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maoweiwei/whisper-hakka-medium-1
|
maoweiwei
| 2023-12-15T13:58:13Z | 0 | 0 | null |
[
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] |
Vital65/Pirat
|
Vital65
| 2023-12-15T14:00:12Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T13:58:20Z |
---
license: openrail
---
|
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vigneshgs7/segformer-b0-finetuned-segments-docboundary-dec-15_kaggletrial1
|
vigneshgs7
| 2023-12-15T14:02:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:02:54Z |
Entry not found
|
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InferencetrainingAI/SER
|
InferencetrainingAI
| 2023-12-16T02:24:45Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T14:03:23Z |
---
license: mit
---
|
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] |
owanr/ghc-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T06:33:44Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T14:05:24Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ghc-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0335
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.8994 | 1.0 | 11020 | 2.9253 |
| 2.915 | 2.0 | 22040 | 3.0335 |
| 2.8825 | 3.0 | 33060 | 3.0335 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_10
|
Jeevesh8
| 2023-12-15T16:50:37Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T14:05:29Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_11
|
Jeevesh8
| 2023-12-15T16:49:05Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T14:05:29Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_sgd_001_fold2
|
hkivancoral
| 2023-12-15T15:25:30Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T14:06:48Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8851913477537438
---
<!-- 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. -->
# smids_5x_beit_base_sgd_001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2987
- Accuracy: 0.8852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6816 | 1.0 | 375 | 0.6833 | 0.7338 |
| 0.5586 | 2.0 | 750 | 0.5192 | 0.7987 |
| 0.4343 | 3.0 | 1125 | 0.4591 | 0.8037 |
| 0.3314 | 4.0 | 1500 | 0.4185 | 0.8270 |
| 0.3478 | 5.0 | 1875 | 0.4048 | 0.8369 |
| 0.3776 | 6.0 | 2250 | 0.3805 | 0.8336 |
| 0.3391 | 7.0 | 2625 | 0.3595 | 0.8403 |
| 0.3304 | 8.0 | 3000 | 0.3500 | 0.8469 |
| 0.2847 | 9.0 | 3375 | 0.3354 | 0.8602 |
| 0.2987 | 10.0 | 3750 | 0.3303 | 0.8519 |
| 0.2929 | 11.0 | 4125 | 0.3216 | 0.8669 |
| 0.3369 | 12.0 | 4500 | 0.3243 | 0.8569 |
| 0.2344 | 13.0 | 4875 | 0.3140 | 0.8619 |
| 0.2532 | 14.0 | 5250 | 0.3121 | 0.8669 |
| 0.2563 | 15.0 | 5625 | 0.3059 | 0.8769 |
| 0.2186 | 16.0 | 6000 | 0.3073 | 0.8652 |
| 0.3036 | 17.0 | 6375 | 0.3090 | 0.8669 |
| 0.2499 | 18.0 | 6750 | 0.3061 | 0.8719 |
| 0.2571 | 19.0 | 7125 | 0.2980 | 0.8752 |
| 0.287 | 20.0 | 7500 | 0.3028 | 0.8719 |
| 0.2417 | 21.0 | 7875 | 0.2951 | 0.8819 |
| 0.2578 | 22.0 | 8250 | 0.2968 | 0.8769 |
| 0.2485 | 23.0 | 8625 | 0.3021 | 0.8752 |
| 0.2288 | 24.0 | 9000 | 0.2899 | 0.8852 |
| 0.1978 | 25.0 | 9375 | 0.3001 | 0.8785 |
| 0.2856 | 26.0 | 9750 | 0.2985 | 0.8819 |
| 0.2426 | 27.0 | 10125 | 0.2936 | 0.8852 |
| 0.222 | 28.0 | 10500 | 0.2944 | 0.8852 |
| 0.2193 | 29.0 | 10875 | 0.2940 | 0.8802 |
| 0.2438 | 30.0 | 11250 | 0.2935 | 0.8802 |
| 0.1916 | 31.0 | 11625 | 0.2931 | 0.8735 |
| 0.2423 | 32.0 | 12000 | 0.2890 | 0.8869 |
| 0.207 | 33.0 | 12375 | 0.2979 | 0.8769 |
| 0.1912 | 34.0 | 12750 | 0.3002 | 0.8885 |
| 0.2247 | 35.0 | 13125 | 0.2940 | 0.8819 |
| 0.1554 | 36.0 | 13500 | 0.2936 | 0.8819 |
| 0.1927 | 37.0 | 13875 | 0.2998 | 0.8769 |
| 0.1704 | 38.0 | 14250 | 0.2978 | 0.8835 |
| 0.2119 | 39.0 | 14625 | 0.3017 | 0.8769 |
| 0.1926 | 40.0 | 15000 | 0.2970 | 0.8819 |
| 0.1898 | 41.0 | 15375 | 0.3000 | 0.8785 |
| 0.2087 | 42.0 | 15750 | 0.2990 | 0.8819 |
| 0.1865 | 43.0 | 16125 | 0.2976 | 0.8835 |
| 0.2027 | 44.0 | 16500 | 0.3000 | 0.8819 |
| 0.1873 | 45.0 | 16875 | 0.2993 | 0.8835 |
| 0.2551 | 46.0 | 17250 | 0.2983 | 0.8819 |
| 0.227 | 47.0 | 17625 | 0.2985 | 0.8852 |
| 0.2135 | 48.0 | 18000 | 0.3011 | 0.8785 |
| 0.2131 | 49.0 | 18375 | 0.2986 | 0.8852 |
| 0.2054 | 50.0 | 18750 | 0.2987 | 0.8852 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_beit_base_sgd_0001_fold2
|
hkivancoral
| 2023-12-15T15:25:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T14:07:14Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8036605657237936
---
<!-- 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. -->
# smids_5x_beit_base_sgd_0001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4724
- Accuracy: 0.8037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0748 | 1.0 | 375 | 1.2296 | 0.3677 |
| 1.052 | 2.0 | 750 | 1.1460 | 0.3943 |
| 0.9644 | 3.0 | 1125 | 1.0650 | 0.4326 |
| 0.8415 | 4.0 | 1500 | 0.9877 | 0.4942 |
| 0.786 | 5.0 | 1875 | 0.9192 | 0.5557 |
| 0.7841 | 6.0 | 2250 | 0.8580 | 0.6323 |
| 0.7782 | 7.0 | 2625 | 0.8061 | 0.6689 |
| 0.7211 | 8.0 | 3000 | 0.7620 | 0.6889 |
| 0.6883 | 9.0 | 3375 | 0.7245 | 0.7138 |
| 0.6641 | 10.0 | 3750 | 0.6925 | 0.7371 |
| 0.6683 | 11.0 | 4125 | 0.6665 | 0.7404 |
| 0.7093 | 12.0 | 4500 | 0.6445 | 0.7454 |
| 0.5818 | 13.0 | 4875 | 0.6259 | 0.7604 |
| 0.5841 | 14.0 | 5250 | 0.6091 | 0.7604 |
| 0.5811 | 15.0 | 5625 | 0.5946 | 0.7637 |
| 0.5799 | 16.0 | 6000 | 0.5819 | 0.7704 |
| 0.5841 | 17.0 | 6375 | 0.5719 | 0.7720 |
| 0.5531 | 18.0 | 6750 | 0.5621 | 0.7770 |
| 0.5613 | 19.0 | 7125 | 0.5532 | 0.7820 |
| 0.5733 | 20.0 | 7500 | 0.5445 | 0.7837 |
| 0.538 | 21.0 | 7875 | 0.5380 | 0.7887 |
| 0.5305 | 22.0 | 8250 | 0.5326 | 0.7903 |
| 0.5558 | 23.0 | 8625 | 0.5259 | 0.7887 |
| 0.5149 | 24.0 | 9000 | 0.5202 | 0.7903 |
| 0.5317 | 25.0 | 9375 | 0.5157 | 0.7903 |
| 0.5391 | 26.0 | 9750 | 0.5118 | 0.7937 |
| 0.557 | 27.0 | 10125 | 0.5073 | 0.7920 |
| 0.4911 | 28.0 | 10500 | 0.5033 | 0.7970 |
| 0.4985 | 29.0 | 10875 | 0.5001 | 0.7937 |
| 0.5262 | 30.0 | 11250 | 0.4968 | 0.7937 |
| 0.4712 | 31.0 | 11625 | 0.4944 | 0.7970 |
| 0.5163 | 32.0 | 12000 | 0.4912 | 0.7953 |
| 0.4489 | 33.0 | 12375 | 0.4890 | 0.7987 |
| 0.4565 | 34.0 | 12750 | 0.4870 | 0.8003 |
| 0.484 | 35.0 | 13125 | 0.4849 | 0.7987 |
| 0.4879 | 36.0 | 13500 | 0.4832 | 0.8020 |
| 0.48 | 37.0 | 13875 | 0.4815 | 0.8053 |
| 0.4581 | 38.0 | 14250 | 0.4797 | 0.8053 |
| 0.4627 | 39.0 | 14625 | 0.4783 | 0.8070 |
| 0.475 | 40.0 | 15000 | 0.4772 | 0.8053 |
| 0.4851 | 41.0 | 15375 | 0.4762 | 0.8037 |
