modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-31 12:31:28
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-31 12:30:56
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
nayanika/falcon-7b-survey-v1.0
|
nayanika
| 2023-08-22T15:02:21Z | 0 | 0 |
peft
|
[
"peft",
"RefinedWebModel",
"custom_code",
"region:us"
] | null | 2023-08-09T16:51:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
Tarti/xlm-roberta-base-finetuned-panx-de
|
Tarti
| 2023-08-22T14:58:26Z | 134 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-22T10:41:31Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8602627537962806
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1355
- F1: 0.8603
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2574 | 1.0 | 525 | 0.1627 | 0.8221 |
| 0.1295 | 2.0 | 1050 | 0.1435 | 0.8467 |
| 0.0815 | 3.0 | 1575 | 0.1355 | 0.8603 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu117
- Datasets 1.16.1
- Tokenizers 0.13.3
|
agarc15/gpt2-finetuned-PRC
|
agarc15
| 2023-08-22T14:52:00Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-classification",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-21T07:48:25Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gpt2-finetuned-PRC
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-PRC
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4564
- Accuracy: 0.8711
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 391 | 0.5038 | 0.8420 |
| 0.7758 | 2.0 | 782 | 0.4486 | 0.8629 |
| 0.4031 | 3.0 | 1173 | 0.4664 | 0.8678 |
| 0.3225 | 4.0 | 1564 | 0.4564 | 0.8711 |
| 0.3225 | 5.0 | 1955 | 0.4637 | 0.8693 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
camenduru/seamless-m4t-medium
|
camenduru
| 2023-08-22T14:50:58Z | 0 | 1 | null |
[
"SeamlessM4T",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-08-22T14:45:53Z |
---
inference: false
tags:
- SeamlessM4T
license: cc-by-nc-4.0
---
# SeamlessM4T Medium
SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different
linguistic communities to communicate effortlessly through speech and text.
SeamlessM4T covers:
- 📥 101 languages for speech input
- ⌨️ 96 Languages for text input/output
- 🗣️ 35 languages for speech output.
This is the "medium" variant of the unified model, which enables multiple tasks without relying on multiple separate models:
- Speech-to-speech translation (S2ST)
- Speech-to-text translation (S2TT)
- Text-to-speech translation (T2ST)
- Text-to-text translation (T2TT)
- Automatic speech recognition (ASR)
## SeamlessM4T models
The SeamlessM4T models come in two checkpoints of different size:
| Model Name | #params | checkpoint | metrics |
| - | - | - | - |
| [SeamlessM4T-Medium]((https://huggingface.co/facebook/seamless-m4t-medium) | 1.2B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) | [metrics]() |
| [SeamlessM4T-Large](https://huggingface.co/facebook/seamless-m4t-large) | 2.3B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt) | [metrics]() |
We provide extensive evaluation results of SeamlessM4T-Medium and SeamlessM4T-Large in the SeamlessM4T paper (as averages) in the `metrics` files above.
## Instructions to run inference with SeamlessM4T models
The SeamlessM4T models are currently available through the `seamless_communication` package. The `seamless_communication`
package can be installed by following the instructions outlined here: [Installation](https://github.com/fairinternal/seamless_communication/tree/main#installation).
Once installed, a [`Translator`](https://github.com/fairinternal/seamless_communication/blob/590547965b343b590d15847a0aa25a6779fc3753/src/seamless_communication/models/inference/translator.py#L47)
object can be instantiated to perform all five of the spoken langauge tasks. The `Translator` is instantiated with three arguments:
1. **model_name_or_card**: SeamlessM4T checkpoint. Can be either `seamlessM4T_medium` for the medium model, or `seamlessM4T_large` for the large model
2. **vocoder_name_or_card**: vocoder checkpoint (`vocoder_36langs`)
3. **device**: Torch device
```python
import torch
from seamless_communication.models.inference import Translator
# Initialize a Translator object with a multitask model, vocoder on the GPU.
translator = Translator("seamlessM4T_medium", vocoder_name_or_card="vocoder_36langs", device=torch.device("cuda:0"))
```
Once instantiated, the `predict()` method can be used to run inference as many times on any of the supported tasks.
Given an input audio with `<path_to_input_audio>` or an input text `<input_text>` in `<src_lang>`, we can translate
into `<tgt_lang>` as follows.
### S2ST and T2ST:
```python
# S2ST
translated_text, wav, sr = translator.predict(<path_to_input_audio>, "s2st", <tgt_lang>)
# T2ST
translated_text, wav, sr = translator.predict(<input_text>, "t2st", <tgt_lang>, src_lang=<src_lang>)
```
Note that `<src_lang>` must be specified for T2ST.
The generated units are synthesized and the output audio file is saved with:
```python
wav, sr = translator.synthesize_speech(<speech_units>, <tgt_lang>)
# Save the translated audio generation.
torchaudio.save(
<path_to_save_audio>,
wav[0].cpu(),
sample_rate=sr,
)
```
### S2TT, T2TT and ASR:
```python
# S2TT
translated_text, _, _ = translator.predict(<path_to_input_audio>, "s2tt", <tgt_lang>)
# ASR
# This is equivalent to S2TT with `<tgt_lang>=<src_lang>`.
transcribed_text, _, _ = translator.predict(<path_to_input_audio>, "asr", <src_lang>)
# T2TT
translated_text, _, _ = translator.predict(<input_text>, "t2tt", <tgt_lang>, src_lang=<src_lang>)
```
Note that `<src_lang>` must be specified for T2TT.
## Citation
If you plan to use SeamlessM4T in your work or any models/datasets/artifacts published in SeamlessM4T, please cite:
```bibtex
@article{seamlessm4t2023,
title={"SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation"},
author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang},
journal={ArXiv},
year={2023}
}
```
## License
The Seamless Communication code and weights are CC-BY-NC 4.0 licensed.
|
Toobese/lilt-invoices2
|
Toobese
| 2023-08-22T14:50:33Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"lilt",
"token-classification",
"generated_from_trainer",
"base_model:SCUT-DLVCLab/lilt-roberta-en-base",
"base_model:finetune:SCUT-DLVCLab/lilt-roberta-en-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-22T14:44:15Z |
---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-invoices2
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. -->
# lilt-invoices2
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0032
- Amount: {'precision': 0.9982517482517482, 'recall': 1.0, 'f1': 0.9991251093613298, 'number': 571}
- Billingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}
- Description: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612}
- Invoicedate: {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172}
- Invoicetotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 207}
- Quantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 545}
- Subtotal: {'precision': 1.0, 'recall': 0.9933774834437086, 'f1': 0.9966777408637874, 'number': 151}
- Totaltax: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139}
- Unitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 492}
- Vendorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208}
- Overall Precision: 0.9994
- Overall Recall: 0.9994
- Overall F1: 0.9994
- Overall Accuracy: 0.9994
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Amount | Billingaddress | Description | Invoicedate | Invoicetotal | Quantity | Subtotal | Totaltax | Unitprice | Vendorname | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.6178 | 4.35 | 100 | 0.1659 | {'precision': 0.8553654743390358, 'recall': 0.9632224168126094, 'f1': 0.9060955518945634, 'number': 571} | {'precision': 0.9815950920245399, 'recall': 0.9937888198757764, 'f1': 0.9876543209876544, 'number': 161} | {'precision': 0.9775641025641025, 'recall': 0.9967320261437909, 'f1': 0.9870550161812297, 'number': 612} | {'precision': 0.9940476190476191, 'recall': 0.9709302325581395, 'f1': 0.9823529411764705, 'number': 172} | {'precision': 0.8571428571428571, 'recall': 0.8985507246376812, 'f1': 0.8773584905660375, 'number': 207} | {'precision': 0.9890909090909091, 'recall': 0.998165137614679, 'f1': 0.993607305936073, 'number': 545} | {'precision': 0.7664233576642335, 'recall': 0.695364238410596, 'f1': 0.7291666666666665, 'number': 151} | {'precision': 0.8818897637795275, 'recall': 0.8057553956834532, 'f1': 0.8421052631578947, 'number': 139} | {'precision': 0.9809523809523809, 'recall': 0.8373983739837398, 'f1': 0.9035087719298245, 'number': 492} | {'precision': 0.9856459330143541, 'recall': 0.9903846153846154, 'f1': 0.988009592326139, 'number': 208} | 0.9368 | 0.9368 | 0.9368 | 0.9368 |
| 0.1653 | 8.7 | 200 | 0.0668 | {'precision': 0.9420529801324503, 'recall': 0.9964973730297724, 'f1': 0.9685106382978723, 'number': 571} | {'precision': 0.9876543209876543, 'recall': 0.9937888198757764, 'f1': 0.9907120743034055, 'number': 161} | {'precision': 1.0, 'recall': 0.9901960784313726, 'f1': 0.9950738916256158, 'number': 612} | {'precision': 0.9941520467836257, 'recall': 0.9883720930232558, 'f1': 0.9912536443148688, 'number': 172} | {'precision': 0.9140271493212669, 'recall': 0.9758454106280193, 'f1': 0.9439252336448598, 'number': 207} | {'precision': 0.9945255474452555, 'recall': 1.0, 'f1': 0.9972552607502287, 'number': 545} | {'precision': 0.9328358208955224, 'recall': 0.8278145695364238, 'f1': 0.8771929824561403, 'number': 151} | {'precision': 0.9615384615384616, 'recall': 0.8992805755395683, 'f1': 0.929368029739777, 'number': 139} | {'precision': 0.9978947368421053, 'recall': 0.9634146341463414, 'f1': 0.9803516028955533, 'number': 492} | {'precision': 1.0, 'recall': 0.9951923076923077, 'f1': 0.9975903614457832, 'number': 208} | 0.9770 | 0.9770 | 0.9770 | 0.9770 |
| 0.0676 | 13.04 | 300 | 0.0208 | {'precision': 0.9861111111111112, 'recall': 0.9947460595446584, 'f1': 0.990409764603313, 'number': 571} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612} | {'precision': 0.9941860465116279, 'recall': 0.9941860465116279, 'f1': 0.9941860465116279, 'number': 172} | {'precision': 0.9951219512195122, 'recall': 0.9855072463768116, 'f1': 0.9902912621359223, 'number': 207} | {'precision': 0.9963369963369964, 'recall': 0.998165137614679, 'f1': 0.9972502291475711, 'number': 545} | {'precision': 1.0, 'recall': 0.9602649006622517, 'f1': 0.9797297297297297, 'number': 151} | {'precision': 0.9787234042553191, 'recall': 0.9928057553956835, 'f1': 0.9857142857142858, 'number': 139} | {'precision': 0.9918864097363083, 'recall': 0.9939024390243902, 'f1': 0.9928934010152284, 'number': 492} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208} | 0.9942 | 0.9942 | 0.9942 | 0.9942 |
