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Wanfq/distillchat_v2.0 | Wanfq | 2023-11-29T17:35:21Z | 1 | 0 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:35:21Z | 2023-11-29T15:49:41.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Wanfq/distillchat_v2.0 | [
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JNewber/friendagent | JNewber | 2023-11-29T15:59:02Z | 1 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:59:02Z | 2023-11-29T15:52:34.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | JNewber/friendagent | [
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meryemnar/layoutlmv3-full-data-02 | meryemnar | 2023-11-29T16:08:50Z | 1 | 0 | null | [
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:08:50Z | 2023-11-29T16:06:14.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | meryemnar/layoutlmv3-full-data-02 | [
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erolb/test3 | erolb | 2023-11-29T16:11:27Z | 1 | 0 | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:Mousumi/finetuned_bart",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:11:27Z | 2023-11-29T16:11:22.000Z | null | null | ---
base_model: Mousumi/finetuned_bart
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: test3
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. -->
# test3
This model is a fine-tuned version of [Mousumi/finetuned_bart](https://huggingface.co/Mousumi/finetuned_bart) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3363
- Bleu: 0.2728
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 313 | 1.3791 | 0.1806 | 20.0 |
| 1.6735 | 2.0 | 626 | 1.3363 | 0.2728 | 20.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | erolb/test3 | [
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Jungwonchang/wav2vec2-large-mms-300m-FULL-SPGI-xs | Jungwonchang | 2023-11-29T17:39:37Z | 1 | 0 | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:spgispeech_xs",
"base_model:facebook/mms-300m",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:39:37Z | 2023-11-29T16:18:22.000Z | null | null | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
datasets:
- spgispeech_xs
base_model: facebook/mms-300m
model-index:
- name: wav2vec2-large-mms-300m-FULL-SPGI-xs
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Test set for spgispeech
type: kensho/spgispeech
config: test
split: test
metrics:
- type: wer
value: 100.0
name: WER
- type: cer
value: 99.3
name: CER
---
<!-- 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-300m-FULL-SPGI-xs
This model is a fine-tuned version of [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) on the spgispeech_xs 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 100
- training_steps: 120
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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bruchansky/plates-bowls | bruchansky | 2023-11-29T16:31:02Z | 1 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:31:02Z | 2023-11-29T16:30:54.000Z | null | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: plates-bowls
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9111111164093018
---
# plates-bowls
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### bowl
#### burger
#### pizza
#### plate
 | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | bruchansky/plates-bowls | [
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MarkrAI/DopeorNope-maestro-v1-DPO-13b | MarkrAI | 2023-11-29T17:08:30Z | 1 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:08:30Z | 2023-11-29T16:40:41.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | MarkrAI/DopeorNope-maestro-v1-DPO-13b | [
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TimelyFormulation74/TestingLlama | TimelyFormulation74 | 2023-11-29T16:47:30Z | 1 | 0 | null | [
"transformers",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T16:47:30Z | 2023-11-29T16:42:12.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | TimelyFormulation74/TestingLlama | [
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emilstabil/DanSumT5-smallV_59491 | emilstabil | 2023-11-29T18:50:35Z | 1 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:50:35Z | 2023-11-29T16:51:24.000Z | null | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-smallV_59491
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. -->
# DanSumT5-smallV_59491
This model is a fine-tuned version of [Danish-summarisation/DanSumT5-small](https://huggingface.co/Danish-summarisation/DanSumT5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4715
- Rouge1: 34.3324
- Rouge2: 11.2932
- Rougel: 20.8529
- Rougelsum: 31.803
- Gen Len: 126.0591
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 0.97 | 29 | 2.6350 | 32.3407 | 9.3733 | 18.8907 | 29.6731 | 126.4726 |
| No log | 1.98 | 59 | 2.5732 | 33.2864 | 10.0998 | 19.4234 | 30.5031 | 126.5021 |
| No log | 2.99 | 89 | 2.5433 | 34.0216 | 10.7327 | 20.0836 | 31.3728 | 125.9873 |
| No log | 4.0 | 119 | 2.5209 | 34.1794 | 10.8803 | 20.414 | 31.4901 | 126.654 |
| No log | 4.97 | 148 | 2.5065 | 34.1598 | 10.9122 | 20.5029 | 31.5882 | 126.443 |
| No log | 5.98 | 178 | 2.4942 | 34.3057 | 10.8906 | 20.6468 | 31.628 | 126.8354 |
| No log | 6.99 | 208 | 2.4843 | 34.2147 | 10.9465 | 20.4998 | 31.5286 | 126.3418 |
| No log | 8.0 | 238 | 2.4768 | 34.3636 | 11.2347 | 20.6723 | 31.7775 | 126.3165 |
| No log | 8.97 | 267 | 2.4736 | 34.1256 | 11.3376 | 20.6823 | 31.6962 | 126.2447 |
| No log | 9.98 | 297 | 2.4718 | 34.3458 | 11.2773 | 20.8056 | 31.8051 | 125.9789 |
| No log | 10.72 | 319 | 2.4715 | 34.3324 | 11.2932 | 20.8529 | 31.803 | 126.0591 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1+git7548e2f
- Datasets 2.13.2
- Tokenizers 0.13.3
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rdecoupes/tetis-textmine-2024-camembert-large-based | rdecoupes | 2023-11-29T17:06:50Z | 1 | 0 | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:06:50Z | 2023-11-29T16:57:22.000Z | null | null | ---
license: cc-by-nc-4.0
---
# [TETIS](https://www.umr-tetis.fr) @ [Challenge TextMine 2024](https://textmine.sciencesconf.org/resource/page/id/9)
---
## This model is a NER based on Camembert-Large for the Kaggle Competition (in French): https://www.kaggle.com/competitions/defi-textmine-2024/
This model could be re-use with HuggingFace transormers pipeline. To use it, please refer to its [Github](https://github.com/tetis-nlp/tetis-challenge_textmine_2024)
---
<img align="left" src="https://www.umr-tetis.fr/images/logo-header-tetis.png">
| Participants |
|----------------------|
| Rémy Decoupes |
| Roberto Interdonato |
| Rodrique Kafando |
| Mehtab Syed Alam |
| Maguelonne Teisseire |
| Mathieu Roche |
| Sarah Valentin |
---
| null | transformers | token-classification | null | null | null | null | null | null | null | null | null | rdecoupes/tetis-textmine-2024-camembert-large-based | [
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Jacaranda/Twi_Adapted_Masked_LM | Jacaranda | 2023-11-29T17:08:57Z | 1 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:08:57Z | 2023-11-29T17:06:33.000Z | null | null | Entry not found | null | transformers | fill-mask | null | null | null | null | null | null | null | null | null | Jacaranda/Twi_Adapted_Masked_LM | [
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Matupom/thainer-corpus-v2-dataset-old_new | Matupom | 2023-11-29T17:16:58Z | 1 | 0 | null | [
"transformers",
"safetensors",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:16:58Z | 2023-11-29T17:16:33.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | Matupom/thainer-corpus-v2-dataset-old_new | [
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mdosama39/xlm-roberta-base-Stress-identification-task-2 | mdosama39 | 2023-11-29T18:05:53Z | 1 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:05:53Z | 2023-11-29T17:34:29.000Z | null | null | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-Stress-identification-task-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-Stress-identification-task-2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- 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
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
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tanvirsrbd1/testvalue_t5_model3 | tanvirsrbd1 | 2023-11-29T17:40:09Z | 1 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:40:09Z | 2023-11-29T17:36:36.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | tanvirsrbd1/testvalue_t5_model3 | [
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yantli/forward_wiki40b_zh-cn | yantli | 2023-11-29T18:09:20Z | 1 | 0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:09:20Z | 2023-11-29T17:50:14.000Z | null | null | GPT2 model trained on the wiki40b zh-cn dataset (https://huggingface.co/datasets/wiki40b).