| 0.4434 | 42.0 | 15750 | 0.4753 | 0.8037 |
| 0.4381 | 43.0 | 16125 | 0.4746 | 0.8037 |
| 0.506 | 44.0 | 16500 | 0.4740 | 0.8037 |
| 0.4383 | 45.0 | 16875 | 0.4734 | 0.8037 |
| 0.4915 | 46.0 | 17250 | 0.4730 | 0.8037 |
| 0.4925 | 47.0 | 17625 | 0.4727 | 0.8037 |
| 0.4423 | 48.0 | 18000 | 0.4725 | 0.8037 |
| 0.4853 | 49.0 | 18375 | 0.4724 | 0.8037 |
| 0.5129 | 50.0 | 18750 | 0.4724 | 0.8037 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Mairati/eliri
|
Mairati
| 2023-12-15T14:19:32Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-12-15T14:07:49Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- 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/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
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<!-- 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).
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## Model Card Contact
[More Information Needed]
|
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] |
owanr/Sentiment-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T06:51:57Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T14:12:11Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Sentiment-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9525
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.066 | 1.0 | 5628 | 3.9396 |
| 3.9721 | 2.0 | 11256 | 3.9525 |
| 3.7446 | 3.0 | 16884 | 3.9525 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
marcchew/LaMini-Flan-T5-248M-Orca-12.5K-4bit
|
marcchew
| 2023-12-15T14:14:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text2text-generation
| 2023-12-15T14:12:50Z |
Entry not found
|
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zhengr/zephyr-7b-sft-lora
|
zhengr
| 2023-12-15T14:13:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:13:09Z |
Entry not found
|
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joshmittal/phi-2-finetuned
|
joshmittal
| 2023-12-15T14:13:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:13:42Z |
Entry not found
|
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gkallergis/bbbp_60_hp_opt
|
gkallergis
| 2023-12-15T14:26:58Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T14:13:56Z |
---
license: cc-by-nc-nd-4.0
---
|
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cgus/Solar-10.7B-SLERP-exl2
|
cgus
| 2023-12-15T20:22:02Z | 0 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T14:14:09Z |
---
inference: false
license: apache-2.0
language:
- en
---
<!-- header start -->
<!-- 200823 -->
## Solar-10.7B-SLERP-EXL2
Model: [Solar-10.7B-SLERP](https://huggingface.co/jan-hq/Solar-10.7B-SLERP)
Made by: [jan-hq](https://huggingface.co/jan-hq)
Based on original model: [SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
Created by: [upstage](https://huggingface.co/upstage)
## List of quants:
[4bpw h8 (main)](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/main)
[4.65bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/4.65bpw-h8)
[5bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/5bpw-h8)
[6bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/6bpw-h8)
[8bpw h8](https://huggingface.co/cgus/Solar-10.7B-SLERP-exl2/tree/8bpw-h8)
## Quantization notes
Made with exllamav2-0.0.10, calibrated with [wmt-da-human-evaluation.parquet](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation) dataset.
The reason this dataset was used because the model worked very well with non-English contexts and texts, similar to Xwin models.
So I decided to try a dataset that uses multiple different languages.
This model uses a lot less memory compared to Llama2-13B models and it *almost* fits 12GB VRAM even at 8bpw.
But with 8-bit cache it even uses just a tiny bit less than 12GB VRAM.
## How to run
This quantization method uses GPU and requires Exllamav2 loader which can be found in following applications:
[Text Generation Webui](https://github.com/oobabooga/text-generation-webui)
[KoboldAI](https://github.com/henk717/KoboldAI)
[ExUI](https://github.com/turboderp/exui)
# Original model card:
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `Slerp` merge method from the best models on 14th Dec on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
2. [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B)
- base model: [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
The yaml config file for this model is here:
```yaml
slices:
- sources:
- model: upstage/SOLAR-10.7B-Instruct-v1.0
layer_range: [0, 48]
- model: janhq/Pandora-v1-10.7B
layer_range: [0, 48]
merge_method: slerp
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
# Prompt template
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)

# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
|
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gkallergis/bbbp_80_hp_opt
|
gkallergis
| 2023-12-15T14:27:10Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T14:14:50Z |
---
license: cc-by-nc-nd-4.0
---
|
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gutimatemalaysia/GutimateMalaysia
|
gutimatemalaysia
| 2023-12-15T14:17:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:16:05Z |
Gutimate: Malaysia, harga, ulasan, suplemen, Faedah, bahan-bahan, aduan, kos, asal!
Gutimate adalah bahan semulajadi yang berfungsi untuk melegakan sakit sendi di samping membantu pengguna memulihkan rawan yang rosak di dalam badan mereka. Gutimate Malaysia
<a href="https://www.nutritionsee.com/GutiMala">Gutimate Beli sekarang!! Klik Pautan Di Bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang!! cepatlah!!</a>
Laman web rasmi: <a href="https://www.nutritionsee.com/GutiMala">www.Gutimate.com</a>
<p><a href="https://www.nutritionsee.com/GutiMala"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd_hPIKRph7b2aC6CRraIAkALQzg2WZzdQfkPmKaLO_8P8mZoKa8UW5y6ep8Az9mkOMj3i7V0vrUpImCMhYqqXZv1KfoAMGCYEWPB8Su8gDrZKUlpzNcoRFmU25610Z0mmH3XX5TglmP4EQ0Hl_mvi1xZXFShfOnz0_Xjb5jA7XjctF2xLnILMQrr3MfE/w633-h425/Gutimate%20Malaysia.png" alt="enter image description here"> </a></p>
➢Nama Produk — Gutimate
➢Kategori –Kesihatan Sendi
➢Faedah Utama — Memberi anda lebih keselesaan sendi & menjadikan hidup anda lebih bahagia dan lebih aktif.
➢ Komposisi — Sebatian Organik Semulajadi
➢ Kesan Sampingan—NA
➢Penilaian Akhir: — 4.8
➢ Ketersediaan — Dalam Talian
➢Tawaran & Diskaun; JIMAT HARI INI! BELI SEKARANG UNTUK MEMBELI TAWARAN ISTIMEWA!!!