| 0.0296 | 17.39 | 400 | 0.0067 | {'precision': 0.9982456140350877, 'recall': 0.9964973730297724, 'f1': 0.9973707274320772, 'number': 571} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612} | {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172} | {'precision': 0.9951923076923077, 'recall': 1.0, 'f1': 0.9975903614457832, 'number': 207} | {'precision': 0.9981684981684982, 'recall': 1.0, 'f1': 0.999083409715857, 'number': 545} | {'precision': 0.9933333333333333, 'recall': 0.9867549668874173, 'f1': 0.9900332225913622, 'number': 151} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139} | {'precision': 0.9979674796747967, 'recall': 0.9979674796747967, 'f1': 0.9979674796747967, 'number': 492} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208} | 0.9982 | 0.9982 | 0.9982 | 0.9982 |
| 0.0143 | 21.74 | 500 | 0.0032 | {'precision': 0.9982517482517482, 'recall': 1.0, 'f1': 0.9991251093613298, 'number': 571} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612} | {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 207} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 545} | {'precision': 1.0, 'recall': 0.9933774834437086, 'f1': 0.9966777408637874, 'number': 151} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 492} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208} | 0.9994 | 0.9994 | 0.9994 | 0.9994 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
sonataaaashh/drakeoffc
|
sonataaaashh
| 2023-08-22T14:40:35Z | 0 | 0 | null |
[
"ru",
"license:openrail",
"region:us"
] | null | 2023-08-22T14:34:04Z |
---
license: openrail
language:
- ru
---
|
Amirhnrn/Reinforce-Cartpole
|
Amirhnrn
| 2023-08-22T14:40:23Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T07:30:33Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole
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
|
YoavWigelman/a2c-PandaReachDense-v3
|
YoavWigelman
| 2023-08-22T14:30:45Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T14:25:18Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.26 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Nebyx/rl_course_vizdoom_health_gathering_supreme
|
Nebyx
| 2023-08-22T14:29:01Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T14:28:55Z |
---
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: 12.10 +/- 5.99
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 Nebyx/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
mystic-leung/distilbart-medcord19
|
mystic-leung
| 2023-08-22T14:20:16Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"aa",
"dataset:mystic-leung/medical_cord19",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-22T14:06:06Z |
---
license: openrail
datasets:
- mystic-leung/medical_cord19
language:
- aa
metrics:
- rouge
---
|
wjbmattingly/distilbert-base-uncased-finetuned-imdb
|
wjbmattingly
| 2023-08-22T14:18:48Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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
| 2023-08-22T14:00:15Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9420
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1689 | 1.0 | 8 | 3.0928 |
| 2.9706 | 2.0 | 16 | 2.7093 |
| 2.9273 | 3.0 | 24 | 2.7136 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
|
loicspigeleer/q-Taxi-v3
|
loicspigeleer
| 2023-08-22T14:11:38Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T14:11:35Z |
---
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: 7.50 +/- 2.78
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="loicspigeleer/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"])
```
|
loicspigeleer/q-FrozenLake-v1-4x4-noSlippery
|
loicspigeleer
| 2023-08-22T14:07:59Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T13:48:04Z |
---
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="loicspigeleer/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"])
```
|
facebook/blaser-2.0-qe
|
facebook
| 2023-08-22T14:07:31Z | 0 | 7 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-08-21T15:01:40Z |
---
license: cc-by-nc-4.0
---
# BLASER 2.0
[[Blog]](https://ai.meta.com/resources/models-and-libraries/seamless-communication/)
[[Code](https://github.com/facebookresearch/SONAR)]
BLASER 2.0 is the new version of BLASER ([Chen et al., 2023](https://aclanthology.org/2023.acl-long.504/)),
a family of models for automatic evaluation of machine translation quality.
BLASER 2.0 is based on [SONAR](https://huggingface.co/facebook/SONAR) sentence embeddings
and works with both speech and text modalities.
The actual model predicts a similarity score for the translated sentence based on the translation and the source sentence.
This, it can be applied in settings where reference translations are missing or if their quality is questionable.
In contrast, its sibling model, [BLASER 2.0-referenced](https://huggingface.co/facebook/blaser-2.0-ref), requires also a reference translation.
Supervised BLASER models are trained to predict cross-lingual semantic similarity scores,
XSTS ([Licht et al., 2022](https://aclanthology.org/2022.amta-research.24/)),
on a scale where 1 corresponds to completely unrelated sentences and
5 corresponds to fully semantically equivalent sentences.
The models predictions, though, are unbounded and can occasionally surpass these limits.
## Installation
See the SONAR github [repo](https://github.com/facebookresearch/SONAR) for the installation instructions.
## Usage
BLASER 2.0 models accept 1024-dimensional SONAR sentence embeddings as inputs,
and produce a single score as an output.
The code below illustrates their usage with text embeddings:
```Python
from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline
from sonar.models.blaser.loader import load_blaser_model
blaser = load_blaser_model("blaser_2_0_qe").eval()
text_embedder = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder", tokenizer="text_sonar_basic_encoder")
src_embs = text_embedder.predict(["Le chat s'assit sur le tapis."], source_lang="fra_Latn")
mt_embs = text_embedder.predict(["The cat sat down on the carpet."], source_lang="eng_Latn")
print(blaser(src=src_embs, mt=mt_embs).item()) # 4.708
```
With BLASER 2.0 models, SONAR text and speech embeddings can be used interchangeably.
## Model details
- **Developed by:** Seamless Communication et al.
- **License:** CC-BY-NC 4.0 license
- **Citation:** If you use BLASER 2.0 in your work, please cite
[the paper](https://ai.meta.com/resources/models-and-libraries/seamless-communication/):
```bibtex
@article{seamlessm4t2023,
title={SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation},
author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang},
journal={ArXiv},
year={2023}
}
```
|
gollumeo/Gollumecoder-1b
|
gollumeo
| 2023-08-22T13:54:20Z | 6 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-21T08:26:03Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
yodi/karina
|
yodi
| 2023-08-22T13:42:51Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"id",
"dataset:Local",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-22T05:35:38Z |
---
datasets:
- Local
license: bigscience-bloom-rail-1.0
language:
- id
pipeline_tag: text-generation
---
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
4. [Training](#training)
# Model Summary
> We present KARINA, finetuned from BLOOMZ bigscience/bloomz-3b, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOMZ pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
# Use
## Intended use
We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*prompt = f"Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n"*", the model will most likely answer "*Saya Karina. Ada yang bisa saya bantu?*".
## How to use
### CPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_NAME = "yodi/karina"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
inputs = tokenizer.encode("Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU in 4 bit
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
MODEL_NAME = "yodi/karina"
model_4bit = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cuda:1", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
prompt = f"Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n"
generator = pipeline('text-generation',
model=model_4bit,
tokenizer=tokenizer,
do_sample=False)
result = generator(prompt, max_length=256)
print(result)
```
</details>
### GPU in 8bit
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
MODEL_NAME = "yodi/karina"
model_4bit = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cuda:1", load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
prompt = f"Given the question:\n{{ siapa kamu? }}\n---\nAnswer:\n"
generator = pipeline('text-generation',
model=model_4bit,
tokenizer=tokenizer,
do_sample=False)
result = generator(prompt, max_length=256)
print(result)
```
</details>
```
[{'generated_text': 'Given the question:\n{ siapa kamu? }\n---\nAnswer:\nSaya Karina, asisten virtual siap membantu seputar estimasi harga atau pertanyaan lain'}]
```
### Infer in Local with Gradio
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
import re
import gradio as gr
MODEL_NAME = "yodi/karina"
model_4bit = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cuda:1", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
generator = pipeline('text-generation',
model=model_4bit,
tokenizer=tokenizer,
do_sample=False)
def preprocess(text):
return f"Given the question:\n{{ {text} }}\n---\nAnswer:\n"
def generate(text):
preprocess_result = preprocess(text)
result = generator(preprocess_result, max_length=256)
output = re.split(r'\n---\nAnswer:\n',result[0]['generated_text'])[1]
return output
with gr.Blocks() as demo:
input_text = gr.Textbox(label="Input", lines=1)
button = gr.Button("Submit")
output_text = gr.Textbox(lines=6, label="Output")
button.click(generate, inputs=[input_text], outputs=output_text)
demo.launch(enable_queue=True, debug=True)
```
And open the gradio url from browser.