Codes used for training are from https://github.com/Futrell/trainlm/tree/master
---
license: mit
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | yantli/forward_wiki40b_zh-cn | [
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mghiasvandm/aste-rest16-base-agents | mghiasvandm | 2023-11-29T17:52:49Z | 1 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:52:49Z | 2023-11-29T17:52:14.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | mghiasvandm/aste-rest16-base-agents | [
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mghiasvandm/tasd-rest15-base-agents | mghiasvandm | 2023-11-29T17:56:58Z | 1 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:56:58Z | 2023-11-29T17:56:24.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | mghiasvandm/tasd-rest15-base-agents | [
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mbiskho/text | mbiskho | 2023-11-29T18:07:54Z | 1 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:07:54Z | 2023-11-29T18:06:54.000Z | null | null | ---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: text
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text
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: 1.6079
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4887 | 1.0 | 18 | 1.6041 |
| 1.0425 | 2.0 | 36 | 1.6079 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.15.0
- Tokenizers 0.15.0
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FpOliveira/tupi-gpt2-small | FpOliveira | 2023-11-29T22:58:06Z | 1 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:58:06Z | 2023-11-29T18:29:28.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | FpOliveira/tupi-gpt2-small | [
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LemTenku/20b | LemTenku | 2023-11-29T18:44:55Z | 1 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:44:55Z | 2023-11-29T18:35:47.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LemTenku/20b | [
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hiiamsid/orca-13b-sft-2 | hiiamsid | 2023-11-29T19:38:51Z | 1 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/Orca-2-13b",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T19:38:51Z | 2023-11-29T18:40:36.000Z | null | null | ---
license: other
base_model: microsoft/Orca-2-13b
tags:
- generated_from_trainer
model-index:
- name: orca-13b-sft-2
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. -->
# orca-13b-sft-2
This model is a fine-tuned version of [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5592
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- total_train_batch_size: 28
- total_eval_batch_size: 28
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.611 | 1.0 | 36 | 1.5833 |
| 0.6432 | 2.0 | 72 | 0.6268 |
| 0.569 | 3.0 | 108 | 0.5802 |
| 0.5642 | 4.0 | 144 | 0.5675 |
| 0.5627 | 5.0 | 180 | 0.5619 |
| 0.5504 | 6.0 | 216 | 0.5595 |
| 0.5553 | 7.0 | 252 | 0.5592 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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mghiasvandm/aste-rest16-base-tuples | mghiasvandm | 2023-11-29T18:49:24Z | 1 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:49:24Z | 2023-11-29T18:48:46.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | mghiasvandm/aste-rest16-base-tuples | [
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TheBloke/LLaMA2-13B-Psyfighter2-AWQ | TheBloke | 2023-11-30T00:52:17Z | 1 | 1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:KoboldAI/LLaMA2-13B-Psyfighter2",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-30T00:52:17Z | 2023-11-29T18:51:04.000Z | null | null | ---
base_model: KoboldAI/LLaMA2-13B-Psyfighter2
inference: false
license: llama2
model_creator: KoboldAI
model_name: Llama2 13B Psyfighter2
model_type: llama
prompt_template: "### Instruction: \n{prompt}\n### Response:\n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
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# Llama2 13B Psyfighter2 - AWQ
- Model creator: [KoboldAI](https://huggingface.co/KoboldAI)
- Original model: [Llama2 13B Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2)
<!-- description start -->
## Description
This repo contains AWQ model files for [KoboldAI's Llama2 13B Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF)
* [KoboldAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca-Tiefighter
```
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.25 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/LLaMA2-13B-Psyfighter2-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `LLaMA2-13B-Psyfighter2-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/LLaMA2-13B-Psyfighter2-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/LLaMA2-13B-Psyfighter2-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/LLaMA2-13B-Psyfighter2-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/LLaMA2-13B-Psyfighter2-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''### Instruction:
{prompt}
### Response:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: KoboldAI's Llama2 13B Psyfighter2
# LLAMA2-13B-Psyfighter2
Psyfighter is a merged model created by the KoboldAI community members Jeb Carter and TwistedShadows and was made possible thanks to the KoboldAI merge request service.
The intent was to add medical data to supplement the models fictional ability with more details on anatomy and mental states. Due to the low ratio's of medical data and the high ratio's of fiction this model should not be used for medical advice or therapy because of its high chance of pulling in fictional data.
The following mergekit recipe was used:
```
merge_method: task_arithmetic
base_model: TheBloke/Llama-2-13B-fp16
models:
- model: TheBloke/Llama-2-13B-fp16
- model: KoboldAI/LLaMA2-13B-Tiefighter
parameters:
weight: 1.0
- model: Doctor-Shotgun/cat-v1.0-13b
parameters:
weight: 0.01
- model: Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
parameters:
weight: 0.02
dtype: float16
```
*V1 of this model was published under the account of the creator of the merge
This model contains the following ingredients from their upstream models for as far as we can track them:
- KoboldAI/LLaMA2-13B-Tiefighter
- Undi95/Xwin-MLewd-13B-V0.2
- - Undi95/ReMM-S-Light
- Undi95/CreativeEngine
- Brouz/Slerpeno
- - elinas/chronos-13b-v2
- jondurbin/airoboros-l2-13b-2.1
- NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2
- CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2
- - KoboldAI/LLAMA2-13B-Holodeck-1
- NousResearch/Nous-Hermes-13b
- OpenAssistant/llama2-13b-orca-8k-3319
- ehartford/WizardLM-1.0-Uncensored-Llama2-13b
- Henk717/spring-dragon
- The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible)
- - SuperCOT (Undisclosed version)
- elinas/chronos-13b-v2 (Version assumed)
- NousResearch/Nous-Hermes-Llama2-13b
- stabilityai/StableBeluga-13B (Version assumed)
- zattio770/120-Days-of-LORA-v2-13B
- PygmalionAI/pygmalion-2-13b
- Undi95/Storytelling-v1-13B-lora
- TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter
- nRuaif/Kimiko-v2-13B
- The-Face-Of-Goonery/Huginn-13b-FP16
- - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp"
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Xwin-LM/Xwin-LM-13B-V0.2
- PocketDoc/Dans-RetroRodeo-13b
- Blackroot/Llama-2-13B-Storywriter-LORA
- Doctor-Shotgun/cat-v1.0-13b
- Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
- meta-llama/Llama-2-13b-chat-hf
- lemonilia/limarp-llama2-v2
While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible!
Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible.
# Usage
This model is meant to be creative, If you let it improvise you get better results than if you drown it in details.
## Story Writing
Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story.
### Generate a story on demand
To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong.
## Chatbots and persona's
This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further.
For example, you can put this in memory in regular chat mode:
```
### Instruction:
Generate a conversation between Alice and Jeb where they discuss language models.
In this conversation Henk is excited to teach Alice about Psyfighter.
### Response:
```
Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format.
## Instruct Prompting
This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work.
During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias.
Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up.
## Adventuring and Adventure Games
This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode).
It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns.
## Discovered something cool and want to engage with us?
Join our community at https://koboldai.org/discord !
We can also provide assistance in making your own merges.