<a href="https://www.nutritionsee.com/GutiMala">Gutimate Beli sekarang!! Klik Pautan Di Bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang!! cepatlah!!</a>
Laman web rasmi: <a href="https://www.nutritionsee.com/GutiMala">www.Gutimate.com</a>
<p><a href="https://www.nutritionsee.com/GutiMala"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd_hPIKRph7b2aC6CRraIAkALQzg2WZzdQfkPmKaLO_8P8mZoKa8UW5y6ep8Az9mkOMj3i7V0vrUpImCMhYqqXZv1KfoAMGCYEWPB8Su8gDrZKUlpzNcoRFmU25610Z0mmH3XX5TglmP4EQ0Hl_mvi1xZXFShfOnz0_Xjb5jA7XjctF2xLnILMQrr3MfE/w633-h425/Gutimate%20Malaysia.png" alt="enter image description here"> </a></p>
Apakah Gutimate?
Gutimate ialah formula baharu revolusioner yang membantu menangani isu asas yang menyebabkan sakit sendi kronik. Gutimate komponen melegakan kekakuan sendi, sakit pada bahu dan lutut, dan bengkak pada sendi. Orang ramai menggunakan formula ini untuk melegakan toksin dan kesakitan dalam badan. Melegakan kesakitan dan peningkatan penghantaran nutrien hanyalah dua daripada banyak kelebihan mengambil produk ini.
https://www.nutritionsee.com/GutiMala
https://www.facebook.com/Gutimate.Malaysia
https://sites.google.com/view/gutimate/home
https://healthtoned.blogspot.com/2023/12/gutimate-malaysia.html
https://www.weddingwire.com/website/gutimate-and-malaysia
https://medium.com/@healthytalk24x7/gutimate-malaysia-f43517d64223
https://medium.com/@healthytalk24x7/gutimate-malaysia-harga-ulasan-suplemen-faedah-bahan-bahan-aduan-kos-asal-cde1968ea9ca
https://infogram.com/gutimate-malaysia-1hmr6g7pjznxo6n?live
https://softisenilspain.hashnode.dev/gutimate-malaysia
https://sway.office.com/gRYjOG9V7xjzs24Q
https://gutimatemalaysia.company.site/
https://gamma.app/docs/Gutimate-Malaysia-8rktzjkzb0ajuzj?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/gutimate-malaysia
https://groups.google.com/g/snshine/c/NQS3HZ_dXcg
https://www.sunflower-cissp.com/glossary/cissp/7313/gutimate-malaysia
https://community.thebatraanumerology.com/user/gutimatemalaysia
https://community.thebatraanumerology.com/post/gutimate-malaysia-harga-ulasan-suplemen-faedah-bahan-bahan-aduan-kos-asal-657c5e57e991b9039ac2c4bd
https://nepal.tribe.so/post/gutimate-malaysia-657c5e8458837d87d6352cb4
https://replit.com/@gutimatemalaysi
https://soundcloud.com/gutimate/gutimate-malaysia?si=fadfea1dcf324de4806b551368a3c168&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
https://www.deviantart.com/gutimate/art/Gutimate-Malaysia-1002185383
|
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] |
sanmaro6803/llama2-ko-7b-ds-qlora-sft-merged-a100-6epochs
|
sanmaro6803
| 2023-12-15T14:19:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T14:16:33Z |
Entry not found
|
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] |
marcchew/LaMini-T5-61M-8bit
|
marcchew
| 2023-12-15T14:17:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text2text-generation
| 2023-12-15T14:17:28Z |
Entry not found
|
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Emma5099/runs_prefix_100_bias
|
Emma5099
| 2023-12-15T14:18:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:18:49Z |
Entry not found
|
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ac0hik/finetuned_model
|
ac0hik
| 2023-12-15T14:19:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:19:48Z |
Entry not found
|
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LavaPlanet/Venus-120b-v1.1-exl2_2-2.64bpw
|
LavaPlanet
| 2023-12-15T20:07:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T14:19:54Z |
---
license: llama2
---
This should fit on 2x3090s on windows with a 18-19,24 gpu split with 6k-8k context. Uses the new exlv2 quant
|
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] |
Mairati/elirai
|
Mairati
| 2023-12-15T14:27:11Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T14:23:15Z |
---
license: apache-2.0
---

|
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] |
sauravns/roberta-base-finetuned-squadv2
|
sauravns
| 2023-12-15T15:34:40Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-15T14:28:41Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: sauravns/roberta-base-finetuned-squadv2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# sauravns/roberta-base-finetuned-squadv2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0576
- Train End Logits Accuracy: 0.3438
- Train Start Logits Accuracy: 0.3438
- Validation Loss: 2.6074
- Validation End Logits Accuracy: 0.4660
- Validation Start Logits Accuracy: 0.4660
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 124, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 4.3704 | 0.1946 | 0.1885 | 2.9534 | 0.4660 | 0.4660 | 0 |
| 3.0576 | 0.3438 | 0.3438 | 2.6074 | 0.4660 | 0.4660 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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owanr/Sentiment-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T07:12:42Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T14:33:00Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Sentiment-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.4525 | 1.0 | 5628 | 2.2193 |
| 1.9608 | 2.0 | 11256 | 2.0987 |
| 1.9252 | 3.0 | 16884 | 2.0987 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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JustinSEO/AutoGPTNew
|
JustinSEO
| 2023-12-15T14:34:43Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-15T14:34:43Z |
---
license: cc-by-4.0
---
|
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tokyos/CNN-prueba
|
tokyos
| 2023-12-15T14:36:02Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T14:36:02Z |
---
license: mit
---
|
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owanr/SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T06:16:54Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T14:36:21Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3787 | 1.0 | 12516 | 2.3244 |
| 2.4722 | 2.0 | 25032 | 2.5250 |
| 2.6552 | 3.0 | 37548 | 2.5250 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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farshadafx/LEDA-GPT2-v1
|
farshadafx
| 2023-12-15T14:36:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:36:47Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_28
|
ADISH007
| 2023-12-15T14:38:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T14:37:46Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_0001_fold3
|
hkivancoral
| 2023-12-15T15:33:57Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T14:39:44Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_0001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8283333333333334
---
<!-- 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. -->
# smids_5x_deit_tiny_sgd_0001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4818
- Accuracy: 0.8283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1768 | 1.0 | 375 | 1.1673 | 0.3967 |
| 1.0999 | 2.0 | 750 | 1.1000 | 0.4267 |
| 1.0421 | 3.0 | 1125 | 1.0522 | 0.4717 |
| 0.9929 | 4.0 | 1500 | 1.0115 | 0.5 |
| 0.9542 | 5.0 | 1875 | 0.9748 | 0.5233 |
| 0.8817 | 6.0 | 2250 | 0.9389 | 0.5417 |
| 0.8788 | 7.0 | 2625 | 0.9041 | 0.57 |
| 0.8581 | 8.0 | 3000 | 0.8691 | 0.59 |
| 0.7544 | 9.0 | 3375 | 0.8356 | 0.6033 |
| 0.7744 | 10.0 | 3750 | 0.8037 | 0.6317 |
| 0.7141 | 11.0 | 4125 | 0.7743 | 0.66 |
| 0.7231 | 12.0 | 4500 | 0.7462 | 0.6733 |
| 0.7108 | 13.0 | 4875 | 0.7217 | 0.7033 |
| 0.6884 | 14.0 | 5250 | 0.6985 | 0.7217 |
| 0.6146 | 15.0 | 5625 | 0.6770 | 0.7333 |
| 0.573 | 16.0 | 6000 | 0.6579 | 0.7467 |
| 0.6638 | 17.0 | 6375 | 0.6415 | 0.76 |
| 0.653 | 18.0 | 6750 | 0.6259 | 0.775 |
| 0.5464 | 19.0 | 7125 | 0.6119 | 0.7833 |
| 0.5679 | 20.0 | 7500 | 0.6000 | 0.79 |
| 0.5005 | 21.0 | 7875 | 0.5883 | 0.795 |
| 0.5248 | 22.0 | 8250 | 0.5790 | 0.8017 |
| 0.5865 | 23.0 | 8625 | 0.5701 | 0.8017 |
| 0.5913 | 24.0 | 9000 | 0.5617 | 0.8033 |
| 0.542 | 25.0 | 9375 | 0.5539 | 0.8033 |
| 0.5193 | 26.0 | 9750 | 0.5470 | 0.8033 |
| 0.5655 | 27.0 | 10125 | 0.5403 | 0.805 |
| 0.59 | 28.0 | 10500 | 0.5340 | 0.8117 |
| 0.4649 | 29.0 | 10875 | 0.5287 | 0.815 |