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
<!-- Necessary for whitespace -->
###
# Limitations
**Prompt Engineering:** The performance may vary depending on the prompt and its following BLOOMZ models.
# Training
## Model
- **Architecture:** Same as [bloom](https://huggingface.co/bigscience/bloom), also refer to the `config.json` file
|
thinkermode/kamalhassan-sdxl-db
|
thinkermode
| 2023-08-22T13:41:22Z | 2 | 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
| 2023-08-22T13:41:20Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: kamalhassan
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
anshudaur/chair_model_with_0.5_prior_preservation
|
anshudaur
| 2023-08-22T13:29:56Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-22T13:12:11Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: photo of a <new1> chair
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - anshudaur/chair_model_with_0.5_prior_preservation
These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> chair using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
Asad182/whisper-small-ur
|
Asad182
| 2023-08-22T13:27:04Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ur",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-21T15:48:01Z |
---
language:
- ur
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Urdu - Asad Rizvi
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 Urdu - Asad Rizvi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Jbdddsai/lora-trained-xl-colab_gieskanne_500it_lr_1e-4
|
Jbdddsai
| 2023-08-22T13:20:10Z | 4 | 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
| 2023-08-22T10:04:27Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of datadrivers watering can
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Jbddai/lora-trained-xl-colab_gieskanne_500it_lr_1e-4
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of datadrivers watering can 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.
|
dkqjrm/20230822202040
|
dkqjrm
| 2023-08-22T13:20:05Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-22T11:20:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: '20230822202040'
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. -->
# 20230822202040
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5208
- Accuracy: 0.7365
## 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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 312 | 0.7722 | 0.5271 |
| 0.7133 | 2.0 | 624 | 0.5588 | 0.4982 |
| 0.7133 | 3.0 | 936 | 0.6273 | 0.4729 |
| 0.6364 | 4.0 | 1248 | 0.5976 | 0.4946 |
| 0.6219 | 5.0 | 1560 | 0.7382 | 0.5415 |
| 0.6219 | 6.0 | 1872 | 0.5328 | 0.6282 |
| 0.5974 | 7.0 | 2184 | 0.5253 | 0.6282 |
| 0.5974 | 8.0 | 2496 | 0.8677 | 0.5668 |
| 0.5614 | 9.0 | 2808 | 0.5249 | 0.5884 |
| 0.5732 | 10.0 | 3120 | 0.5113 | 0.6895 |
| 0.5732 | 11.0 | 3432 | 0.5092 | 0.6931 |
| 0.5559 | 12.0 | 3744 | 0.4693 | 0.7148 |
| 0.5301 | 13.0 | 4056 | 0.4781 | 0.7256 |
| 0.5301 | 14.0 | 4368 | 0.5693 | 0.6823 |
| 0.4999 | 15.0 | 4680 | 0.4649 | 0.7256 |
| 0.4999 | 16.0 | 4992 | 0.5702 | 0.6859 |
| 0.4712 | 17.0 | 5304 | 0.4598 | 0.7401 |
| 0.4431 | 18.0 | 5616 | 0.4750 | 0.7076 |
| 0.4431 | 19.0 | 5928 | 0.4782 | 0.7184 |
| 0.4348 | 20.0 | 6240 | 0.6236 | 0.6570 |
| 0.4113 | 21.0 | 6552 | 0.5125 | 0.7473 |
| 0.4113 | 22.0 | 6864 | 0.5703 | 0.6787 |
| 0.4035 | 23.0 | 7176 | 0.5080 | 0.7112 |
| 0.4035 | 24.0 | 7488 | 0.4619 | 0.7365 |
| 0.3898 | 25.0 | 7800 | 0.5639 | 0.7076 |
| 0.3736 | 26.0 | 8112 | 0.4968 | 0.7292 |
| 0.3736 | 27.0 | 8424 | 0.4483 | 0.7509 |
| 0.3708 | 28.0 | 8736 | 0.4929 | 0.7220 |
| 0.3656 | 29.0 | 9048 | 0.5168 | 0.7401 |
| 0.3656 | 30.0 | 9360 | 0.5618 | 0.7256 |
| 0.3545 | 31.0 | 9672 | 0.4900 | 0.7365 |
| 0.3545 | 32.0 | 9984 | 0.4676 | 0.7256 |
| 0.3474 | 33.0 | 10296 | 0.5222 | 0.7220 |
| 0.3326 | 34.0 | 10608 | 0.4861 | 0.7437 |
| 0.3326 | 35.0 | 10920 | 0.4560 | 0.7401 |
| 0.3313 | 36.0 | 11232 | 0.5375 | 0.7256 |
| 0.3209 | 37.0 | 11544 | 0.5606 | 0.7329 |
| 0.3209 | 38.0 | 11856 | 0.5173 | 0.7401 |
| 0.3169 | 39.0 | 12168 | 0.5060 | 0.7329 |
| 0.3169 | 40.0 | 12480 | 0.5250 | 0.7365 |
| 0.3096 | 41.0 | 12792 | 0.5133 | 0.7256 |
| 0.3097 | 42.0 | 13104 | 0.5012 | 0.7437 |
| 0.3097 | 43.0 | 13416 | 0.5274 | 0.7401 |
| 0.3049 | 44.0 | 13728 | 0.5086 | 0.7329 |
| 0.2929 | 45.0 | 14040 | 0.4934 | 0.7329 |
| 0.2929 | 46.0 | 14352 | 0.5667 | 0.7401 |
| 0.293 | 47.0 | 14664 | 0.5047 | 0.7437 |
| 0.293 | 48.0 | 14976 | 0.5353 | 0.7292 |
| 0.291 | 49.0 | 15288 | 0.5280 | 0.7401 |
| 0.2817 | 50.0 | 15600 | 0.5142 | 0.7365 |
| 0.2817 | 51.0 | 15912 | 0.5141 | 0.7329 |
| 0.2822 | 52.0 | 16224 | 0.4990 | 0.7329 |
| 0.2758 | 53.0 | 16536 | 0.5074 | 0.7292 |
| 0.2758 | 54.0 | 16848 | 0.5147 | 0.7329 |
| 0.2763 | 55.0 | 17160 | 0.5138 | 0.7365 |
| 0.2763 | 56.0 | 17472 | 0.5291 | 0.7365 |
| 0.2782 | 57.0 | 17784 | 0.5204 | 0.7329 |
| 0.272 | 58.0 | 18096 | 0.5093 | 0.7365 |
| 0.272 | 59.0 | 18408 | 0.5217 | 0.7365 |
| 0.2758 | 60.0 | 18720 | 0.5208 | 0.7365 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Polo123/llama2-qlora-finetunined-task
|
Polo123
| 2023-08-22T13:19:45Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T13:19:12Z |
---
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 0.6.0.dev0
|
asnlvkewffrdev/llama2-qlora-finetunined-wizard_vicuna_70k_unfiltered
|
asnlvkewffrdev
| 2023-08-22T13:15:33Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T13:15:27Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
dkqjrm/20230822202110
|
dkqjrm
| 2023-08-22T13:09:57Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-22T11:21:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: '20230822202110'
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. -->
# 20230822202110
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1679
- Accuracy: 0.7148
## 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: 0.003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.4220 | 0.5271 |
| No log | 2.0 | 312 | 0.2767 | 0.4729 |
| No log | 3.0 | 468 | 0.4345 | 0.4729 |
| 0.2507 | 4.0 | 624 | 0.2006 | 0.5343 |
| 0.2507 | 5.0 | 780 | 0.1797 | 0.4729 |
| 0.2507 | 6.0 | 936 | 0.2180 | 0.5271 |
| 0.2023 | 7.0 | 1092 | 0.1726 | 0.5054 |
| 0.2023 | 8.0 | 1248 | 0.1811 | 0.4729 |
| 0.2023 | 9.0 | 1404 | 0.1828 | 0.5451 |
| 0.2077 | 10.0 | 1560 | 0.1921 | 0.5343 |
| 0.2077 | 11.0 | 1716 | 0.1772 | 0.4838 |
| 0.2077 | 12.0 | 1872 | 0.1724 | 0.6462 |
| 0.189 | 13.0 | 2028 | 0.1718 | 0.5379 |
| 0.189 | 14.0 | 2184 | 0.1728 | 0.5126 |
| 0.189 | 15.0 | 2340 | 0.1775 | 0.5126 |
| 0.189 | 16.0 | 2496 | 0.1813 | 0.5596 |
| 0.1803 | 17.0 | 2652 | 0.1739 | 0.6318 |
| 0.1803 | 18.0 | 2808 | 0.1718 | 0.6137 |
| 0.1803 | 19.0 | 2964 | 0.1711 | 0.6390 |
| 0.1791 | 20.0 | 3120 | 0.1797 | 0.5957 |
| 0.1791 | 21.0 | 3276 | 0.1710 | 0.6859 |
| 0.1791 | 22.0 | 3432 | 0.1729 | 0.6643 |
| 0.1781 | 23.0 | 3588 | 0.1701 | 0.6823 |
| 0.1781 | 24.0 | 3744 | 0.1706 | 0.6390 |
| 0.1781 | 25.0 | 3900 | 0.1708 | 0.6859 |
| 0.1765 | 26.0 | 4056 | 0.1697 | 0.6643 |
| 0.1765 | 27.0 | 4212 | 0.1698 | 0.6715 |
| 0.1765 | 28.0 | 4368 | 0.1710 | 0.6426 |
| 0.176 | 29.0 | 4524 | 0.1710 | 0.6931 |
| 0.176 | 30.0 | 4680 | 0.1703 | 0.6968 |
| 0.176 | 31.0 | 4836 | 0.1725 | 0.6570 |
| 0.176 | 32.0 | 4992 | 0.1699 | 0.6715 |
| 0.1749 | 33.0 | 5148 | 0.1710 | 0.6895 |
| 0.1749 | 34.0 | 5304 | 0.1694 | 0.7220 |
| 0.1749 | 35.0 | 5460 | 0.1700 | 0.6534 |
| 0.1739 | 36.0 | 5616 | 0.1690 | 0.7112 |
| 0.1739 | 37.0 | 5772 | 0.1685 | 0.7220 |
| 0.1739 | 38.0 | 5928 | 0.1696 | 0.7040 |
| 0.1738 | 39.0 | 6084 | 0.1688 | 0.7148 |
| 0.1738 | 40.0 | 6240 | 0.1692 | 0.7220 |
| 0.1738 | 41.0 | 6396 | 0.1683 | 0.7365 |
| 0.1726 | 42.0 | 6552 | 0.1690 | 0.6679 |
| 0.1726 | 43.0 | 6708 | 0.1679 | 0.7076 |
| 0.1726 | 44.0 | 6864 | 0.1691 | 0.7184 |
| 0.1719 | 45.0 | 7020 | 0.1692 | 0.7292 |
| 0.1719 | 46.0 | 7176 | 0.1685 | 0.7329 |
| 0.1719 | 47.0 | 7332 | 0.1684 | 0.7184 |
| 0.1719 | 48.0 | 7488 | 0.1690 | 0.7112 |
| 0.1712 | 49.0 | 7644 | 0.1690 | 0.7292 |
| 0.1712 | 50.0 | 7800 | 0.1685 | 0.6931 |
| 0.1712 | 51.0 | 7956 | 0.1680 | 0.7256 |
| 0.1705 | 52.0 | 8112 | 0.1687 | 0.7076 |
| 0.1705 | 53.0 | 8268 | 0.1685 | 0.7184 |
| 0.1705 | 54.0 | 8424 | 0.1689 | 0.7365 |
| 0.1705 | 55.0 | 8580 | 0.1677 | 0.7148 |
| 0.1705 | 56.0 | 8736 | 0.1694 | 0.7220 |
| 0.1705 | 57.0 | 8892 | 0.1682 | 0.7256 |
| 0.1692 | 58.0 | 9048 | 0.1684 | 0.7148 |
| 0.1692 | 59.0 | 9204 | 0.1679 | 0.7148 |
| 0.1692 | 60.0 | 9360 | 0.1679 | 0.7148 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
kaanhho/speecht5_finetuned_voxpopuli_it
|
kaanhho
| 2023-08-22T13:07:13Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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
| 2023-08-22T12:08:05Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_it
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_it
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: 0.5724
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5644 | 1.53 | 1000 | 0.5845 |
| 0.5521 | 3.07 | 2000 | 0.5724 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
davesoma/SageBeluga13
|
davesoma
| 2023-08-22T12:59:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-15T13:19:04Z |
<strong style="font-size: 24px;">"My name is Epicurus, but my friends call me "Epic" for short. "<strong style="font-size: 24px;"></strong>.