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] |
magixxixx/roberta-shopee-sentiment-gadgets | magixxixx | 2023-11-29T19:09:32Z | 1 | 0 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"tl",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:09:32Z | 2023-11-29T18:51:58.000Z | null | null | ---
metrics:
- accuracy
pipeline_tag: text-classification
language:
- tl
---
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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#### Software
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## Glossary [optional]
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## Model Card Authors [optional]
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mghiasvandm/tasd-rest15-base-tuples | mghiasvandm | 2023-11-29T18:58:38Z | 1 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:58:38Z | 2023-11-29T18:57:56.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | mghiasvandm/tasd-rest15-base-tuples | [
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Rofoman/GTS-Noromaid-V1.00 | Rofoman | 2023-11-29T20:31:12Z | 1 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:31:12Z | 2023-11-29T20:13:45.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Rofoman/GTS-Noromaid-V1.00 | [
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mghiasvandm/aste-rest14-base-tuples | mghiasvandm | 2023-11-29T20:23:11Z | 1 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:23:11Z | 2023-11-29T20:22:36.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | mghiasvandm/aste-rest14-base-tuples | [
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LemTenku/13B | LemTenku | 2023-11-29T20:53:14Z | 1 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:53:14Z | 2023-11-29T20:25:03.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LemTenku/13B | [
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Angelinis/electra-small-discriminator-finetuned-enem | Angelinis | 2023-11-29T20:33:28Z | 1 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"electra",
"multiple-choice",
"generated_from_trainer",
"base_model:google/electra-small-discriminator",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T20:33:28Z | 2023-11-29T20:28:10.000Z | null | null | ---
license: apache-2.0
base_model: google/electra-small-discriminator
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: electra-small-discriminator-finetuned-enem
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. -->
# electra-small-discriminator-finetuned-enem
This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.2551
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6117 | 1.0 | 933 | 1.6094 | 0.1967 |
| 1.6088 | 2.0 | 1866 | 1.6094 | 0.2492 |
| 1.6091 | 3.0 | 2799 | 1.6094 | 0.2551 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0
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MatteoWood/bert-sexism-classifier-0 | MatteoWood | 2023-11-29T20:43:08Z | 1 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain",
"dataset:MatteoWood/autotrain-data-bert-sexism-classifier",
"endpoints_compatible",
"region:us"
] | 2023-11-29T20:43:08Z | 2023-11-29T20:42:37.000Z | null | null |
---
tags:
- autotrain
- text-classification
widget:
- text: "I love AutoTrain"
datasets:
- MatteoWood/autotrain-data-bert-sexism-classifier
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.41215935349464417
f1: 0.6691176470588236
precision: 0.629757785467128
recall: 0.7137254901960784
auc: 0.8707093038557704
accuracy: 0.82
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | MatteoWood/bert-sexism-classifier-0 | [
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IlyaGusev/iamblichus_d38_m2 | IlyaGusev | 2023-11-29T21:02:44Z | 1 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:02:44Z | 2023-11-29T20:57:38.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | IlyaGusev/iamblichus_d38_m2 | [
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IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw | IconicAI | 2023-11-29T21:32:10Z | 1 | 1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"exl2",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:32:10Z | 2023-11-29T21:10:28.000Z | null | null | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
- exl2
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---
EXL2 quantisation of NeuralHermes-2.5-Mistral-7B, for use with ExLLamaV2.
[Original model](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) by @mlabonne.
**Model size:** 4.6GB (3x reduction), 5 bits-per-weight average, 6bpw on head.
**Calibration Data:** Wikitext [(parquet)](https://huggingface.co/datasets/wikitext/blob/refs%2Fconvert%2Fparquet/wikitext-2-v1/train/0000.parquet)
**Command:** `python convert.py -i convert/NeuralHermes-2.5-Mistral-7B -c convert/0000.parquet -o convert/temp2 -cf convert/nh-5bpw -b 5.0 -hb 6`
Layer measurements are provided in `measurement.json`` for further quantisation.
---
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is an [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset.
It is directly inspired by the RLHF process described by [neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template. I haven't performed a comprehensive evaluation of the model, but it works great, nothing broken apparently! :)
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
GGUF versions of this model are available here: [mlabonne/NeuralHermes-2.5-Mistral-7B-GGUF](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-GGUF).
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16,
* lora_alpha=16,
* lora_dropout=0.05,
* bias="none",
* task_type="CAUSAL_LM",
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4,
* gradient_accumulation_steps=4,
* gradient_checkpointing=True,
* learning_rate=5e-5,
* lr_scheduler_type="cosine",
* max_steps=200,
* optim="paged_adamw_32bit",
* warmup_steps=100,
**DPOTrainer**:
* beta=0.1,
* max_prompt_length=1024,
* max_length=1536, | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw | [
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Hasan-Mesbaul-420/whisper-small-hi | Hasan-Mesbaul-420 | 2023-11-29T21:41:42Z | 1 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:41:42Z | 2023-11-29T21:35:49.000Z | null | null | ---
language:
- hi
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 Hi - Sanchit Gandhi
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 Hi - Sanchit Gandhi
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: 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: 500
- training_steps: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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typeof/mini-mistral | typeof | 2023-11-29T22:08:19Z | 1 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:08:19Z | 2023-11-29T22:07:02.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | typeof/mini-mistral | [
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csukuangfj/wheels | csukuangfj | 2023-11-29T10:44:20Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T10:44:20Z | 2022-07-11T02:40:59.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/wheels | [
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scikit-learn/Fish-Weight | scikit-learn | 2023-11-29T12:48:44Z | 0 | 2 | null | [
"sklearn",
"skops",
"tabular-regression",
"license:mit",
"has_space",
"region:us"
] | 2023-11-29T12:48:44Z | 2022-10-06T20:37:41.000Z | null | null | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
widget:
- structuredData:
Height:
- 11.52
- 12.48
- 12.3778
Length1:
- 23.2
- 24.0
- 23.9
Length2:
- 25.4
- 26.3
- 26.5
Length3:
- 30.0
- 31.2
- 31.1
Species:
- Bream
- Bream
- Bream
Width:
- 4.02
- 4.3056
- 4.6961
---
# Model description
This is a GradientBoostingRegressor on a fish dataset.
## Intended uses & limitations
This model is intended for educational purposes.
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-----------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('columntransformer', ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])), ('gradientboostingregressor', GradientBoostingRegressor(random_state=42))] |
| verbose | False |
| columntransformer | ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)]) |
| gradientboostingregressor | GradientBoostingRegressor(random_state=42) |
| columntransformer__n_jobs | |
| columntransformer__remainder | passthrough |
| columntransformer__sparse_threshold | 0.3 |
| columntransformer__transformer_weights | |
| columntransformer__transformers | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False), <sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)] |
| columntransformer__verbose | False |
| columntransformer__verbose_feature_names_out | True |
| columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) |
| columntransformer__onehotencoder__categories | auto |
| columntransformer__onehotencoder__drop | |
| columntransformer__onehotencoder__dtype | <class 'numpy.float64'> |
| columntransformer__onehotencoder__handle_unknown | ignore |
| columntransformer__onehotencoder__sparse | False |
| gradientboostingregressor__alpha | 0.9 |
| gradientboostingregressor__ccp_alpha | 0.0 |
| gradientboostingregressor__criterion | friedman_mse |
| gradientboostingregressor__init | |
| gradientboostingregressor__learning_rate | 0.1 |
| gradientboostingregressor__loss | squared_error |
| gradientboostingregressor__max_depth | 3 |
| gradientboostingregressor__max_features | |
| gradientboostingregressor__max_leaf_nodes | |
| gradientboostingregressor__min_impurity_decrease | 0.0 |
| gradientboostingregressor__min_samples_leaf | 1 |
| gradientboostingregressor__min_samples_split | 2 |
| gradientboostingregressor__min_weight_fraction_leaf | 0.0 |
| gradientboostingregressor__n_estimators | 100 |
| gradientboostingregressor__n_iter_no_change | |
| gradientboostingregressor__random_state | 42 |
| gradientboostingregressor__subsample | 1.0 |
| gradientboostingregressor__tol | 0.0001 |
| gradientboostingregressor__validation_fraction | 0.1 |
| gradientboostingregressor__verbose | 0 |
| gradientboostingregressor__warm_start | False |
</details>
### Model Plot
The model plot is below.