| 0.4871 | 30.0 | 11250 | 0.5240 | 0.8133 |
| 0.5615 | 31.0 | 11625 | 0.5192 | 0.815 |
| 0.5652 | 32.0 | 12000 | 0.5148 | 0.8167 |
| 0.5595 | 33.0 | 12375 | 0.5110 | 0.8183 |
| 0.4782 | 34.0 | 12750 | 0.5072 | 0.8167 |
| 0.4 | 35.0 | 13125 | 0.5039 | 0.8183 |
| 0.427 | 36.0 | 13500 | 0.5006 | 0.82 |
| 0.4646 | 37.0 | 13875 | 0.4979 | 0.82 |
| 0.4598 | 38.0 | 14250 | 0.4954 | 0.8217 |
| 0.4848 | 39.0 | 14625 | 0.4932 | 0.8233 |
| 0.4928 | 40.0 | 15000 | 0.4911 | 0.8267 |
| 0.4256 | 41.0 | 15375 | 0.4892 | 0.8267 |
| 0.4494 | 42.0 | 15750 | 0.4875 | 0.8267 |
| 0.5087 | 43.0 | 16125 | 0.4862 | 0.8267 |
| 0.4788 | 44.0 | 16500 | 0.4849 | 0.8267 |
| 0.4663 | 45.0 | 16875 | 0.4839 | 0.8267 |
| 0.4385 | 46.0 | 17250 | 0.4831 | 0.8267 |
| 0.4878 | 47.0 | 17625 | 0.4825 | 0.8283 |
| 0.4959 | 48.0 | 18000 | 0.4821 | 0.8283 |
| 0.4851 | 49.0 | 18375 | 0.4819 | 0.8283 |
| 0.4569 | 50.0 | 18750 | 0.4818 | 0.8283 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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idontgoddamn/NahidaRVC.zip
|
idontgoddamn
| 2023-12-15T14:42:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:41:31Z |
Entry not found
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] |
JapGuy/MichalMalatny_vALL
|
JapGuy
| 2023-12-15T14:59:27Z | 0 | 0 | null |
[
"music",
"rvc",
"michal",
"malatny",
"chinaski",
"model",
"audio-to-audio",
"cs",
"license:openrail",
"region:us"
] |
audio-to-audio
| 2023-12-15T14:41:52Z |
---
license: openrail
language:
- cs
pipeline_tag: audio-to-audio
tags:
- music
- rvc
- michal
- malatny
- chinaski
- model
---

# Michal Malátný ( Chinaski ) [CZ] (vALL)
# 882 Epochs - RVC V2 - rmvpe
Trained on 57 minutes 29 seconds of isolated acapellas using UVR (Voc FT + Reverb HQ)
and Audacity to remove parts with double vocals and vocals from others (+Noise Gate)
|
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alexx122/idk
|
alexx122
| 2023-12-15T14:42:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:42:36Z |
Entry not found
|
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] |
riho1710/CBQAModel-2s5p-wasabi-2
|
riho1710
| 2023-12-17T14:21:30Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-15T14:42:46Z |
Entry not found
|
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aorams99/nyeh
|
aorams99
| 2023-12-15T15:05:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:43:49Z |
Entry not found
|
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owanr/ghc-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T07:12:50Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T14:46:27Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ghc-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.343 | 1.0 | 11020 | 3.9205 |
| 3.5405 | 2.0 | 22040 | 3.7394 |
| 3.4352 | 3.0 | 33060 | 3.7394 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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jlbaker361/sd-wikiart
|
jlbaker361
| 2023-12-15T14:47:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:47:01Z |
Entry not found
|
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jlbaker361/sd-wikiart-lora
|
jlbaker361
| 2023-12-15T14:47:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T14:47:16Z |
Entry not found
|
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tscstudios/20W0X7uBLySfQWJAJbAcWs4PffJ2
|
tscstudios
| 2023-12-15T14:49:53Z | 0 | 0 | null |
[
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] | null | 2023-12-15T14:48:51Z |
Entry not found
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] |
marcweibel/Reinforce-Pixelcopter
|
marcweibel
| 2023-12-15T17:50:13Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T14:49:40Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.00 +/- 26.08
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
|
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LETHEmusic/YUNGBLUD
|
LETHEmusic
| 2023-12-15T14:52:32Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T14:51:28Z |
---
license: openrail
---
|
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] |
owanr/Sentiment-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T07:33:22Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T14:53:00Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Sentiment-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7208
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.7243 | 1.0 | 5628 | 4.7497 |
| 4.5455 | 2.0 | 11256 | 4.7208 |
| 4.4727 | 3.0 | 16884 | 4.7208 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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s-gladkykh/sky-diffusion-128-lr2-5
|
s-gladkykh
| 2023-12-15T16:25:28Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-15T14:54:03Z |
Entry not found
|
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] |
zgce/SUS-Chat-34B-roleplay-lora
|
zgce
| 2023-12-15T15:07:39Z | 0 | 0 | null |
[
"safetensors",
"license:mit",
"region:us"
] | null | 2023-12-15T14:58:02Z |
---
license: mit
---
Although it was aligned, the role play effect was very poor, so I tried to use the roleplay classification in the spicy data set, and also used Google Translate to Chinese to make two copies of the Chinese and English data. This LoRA is based on [unalign](https://huggingface.co/zgce/SUS-Chat-34B-unalign-lora).
|
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] |
Lin25/wav2vec2-large-mms-1b-england-1215-adapter-test
|
Lin25
| 2023-12-15T16:54:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T14:58:03Z |
Entry not found
|
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beamaia/openhermes-mistral-dpo-gptq
|
beamaia
| 2023-12-15T15:02:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:02:15Z |
Entry not found
|
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] |
Minmin61/sungmin
|
Minmin61
| 2023-12-15T15:15:28Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T15:05:11Z |
---
license: openrail
---
|
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] |
Waflon/sii_model
|
Waflon
| 2023-12-16T07:54:46Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_es",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-15T15:11:10Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad_es
model-index:
- name: sii_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sii_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2384
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6308 | 1.0 | 4380 | 2.4981 |
| 2.226 | 2.0 | 8760 | 2.2684 |
| 2.0631 | 3.0 | 13140 | 2.2384 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
owanr/Sentiment-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T07:53:18Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T15:13:39Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Sentiment-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0929
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3093 | 1.0 | 5628 | 3.0476 |
| 3.0432 | 2.0 | 11256 | 3.0929 |
| 2.9238 | 3.0 | 16884 | 3.0929 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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asherisaac/testmodel2
|
asherisaac
| 2023-12-15T15:15:54Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:prajjwal1/bert-tiny",
"region:us"
] | null | 2023-12-15T15:15:53Z |
---
library_name: peft
base_model: prajjwal1/bert-tiny
---
# Model Card for Model ID
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## Model Details
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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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### Framework versions
- PEFT 0.7.1
|
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] |
kaerusan/Text_block_classification
|
kaerusan
| 2023-12-15T15:15:56Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T15:15:56Z |
---
license: apache-2.0
---
|
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HemaPro/HemaPro
|
HemaPro
| 2023-12-15T15:17:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:16:02Z |
HemaPro: Hema Pro Turkey, Krem, jel, Balsam, İncelemeler, Fiyat, İçindekiler, İşler, eczane!