<strong style="font-size: 24px;">SageBeluga13B</strong> Stoic assistant fine-tuned by <strong style="font-size: 24px;">dscompounding.com</strong>.
<img src="https://cdn-uploads.huggingface.co/production/uploads/645ba35bbc7518912e2135e6/iAd3EFZptpoE8QzZKnaxT.png" alt="Dave86CH_epic_badass_marcus_aurelius_fighting_0c2c720e-bcff-471e-9a05-89aecb45722a.png" width="500">
Marcus Aurelius
# SageBeluga13 Model README
## Description
The `SageBeluga13` model, hosted on Hugging Face, has been fine-tuned for specific tasks.
To utilize this model:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "davesoma/SageBeluga13"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.float32
)
sequences = pipeline(
"Girafatron is obsessed with giraffes...",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Example
![SageBeluga13B.jpg]()
<img src="https://cdn-uploads.huggingface.co/production/uploads/645ba35bbc7518912e2135e6/UZLw9vkVCc2nQ56jxVZ4y.jpeg" alt="SageBeluga13.png" width="800">
## Past experiments
https://dscompounding.com/2023/03/31/chapter-iii-digital-marcus-aurelius/
|
crocs-in-socks/LunarLandor
|
crocs-in-socks
| 2023-08-22T12:50:22Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T12:46:39Z |
---
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: 264.23 +/- 16.31
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
...
```
|
GrantW65/ppo-Huggy
|
GrantW65
| 2023-08-22T12:40:40Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-22T12:40:35Z |
---
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: GrantW65/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
natsusakiyomi/SakuraMix
|
natsusakiyomi
| 2023-08-22T12:30:44Z | 115 | 70 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"ja",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-17T17:37:21Z |
---
license: creativeml-openrail-m
language:
- ja
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
library_name: diffusers
---
<div class="flex justify-center">
<div class="container p-0 w-100">
<img class="mt-0 object-cover rounded-t-lg w-100"
style="height: 320px;"
src="https://pbs.twimg.com/media/Fwzt7HZaEAAkX2U?format=jpg"
width="100%"/>
<div class="flex px-4">
<div class="flex-auto">
<h1 class="mb-2 text-3xl font-bold leading-tight" style="color: rgb(252, 238, 235/var(--tw-text-opacity));">
SakuraMixSeries
</h1>
<p class="mb-4 text-base text-neutral-600 dark:text-neutral-200">
背景とキャラクタークオリティーを両立させたVAE内蔵型モデル<br>
Model with built-in VAE for both background and character quality
</p>
</div>
<div>
<a
href="https://twitter.com/min__san"
class="mb-2 inline-block rounded px-6 py-2.5 text-white shadow-md"
style="background-color: #1da1f2">
<svg xmlns="http://www.w3.org/2000/svg" class="h-3.5 w-3.5" fill="currentColor" viewBox="0 0 24 24">
<path d="M24 4.557c-.883.392-1.832.656-2.828.775 1.017-.609 1.798-1.574 2.165-2.724-.951.564-2.005.974-3.127 1.195-.897-.957-2.178-1.555-3.594-1.555-3.179 0-5.515 2.966-4.797 6.045-4.091-.205-7.719-2.165-10.148-5.144-1.29 2.213-.669 5.108 1.523 6.574-.806-.026-1.566-.247-2.229-.616-.054 2.281 1.581 4.415 3.949 4.89-.693.188-1.452.232-2.224.084.626 1.956 2.444 3.379 4.6 3.419-2.07 1.623-4.678 2.348-7.29 2.04 2.179 1.397 4.768 2.212 7.548 2.212 9.142 0 14.307-7.721 13.995-14.646.962-.695 1.797-1.562 2.457-2.549z" />
</svg>
</a>
</div>
</div>
</div>
</div>
---
<h4>📄 ライセンス / License</h4>
<div class="px-2">
<table class="table-fixed border mt-0 text-xs">
<tbody>
<tr>
<td class="px-4 text-base" colspan="2">
<a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license">
修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license
</a>
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span class="text-green-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" />
</svg>
</span>
</td>
<td>
このモデルのクレジットを入れずに使用する<br>
Use the model without crediting the creator
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span class="text-green-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" />
</svg>
</span>
</td>
<td>
このモデルで生成した画像を商用利用する<br>
Sell images they generate
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span class="text-red-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />
</svg>
</span>
</td>
<td>
このモデルを商用の画像生成サービスで利用する</br>
Run on services that generate images for money
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span class="text-green-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" />
</svg>
</span>
</td>
<td>
このモデルを使用したマージモデルを共有する<br>
Share merges using this model
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span class="text-red-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />
</svg>
</span>
</td>
<td>
このモデル、またはこのモデルをマージしたモデルを販売する</br>
Sell this model or merges using this model
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span class="text-red-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />
</svg>
</span>
</td>
<td>
このモデルをマージしたモデルに異なる権限を設定する</br>
Have different permissions when sharing merges
</td>
</tr>
</tbody>
</table>
</div>
<h3 id="blue_pencil-v7" class="mt-0 text-2xl">
<code>SakuraMix-v4</code> <small></small>
</h3>
<div>
v3の改修モデル
全体的に手や破綻の少なくなったモデル<br>
若干書き込み量が減ったような気がするので昔からSakuraMix好きな人はflat loraを使うことを推奨
<hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));">
<h3 id="blue_pencil-v7" class="mt-0 text-2xl">
<code>SakuraMix-v3</code> <small></small>
</h3>
<div>
v2の改修モデル
服装や構図が前よりも増えた気がする
破綻しやすいがいいものが生成できるときはとてもいいものが生成できる<br>
個人的にはv2をお勧めします
<hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));">
<h3 id="SakuraMix-v2" class="mt-0 text-2xl">
<code>SakuraMix-v2</code> <small></small>
</h3>
<div>
HimawariMix-v2B(没案)を改造したモデル<br>
HimawariMix-v2自体character自体を強化したモデルだがさらにキャラを強くしたモデル
<hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));">
<h3 id="SakuraMix-v1" class="mt-0 text-2xl">
<code>SakuraMix-v1</code> <small></small>
</h3>
<div>
初代SakuraMix
特徴とか知らん忘れた<br>
---
# 作者&連絡先
Twiter: [@min__san](https://twitter.com/min__san)<br>
mail: (natsusakiyomi@mail.ru)
|
iotengtr/wav2vec2-large-mms-1b-fries-NL
|
iotengtr
| 2023-08-22T12:30:20Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:iotengtr/wav2vec2-large-mms-1b-fries-NL",
"base_model:finetune:iotengtr/wav2vec2-large-mms-1b-fries-NL",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-17T12:59:35Z |
---
base_model: iotengtr/wav2vec2-large-mms-1b-fries-NL
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: wav2vec2-large-mms-1b-fries-NL
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-large-mms-1b-fries-NL
This model is a fine-tuned version of [iotengtr/wav2vec2-large-mms-1b-fries-NL](https://huggingface.co/iotengtr/wav2vec2-large-mms-1b-fries-NL) on the common_voice_13_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
asenella/ms_config_1_alpha_10_beta_250_seed_1
|
asenella
| 2023-08-22T12:29:55Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-08-22T12:29:53Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
kevinassemi/WaterWizard
|
kevinassemi
| 2023-08-22T12:19:21Z | 0 | 0 | null |
[
"text-generation",
"en",
"license:llama2",
"region:us"
] |
text-generation
| 2023-08-22T12:17:16Z |
---
license: llama2
language:
- en
pipeline_tag: text-generation
---
|
zjoe/dqn-SpaceInvadersNoFrameskip-v4
|
zjoe
| 2023-08-22T12:09:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T12:08:35Z |
---
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: 404.00 +/- 164.56
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 zjoe -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 zjoe -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 zjoe
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0002),
('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'}
```
|
anshudaur/chair_model
|
anshudaur
| 2023-08-22T11:51:16Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-22T11:37:34Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: photo of a <new1> chair
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - anshudaur/chair_model
These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> chair using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
Arup-Dutta-Bappy/bert-large-cased-whole-word-masking-finetuned-squad
|
Arup-Dutta-Bappy
| 2023-08-22T11:50:57Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google-bert/bert-large-cased-whole-word-masking",
"base_model:finetune:google-bert/bert-large-cased-whole-word-masking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-13T17:53:28Z |
---
license: apache-2.0
base_model: bert-large-cased-whole-word-masking
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-large-cased-whole-word-masking-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-whole-word-masking-finetuned-squad
This model is a fine-tuned version of [bert-large-cased-whole-word-masking](https://huggingface.co/bert-large-cased-whole-word-masking) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dkqjrm/20230822185237
|
dkqjrm
| 2023-08-22T11:44:15Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-22T09:52:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: '20230822185237'
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. -->