<style>#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 {color: black;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 pre{padding: 0;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable {background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator:hover {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-item {z-index: 1;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:only-child::after {width: 0;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-text-repr-fallback {display: none;}</style><div id="sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f6612892-c085-4dd9-8dca-9cb8081c3777" type="checkbox" ><label for="f6612892-c085-4dd9-8dca-9cb8081c3777" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3d74f98b-ae31-452d-af87-2c65b0323ba2" type="checkbox" ><label for="3d74f98b-ae31-452d-af87-2c65b0323ba2" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4af39992-03cf-4522-a288-2db0a787a63c" type="checkbox" ><label for="4af39992-03cf-4522-a288-2db0a787a63c" class="sk-toggleable__label sk-toggleable__label-arrow">onehotencoder</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="519d5e51-5fa6-45d6-a3f7-59c11370402d" type="checkbox" ><label for="519d5e51-5fa6-45d6-a3f7-59c11370402d" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore', sparse=False)</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7ede29a7-2614-4eed-a021-e85f1aaa5659" type="checkbox" ><label for="7ede29a7-2614-4eed-a021-e85f1aaa5659" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>['Length1', 'Length2', 'Length3', 'Height', 'Width']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="69357535-0314-4987-a311-112335d2cb52" type="checkbox" ><label for="69357535-0314-4987-a311-112335d2cb52" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" type="checkbox" ><label for="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div></div></div>
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from skops.hub_utils import download
from skops.io import load
download("brendenc/Fish-Weight", "path_to_folder")
# make sure model file is in skops format
# if model is a pickle file, make sure it's from a source you trust
model = load("path_to_folder/example.pkl")
```
</details>
# Model Card Authors
This model card is written by following authors:
Brenden Connors
| null | sklearn | tabular-regression | null | null | null | null | null | null | null | null | null | scikit-learn/Fish-Weight | [
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CCRss/test | CCRss | 2023-11-29T10:03:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:03:01Z | 2022-10-15T18:05:08.000Z | null | null | What | null | null | null | null | null | null | null | null | null | null | null | null | CCRss/test | [
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GIMG/AIChan_Model | GIMG | 2023-11-29T14:55:33Z | 0 | 104 | null | [
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T14:55:33Z | 2022-12-12T03:09:03.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
---
Free Model Share | null | null | null | null | null | null | null | null | null | null | null | null | GIMG/AIChan_Model | [
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FireInside/OF | FireInside | 2023-11-29T16:26:44Z | 0 | 0 | null | [
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kitou/Any | kitou | 2023-11-29T16:27:36Z | 0 | 4 | null | [
"license:unknown",
"region:us"
] | 2023-11-29T16:27:36Z | 2022-12-28T10:04:15.000Z | null | null | ---
license: unknown
---
| null | null | null | null | null | null | null | null | null | null | null | null | kitou/Any | [
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Tutorograd/NEURO_PIXEL_PROMPTS | Tutorograd | 2023-11-29T08:36:17Z | 0 | 5 | null | [
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] | 2023-11-29T08:36:17Z | 2023-01-01T08:14:49.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Tutorograd/NEURO_PIXEL_PROMPTS | [
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richeonl0/webui | richeonl0 | 2023-11-29T14:07:36Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T14:07:36Z | 2023-01-02T12:03:26.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | richeonl0/webui | [
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delsin/test-model | delsin | 2023-11-29T08:06:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:06:46Z | 2023-01-17T08:12:21.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | delsin/test-model | [
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vicfeuga/PixelCopter | vicfeuga | 2023-11-29T17:20:28Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-29T17:20:28Z | 2023-01-29T22:49:30.000Z | null | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.90 +/- 18.69
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | vicfeuga/PixelCopter | [
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bbbh/a | bbbh | 2023-11-29T17:03:19Z | 0 | 0 | null | [
"license:other",
"region:us"
] | 2023-11-29T17:03:19Z | 2023-02-08T14:52:45.000Z | null | null | ---
license: other
---
| null | null | null | null | null | null | null | null | null | null | null | null | bbbh/a | [
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nyaa314/lora | nyaa314 | 2023-11-29T17:37:07Z | 0 | 9 | null | [
"region:us"
] | 2023-11-29T17:37:07Z | 2023-02-20T07:54:33.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | nyaa314/lora | [
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zhongkaifu/story_writing | zhongkaifu | 2023-11-30T00:11:15Z | 0 | 2 | null | [
"license:bsd-3-clause",
"region:us"
] | 2023-11-30T00:11:15Z | 2023-02-23T19:07:12.000Z | null | null | ---
license: bsd-3-clause
---
| null | null | null | null | null | null | null | null | null | null | null | null | zhongkaifu/story_writing | [
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kollof/test-model | kollof | 2023-11-29T15:42:26Z | 0 | 0 | null | [
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:42:26Z | 2023-02-25T17:49:15.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | kollof/test-model | [
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Norton0924/Joyfusion | Norton0924 | 2023-11-29T15:41:05Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T15:41:05Z | 2023-02-27T13:00:35.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Norton0924/Joyfusion | [
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TeALAiN/GmgkneSN_9 | TeALAiN | 2023-11-29T19:35:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T19:35:02Z | 2023-03-02T09:37:40.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | TeALAiN/GmgkneSN_9 | [
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Seltion/Pretty2D | Seltion | 2023-11-30T00:25:38Z | 0 | 11 | null | [
"license:other",
"region:us"
] | 2023-11-30T00:25:38Z | 2023-03-04T23:20:16.000Z | null | null | ---
license: other
---
| null | null | null | null | null | null | null | null | null | null | null | null | Seltion/Pretty2D | [
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X3gold/r34protogen_1 | X3gold | 2023-11-29T08:50:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:50:55Z | 2023-03-23T22:42:58.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | X3gold/r34protogen_1 | [
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CluelessC/hll-test | CluelessC | 2023-11-29T20:21:39Z | 0 | 19 | null | [
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T20:21:39Z | 2023-03-28T09:02:13.000Z | null | null | ---
license: creativeml-openrail-m
pipeline_tag: text-to-image
--- | null | null | text-to-image | null | null | null | null | null | null | null | null | null | CluelessC/hll-test | [
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zap-thamm/QLearning-FrozenLake-v1 | zap-thamm | 2023-11-29T17:22:03Z | 0 | 0 | null | [
"FrozenLake-v1",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T17:22:03Z | 2023-04-14T19:46:45.000Z | null | null | ---
tags:
- FrozenLake-v1
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: QLearning-FrozenLake-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
metrics:
- type: mean_reward
value: 0.72 +/- 0.45