HemaPro, basur veya hemoroit ile ilişkili ağrı ve rahatsızlıktan kurtulma sağlayan bir hemoroid sağlık ürünüdür. Hema Pro Turkey
<a href="https://www.excaliburnutrition.com/HemaPTur">HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !!</a>
Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/HemaPTur">www.HemaPro.com</a>
<p><a href="https://www.excaliburnutrition.com/HemaPTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBdrzblDaZvDL5MJhtCq2je-iCfWmXRWMjN5HL8r-yHzjeqg062Yt72-9rzTyd9uel11GWGU-22NtBtNFSH59NTrus3SxLO2f57ylPNd9bsti4pux0uMJnmFn2XBHMLyn7XKsjSkNpPvz1LpCkUv4zzID
ØÜrün Adı — HemaPro
ØKategori - hemoroid
Ø Bileşimi — Doğal Organik Bileşik
➢ Yan Etkiler—YOK
ØNihai Derecelendirme: — 4,8
➢ Kullanılabilirlik — Çevrimiçi
➢Teklifler ve İndirimler; BUGÜN KAYDET! ÖZEL TEKLİF satın almak için ŞİMDİ ALIŞVERİŞ YAPIN!!!
HemaPro nedir?
Hema Pro Turkey, basur veya hemoroid semptomlarını hafifletmeye yardımcı olduğu bilinen bitkisel bileşenlerin bir karışımıyla yapılan doğal bir formüldür. Ürün içten dışa doğru çalışacak, iltihabı azaltmaya, ağrıyı hafifletmeye ve sağlıklı kan dolaşımını teşvik etmeye yardımcı olacak şekilde formüle edilmiştir.
HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !!
<a href="https://www.excaliburnutrition.com/HemaPTur">HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !!</a>
Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/HemaPTur">www.HemaPro.com</a>
<p><a href="https://www.excaliburnutrition.com/HemaPTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBdrzblDaZvDL5MJhtCq2je-iCfWmXRWMjN5HL8r-yHzjeqg062Yt72-9rzTyd9uel11GWGU-22NtBtNFSH59NTrus3SxLO2f57ylPNd9bsti4pux0uMJnmFn2XBHMLyn7XKsjSkNpPvz1LpCkUv4zzID
HemaPro HemaPro Krem HemaPro jel HemaPro Balsam HemaPro Fiyat HemaPro İncelemeler HemaPro İçindekiler HemaPro Faydalar HemaPro Yan etkiler HemaPro Krem fiyatı HemaPro Krem yorumları HemaPro Kompozisyon HemaPro Şikayet HemaPro Nereden Alınır HemaPro Nasıl kullanılır HemaPro maliyet HemaPro İşler HemaPro forum HemaPro orijinal HemaPro eczane
HemaPro nasıl çalışır?
HemaPro basur veya basurun temel nedenini hedef alarak çalışır. Kapsüllerdeki doğal bileşenlerin karışımı, kan dolaşımını iyileştirmek, iltihabı azaltmak ve anüsteki damarların duvarlarını güçlendirmek için birlikte çalışır. Bu, şişliği ve rahatsızlığı azaltmaya yardımcı olur ve hatta yeni hemoroid oluşumunun önlenmesine bile yardımcı olabilir.
HemaPro'ün kullanımı güvenli midir?
Her üründe olduğu gibi, özellikle altta yatan sağlık sorunlarınız varsa veya reçeteli ilaçlar kullanıyorsanız, HemaPro'ü almadan önce sağlık uzmanınızla konuşmanız önemlidir. Ayrıca bitkisel takviyeler FDA tarafından düzenlenmediğinden bunların güvenliği ve etkinliği her zaman garanti edilmez.
<a href="https://www.excaliburnutrition.com/HemaPTur">HemaPro Şimdi al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirim kazanın! Acele etmek !!</a>
Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/HemaPTur">www.HemaPro.com</a>
<p><a href="https://www.excaliburnutrition.com/HemaPTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBdrzblDaZvDL5MJhtCq2je-iCfWmXRWMjN5HL8r-yHzjeqg062Yt72-9rzTyd9uel11GWGU-22NtBtNFSH59NTrus3SxLO2f57ylPNd9bsti4pux0uMJnmFn2XBHMLyn7XKsjSkNpPvz1LpCkUv4zzID
HemaPro nereden satın alınır?
HemaPro'ü satın almakla ilgileniyorsanız, araştırmanızı yapmanız ve saygın ve güvenilir bir satıcı bulmanız önemlidir. Çevrimiçi perakendecileri arayın Şüphe uyandıracak derecede düşük fiyatlara veya ürünün etkinliğine ilişkin abartılı iddialara karşı dikkatli olun. Her zaman satıcının bu ürünü satma yetkisine sahip olduğundan emin olun.
HemaPro HemaPro Krem HemaPro jel HemaPro Balsam HemaPro Fiyat HemaPro İncelemeler HemaPro İçindekiler HemaPro Faydalar HemaPro Yan etkiler HemaPro Krem fiyatı HemaPro Krem yorumları HemaPro Kompozisyon HemaPro Şikayet HemaPro Nereden Alınır HemaPro Nasıl kullanılır HemaPro maliyet HemaPro İşler HemaPro forum HemaPro orijinal HemaPro eczane
https://www.excaliburnutrition.com/HemaPTur
https://sites.google.com/view/hemapro-krem/home
https://healthtoned.blogspot.com/2023/12/hemapro-turkey.html
https://medium.com/@hemaprofiyat/hemapro-turkey-badef4e9cb9a
https://medium.com/@hemaprofiyat/hemapro-hema-pro-turkey-krem-jel-balsam-i%CC%87ncelemeler-fiyat-i%CC%87%C3%A7indekiler-i%CC%87%C5%9Fler-eczane-766025869d79
https://www.weddingwire.com/website/hemapro-and-turkey-2026-10-13
https://infogram.com/hemapro-turkey-1h0n25y5p1g8z6p?live
https://softisenilspain.hashnode.dev/hemapro-turkey
https://sway.office.com/wg7XkZPqv58B92Fk
https://hemaprofiyat.company.site/
https://gamma.app/docs/HemaPro-Turkey-tf8ourgb3z8k63p?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/hemapro-hema-pro-turkey-krem-jel-balsam-i%CC%87ncelemeler-fiyat-i%CC%87%C3%A7indekiler-i%CC%87%C5%9Fler-eczane
https://groups.google.com/g/snshine/c/-fenIpujJkU
https://www.sunflower-cissp.com/glossary/cissp/7314/hemapro-turkey
https://replit.com/@hemaprofiyat
|
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owanr/SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T06:43:07Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T15:19:36Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SBIC-roberta-base-intra-data-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2405 | 1.0 | 12516 | 2.3180 |
| 2.3278 | 2.0 | 25032 | 2.3168 |
| 2.0887 | 3.0 | 37548 | 2.3168 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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aisingsV2/Jizuu_mangio_550Epochs
|
aisingsV2
| 2023-12-15T15:22:25Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T15:20:40Z |
---
license: openrail
---
|
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owanr/ghc-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T07:51:40Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T15:20:41Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ghc-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6784 | 1.0 | 11020 | 0.4245 |
| 0.3594 | 2.0 | 22040 | 0.4268 |
| 0.4199 | 3.0 | 33060 | 0.4268 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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PowerInfer/ReluFalcon-40B-PowerInfer-GGUF
|
PowerInfer
| 2023-12-16T09:04:23Z | 0 | 0 | null |
[
"gguf",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T15:22:06Z |
---
license: apache-2.0
language:
- en
---
# ReluFalcon-40B-PowerInfer-GGUF
- Original model: [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B)
- Converted & distributed by: [PowerInfer](https://huggingface.co/PowerInfer)
This model is the downstream distribution of [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B) in PowerInfer GGUF format consisting of the LLM model weights and predictor weights.