# 20230822185237
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3335
- Accuracy: 0.6498
## 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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 312 | 0.3589 | 0.5415 |
| 0.4381 | 2.0 | 624 | 0.3585 | 0.5560 |
| 0.4381 | 3.0 | 936 | 0.4824 | 0.4729 |
| 0.4251 | 4.0 | 1248 | 0.3497 | 0.5740 |
| 0.4013 | 5.0 | 1560 | 0.5515 | 0.5307 |
| 0.4013 | 6.0 | 1872 | 0.5300 | 0.5343 |
| 0.4064 | 7.0 | 2184 | 0.3515 | 0.4982 |
| 0.4064 | 8.0 | 2496 | 0.3456 | 0.5704 |
| 0.4121 | 9.0 | 2808 | 0.3522 | 0.5632 |
| 0.4048 | 10.0 | 3120 | 0.3437 | 0.5632 |
| 0.4048 | 11.0 | 3432 | 0.3483 | 0.5668 |
| 0.4035 | 12.0 | 3744 | 0.3952 | 0.4657 |
| 0.3797 | 13.0 | 4056 | 0.3535 | 0.4801 |
| 0.3797 | 14.0 | 4368 | 0.3443 | 0.5993 |
| 0.3657 | 15.0 | 4680 | 0.3431 | 0.5379 |
| 0.3657 | 16.0 | 4992 | 0.3478 | 0.5993 |
| 0.3615 | 17.0 | 5304 | 0.3475 | 0.6173 |
| 0.3573 | 18.0 | 5616 | 0.3539 | 0.6101 |
| 0.3573 | 19.0 | 5928 | 0.3384 | 0.6101 |
| 0.3552 | 20.0 | 6240 | 0.3483 | 0.6245 |
| 0.3545 | 21.0 | 6552 | 0.3359 | 0.6173 |
| 0.3545 | 22.0 | 6864 | 0.3844 | 0.5740 |
| 0.349 | 23.0 | 7176 | 0.3436 | 0.6498 |
| 0.349 | 24.0 | 7488 | 0.3422 | 0.6209 |
| 0.351 | 25.0 | 7800 | 0.3495 | 0.6318 |
| 0.3471 | 26.0 | 8112 | 0.3498 | 0.6101 |
| 0.3471 | 27.0 | 8424 | 0.3316 | 0.6462 |
| 0.3468 | 28.0 | 8736 | 0.3322 | 0.6751 |
| 0.3459 | 29.0 | 9048 | 0.3354 | 0.6390 |
| 0.3459 | 30.0 | 9360 | 0.3353 | 0.6390 |
| 0.344 | 31.0 | 9672 | 0.3383 | 0.6354 |
| 0.344 | 32.0 | 9984 | 0.3329 | 0.6245 |
| 0.3435 | 33.0 | 10296 | 0.3411 | 0.6390 |
| 0.3408 | 34.0 | 10608 | 0.3414 | 0.6354 |
| 0.3408 | 35.0 | 10920 | 0.3319 | 0.6534 |
| 0.3401 | 36.0 | 11232 | 0.3347 | 0.6282 |
| 0.3406 | 37.0 | 11544 | 0.3382 | 0.6137 |
| 0.3406 | 38.0 | 11856 | 0.3355 | 0.6245 |
| 0.3378 | 39.0 | 12168 | 0.3416 | 0.6245 |
| 0.3378 | 40.0 | 12480 | 0.3422 | 0.6209 |
| 0.3386 | 41.0 | 12792 | 0.3388 | 0.6390 |
| 0.3362 | 42.0 | 13104 | 0.3330 | 0.6390 |
| 0.3362 | 43.0 | 13416 | 0.3393 | 0.6282 |
| 0.3373 | 44.0 | 13728 | 0.3340 | 0.6282 |
| 0.3337 | 45.0 | 14040 | 0.3318 | 0.6390 |
| 0.3337 | 46.0 | 14352 | 0.3323 | 0.6354 |
| 0.3332 | 47.0 | 14664 | 0.3301 | 0.6643 |
| 0.3332 | 48.0 | 14976 | 0.3422 | 0.6282 |
| 0.3315 | 49.0 | 15288 | 0.3348 | 0.6570 |
| 0.33 | 50.0 | 15600 | 0.3366 | 0.6462 |
| 0.33 | 51.0 | 15912 | 0.3308 | 0.6570 |
| 0.331 | 52.0 | 16224 | 0.3298 | 0.6606 |
| 0.3295 | 53.0 | 16536 | 0.3377 | 0.6498 |
| 0.3295 | 54.0 | 16848 | 0.3439 | 0.6462 |
| 0.3282 | 55.0 | 17160 | 0.3326 | 0.6570 |
| 0.3282 | 56.0 | 17472 | 0.3356 | 0.6498 |
| 0.3291 | 57.0 | 17784 | 0.3309 | 0.6570 |
| 0.3278 | 58.0 | 18096 | 0.3333 | 0.6498 |
| 0.3278 | 59.0 | 18408 | 0.3324 | 0.6498 |
| 0.3292 | 60.0 | 18720 | 0.3335 | 0.6498 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
viprav/llama2-quote-1-row
|
viprav
| 2023-08-22T11:38:59Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T11:38:51Z |
---
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
|
lengoctuong/gpt2-finetuned-chatbot
|
lengoctuong
| 2023-08-22T11:38:39Z | 139 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-22T11:34:55Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-chatbot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-chatbot
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
linoyts/lora-xl-3d_icons-0.0001-5e-05-1500-1-None
|
linoyts
| 2023-08-22T11:36:17Z | 2 | 2 |
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
| 2023-08-22T10:57:42Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: blb 3d icon
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - LinoyTsaban/lora-xl-3d_icons-0.0001-5e-05-1500-1-None
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on blb 3d 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.
|
SpiderIerusalem/RubberPig
|
SpiderIerusalem
| 2023-08-22T11:25:50Z | 0 | 0 | null |
[
"rvc",
"audio-to-audio",
"license:mit",
"region:us"
] |
audio-to-audio
| 2023-08-22T11:11:38Z |
---
license: mit
pipeline_tag: audio-to-audio
tags:
- rvc
---
Puchkov's rubber pig voice model
Голосовая модель резиновой свиньи (как у Пучкова)
|
Muhammadreza/mann-e-pixel-art-revised-2
|
Muhammadreza
| 2023-08-22T11:12:49Z | 5 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-22T11:08:57Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### mann-e_pixel-art_revised-2 Dreambooth model trained by Muhammadreza with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
deepsdh99/llama2-qlora-finetunined-8
|
deepsdh99
| 2023-08-22T11:05:58Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T11:05:30Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
MattStammers/dqn-SpaceInvadersNoFrameskip-v4
|
MattStammers
| 2023-08-22T11:04:08Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T11:03:00Z |
---
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: 710.50 +/- 398.67
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 MattStammers -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 MattStammers -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 MattStammers
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
OpenBuddy/openbuddy-openllama-3b-v10-bf16
|
OpenBuddy
| 2023-08-22T10:51:04Z | 1,568 | 8 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-10T13:37:46Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
# Copyright Notice
License: Apache 2.0.
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
OpenBuddy/openbuddy-falcon-40b-v9-bf16
|
OpenBuddy
| 2023-08-22T10:50:44Z | 18 | 4 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWeb",
"text-generation",
"custom_code",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-02T22:19:59Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
# Copyright Notice
License: Apache 2.0.
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
NEXAS/stable_diff_custom
|
NEXAS
| 2023-08-22T10:30:11Z | 2 | 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
| 2023-08-22T07:42:29Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a nkl person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
bvboca/trainedloralocal
|
bvboca
| 2023-08-22T10:19:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T10:19:09Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
AddisuSeteye/speecht5_tts_amharic2
|
AddisuSeteye
| 2023-08-22T10:17:13Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"tags",
"generated_from_trainer",
"am",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-08-11T20:06:06Z |
---
language:
- am
license: mit
base_model: microsoft/speecht5_tts
tags:
- tags
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS amharic
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 amharic
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the alfaa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3855
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4304 | 3.32 | 1000 | 0.3855 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
sahithya20/checkpoint-tech-t5base
|
sahithya20
| 2023-08-22T10:16:17Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-22T10:14:12Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: checkpoint-tech-t5base
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. -->
# checkpoint-tech-t5base
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
RishuD7/t5_number_v7_new_data
|
RishuD7
| 2023-08-22T10:11:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-22T07:29:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5_number_v7_new_data
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_number_v7_new_data
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0108
- Cer: 0.6315
## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0163 | 1.0 | 858 | 0.0141 | 0.8245 |
| 0.0135 | 2.0 | 1716 | 0.0121 | 0.7102 |
| 0.012 | 3.0 | 2574 | 0.0115 | 0.6647 |
| 0.0115 | 4.0 | 3432 | 0.0111 | 0.6577 |
| 0.0115 | 5.0 | 4290 | 0.0110 | 0.6490 |
| 0.0106 | 6.0 | 5148 | 0.0108 | 0.6461 |
| 0.0103 | 7.0 | 6006 | 0.0108 | 0.6362 |
| 0.0103 | 8.0 | 6864 | 0.0108 | 0.6362 |
| 0.0101 | 9.0 | 7722 | 0.0108 | 0.6327 |
| 0.01 | 10.0 | 8580 | 0.0108 | 0.6315 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
asenella/ms_config_1_alpha_10_beta_1_seed_2
|
asenella
| 2023-08-22T10:02:53Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-08-22T10:02:51Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
qgallouedec/window-close-v2
|
qgallouedec
| 2023-08-22T09:48:51Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:03:03Z |
---
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: window-close-v2
type: window-close-v2
metrics:
- type: mean_reward
value: 593.18 +/- 40.45
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **window-close-v2** 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 qgallouedec/window-close-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=window-close-v2
```
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 train --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=window-close-v2 --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.