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="zap-thamm/QLearning-FrozenLake-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | zap-thamm/QLearning-FrozenLake-v1 | [
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zap-thamm/QLearning-Taxi-v3 | zap-thamm | 2023-11-29T17:23:03Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T17:23:03Z | 2023-04-14T20:21:36.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: QLearning-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 8.04 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="zap-thamm/QLearning-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | zap-thamm/QLearning-Taxi-v3 | [
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stablediffusionapi/load_lora_embeddings | stablediffusionapi | 2023-11-29T16:12:52Z | 0 | 10 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T16:12:52Z | 2023-04-22T03:41:17.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | stablediffusionapi/load_lora_embeddings | [
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svalovavalentin/disimon001 | svalovavalentin | 2023-11-29T23:30:29Z | 0 | 0 | null | [
"onnx",
"license:openrail",
"region:us"
] | 2023-11-29T23:30:29Z | 2023-05-07T18:21:49.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | svalovavalentin/disimon001 | [
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stanford-nlpxed/transcript-analysis | stanford-nlpxed | 2023-11-29T18:49:30Z | 0 | 0 | null | [
"license:gpl",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:49:30Z | 2023-05-13T04:05:30.000Z | null | null | ---
license: gpl
---
| null | null | null | null | null | null | null | null | null | null | null | null | stanford-nlpxed/transcript-analysis | [
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baqu2213/PoemForSmallFThings | baqu2213 | 2023-11-29T17:04:28Z | 0 | 44 | null | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T17:04:28Z | 2023-05-27T03:34:45.000Z | null | null | ---
license: creativeml-openrail-m
---
### Chibi Pixie
### Chibi Pixie v2
### Fizzlepop
Not recommend horizontal-wide resolution (640 x 512 ~)
Recommend hires 2.5x
### Antifreeze soda water
### Antifreeze soda water v2
### Spicy cotton candy
### Communitymodel c001
Not stable
If you want using this model, **use lora : <lora:antifreez3Artist_v20:0.25>** (You can get it from civitai)
### Communitymodel c002
Not stable
boiled shirimp : loha adjusted model (add specific style)
### Communitymodel c003 (testmodel)
Not stable
### Communitymodel c003 (testmodel_2)
Not stable
### Communitymodel a001
* Add prompt -> **3d:0.2**
### Communitymodel a002
### Communitymodel n001
### Communitymodel g001
### Communitymodel g002
* Focus on background quality, sacrifice a little of character quality from g001
### Communitymodel m001
* Add prompt -> **semi-realistic**
### Communitymodel m002
LQ version of m001
### Communitymodel m004
### FlipBomb
| null | null | null | null | null | null | null | null | null | null | null | null | baqu2213/PoemForSmallFThings | [
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tktkdrrrrrrrrrrr/CivitAI_model_info | tktkdrrrrrrrrrrr | 2023-11-30T01:08:23Z | 0 | 8 | null | [
"has_space",
"region:us"
] | 2023-11-30T01:08:23Z | 2023-05-27T13:01:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | tktkdrrrrrrrrrrr/CivitAI_model_info | [
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amandaic1805/MoondropCollection | amandaic1805 | 2023-11-29T21:05:06Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T21:05:06Z | 2023-06-05T12:38:24.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | amandaic1805/MoondropCollection | [
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Purupuru188/ai_voice_models_no_permission | Purupuru188 | 2023-11-29T15:02:52Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T15:02:52Z | 2023-06-13T14:30:35.000Z | null | null | ---
license: openrail
--- | null | null | null | null | null | null | null | null | null | null | null | null | Purupuru188/ai_voice_models_no_permission | [
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taini/SdTraining | taini | 2023-11-29T03:47:57Z | 0 | 0 | null | [
"license:gpl",
"region:us"
] | 2023-11-29T03:47:57Z | 2023-06-15T01:39:51.000Z | null | null | ---
license: gpl
---
| null | null | null | null | null | null | null | null | null | null | null | null | taini/SdTraining | [
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takuya23/SD-model-fp16 | takuya23 | 2023-11-29T09:25:08Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-29T09:25:08Z | 2023-06-17T07:23:07.000Z | null | null | ---
license: unknown
---
Converted with model-toolkit<br>
DarkFruitMix-fp16.safetensors<br>
https://civitai.com/models/1845/darkfruit-mix | null | null | null | null | null | null | null | null | null | null | null | null | takuya23/SD-model-fp16 | [
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zaveid/z2 | zaveid | 2023-11-29T07:05:24Z | 0 | 0 | null | [
"license:other",
"region:us"
] | 2023-11-29T07:05:24Z | 2023-06-18T13:34:09.000Z | null | null | ---
license: other
---
| null | null | null | null | null | null | null | null | null | null | null | null | zaveid/z2 | [
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KJAV/RVC | KJAV | 2023-11-29T14:18:17Z | 0 | 7 | null | [
"ja",
"en",
"ko",
"license:openrail",
"region:us"
] | 2023-11-29T14:18:17Z | 2023-06-23T18:19:54.000Z | null | null | ---
license: openrail
language:
- ja
- en
- ko
---
# <a name="Discord"></a>[North East Asian RVC Hub](https://discord.gg/6vJUDgxu2G)
imase.zip - imase (800 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
Whoppa.zip - Burger King Ad Guy (700 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
Ayase.zip - Ayase (From YOASOBI) (600 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
LilasIkuta.zip - 幾田りら (From YOASOBI) (2k Epochs 143.8k Steps) [RVC v2] [40k] [crepe] [Batch Size 6]
WelvenDaGreat.zip - Welven da Great (Deez Nuts Guy) (300 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
AiHoshinoTTS.zip - 星野アイ (From 【推しの子】) (TTS/Realtime) (250 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
BoeingB738_GPWS_Callouts.zip - Boeing B737-800 Various Epochs (GPWS Callouts 450 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
BoeingB738_GPWS_Warnings.zip - Boeing B737-800 Various Epochs (GPWS Warnings 300 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
BoeingB738_TCAS.zip - Boeing B737-800 Various Epochs (TCAS 450 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
when_you_stub_your_toe.zip - when you stub your toe (470 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
AiHoshino.zip - 星野アイ (From 【推しの子】) (2k Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
Pieyon.zip - ぴえヨン (From 【推しの子】) (2k Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
AkaneKurokawa.zip - 黒川あかね (From 【推しの子】) (500 Epochs) [RVC v2] [40k] [crepe] [Batch Size 6]
Yuu.zip - ゆう。 (2.1k Epochs 103k Steps) [RVC v2] [48k] [rmvpe] [Batch Size 6]
MiyakoShikimori.zip - 式守都 (From 可愛いだけじゃない式守さん) (1.5k Epochs) [RVC v2] [48k] [rmvpe] [Batch Size 6]
YuukiIzumi.zip - 和泉幽希 (From 可愛いだけじゃない式守さん) (1.5k Epochs) [RVC v2] [48k] [rmvpe] [Batch Size 6]
YoonSukYeol.zip - 윤석열 (600 Epochs 139.6k Steps) [RVC v2] [48k] [rmvpe] [Batch Size 6]
OliverShanGrant.zip - Oliver Shan Grant (올리버쌤) (1,000 Epochs) [RVC v2] [48k] [rmvpe] [Batch Size 8]
YudaiTakenaka.zip - 竹中雄大 (From Novelbright) (2,830 Epochs 101.8k Steps) [RVC v2] [48k] [rmvpe] [Batch Size 4]
---
PLANNED (2023)
---
GenHoshino.zip - 星野源 (???? Epochs) [RVC v2] [48k] [rmvpe] [Batch Size 8]
MikuNakano_Singing.zip - 中野三玖 (From 五等分の花嫁) (???? Epochs) [RVC v2] [48k] [rmvpe]
MikuNakano_Talking.zip - 中野三玖 (From 五等分の花嫁) (???? Epochs) [RVC v2] [48k] [rmvpe]
ItsukiNakano.zip - 中野五月 (From 五等分の花嫁) (???? Epochs) [RVC v2] [48k] [rmvpe]
YotsubaNakano.zip - 中野四葉 (From 五等分の花嫁) (???? Epochs) [RVC v2] [48k] [rmvpe]
NinoNakano.zip - 中野二乃 (From 五等分の花嫁) (???? Epochs) [RVC v2] [48k] [rmvpe]
IchikaNakano.zip - 中野一花 (From 五等分の花嫁) (???? Epochs) [RVC v2] [48k] [rmvpe]
FuutarouUesugi.zip - 上杉風太郎 (From 五等分の花嫁) (???? Epochs) [RVC v2] [48k] [rmvpe]