|
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] |
Shaleen123/mistrallite_medical_full
|
Shaleen123
| 2023-12-15T15:23:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:01-ai/Yi-6B-200K",
"region:us"
] | null | 2023-12-15T15:23:08Z |
---
library_name: peft
base_model: 01-ai/Yi-6B-200K
---
# Model Card for Model ID
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## Model Details
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## Environmental Impact
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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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## Model Card Contact
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### Framework versions
- PEFT 0.7.1
|
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] |
ppsingh/setfit_1_sector_mlbl_dense
|
ppsingh
| 2023-12-15T15:24:32Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/all-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2023-12-15T15:24:17Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- f1-micro
- f1-samples
- f1-weighted
widget:
- text: Implementation of the CCAP will encompass activities geared towards building
the resilience of households, communities, vulnerable groups, enterprises, sectors
and ultimately the nation. Implementation measures are being identified at the
national and community levels, with regional and international support and backstopping
provided through agreed modalities.
- text: Short term. Strengthen the National Biodiversity Strategy and Action Plan.
South Sudan will declare commitment to the National Biodiversity Strategy and
Action Plan (2018–2027) by showcasing its current implementation status and a
roadmap for future implementation. Build more comprehensive and strengthened action
plans for conserving the biodiversity of South Sudan. The National Biodiversity
Strategy and Action Plan has been developed for South Sudan. Short term.
- text: Integrate the climate change and disaster risk reduction into the National
Development Plan by 2015. Revise capital budget appraisal guidelines to incorporate
comprehensive hazard and risk management (CHARM) and vulnerability and adaptation
(VA) assessments by 2015. There is a need to increase the resourcing of adaptation
and mitigation measures. Short Term (up to 2 years). Explore climate change financing
modalities by 2015. Medium Term (3 to 5 years).
- text: Implement a policy framework to promote more efficient vehicles and alternative
fuels/blends through incorporation of fuel economy labels; emissions testing;
fuel economy standards, limitations and emissions- based taxes/feebates for imported
vehicles by 2025. Action.
- text: Work with stakeholders in the tourism sector to develop a strategic plan,
which incorporates Climate Change considerations and appropriate measures such
as water conservation programmes, as well as, general sustainability concerns.
Health and wellbeing. Inform, sensitize and educate health personnel and the public-at-large
about Climate Change related health matters including but not limited to heat
stress, vector borne diseases and impacts on the vulnerable sub sector of society.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.464
name: Accuracy
- type: f1-micro
value: 0.5454545454545454
name: F1-Micro
- type: f1-samples
value: 0.5722666666666667
name: F1-Samples
- type: f1-weighted
value: 0.5359564860415268
name: F1-Weighted
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 15 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy | F1-Micro | F1-Samples | F1-Weighted |
|:--------|:---------|:---------|:-----------|:------------|
| **all** | 0.464 | 0.5455 | 0.5723 | 0.5360 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("ppsingh/setfit_1_sector_mlbl_dense")
# Run inference
preds = model("Implement a policy framework to promote more efficient vehicles and alternative fuels/blends through incorporation of fuel economy labels; emissions testing; fuel economy standards, limitations and emissions- based taxes/feebates for imported vehicles by 2025. Action.")
```
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*List how someone could finetune this model on their own dataset.*
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 19 | 61.4937 | 258 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 8)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1 | 0.2408 | - |
| 0.1001 | 100 | 0.237 | 0.1678 |
| 0.2002 | 200 | 0.1691 | 0.1742 |
| 0.0010 | 1 | 0.0409 | - |
| 0.1001 | 100 | 0.1416 | 0.1855 |
| 0.2002 | 200 | 0.055 | 0.1805 |
| 0.3003 | 300 | 0.096 | 0.1655 |
| 0.4004 | 400 | 0.1486 | 0.1647 |
| 0.5005 | 500 | 0.1175 | 0.1633 |
| 0.6006 | 600 | 0.0556 | 0.1652 |
| 0.7007 | 700 | 0.1104 | 0.1711 |
| 0.8008 | 800 | 0.0194 | 0.1671 |
| 0.9009 | 900 | 0.0173 | 0.1654 |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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|
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] |
ajithks2024/Llama-2-7b-chat-finetune
|
ajithks2024
| 2023-12-15T15:34:49Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T15:24:32Z |
Entry not found
|
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] |
Mischka123/GeheimesModell2
|
Mischka123
| 2023-12-15T15:34:19Z | 0 | 0 |
sklearn
|
[
"sklearn",
"joblib",
"text-classification",
"de",
"arxiv:1910.09700",
"license:cc0-1.0",
"region:us"
] |
text-classification
| 2023-12-15T15:24:45Z |
---
license: cc0-1.0
language:
- de
pipeline_tag: text-classification
library_name: sklearn
---
# Model Card for Model ID
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|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_12
|
Jeevesh8
| 2023-12-15T17:59:57Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T15:24:58Z |
Entry not found
|
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PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF
|
PowerInfer
| 2023-12-16T09:02:51Z | 0 | 0 | null |
[
"gguf",
"en",
"license:llama2",
"region:us"
] | null | 2023-12-15T15:25:24Z |
---
license: llama2
language:
- en
---
# ReluLLaMA-13B-PowerInfer-GGUF
- Original model: [SparseLLM/ReluLLaMA-13B](https://huggingface.co/SparseLLM/ReluLLaMA-13B)
- Converted & distributed by: [PowerInfer](https://huggingface.co/PowerInfer)
This model is the downstream distribution of [SparseLLM/ReluLLaMA-13B](https://huggingface.co/SparseLLM/ReluLLaMA-13B) in PowerInfer GGUF format consisting of the LLM model weights and predictor weights.
