) |
| 300 |  | [<NSFW, click to see>](300/previews/pattern_2.png) | [<NSFW, click to see>](300/previews/pattern_3.png) |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/viola_pokemon.zip) |
| 200 |  | [<NSFW, click to see>](200/previews/pattern_2.png) | [<NSFW, click to see>](200/previews/pattern_3.png) |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/viola_pokemon.zip) |
| 100 |  | [<NSFW, click to see>](100/previews/pattern_2.png) | [<NSFW, click to see>](100/previews/pattern_3.png) |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/viola_pokemon.zip) |
|
SUPERSOKOL/marian-finetuned-kde4-en-to-uk
|
SUPERSOKOL
| 2023-08-17T13:59:11Z | 126 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-08-17T11:28:22Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-uk
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-uk
split: train
args: en-uk
metrics:
- name: Bleu
type: bleu
value: 50.09005982889118
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-uk
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-uk](https://huggingface.co/Helsinki-NLP/opus-mt-en-uk) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7624
- Bleu: 50.0901
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
viswavi/datafinder-scibert-nl-queries
|
viswavi
| 2023-08-17T13:54:02Z | 116 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"fill-mask",
"arxiv:2305.16636",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-07T19:40:25Z |
---
license: mit
pipeline_tag: fill-mask
---
This is a version of the SciBERT encoder trained for the purpose of retrieving datasets by textual description given a natural language query.
If useful, please cite
```
@inproceedings{viswanathan23acl,
title = {DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions},
author = {Vijay Viswanathan and Luyu Gao and Tongshuang Wu and Pengfei Liu and Graham Neubig},
booktitle = {Annual Conference of the Association for Computational Linguistics (ACL)},
address = {Toronto, Canada},
month = {July},
url = {https://arxiv.org/abs/2305.16636},
year = {2023}
}
```
|
CyberHarem/furisode_girl_pokemon
|
CyberHarem
| 2023-08-17T13:40:46Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/furisode_girl_pokemon",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-17T13:35:04Z |
---
license: mit
datasets:
- CyberHarem/furisode_girl_pokemon
pipeline_tag: text-to-image
tags:
- art
---
# Lora of furisode_girl_pokemon
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).
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 1500, you need to download `1500/furisode_girl_pokemon.pt` as the embedding and `1500/furisode_girl_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `furisode_girl_pokemon`.**
These are available steps:
| Steps | pattern_1 | bikini | free | nude | Download |
|--------:|:----------------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:-------------------------------------------|
| 1500 | [<NSFW, click to see>](1500/previews/pattern_1.png) |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/furisode_girl_pokemon.zip) |
| 1400 | [<NSFW, click to see>](1400/previews/pattern_1.png) |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/furisode_girl_pokemon.zip) |
| 1300 | [<NSFW, click to see>](1300/previews/pattern_1.png) |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/furisode_girl_pokemon.zip) |
| 1200 | [<NSFW, click to see>](1200/previews/pattern_1.png) |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/furisode_girl_pokemon.zip) |
| 1100 | [<NSFW, click to see>](1100/previews/pattern_1.png) |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/furisode_girl_pokemon.zip) |
| 1000 | [<NSFW, click to see>](1000/previews/pattern_1.png) |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/furisode_girl_pokemon.zip) |
| 900 | [<NSFW, click to see>](900/previews/pattern_1.png) |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/furisode_girl_pokemon.zip) |
| 800 | [<NSFW, click to see>](800/previews/pattern_1.png) |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/furisode_girl_pokemon.zip) |
| 700 | [<NSFW, click to see>](700/previews/pattern_1.png) |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/furisode_girl_pokemon.zip) |
| 600 | [<NSFW, click to see>](600/previews/pattern_1.png) |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/furisode_girl_pokemon.zip) |
| 500 | [<NSFW, click to see>](500/previews/pattern_1.png) |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/furisode_girl_pokemon.zip) |
| 400 | [<NSFW, click to see>](400/previews/pattern_1.png) |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/furisode_girl_pokemon.zip) |
| 300 | [<NSFW, click to see>](300/previews/pattern_1.png) |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/furisode_girl_pokemon.zip) |
| 200 | [<NSFW, click to see>](200/previews/pattern_1.png) |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/furisode_girl_pokemon.zip) |
| 100 | [<NSFW, click to see>](100/previews/pattern_1.png) |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/furisode_girl_pokemon.zip) |
|
paarth-sachan/taxi_gymnasium
|
paarth-sachan
| 2023-08-17T13:38:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T13:38:03Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_gymnasium
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.69
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="paarth-sachan/taxi_gymnasium", 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"])
```
|
paarth-sachan/q-FrozenLake-v1-4x4-noSlippery
|
paarth-sachan
| 2023-08-17T13:34:00Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-17T13:33:58Z |
---
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="paarth-sachan/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"])
```
|
linoyts/lora-trained-xl-colab-3d-icon-0.0001-1500-1
|
linoyts
| 2023-08-17T13:33:53Z | 6 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-17T08:46:49Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: sdxl3dicon digital art
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - LinoyTsaban/lora-trained-xl-colab-3d-icon-0.0001-1500-1
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on sdxl3dicon digital art using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.