SatoshiFujiwara.zip - 藤原聡 (From Official髭男dism) (???? Epochs) [RVC v2] [48k] [rmvpe]
DaiHirai.zip - 平井大 (???? Epochs) [RVC v2] [48k] [rmvpe]
---
PLANNED (2024)
---
suis.zip - スイ (From ヨルシカ) (???? Epochs) Epochs [RVC v2] [48k] [rmvpe]
Rei.zip - 礼衣 (From ツユ) (???? Epochs) / Aizawa.zip - 相沢 (???? Epochs) [RVC v2] [48k] [rmvpe]
ACAne.zip - ACAね (From ずっと真夜中でいいのに。) (???? Epochs) [RVC v2] [48k] [rmvpe]
MotokiOhmori.zip - 大森元貴 (From Mrs. GREEN APPLE) (???? Epochs) [RVC v2] [48k] [rmvpe]
HitoriGotoh.zip - 後藤ひとり (From ぼっち・ざ・ろっく！) (???? Epochs) [RVC v2] [48k] [rmvpe]
NijikaIjichi.zip - 伊地知虹夏 (From ぼっち・ざ・ろっく！) (???? Epochs) [RVC v2] [48k] [rmvpe]
IkuyoKita.zip - 喜多郁代 (From ぼっち・ざ・ろっく！) (???? Epochs) [RVC v2] [48k] [rmvpe]
RyoYamada.zip - 山田リョウ (From ぼっち・ざ・ろっく！) (???? Epochs) [RVC v2] [48k] [rmvpe]
LilasIkuta_ALL.zip - 幾田りら (From YOASOBI) Various Epochs (All ???? Epochs) [RVC v2] [48k] [rmvpe]
LilasIkuta_YOASOBI_JP.zip - 幾田りら (From YOASOBI) Various Epochs (Japanese ???? Epochs) [RVC v2] [48k] [rmvpe]
LilasIkuta_YOASOBI_EN.zip - 幾田りら (From YOASOBI) Various Epochs (English ???? Epochs) [RVC v2] [48k] [rmvpe]
LilasIkuta_LilasIkuta.zip - 幾田りら (From YOASOBI) Various Epochs (幾田りら ???? Epochs) [RVC v2] [48k] [rmvpe]
LilasIkuta_THE-FIRST-TAKE.zip - 幾田りら (From YOASOBI) Various Epochs (THE FIRST TAKE ???? Epochs) [RVC v2] [48k] [rmvpe]
LilasIkuta_TTS.zip - 幾田りら (From YOASOBI) Various Epochs (TTS ???? Epochs) [RVC v2] [48k] [rmvpe]
SakutaAzusagawa.zip - 梓川咲太 (From 『青春ブタ野郎』シリーズ) (???? Epochs) [RVC v2] [48k] [rmvpe]
MaiSakurajima.zip - 桜島麻衣 (From 『青春ブタ野郎』シリーズ) (???? Epochs) [RVC v2] [48k] [rmvpe]
KazuyaKinoshita.zip - 木ノ下和也 (From 彼女、お借りします) (???? Epochs) [RVC v2] [48k] [rmvpe]
ChizuruMizuhara.zip - 水原千鶴 (From 彼女、お借りします) (???? Epochs) [RVC v2] [48k] [rmvpe]
RukaSarashina.zip - 更科瑠夏 (From 彼女、お借りします) (???? Epochs) [RVC v2] [48k] [rmvpe] | null | null | null | null | null | null | null | null | null | null | null | null | KJAV/RVC | [
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comehu/COMEHU_RVCmodels | comehu | 2023-11-29T22:28:20Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:28:20Z | 2023-06-23T18:25:45.000Z | null | null | My RVC models, only for fun/research purpose. Making money off of them is prohibited (except the talkbox ones, music producers are free to use those models without giving credits)
RVC v1 MODELS (All are 40kHz):
Aaliyah
Amy Lee (from Evanescence)
Mac Miller
Nicki Minaj (sing / rap)
Romeo Santos
Skrillex
Talkbox
Tinashe
Usher
Whitney Houston
RVC v2 MODELS (All are 48kHz):
Israel Kamakawiwo'ole
AaliyahWIP
Aaliyah's Aaliyah [BEST OPTION]
Whitney Houston (My Love is Your Love ERA) *named MLIYLWhitneyCOMEHU.zip* [BEST OPTION]
TalkboxDRY (Realistic voice talkbox effect) [BEST OPTION]
| null | null | null | null | null | null | null | null | null | null | null | null | comehu/COMEHU_RVCmodels | [
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jajca/rvc_v2 | jajca | 2023-11-29T17:34:04Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T17:34:04Z | 2023-06-23T18:41:20.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jajca/rvc_v2 | [
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EvanTheToonGuy40/RVC_Models | EvanTheToonGuy40 | 2023-11-29T02:08:07Z | 0 | 5 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T02:08:07Z | 2023-06-23T19:23:47.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | EvanTheToonGuy40/RVC_Models | [
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sxndypz/rvc-v2-models | sxndypz | 2023-11-29T12:46:44Z | 0 | 7 | null | [
"RVC v2",
"audio-to-audio",
"ja",
"license:openrail",
"region:us"
] | 2023-11-29T12:46:44Z | 2023-06-25T10:20:16.000Z | null | null | ---
license: openrail
language:
- ja
pipeline_tag: audio-to-audio
tags:
- RVC v2
--- | null | null | audio-to-audio | null | null | null | null | null | null | null | null | null | sxndypz/rvc-v2-models | [
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zukoochan/ZukooRVC | zukoochan | 2023-11-29T16:44:05Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T16:44:05Z | 2023-06-26T16:27:13.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | zukoochan/ZukooRVC | [
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DelinaresMassates/Artoids | DelinaresMassates | 2023-11-29T05:03:46Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T05:03:46Z | 2023-06-26T20:22:22.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | DelinaresMassates/Artoids | [
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toasterroasterp1/ABDL_Pack | toasterroasterp1 | 2023-11-29T03:51:33Z | 0 | 10 | null | [
"region:us"
] | 2023-11-29T03:51:33Z | 2023-07-08T16:26:53.000Z | null | null | # Model Description
This is a pack of ABDL related NSFW Loras for anime art that currently includes 4 versions of a diaper mess model, a model for characters in cribs, a model that creates ABDL spaces with things like cribs,
changing tables, baby toys, bottles, stuffies, and onesies, as well as not so refined Lora for hanging bouncers. V4 has not been tested much yet but seems to be the best
model for visuals/colors by far.
Update: New onesie lora, works well with abdl and mess loras, provided mess is set around 0.5
I did not make diapermix, but felt it was a really good Lora and it was unfortunate that it got removed. I wanted to make sure you could still get it with
the rest of these. It creates really nice ABDL diapers and works well with other ABDL related Loras.
I may or may not add to this pack in the future.
For examples and results, check my civitai posts here: https://civitai.com/user/toasterroasterp1220
(most of these are outdated, will try to update these in the near future)
- **Developed by:** Toaster Roaster
- **Model type:** Lora Pack
- **Language:** English
- **Finetuned from model:** https://civitai.com/models/4714?modelVersionId=9623 | 4th version made with https://civitai.com/models/88065
### Out-of-Scope Use/Limitations
<!-- This section addresses uses that the model will not work well for. !-->
- With multiple Loras put together, images generated generally have terrible coloring (except in the case of V4 from the little testing done). Different Loras have
different results,and I recommend mixing different Lorasfor better results.
- Generally struggles with anatomy, just keep generating for better anatomy. My experience is roughly 50/50.
- The ABDL lora is not at all perfect. Supplementary Loras with ABDL at a higher strength generates great results in my experience (cribs work great).
- These Loras tend to place random diapers around as objects on the floor and rarely even as characters. Regenerating images or inpainting can work around them nicely.
- The bouncer lora is very overset with Trap Diaper's art and generally creates purple backgrounds and struggles with placing the hanging straps in the correct places
or number.
- Does not create hyper messes, but does not create realistic ones either.
- Does not always understand how to make diaper messes underneath clothing, but can work. Upsirt shots work well, however.
- InCrib should use 0.5-0.7 strength for best results on it's own, but works much better paired with ABDL Lora.
- onesie lora works well with mess loras, provided mess is set around 0.5 and not higher than 0.7
New! - Rattle and Bottle loras
- Not much testing done. Trained and works best on Omarashi Omutsu Yuzu v2.0
# Closing
Remember, generating more images helps (or just img2img) and inpainting makes perfect!
Thank you for downloading!
Toaster Roaster P1 | null | null | null | null | null | null | null | null | null | null | null | null | toasterroasterp1/ABDL_Pack | [
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badmonk/om | badmonk | 2023-11-29T14:28:31Z | 0 | 3 | null | [
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T14:28:31Z | 2023-07-08T17:11:17.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# Model Card for OM
## Model Description
- **Collection by:** BADMONK
- **Language(s) (NLP):** EN
- **License:** Creativeml-Openrail-M