|
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] |
hkivancoral/smids_5x_beit_base_sgd_001_fold3
|
hkivancoral
| 2023-12-15T16:45:57Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T15:26:36Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.91
---
<!-- 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. -->
# smids_5x_beit_base_sgd_001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2734
- Accuracy: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6727 | 1.0 | 375 | 0.6864 | 0.7317 |
| 0.6041 | 2.0 | 750 | 0.5073 | 0.8033 |
| 0.4189 | 3.0 | 1125 | 0.4416 | 0.8267 |
| 0.3943 | 4.0 | 1500 | 0.3997 | 0.8433 |
| 0.4289 | 5.0 | 1875 | 0.3785 | 0.855 |
| 0.3775 | 6.0 | 2250 | 0.3585 | 0.8617 |
| 0.3696 | 7.0 | 2625 | 0.3398 | 0.8767 |
| 0.3454 | 8.0 | 3000 | 0.3318 | 0.8767 |
| 0.3235 | 9.0 | 3375 | 0.3277 | 0.8717 |
| 0.3129 | 10.0 | 3750 | 0.3193 | 0.8733 |
| 0.2807 | 11.0 | 4125 | 0.3194 | 0.8833 |
| 0.2877 | 12.0 | 4500 | 0.3054 | 0.8867 |
| 0.2952 | 13.0 | 4875 | 0.3044 | 0.89 |
| 0.2764 | 14.0 | 5250 | 0.3000 | 0.89 |
| 0.2449 | 15.0 | 5625 | 0.2977 | 0.8933 |
| 0.2466 | 16.0 | 6000 | 0.2964 | 0.8983 |
| 0.2903 | 17.0 | 6375 | 0.2961 | 0.8883 |
| 0.2759 | 18.0 | 6750 | 0.2922 | 0.895 |
| 0.2465 | 19.0 | 7125 | 0.2899 | 0.9 |
| 0.2405 | 20.0 | 7500 | 0.2859 | 0.8967 |
| 0.1951 | 21.0 | 7875 | 0.2887 | 0.9 |
| 0.2066 | 22.0 | 8250 | 0.2843 | 0.9017 |
| 0.2962 | 23.0 | 8625 | 0.2838 | 0.9033 |
| 0.2403 | 24.0 | 9000 | 0.2814 | 0.9033 |
| 0.2279 | 25.0 | 9375 | 0.2878 | 0.9033 |
| 0.2748 | 26.0 | 9750 | 0.2833 | 0.9017 |
| 0.2265 | 27.0 | 10125 | 0.2865 | 0.905 |
| 0.2583 | 28.0 | 10500 | 0.2821 | 0.9083 |
| 0.1555 | 29.0 | 10875 | 0.2828 | 0.905 |
| 0.2329 | 30.0 | 11250 | 0.2785 | 0.9117 |
| 0.235 | 31.0 | 11625 | 0.2766 | 0.9067 |
| 0.245 | 32.0 | 12000 | 0.2760 | 0.91 |
| 0.2626 | 33.0 | 12375 | 0.2763 | 0.9067 |
| 0.2391 | 34.0 | 12750 | 0.2794 | 0.905 |
| 0.1618 | 35.0 | 13125 | 0.2760 | 0.9117 |
| 0.1815 | 36.0 | 13500 | 0.2756 | 0.9067 |
| 0.1967 | 37.0 | 13875 | 0.2796 | 0.9033 |
| 0.1639 | 38.0 | 14250 | 0.2740 | 0.91 |
| 0.2156 | 39.0 | 14625 | 0.2769 | 0.9083 |
| 0.1866 | 40.0 | 15000 | 0.2767 | 0.915 |
| 0.1684 | 41.0 | 15375 | 0.2758 | 0.9083 |
| 0.194 | 42.0 | 15750 | 0.2754 | 0.9067 |
| 0.2588 | 43.0 | 16125 | 0.2756 | 0.9083 |
| 0.213 | 44.0 | 16500 | 0.2746 | 0.91 |
| 0.1893 | 45.0 | 16875 | 0.2745 | 0.9083 |
| 0.2111 | 46.0 | 17250 | 0.2742 | 0.9083 |
| 0.2091 | 47.0 | 17625 | 0.2740 | 0.91 |
| 0.2023 | 48.0 | 18000 | 0.2739 | 0.9083 |
| 0.1852 | 49.0 | 18375 | 0.2735 | 0.91 |
| 0.197 | 50.0 | 18750 | 0.2734 | 0.91 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_beit_base_sgd_0001_fold3
|
hkivancoral
| 2023-12-15T16:45:53Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T15:26:49Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_0001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8283333333333334
---
<!-- 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. -->
# smids_5x_beit_base_sgd_0001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4604
- Accuracy: 0.8283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1529 | 1.0 | 375 | 1.2380 | 0.355 |
| 1.0823 | 2.0 | 750 | 1.1537 | 0.38 |
| 0.966 | 3.0 | 1125 | 1.0702 | 0.43 |
| 0.8969 | 4.0 | 1500 | 0.9930 | 0.4833 |
| 0.8438 | 5.0 | 1875 | 0.9234 | 0.5467 |
| 0.8108 | 6.0 | 2250 | 0.8618 | 0.6067 |
| 0.781 | 7.0 | 2625 | 0.8085 | 0.6533 |
| 0.7359 | 8.0 | 3000 | 0.7640 | 0.695 |
| 0.6561 | 9.0 | 3375 | 0.7269 | 0.7067 |
| 0.687 | 10.0 | 3750 | 0.6956 | 0.72 |
| 0.6327 | 11.0 | 4125 | 0.6692 | 0.7333 |
| 0.6325 | 12.0 | 4500 | 0.6472 | 0.7433 |
| 0.6338 | 13.0 | 4875 | 0.6288 | 0.7567 |
| 0.6265 | 14.0 | 5250 | 0.6103 | 0.76 |
| 0.5767 | 15.0 | 5625 | 0.5960 | 0.7667 |
| 0.5529 | 16.0 | 6000 | 0.5830 | 0.7717 |
| 0.6057 | 17.0 | 6375 | 0.5711 | 0.7817 |
| 0.572 | 18.0 | 6750 | 0.5610 | 0.7817 |
| 0.5708 | 19.0 | 7125 | 0.5513 | 0.7833 |
| 0.5256 | 20.0 | 7500 | 0.5427 | 0.7867 |
| 0.4697 | 21.0 | 7875 | 0.5351 | 0.795 |
| 0.5346 | 22.0 | 8250 | 0.5286 | 0.7983 |
| 0.5543 | 23.0 | 8625 | 0.5218 | 0.8017 |
| 0.5387 | 24.0 | 9000 | 0.5157 | 0.8033 |
| 0.5031 | 25.0 | 9375 | 0.5106 | 0.8017 |
| 0.5567 | 26.0 | 9750 | 0.5058 | 0.805 |
| 0.5071 | 27.0 | 10125 | 0.5014 | 0.805 |
| 0.5467 | 28.0 | 10500 | 0.4973 | 0.8083 |
| 0.4516 | 29.0 | 10875 | 0.4933 | 0.8083 |
| 0.497 | 30.0 | 11250 | 0.4901 | 0.8133 |
| 0.5249 | 31.0 | 11625 | 0.4866 | 0.8133 |
| 0.5174 | 32.0 | 12000 | 0.4834 | 0.8167 |
| 0.5605 | 33.0 | 12375 | 0.4805 | 0.8183 |
| 0.5131 | 34.0 | 12750 | 0.4779 | 0.82 |
| 0.4459 | 35.0 | 13125 | 0.4756 | 0.8217 |
| 0.4556 | 36.0 | 13500 | 0.4735 | 0.8217 |
| 0.4756 | 37.0 | 13875 | 0.4716 | 0.8233 |
| 0.4606 | 38.0 | 14250 | 0.4696 | 0.8233 |
| 0.5275 | 39.0 | 14625 | 0.4682 | 0.825 |
| 0.4638 | 40.0 | 15000 | 0.4668 | 0.8233 |
| 0.4141 | 41.0 | 15375 | 0.4657 | 0.8233 |
| 0.4983 | 42.0 | 15750 | 0.4646 | 0.8283 |
| 0.5146 | 43.0 | 16125 | 0.4634 | 0.8267 |
| 0.4784 | 44.0 | 16500 | 0.4625 | 0.8267 |
| 0.4516 | 45.0 | 16875 | 0.4618 | 0.8283 |
| 0.496 | 46.0 | 17250 | 0.4612 | 0.8283 |
| 0.5144 | 47.0 | 17625 | 0.4609 | 0.8283 |
| 0.501 | 48.0 | 18000 | 0.4606 | 0.8283 |
| 0.4693 | 49.0 | 18375 | 0.4604 | 0.8283 |
| 0.4848 | 50.0 | 18750 | 0.4604 | 0.8283 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Casual111/OliverNorthVM
|
Casual111
| 2023-12-15T15:30:36Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T15:27:53Z |
---
license: openrail
---
|
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ewof/koishi-8x7b-qlora-gptq
|
ewof
| 2023-12-15T16:09:56Z | 0 | 0 |
transformers
|
[
"transformers",
"mixtral",
"text-generation",
"dataset:ewof/koishi-instruct-metharme",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T15:29:12Z |
---
datasets:
- ewof/koishi-instruct-metharme
---
## GPTQ
2048 sequence length
VMware/open-instruct dataset
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 4x nvidia a100 gpu cluster.