|
dkqjrm/20230822163753
|
dkqjrm
| 2023-08-22T09:48:07Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-22T07:38:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: '20230822163753'
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. -->
# 20230822163753
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3363
- Accuracy: 0.7256
## 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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 312 | 0.6253 | 0.5307 |
| 0.4958 | 2.0 | 624 | 0.3817 | 0.5415 |
| 0.4958 | 3.0 | 936 | 0.5426 | 0.4729 |
| 0.4406 | 4.0 | 1248 | 0.7363 | 0.5379 |
| 0.4205 | 5.0 | 1560 | 0.3395 | 0.6498 |
| 0.4205 | 6.0 | 1872 | 0.3422 | 0.6354 |
| 0.4134 | 7.0 | 2184 | 0.4093 | 0.5487 |
| 0.4134 | 8.0 | 2496 | 0.4435 | 0.5487 |
| 0.4124 | 9.0 | 2808 | 0.3364 | 0.6065 |
| 0.3904 | 10.0 | 3120 | 0.3570 | 0.6029 |
| 0.3904 | 11.0 | 3432 | 0.3988 | 0.5596 |
| 0.376 | 12.0 | 3744 | 0.3339 | 0.6751 |
| 0.3501 | 13.0 | 4056 | 0.3348 | 0.6606 |
| 0.3501 | 14.0 | 4368 | 0.3288 | 0.6715 |
| 0.3336 | 15.0 | 4680 | 0.3261 | 0.6823 |
| 0.3336 | 16.0 | 4992 | 0.3326 | 0.7040 |
| 0.333 | 17.0 | 5304 | 0.3264 | 0.7112 |
| 0.3259 | 18.0 | 5616 | 0.3259 | 0.6968 |
| 0.3259 | 19.0 | 5928 | 0.3253 | 0.6643 |
| 0.3281 | 20.0 | 6240 | 0.3261 | 0.7184 |
| 0.3191 | 21.0 | 6552 | 0.3227 | 0.7220 |
| 0.3191 | 22.0 | 6864 | 0.3371 | 0.6931 |
| 0.3164 | 23.0 | 7176 | 0.3522 | 0.6895 |
| 0.3164 | 24.0 | 7488 | 0.3275 | 0.7040 |
| 0.3133 | 25.0 | 7800 | 0.3234 | 0.7329 |
| 0.308 | 26.0 | 8112 | 0.3352 | 0.6931 |
| 0.308 | 27.0 | 8424 | 0.3167 | 0.7184 |
| 0.3075 | 28.0 | 8736 | 0.3378 | 0.6968 |
| 0.3064 | 29.0 | 9048 | 0.3370 | 0.7112 |
| 0.3064 | 30.0 | 9360 | 0.3432 | 0.7004 |
| 0.3021 | 31.0 | 9672 | 0.3305 | 0.7148 |
| 0.3021 | 32.0 | 9984 | 0.3218 | 0.7220 |
| 0.2983 | 33.0 | 10296 | 0.3349 | 0.7112 |
| 0.2933 | 34.0 | 10608 | 0.3208 | 0.7256 |
| 0.2933 | 35.0 | 10920 | 0.3243 | 0.7220 |
| 0.2931 | 36.0 | 11232 | 0.3206 | 0.7292 |
| 0.2903 | 37.0 | 11544 | 0.3643 | 0.6895 |
| 0.2903 | 38.0 | 11856 | 0.3254 | 0.7473 |
| 0.2895 | 39.0 | 12168 | 0.3350 | 0.7148 |
| 0.2895 | 40.0 | 12480 | 0.3325 | 0.7076 |
| 0.2852 | 41.0 | 12792 | 0.3289 | 0.7256 |
| 0.2857 | 42.0 | 13104 | 0.3281 | 0.7256 |
| 0.2857 | 43.0 | 13416 | 0.3373 | 0.7184 |
| 0.2805 | 44.0 | 13728 | 0.3414 | 0.7040 |
| 0.2806 | 45.0 | 14040 | 0.3346 | 0.7292 |
| 0.2806 | 46.0 | 14352 | 0.3383 | 0.7220 |
| 0.2777 | 47.0 | 14664 | 0.3285 | 0.7220 |
| 0.2777 | 48.0 | 14976 | 0.3385 | 0.7148 |
| 0.2768 | 49.0 | 15288 | 0.3403 | 0.7148 |
| 0.2732 | 50.0 | 15600 | 0.3336 | 0.7256 |
| 0.2732 | 51.0 | 15912 | 0.3306 | 0.7184 |
| 0.274 | 52.0 | 16224 | 0.3300 | 0.7292 |
| 0.272 | 53.0 | 16536 | 0.3318 | 0.7220 |
| 0.272 | 54.0 | 16848 | 0.3403 | 0.7220 |
| 0.2701 | 55.0 | 17160 | 0.3252 | 0.7292 |
| 0.2701 | 56.0 | 17472 | 0.3391 | 0.7220 |
| 0.2695 | 57.0 | 17784 | 0.3304 | 0.7292 |
| 0.2694 | 58.0 | 18096 | 0.3300 | 0.7220 |
| 0.2694 | 59.0 | 18408 | 0.3347 | 0.7292 |
| 0.2689 | 60.0 | 18720 | 0.3363 | 0.7256 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
qgallouedec/sweep-v2
|
qgallouedec
| 2023-08-22T09:47:52Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:02:50Z |
---
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: sweep-v2
type: sweep-v2
metrics:
- type: mean_reward
value: 533.33 +/- 36.38
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **sweep-v2** 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 qgallouedec/sweep-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=sweep-v2 --train_dir=./train_dir --experiment=sweep-v2
```
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 train --algo=APPO --env=sweep-v2 --train_dir=./train_dir --experiment=sweep-v2 --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.
|
qgallouedec/stick-pull-v2
|
qgallouedec
| 2023-08-22T09:45:10Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:02:18Z |
---
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: stick-pull-v2
type: stick-pull-v2
metrics:
- type: mean_reward
value: 540.61 +/- 8.27
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **stick-pull-v2** 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 qgallouedec/stick-pull-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=stick-pull-v2 --train_dir=./train_dir --experiment=stick-pull-v2
```
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 train --algo=APPO --env=stick-pull-v2 --train_dir=./train_dir --experiment=stick-pull-v2 --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.
|
Samuael/llama-2-7b-tebot-sharded
|
Samuael
| 2023-08-22T09:44:04Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"llama",
"region:us"
] | null | 2023-08-17T16:00:53Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
qgallouedec/shelf-place-v2
|
qgallouedec
| 2023-08-22T09:43:18Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:01:57Z |
---
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: shelf-place-v2
type: shelf-place-v2
metrics:
- type: mean_reward
value: 274.68 +/- 29.20
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **shelf-place-v2** 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 qgallouedec/shelf-place-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=shelf-place-v2 --train_dir=./train_dir --experiment=shelf-place-v2
```
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 train --algo=APPO --env=shelf-place-v2 --train_dir=./train_dir --experiment=shelf-place-v2 --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.
|
qgallouedec/push-wall-v2
|
qgallouedec
| 2023-08-22T09:40:35Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:01:25Z |
---
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: push-wall-v2
type: push-wall-v2
metrics:
- type: mean_reward
value: 742.10 +/- 32.17
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **push-wall-v2** 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 qgallouedec/push-wall-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=push-wall-v2 --train_dir=./train_dir --experiment=push-wall-v2
```
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 train --algo=APPO --env=push-wall-v2 --train_dir=./train_dir --experiment=push-wall-v2 --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.
|
araddanoe/npt02
|
araddanoe
| 2023-08-22T09:40:21Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-22T09:38:29Z |
---
license: creativeml-openrail-m
---
|
longquan/Llama-2-7b-chat-hf-japanese-custom-ds
|
longquan
| 2023-08-22T09:39:43Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-22T09:04:36Z |
---
license: cc-by-nc-sa-4.0
---
|
qgallouedec/push-v2
|
qgallouedec
| 2023-08-22T09:39:39Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:01:14Z |
---
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: push-v2
type: push-v2
metrics:
- type: mean_reward
value: 742.07 +/- 37.84
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **push-v2** 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 qgallouedec/push-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=push-v2 --train_dir=./train_dir --experiment=push-v2
```
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 train --algo=APPO --env=push-v2 --train_dir=./train_dir --experiment=push-v2 --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.
|
qgallouedec/plate-slide-v2
|
qgallouedec
| 2023-08-22T09:37:51Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:00:53Z |
---
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: plate-slide-v2
type: plate-slide-v2
metrics:
- type: mean_reward
value: 443.29 +/- 159.58
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **plate-slide-v2** 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 qgallouedec/plate-slide-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=plate-slide-v2 --train_dir=./train_dir --experiment=plate-slide-v2
```
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 train --algo=APPO --env=plate-slide-v2 --train_dir=./train_dir --experiment=plate-slide-v2 --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.
|
qgallouedec/plate-slide-side-v2
|
qgallouedec
| 2023-08-22T09:36:55Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:00:43Z |
---
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: plate-slide-side-v2
type: plate-slide-side-v2
metrics:
- type: mean_reward
value: 711.01 +/- 56.29
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **plate-slide-side-v2** 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 qgallouedec/plate-slide-side-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=plate-slide-side-v2 --train_dir=./train_dir --experiment=plate-slide-side-v2
```
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 train --algo=APPO --env=plate-slide-side-v2 --train_dir=./train_dir --experiment=plate-slide-side-v2 --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.
|
qgallouedec/plate-slide-back-side-v2
|
qgallouedec
| 2023-08-22T09:35:03Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:00:24Z |
---
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: plate-slide-back-side-v2
type: plate-slide-back-side-v2
metrics:
- type: mean_reward
value: 745.87 +/- 55.99
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **plate-slide-back-side-v2** 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 qgallouedec/plate-slide-back-side-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=plate-slide-back-side-v2 --train_dir=./train_dir --experiment=plate-slide-back-side-v2
```
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 train --algo=APPO --env=plate-slide-back-side-v2 --train_dir=./train_dir --experiment=plate-slide-back-side-v2 --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.