### 🕉️ ###
| null | null | text-to-image | null | null | null | null | null | null | null | null | null | badmonk/om | [
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One-2-3-45/code | One-2-3-45 | 2023-11-29T21:32:25Z | 0 | 4 | null | [
"text-to-3d",
"image-to-3d",
"arxiv:2306.16928",
"license:mit",
"has_space",
"region:us"
] | 2023-11-29T21:32:25Z | 2023-07-09T00:08:09.000Z | null | null | ---
license: mit
tags:
- text-to-3d
- image-to-3d
---
# One-2-3-45's Inference Model
<div>
<a style="display:inline-block" href="http://one-2-3-45.com"><img src="https://img.shields.io/badge/Project_Homepage-f9f7f7?logo=data:image/webp;base64,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<a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2306.16928"><img src="https://img.shields.io/badge/2306.16928-f9f7f7?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADcAAABMCAYAAADJPi9EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAuIwAALiMBeKU/dgAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAAAa2SURBVHja3Zt7bBRFGMAXUCDGF4rY7m7bAwuhlggKStFgLBgFEkCIIRJEEoOBYHwRFYKilUgEReVNJEGCJJpehHI3M9vZvd3bUP1DjNhEIRQQsQgSHiJgQZ5dv7krWEvvdmZ7d7vHJN+ft/f99pv5XvOtJMFCqvoCUpTdIEeRLC+L9Ox5i3Q9LACaCeK0kXoSChVcD3C/tQPHpAEsquQ73IkUcEz2kcLCknyGW5MGjkljRFVL8xJOKyi4CwCOuQAeAkfTP1+tNxLkogvgEbDgffkJqKqvuMA5ifOpqg/5qWecRstNg7xoUTI1Fovdxg8oy2s5AP8CGeYHmGngeZaOL4I4LXLcpHg4149/GDz4xqgsb+UAbMKKUpkrqHA43MUyyJpWUK0EHeG2YKRXr7tB+QMcgGewLD+ebTDbtrtbBt7UPlhS4rV4IvcDI7J8P1OeA/AcAI7LHljN7aB8XTowJmZt9EFRD/o0SDMH4HlwMhMyDWZZSAHFf3YDs3RS49WDLuaAY3IJq+qzmQKLxXAZKN7oDoYbdV3v5elPqiSpMyiOuAEVZVqHXb1OhloUH+MA+ztO0cAO/RkrfyBE7OAEbAZvO8vzVtTRWFD6DAfY5biBM3PWiaL0a4lvXICwnV8WjmE6ntYmhqX2jjp5LbMZjCw/wbYeN6CizOa2GMVzQOlmHjB4Ceuyk6LJ8huccEmR5Xddg7OOV/NAtchW+E3XbOag60QA4Qwuarca0bRuEJyr+cFQwzcY98huxhAKdQelt4kAQpj4qJ3gvFXAYn+aJumXk1yPlpQUgtIHhbYoFMUstNRRWgjnpl4A7IKlayNymqFHFaWCpV9CFry3LGxR1CgA5kB5M8OX2goApwpaz6mdOMGxtAgXWJySxb4WuQD4qTDgU+N5AAnzpr7ChSWpCyisiQJqY0Y7FtmSKpbV23b45kC0KHBxcQ9QeI8w4KgnHRPVtIU7rOtbioLVg5Hl/qDwSVFAMqLSMSObroCdZYlzIJtMRFVHCaRo/wFWPgaAXzdbBpkc2A4aKzCNd97+URQuESYGDDhIVfWOQIKZJu4D2+oXlgDTV1865gUQZDts756BArMNMoR1oa46BYqbyPixZz1ZUFV3sgwoGBajuBKATl3btIn8QYYMuezRgrsiRUWyr2BxA40EkPMpA/Hm6gbUu7fjEXA3azP6AsbKD9bxdUuhjM9W7fII52BF+daRpE4+WA3P501+jbfmHvQKyFqMuXf7Ot4mkN2fr50y+bRH61X7AXdUpHSxaPQ4GVbR5AGw3g+434XgQGKfr72I+vQRhfsu92dOx7WicInzt3CBg1RVpMm0NveWo2SqFzgmdNZMbriILD+S+zoueWf2vSdAipzacWN5nMl6XxNlUHa/J8DoJodUDE0HR8Ll5V0lPxcrLEHZPV4AzS83OLis7FowVa3RSku7BSNxJqQAlN3hBTC2apmDSkpaw22wJemGQFUG7J4MlP3JC6A+f96V7vRyX9It3nzT/GrjIU8edM7rMSnIi10f476lzbE1K7yEiEuWro0OJBguLCwDuFOJc1Na6sRWL/cCeMIwUN9ggSVbe3v/5/EgzTKWLvEAiBrYRUkgwNI2ZaFQNT75UDxEUEx97zYnzpmiLEmbaYCbNxYtFAb0/Z4AztgUrhyxuNgxPnhfHFDHz/vTgFWUQZxTRkkJhQ6YNdVUEPAfO6ZV5BRss6LcCVb7VaAma9giy0XJZBt9IQh42NY0NSdgbLIPlLUF6rEdrdt0CUCK1wsCbkcI3ZSLc7ZSwGLbmJXbPsNxnE5xilYKAobZ77LpGZ8TAIun+/iCKQoF71IxQDI3K2CCd+ARNvXg9sykBcnHAoCZG4u66hlDoQLe6QV4CRtFSxZQ+D0BwNO2jgdkzoGoah1nj3FVlSR19taTSYxI8QLut23U8dsgzqHulJNCQpcqBnpTALCuQ6NSYLHpmR5i42gZzuIdcrMMvMJbQlxe3jXxyZnLACl7ARm/FjPIDOY8ODtpM71sxwfcZpvBeUzKWmfNINM5AS+wO0Khh7dMqKccu4+qatarZjYAwDlgetzStHtEt+XedsBOQtU9XMrRgjg4KTnc5nr+dmqadit/4C4uLm8DuA9koJTj1TL7fI5nDL+qqoo/FLGAzL7dYT17PzvAcQONYSUQRxW/QMrHZVIyik0ZuQA2mzp+Ji8BW4YM3Mbzm9inaHkJCGfrUZZjujiYailfFwA8DHIy3acwUj4v9vUVa+SmgNsl5fuyDTKovW9/IAmfLV0Pi2UncA515kjYdrwC9i9rpuHiq3JwtAAAAABJRU5ErkJggg=="></a>
<a style="display:inline-block; margin-left: .5em" href='https://github.com/One-2-3-45/One-2-3-45'><img src='https://img.shields.io/github/stars/One-2-3-45/One-2-3-45?style=social' /></a>
</div>
This inference model supports the demo for [One-2-3-45](http://One-2-3-45.com).
Try out our 🤗 Hugging Face Demo:
<a target="_blank" href="https://huggingface.co/spaces/One-2-3-45/One-2-3-45">
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
</a>
Please refer to our [GitHub repo](https://github.com/One-2-3-45/One-2-3-45) for full code release and local deployment.
## Citation
```bibtex
@misc{liu2023one2345,
title={One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization},
author={Minghua Liu and Chao Xu and Haian Jin and Linghao Chen and Mukund Varma T and Zexiang Xu and Hao Su},
year={2023},
eprint={2306.16928},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` | null | null | null | null | null | null | null | null | null | null | null | null | One-2-3-45/code | [
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] |
alexandra234/kurdish_central_whisper | alexandra234 | 2023-11-30T01:06:40Z | 0 | 0 | null | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-large-v2",
"region:us"
] | 2023-11-30T01:06:40Z | 2023-07-15T09:03:17.000Z | null | null | ---
library_name: peft
base_model: openai/whisper-large-v2
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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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).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
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RVC123/USB | RVC123 | 2023-11-29T18:33:17Z | 0 | 0 | null | [
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WoomyPearl/RVC-Model-Palace | WoomyPearl | 2023-11-30T00:37:27Z | 0 | 4 | null | [
"region:us"
] | 2023-11-30T00:37:27Z | 2023-07-22T23:24:40.000Z | null | null | Hello and welcome to my RVC voice model repository, here you can find models of various characters!
Use them for anything from memes, song covers, to even masking your voice in Discord voice calls!
Don't forget to credit me when using my models!
---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | WoomyPearl/RVC-Model-Palace | [
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Nemani/no-ema-fp16_models_achive | Nemani | 2023-11-29T14:03:05Z | 0 | 1 | null | [
"region:us"
] | 2023-11-29T14:03:05Z | 2023-07-27T09:20:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Nemani/no-ema-fp16_models_achive | [
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Rootreck/so-vits-svc-4.0-ru-The_Witcher_3_Wild_Hunt | Rootreck | 2023-11-29T17:06:16Z | 0 | 2 | null | [
"transformers",
"ru",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:06:16Z | 2023-07-30T06:07:22.000Z | null | null | ---
language:
- ru
library_name: transformers
---
Это модели голосов персонажей из Ведьмак 3: Дикая охота, обученные для so-vits-svc-4.1.1
These are character voice models from The Witcher 3: Wild Hunt, trained for so-vits-svc-4.1.1 | null | transformers | null | null | null | null | null | null | null | null | null | null | Rootreck/so-vits-svc-4.0-ru-The_Witcher_3_Wild_Hunt | [
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KhryausOHowl/KhrysRVC | KhryausOHowl | 2023-11-29T20:35:21Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T20:35:21Z | 2023-07-31T01:20:46.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | KhryausOHowl/KhrysRVC | [
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NoIdea4Username/NoIdeaRVCCollection | NoIdea4Username | 2023-11-29T04:42:29Z | 0 | 2 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T04:42:29Z | 2023-07-31T02:41:22.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | NoIdea4Username/NoIdeaRVCCollection | [
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Zhongyuan/sd-models | Zhongyuan | 2023-11-29T08:16:50Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T08:16:50Z | 2023-07-31T09:35:57.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Zhongyuan/sd-models | [
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BlinkDL/temp | BlinkDL | 2023-11-29T20:07:54Z | 0 | 13 | null | [
"has_space",
"region:us"
] | 2023-11-29T20:07:54Z | 2023-08-05T11:20:33.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | BlinkDL/temp | [
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EarthnDusk/SDXL_Loras | EarthnDusk | 2023-11-29T07:20:08Z | 0 | 0 | null | [
"lora",
"sdxl",
"anime",
"earthndusk",
"autistic art",
"en",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T07:20:08Z | 2023-08-08T01:42:18.000Z | null | null | ---
license: creativeml-openrail-m
language:
- en
tags:
- lora
- sdxl
- anime
- earthndusk
- autistic art
---
Earth & Dusk SDXL Loras, please do not re upload.
Join our Reddit: https://www.reddit.com/r/earthndusk/
WE ARE PROUDLY SPONSORED BY: https://www.piratediffusion.com/
Listen to the music that we've made that goes with our art: https://open.spotify.com/playlist/00R8x00YktB4u541imdSSf?si=b60d209385a74b38
We stream a lot of our testing on twitch: https://www.twitch.tv/duskfallcrew
any chance you can spare a coffee or three? https://ko-fi.com/DUSKFALLcrew
[](https://ko-fi.com/Z8Z8L4EO)
Merge permissions: MUST CREDIT, linkback to Earth & DUSK or to the respective civitAI profile. You may not sell your merge loras or lora bind checkpoints, or merged models you make from our content but you may use them on generative services. | null | null | null | null | null | null | null | null | null | null | null | null | EarthnDusk/SDXL_Loras | [
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Cmanflip/OtherRVCModels | Cmanflip | 2023-11-29T04:18:06Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T04:18:06Z | 2023-08-14T17:36:36.000Z | null | null | ---
license: openrail
---
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feffy/fluffyrock-quality-tags | feffy | 2023-11-30T00:53:26Z | 0 | 1 | null | [
"region:us"
] | 2023-11-30T00:53:26Z | 2023-08-15T05:45:45.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | feffy/fluffyrock-quality-tags | [
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vibeus/sherpa-onnx-int8 | vibeus | 2023-11-29T09:11:15Z | 0 | 0 | null | [
"onnx",
"region:us"
] | 2023-11-29T09:11:15Z | 2023-08-17T07:36:00.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | vibeus/sherpa-onnx-int8 | [
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FlazO0/FlazO0 | FlazO0 | 2023-11-29T16:43:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:43:51Z | 2023-08-18T14:40:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | FlazO0/FlazO0 | [
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peekaboo682/fashion | peekaboo682 | 2023-11-29T06:34:39Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T06:34:39Z | 2023-08-19T17:08:47.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | peekaboo682/fashion | [
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Teamrt/SOFTNET | Teamrt | 2023-11-29T08:50:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T08:50:24Z | 2023-08-21T07:01:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Teamrt/SOFTNET | [
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teradakokoro/irimlla | teradakokoro | 2023-11-29T12:21:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T12:21:21Z | 2023-08-21T11:05:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | teradakokoro/irimlla | [
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netgvarun2005/MultiModalBertHubert | netgvarun2005 | 2023-11-29T13:20:54Z | 0 | 0 | null | [
"has_space",
"region:us"
] | 2023-11-29T13:20:54Z | 2023-08-23T18:47:21.000Z | null | null | ---
# For reference on model card metadata, see the spec: https://github.com/netgvarun2012/VirtualTherapist
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/netgvarun2012/VirtualTherapist).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
A MultiModal architecture model that was created and finetuned jointly by concatenating Hubert and BERT embeddings.
Hubert model was fine-tuned with a classification head on preprocessed audio and emotion labels in supervised manner.
BERT was trained on text transcrition embeddings.
Model can accurately recognize emotions classes- Angry,Sad,Fearful,Happy,Disgusted,Surprised,Calm with ~80% accuracy.
- **Developed by:** [https://www.linkedin.com/in/sharmavaruncs/]
- **Model type:** [MultiModal - Text and Audio based]
- **Language(s) (NLP):** [NLP, Speech processing]
- **Finetuned from model [optional]:** [https://huggingface.co/docs/transformers/model_doc/hubert]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/netgvarun2012/VirtualTherapist/]
- **Paper [optional]:** [https://github.com/netgvarun2012/VirtualTherapist/blob/main/documentation/Speech_and_Text_based_MultiModal_Emotion_Recognizer.pdf]
- **Demo [optional]:** [https://huggingface.co/spaces/netgvarun2005/VirtualTherapist]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
'Virtual Therapist' app - an Intelligent speech and text input based assistant that can decipher emotions and generate therapeutic messages based on the Emotional state of the user.
Emotions recognized - Angry,Sad,Fearful,Happy,Disgusted,Surprised,Calm with ~80% accuracy.
Use the code below to get started with the model:
class MultimodalModel(nn.Module):
'''
Custom PyTorch model that takes as input both the audio features and the text embeddings, and concatenates the last hidden states from the Hubert and BERT models.
'''
def __init__(self, bert_model_name, num_labels):
super().__init__()
self.hubert = HubertForSequenceClassification.from_pretrained("netgvarun2005/HubertStandaloneEmoDetector", num_labels=num_labels).hubert
self.bert = AutoModel.from_pretrained(bert_model_name)
self.classifier = nn.Linear(self.hubert.config.hidden_size + self.bert.config.hidden_size, num_labels)
def forward(self, input_values, text):
hubert_output = self.hubert(input_values).last_hidden_state
bert_output = self.bert(text).last_hidden_state
# Apply mean pooling along the sequence dimension
hubert_output = hubert_output.mean(dim=1)
bert_output = bert_output.mean(dim=1)
concat_output = torch.cat((hubert_output, bert_output), dim=-1)
logits = self.classifier(concat_output)
return logits
def load_model():
"""
Load and configure various models and tokenizers for a multi-modal application.
This function loads a multi-modal model and its weights from a specified source,
initializes tokenizers for the model and an additional language model, and returns
these components for use in a multi-modal application.
Returns:
tuple: A tuple containing the following components:
- multiModel (MultimodalModel): The multi-modal model.
- tokenizer (AutoTokenizer): Tokenizer for the multi-modal model.
- model_gpt (AutoModelForCausalLM): Language model for text generation.
- tokenizer_gpt (AutoTokenizer): Tokenizer for the language model.
"""
# Load the model
multiModel = MultimodalModel(bert_model_name, num_labels)
# Load the model weights and tokenizer directly from Hugging Face Spaces
multiModel.load_state_dict(torch.hub.load_state_dict_from_url(model_weights_path, map_location=device), strict=False)
tokenizer = AutoTokenizer.from_pretrained("netgvarun2005/MultiModalBertHubertTokenizer")
# GenAI
tokenizer_gpt = AutoTokenizer.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedTokenizer", pad_token='<|pad|>',bos_token='<|startoftext|>',eos_token='<|endoftext|>')
model_gpt = AutoModelForCausalLM.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedModel")
return multiModel,tokenizer,model_gpt,tokenizer_gpt
## Model Card Authors [Varun Sharma]
| null | null | null | null | null | null | null | null | null | null | null | null | netgvarun2005/MultiModalBertHubert | [
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Cool629/GamePack | Cool629 | 2023-11-29T05:19:48Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T05:19:48Z | 2023-08-26T22:44:42.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Cool629/GamePack | [
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nRuaif/HH2-temp | nRuaif | 2023-11-29T11:15:11Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T11:15:11Z | 2023-09-01T16:50:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | nRuaif/HH2-temp | [
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qod/s | qod | 2023-11-29T19:11:48Z | 0 | 0 | null | [
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qod/loras | qod | 2023-11-29T18:55:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T18:55:51Z | 2023-09-05T18:42:50.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | qod/loras | [
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navmesh/SDCommands | navmesh | 2023-11-29T02:50:26Z | 0 | 0 | null | [
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MrNeon/lora_backup | MrNeon | 2023-11-29T23:48:13Z | 0 | 1 | null | [
"region:us"
] | 2023-11-29T23:48:13Z | 2023-09-12T13:07:58.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | MrNeon/lora_backup | [
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houck2040/utility_pole | houck2040 | 2023-11-29T16:17:51Z | 0 | 0 | null | [
"transformers",
"pytorch",
"detr",
"object-detection",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:17:51Z | 2023-09-13T02:15:14.000Z | null | null | ---
license: mit
---
Fine tuned facebook/detr-resnet-50 to identify Telepone Poles using google street view api.
| null | transformers | object-detection | null | null | null | null | null | null | null | null | null | houck2040/utility_pole | [
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