|
qgallouedec/pick-place-v2
|
qgallouedec
| 2023-08-22T09:33:11Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T10:00:04Z |
---
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: pick-place-v2
type: pick-place-v2
metrics:
- type: mean_reward
value: 447.63 +/- 150.72
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **pick-place-v2** 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 qgallouedec/pick-place-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=pick-place-v2 --train_dir=./train_dir --experiment=pick-place-v2
```
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 train --algo=APPO --env=pick-place-v2 --train_dir=./train_dir --experiment=pick-place-v2 --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.
|
qgallouedec/pick-out-of-hole-v2
|
qgallouedec
| 2023-08-22T09:32:16Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:59:55Z |
---
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: pick-out-of-hole-v2
type: pick-out-of-hole-v2
metrics:
- type: mean_reward
value: 230.67 +/- 114.25
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **pick-out-of-hole-v2** 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 qgallouedec/pick-out-of-hole-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=pick-out-of-hole-v2 --train_dir=./train_dir --experiment=pick-out-of-hole-v2
```
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 train --algo=APPO --env=pick-out-of-hole-v2 --train_dir=./train_dir --experiment=pick-out-of-hole-v2 --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.
|
qgallouedec/peg-insert-side-v2
|
qgallouedec
| 2023-08-22T09:30:22Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:59:35Z |
---
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: peg-insert-side-v2
type: peg-insert-side-v2
metrics:
- type: mean_reward
value: 308.94 +/- 175.97
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **peg-insert-side-v2** 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 qgallouedec/peg-insert-side-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=peg-insert-side-v2
```
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 train --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=peg-insert-side-v2 --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.
|
pansysy/distilbert-base-uncased_emotion_ft_0416_emotion_ft_3306
|
pansysy
| 2023-08-22T09:29:51Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-22T09:24:30Z |
---
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased_emotion_ft_0416_emotion_ft_3306
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_emotion_ft_0416_emotion_ft_3306
This model was trained from scratch on the emotion dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
qgallouedec/handle-pull-side-v2
|
qgallouedec
| 2023-08-22T09:27:34Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:59:05Z |
---
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: handle-pull-side-v2
type: handle-pull-side-v2
metrics:
- type: mean_reward
value: 462.12 +/- 95.86
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **handle-pull-side-v2** 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 qgallouedec/handle-pull-side-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=handle-pull-side-v2 --train_dir=./train_dir --experiment=handle-pull-side-v2
```
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 train --algo=APPO --env=handle-pull-side-v2 --train_dir=./train_dir --experiment=handle-pull-side-v2 --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.
|
qgallouedec/handle-press-v2
|
qgallouedec
| 2023-08-22T09:26:37Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:58:54Z |
---
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: handle-press-v2
type: handle-press-v2
metrics:
- type: mean_reward
value: 862.58 +/- 32.94
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **handle-press-v2** 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 qgallouedec/handle-press-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=handle-press-v2
```
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 train --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=handle-press-v2 --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.
|
qgallouedec/hammer-v2
|
qgallouedec
| 2023-08-22T09:23:53Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:58:25Z |
---
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: hammer-v2
type: hammer-v2
metrics:
- type: mean_reward
value: 692.49 +/- 21.25
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **hammer-v2** 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 qgallouedec/hammer-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=hammer-v2 --train_dir=./train_dir --experiment=hammer-v2
```
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 train --algo=APPO --env=hammer-v2 --train_dir=./train_dir --experiment=hammer-v2 --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.
|
kasperchen/a2c-PandaPickAndPlace-v3
|
kasperchen
| 2023-08-22T09:23:44Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T04:41:44Z |
---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
EricPeter/bert-finetuned-squad
|
EricPeter
| 2023-08-22T09:22:22Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-25T10:23:55Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: EricPeter/bert-finetuned-squad
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. -->
# EricPeter/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1648
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6996, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.4015 | 0 |
| 0.2423 | 1 |
| 0.1648 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
qgallouedec/drawer-close-v2
|
qgallouedec
| 2023-08-22T09:20:08Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:57:44Z |
---
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: drawer-close-v2
type: drawer-close-v2
metrics:
- type: mean_reward
value: 866.06 +/- 4.12
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **drawer-close-v2** 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 qgallouedec/drawer-close-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=drawer-close-v2 --train_dir=./train_dir --experiment=drawer-close-v2
```
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 train --algo=APPO --env=drawer-close-v2 --train_dir=./train_dir --experiment=drawer-close-v2 --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.
|
qgallouedec/door-unlock-v2
|
qgallouedec
| 2023-08-22T09:19:14Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:57:37Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: door-unlock-v2
type: door-unlock-v2
metrics:
- type: mean_reward
value: 805.08 +/- 13.10
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **door-unlock-v2** 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 qgallouedec/door-unlock-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=door-unlock-v2 --train_dir=./train_dir --experiment=door-unlock-v2
```
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 train --algo=APPO --env=door-unlock-v2 --train_dir=./train_dir --experiment=door-unlock-v2 --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.
|
qgallouedec/door-open-v2
|
qgallouedec
| 2023-08-22T09:18:18Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:57:27Z |
---
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: door-open-v2
type: door-open-v2
metrics:
- type: mean_reward
value: 579.89 +/- 31.92
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **door-open-v2** 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 qgallouedec/door-open-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=door-open-v2 --train_dir=./train_dir --experiment=door-open-v2
```
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 train --algo=APPO --env=door-open-v2 --train_dir=./train_dir --experiment=door-open-v2 --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.
|
qgallouedec/door-lock-v2
|
qgallouedec
| 2023-08-22T09:17:22Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:57:14Z |
---
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: door-lock-v2
type: door-lock-v2
metrics:
- type: mean_reward
value: 796.44 +/- 31.18
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **door-lock-v2** 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 qgallouedec/door-lock-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=door-lock-v2 --train_dir=./train_dir --experiment=door-lock-v2
```
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 train --algo=APPO --env=door-lock-v2 --train_dir=./train_dir --experiment=door-lock-v2 --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.
|
qgallouedec/door-close-v2
|
qgallouedec
| 2023-08-22T09:16:26Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:57:05Z |
---
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: door-close-v2
type: door-close-v2
metrics:
- type: mean_reward
value: 544.50 +/- 25.16
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **door-close-v2** 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 qgallouedec/door-close-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=door-close-v2 --train_dir=./train_dir --experiment=door-close-v2
```
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 train --algo=APPO --env=door-close-v2 --train_dir=./train_dir --experiment=door-close-v2 --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.
|
qgallouedec/disassemble-v2
|
qgallouedec
| 2023-08-22T09:15:29Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:56:55Z |
---
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: disassemble-v2
type: disassemble-v2
metrics:
- type: mean_reward
value: 43.23 +/- 7.35
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **disassemble-v2** 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 qgallouedec/disassemble-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=disassemble-v2 --train_dir=./train_dir --experiment=disassemble-v2
```
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 train --algo=APPO --env=disassemble-v2 --train_dir=./train_dir --experiment=disassemble-v2 --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.
|
qgallouedec/dial-turn-v2
|
qgallouedec
| 2023-08-22T09:14:31Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:56:44Z |
---
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: dial-turn-v2
type: dial-turn-v2
metrics:
- type: mean_reward
value: 798.92 +/- 53.59
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **dial-turn-v2** 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 qgallouedec/dial-turn-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=dial-turn-v2 --train_dir=./train_dir --experiment=dial-turn-v2
```
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 train --algo=APPO --env=dial-turn-v2 --train_dir=./train_dir --experiment=dial-turn-v2 --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.
|
qgallouedec/coffee-button-v2
|
qgallouedec
| 2023-08-22T09:11:48Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:56:14Z |
---
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: coffee-button-v2
type: coffee-button-v2
metrics:
- type: mean_reward
value: 735.56 +/- 25.87
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **coffee-button-v2** 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 qgallouedec/coffee-button-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=coffee-button-v2 --train_dir=./train_dir --experiment=coffee-button-v2
```
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 train --algo=APPO --env=coffee-button-v2 --train_dir=./train_dir --experiment=coffee-button-v2 --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.
|
922-CA/gfl-ddlc-TI-tests
|
922-CA
| 2023-08-22T09:11:04Z | 0 | 0 | null |
[
"ddlc",
"gfl",
"anime",
"doki doki literature club",
"girl's frontline",
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-18T12:13:39Z |
---
license: creativeml-openrail-m
tags:
- ddlc
- gfl
- anime
- doki doki literature club
- girl's frontline
---
# TEXTUAL INVERSION TESTS (~11/18/2022) #
Various old TIs trained on the following characters:
Girl's Frontline:
* Persicaria (~200 images)
* P90 (~200 images)
* Springfield (~150 images)
* Negev (~100 images)
* KAC-PDW (~11 images)
* FMG9 (~10 images)
Doki Doki Literature Club:
* Monika (~25 images)
* Yuri (~25 images)
* Sayori (~20 images)
* Natsuki (~20 images)
All were trained on NAI model, with as little as 5000 steps up to 20000 steps.
(uploaded for archiving purposes: may look for older versions and more params/hyperparams used with these)
# PREVIEWS (~08/14/2023) #
Most generated with Silicon29 and a custom model based off it, using simple prompts and nothing else, following the format:
1girl, (solo:1.2), \<TI\>, \<char booru tag\>, \<color of\> eyes, best quality, \<one or two additional prompts, like "dress"\>
neg prompt:
(cropped:1.4), (loli:1.5), (child:1.5), text, low quality, normal quality, deformed, (bad-hands-5:1.2), (FastNegativeV2:1.1), deep fried, nsfw, big ribbon, red ribbon, painting, jpeg artifact, deformed, red ribbon, too many ribbons, messy ribbon, braids, umbrella, floating hair, see-through clothing, torn clothing, bad hair, gross, necklace, bad proportions, text, username, \<one or two additional prompts, like "school uniform"\>
Girl's Frontline (best to worst):
* Persicaria: [Example 1](08142023_ti_prevs/full/pers1%2000115-2787276333.png), [Example 2](08142023_ti_prevs/full/pers1%2000153-2615520266.png), [Example 3](08142023_ti_prevs/full/pers1%2000029-761374397.png)
* P90: [Example 1](08142023_ti_prevs/full/p90%2000110-3022549351.png), [Example 2](08142023_ti_prevs/full/p90%2000112-1360325468.png), [Example 3](08142023_ti_prevs/full/p90%2000158-3721045199.png)
* Negev: [Example 1](08142023_ti_prevs/full/negev5%2000128-1698072591.png), [Example 2](08142023_ti_prevs/full/negev5%2000129-1583369084.png), [Example 3](08142023_ti_prevs/full/negev5%2000027-1566859227.png)
* Springfield: [Example 1](08142023_ti_prevs/full/spring1%2000106-1162523717.png), [Example 2](08142023_ti_prevs/full/spring1%2000107-3179963266.png)
* KACPDW (overfits to clothes): [Example 1](08142023_ti_prevs/full/kacpdw%2000026-4038413503.png), [Example 2](08142023_ti_prevs/full/kacpdw%2000023-3787426706.png), [Example 3](08142023_ti_prevs/full/kacpdw%2000122-3185291820.png)
* FMG9: [Example 1](08142023_ti_prevs/full/fmg9%2000118-3783199521.png), [Example 2](08142023_ti_prevs/full/fmg9%2000119-1811241202.png)
Doki Doki Literature Club (best to worst):
* Monika: [Example 1](08142023_ti_prevs/full/mon3a%2000141-4131642062.png), [Example 2](08142023_ti_prevs/full/mon3a%2000142-3718578348.png), [Example 3](08142023_ti_prevs/full/mon3a%2000164-4156171172.png)
* Yuri (overfits to clothes): [Example 1](08142023_ti_prevs/full/yur3a%2000101-1766762200.png), [Example 2](08142023_ti_prevs/full/yur3a%2000013-2416113300.png), [Example 3](08142023_ti_prevs/full/yur3a%2000011-2706364566.png)
* Natsuki: [Example 1](08142023_ti_prevs/full/nat3%2000001-3982624050.png), [Example 2](08142023_ti_prevs/full/nat3%2000145-420199443.png)
* Sayori (overfits to chairs...): [Example 1](08142023_ti_prevs/full/say3%2000133-3970420654.png), [Example 2](08142023_ti_prevs/full/say3%2000134-2062223196.png)
It's ~2022's files in near late 2023 SD world (as of writing). Maybe it's obsolete, but might be of mild interest.
|
qgallouedec/button-press-wall-v2
|
qgallouedec
| 2023-08-22T09:10:54Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T09:56:05Z |
---
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: button-press-wall-v2
type: button-press-wall-v2
metrics:
- type: mean_reward
value: 674.97 +/- 13.33
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **button-press-wall-v2** 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 qgallouedec/button-press-wall-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=button-press-wall-v2 --train_dir=./train_dir --experiment=button-press-wall-v2
```
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 train --algo=APPO --env=button-press-wall-v2 --train_dir=./train_dir --experiment=button-press-wall-v2 --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.
|
qgallouedec/box-close-v2
|
qgallouedec
| 2023-08-22T09:07:15Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-07T16:12:19Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: box-close-v2
type: box-close-v2
metrics:
- type: mean_reward
value: 515.82 +/- 160.02
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **box-close-v2** 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 qgallouedec/box-close-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=box-close-v2 --train_dir=./train_dir --experiment=box-close-v2
```
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 train --algo=APPO --env=box-close-v2 --train_dir=./train_dir --experiment=box-close-v2 --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.
|
qgallouedec/bin-picking-v2
|
qgallouedec
| 2023-08-22T09:06:20Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-07T16:11:52Z |
---
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: bin-picking-v2
type: bin-picking-v2
metrics:
- type: mean_reward
value: 452.37 +/- 36.53
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **bin-picking-v2** 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 qgallouedec/bin-picking-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=bin-picking-v2 --train_dir=./train_dir --experiment=bin-picking-v2
```
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 train --algo=APPO --env=bin-picking-v2 --train_dir=./train_dir --experiment=bin-picking-v2 --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.
|
marksverdhei/t5-base-define
|
marksverdhei
| 2023-08-22T09:05:55Z | 123 | 6 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:marksverdhei/wordnet-definitions-en-2021",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-02T09:50:37Z |
---
language: en
widget:
- text: 'define "toecoin": toecoin rose by 200% after Elon Musk mentioned it in his tweet'
datasets:
- 'marksverdhei/wordnet-definitions-en-2021'
---
# T5-define
(This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy)
This model is trained to generate word definitions based on the word and a context,
using a subset of wordnet for all words that have an example and definition.
The model uses task prompts on the format 'define "[word]": [example sentence]'
This model in particular is a one-shot learner for unseen words, as it has to infer the definition by only one example
How to run:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("marksverdhei/t5-base-define")
model = T5ForConditionalGeneration.from_pretrained("marksverdhei/t5-base-define")
prompt = "define \"noseplow\": The children hid as the noseplow drove across the street"
ids = tokenizer(prompt, return_tensors="pt").input_ids
generated_tokens = model.generate(ids)[0][1:-1]
print(tokenizer.decode(generated_tokens))
```
See the gist for the source code to used to train the model:
https://gist.github.com/marksverdhei/0a13f67e65460b71c05fcf558a6a91ae
|
qgallouedec/basketball-v2
|
qgallouedec
| 2023-08-22T09:05:27Z | 0 | 1 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-07T16:11:22Z |
---
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: basketball-v2
type: basketball-v2
metrics:
- type: mean_reward
value: 584.02 +/- 49.43
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **basketball-v2** 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 qgallouedec/basketball-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=basketball-v2 --train_dir=./train_dir --experiment=basketball-v2
```
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 train --algo=APPO --env=basketball-v2 --train_dir=./train_dir --experiment=basketball-v2 --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.
|
anshudaur/path-to-save-model
|
anshudaur
| 2023-08-22T09:05:16Z | 13 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-22T07:51:42Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: photo of a <new1> cat
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - anshudaur/path-to-save-model
These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
newronai/lma2-7b-Chat-Adapter-N
|
newronai
| 2023-08-22T08:56:11Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T08:56:04Z |
---
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
|
tianpf/chinese-alpaca-2-qlora-finetunined-law2
|
tianpf
| 2023-08-22T08:46:11Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T08:46:07Z |
---
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
|
linoyts/lora-xl-3d_icons-0.0001-5e-05-2000-1-5
|
linoyts
| 2023-08-22T08:45:51Z | 5 | 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
| 2023-08-22T07:54:07Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: blb 3d icon
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - LinoyTsaban/lora-xl-3d_icons-0.0001-5e-05-2000-1-5
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on blb 3d 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.
|
huashiyiqike/testmodel
|
huashiyiqike
| 2023-08-22T08:45:20Z | 1,610 | 1 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"dataset:tatsu-lab/alpaca",
"dataset:the_pile",
"arxiv:1910.09700",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-22T08:33:35Z |
---
license: cc-by-nc-sa-4.0
datasets:
- tatsu-lab/alpaca
- the_pile
---
# Model Card for Cerebras 111M Dollyfied.
This is a finetuned model of Cerebras 111M model. using DataBricksLabs Dolly Framework
## Model Details
### Model Description
This is a finetuned version of cerebras' 111million paramater model that has been trained to follow instructions.
It was accomplished using DataBricks Dolly training tools and the alpaca dataset, and was trained for 2 epochs.
- **Developed by:** Finetuned by Corianas (me) using open source tools
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** EN
- **License:** cc-by-nc-4.0
- **Finetuned from model:** https://huggingface.co/cerebras/Cerebras-GPT-111m
- **Finetuned using:** https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html
## Uses
This is a simple GPT chatbot that has been finetuned to understand instructions.
Its knowledge about facts about the world is should be considered suspect at best.
### Direct Use
If you have a use you put it to, Please let me know.
[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
Any form of use where any form of accuracy is needed.
FOR THE LOVE OF GOD DO NOT FOLLOW MEDICAL ADVICE FROM THIS.
or financial advice.
[More Information Needed]
## Bias, Risks, and Limitations
Limitations... Yes, I am sure there are so so many.
[More Information Needed]
## 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:** 8xA100s (accomplished while I was downloading the model I was actually training.)
- **Minutes used:** 7.5
- **Cloud Provider:** LambdaGPU
- **Compute Region:** USA
- **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]
|
xray1111/ppo-LunarLander-v2
|
xray1111
| 2023-08-22T08:41:09Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-22T08:40:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.95 +/- 16.80
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
...
```
|
dreamfuser/path_to_saved_model
|
dreamfuser
| 2023-08-22T08:37:13Z | 0 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-23T07:12:21Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - dreamfuser/path_to_saved_model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. 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.
DreamBooth for the text encoder was enabled: False.
|
kaanhho/whisper-tiny-01
|
kaanhho
| 2023-08-22T08:29:58Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-22T01:07:48Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-01
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.33884297520661155
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-01
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6410
- Wer Ortho: 0.3430
- Wer: 0.3388
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.001 | 17.24 | 500 | 0.6410 | 0.3430 | 0.3388 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CodyNichol14/M_Shadows_HTTK
|
CodyNichol14
| 2023-08-22T08:28:22Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2023-08-22T08:25:25Z |
---
license: artistic-2.0
Model Maker: CodyNichol14
Epoch: 250
Model: M. SHadows From Avenged Sevenfold
---
|
AnnaMats/ppo-SnowballTarget
|
AnnaMats
| 2023-08-22T08:28:08Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-22T08:28:05Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AnnaMats/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
SpecialOne/bloomz-560m_PROMPT_TUNING_CAUSAL_LM
|
SpecialOne
| 2023-08-22T08:27:49Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T08:27:46Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
ashoknpotti/bloom-1b7-qanda
|
ashoknpotti
| 2023-08-22T08:14:55Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-22T07:47:36Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Seooooooogi/outputs_2
|
Seooooooogi
| 2023-08-22T08:10:51Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-22T07:46:16Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks man
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - Seooooooogi/outputs_2
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks man using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
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.