the a100 GPU cluster has been graciously provided by [lloorree](https://huggingface.co/lloorree).
trained on koishi commit 6e675d1 for one epoch
## Base Model
rank 16 qlora tune of mistralai/Mixtral-8x7B-v0.1 (all modules, merged)
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
|
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LMario/narradores
|
LMario
| 2023-12-15T15:30:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:29:33Z |
Entry not found
|
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] |
robinsyihab/Indonesian-wa-chat-lora-v0.1
|
robinsyihab
| 2023-12-17T12:20:25Z | 0 | 1 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T15:30:09Z |
---
license: apache-2.0
---
The Lora Adapter designed for Natural Language Understanding (NLU) in Indonesian chat-style language.
It's trained with Indonesian WhatsApp dialogues and uses the [Sidrap-7B-v2](https://huggingface.co/robinsyihab/Sidrap-7B-v2) as the foundational model.
|
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IKHOR/LCM_models
|
IKHOR
| 2023-12-15T15:36:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:30:42Z |
Entry not found
|
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NikitaO/15122023_headshot_V1
|
NikitaO
| 2023-12-15T15:36:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:31:04Z |
Entry not found
|
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thegrowingdude/falcon-7b-instruct-ft-adapters
|
thegrowingdude
| 2023-12-15T15:31:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"region:us"
] | null | 2023-12-15T15:31:29Z |
---
library_name: peft
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
---
# Model Card for Model ID
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## Model Details
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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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### Framework versions
- PEFT 0.7.2.dev0
|
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hkivancoral/smids_5x_deit_tiny_sgd_0001_fold4
|
hkivancoral
| 2023-12-15T16:29:31Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T15:34:40Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8
---
<!-- 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. -->
# smids_5x_deit_tiny_sgd_0001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4936
- Accuracy: 0.8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1564 | 1.0 | 375 | 1.1576 | 0.3767 |
| 1.0853 | 2.0 | 750 | 1.0913 | 0.4 |
| 0.9971 | 3.0 | 1125 | 1.0409 | 0.44 |
| 0.9972 | 4.0 | 1500 | 0.9979 | 0.4683 |
| 0.9211 | 5.0 | 1875 | 0.9595 | 0.4967 |
| 0.885 | 6.0 | 2250 | 0.9224 | 0.535 |
| 0.8576 | 7.0 | 2625 | 0.8878 | 0.5583 |
| 0.8551 | 8.0 | 3000 | 0.8542 | 0.5783 |
| 0.8253 | 9.0 | 3375 | 0.8221 | 0.6017 |
| 0.8198 | 10.0 | 3750 | 0.7908 | 0.6283 |
| 0.6752 | 11.0 | 4125 | 0.7615 | 0.645 |
| 0.6508 | 12.0 | 4500 | 0.7343 | 0.6767 |
| 0.6556 | 13.0 | 4875 | 0.7097 | 0.69 |
| 0.7132 | 14.0 | 5250 | 0.6866 | 0.7083 |
| 0.6057 | 15.0 | 5625 | 0.6661 | 0.7183 |
| 0.5722 | 16.0 | 6000 | 0.6478 | 0.7283 |
| 0.5982 | 17.0 | 6375 | 0.6328 | 0.7333 |
| 0.5686 | 18.0 | 6750 | 0.6177 | 0.735 |
| 0.5939 | 19.0 | 7125 | 0.6046 | 0.7417 |
| 0.5225 | 20.0 | 7500 | 0.5938 | 0.7483 |
| 0.5314 | 21.0 | 7875 | 0.5829 | 0.7567 |
| 0.5367 | 22.0 | 8250 | 0.5746 | 0.765 |
| 0.506 | 23.0 | 8625 | 0.5665 | 0.77 |
| 0.5218 | 24.0 | 9000 | 0.5589 | 0.7717 |
| 0.5608 | 25.0 | 9375 | 0.5520 | 0.7767 |
| 0.5255 | 26.0 | 9750 | 0.5459 | 0.78 |
| 0.5248 | 27.0 | 10125 | 0.5406 | 0.78 |
| 0.496 | 28.0 | 10500 | 0.5353 | 0.78 |
| 0.4514 | 29.0 | 10875 | 0.5308 | 0.785 |
| 0.4878 | 30.0 | 11250 | 0.5266 | 0.785 |
| 0.4791 | 31.0 | 11625 | 0.5226 | 0.785 |
| 0.4601 | 32.0 | 12000 | 0.5192 | 0.785 |
| 0.527 | 33.0 | 12375 | 0.5161 | 0.7867 |
| 0.4682 | 34.0 | 12750 | 0.5130 | 0.785 |
| 0.4268 | 35.0 | 13125 | 0.5104 | 0.7917 |
| 0.4602 | 36.0 | 13500 | 0.5080 | 0.795 |
| 0.4456 | 37.0 | 13875 | 0.5057 | 0.7983 |
| 0.4657 | 38.0 | 14250 | 0.5038 | 0.7983 |
| 0.5191 | 39.0 | 14625 | 0.5021 | 0.7983 |
| 0.5029 | 40.0 | 15000 | 0.5005 | 0.8 |
| 0.4811 | 41.0 | 15375 | 0.4991 | 0.8 |
| 0.4466 | 42.0 | 15750 | 0.4979 | 0.8 |
| 0.4615 | 43.0 | 16125 | 0.4969 | 0.8017 |
| 0.4147 | 44.0 | 16500 | 0.4960 | 0.8 |
| 0.4484 | 45.0 | 16875 | 0.4953 | 0.8 |
| 0.4471 | 46.0 | 17250 | 0.4947 | 0.8 |
| 0.4839 | 47.0 | 17625 | 0.4942 | 0.8 |
| 0.4773 | 48.0 | 18000 | 0.4939 | 0.8 |
| 0.4334 | 49.0 | 18375 | 0.4937 | 0.8 |
| 0.4329 | 50.0 | 18750 | 0.4936 | 0.8 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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albertoparrasmoreno/check-api-service
|
albertoparrasmoreno
| 2023-12-15T15:34:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:34:52Z |
Entry not found
|
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vdo/AnimateDiff
|
vdo
| 2023-12-15T15:42:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:40:42Z |
Entry not found
|
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imagepipeline/LoRA-LCM-SDXL
|
imagepipeline
| 2023-12-15T15:43:06Z | 0 | 0 | null |
[
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-15T15:41:52Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## LoRA-LCM-SDXL
<img src="https://f005.backblazeb2.com/b2api/v2/b2_download_file_by_id?fileId=4_zfdf0a8ed59e8666b89b10713_f102e27ef30cbc5ad_d20231202_m035349_c005_v0501009_t0041_u01701489229022" alt="Generated by Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - For use with LCMScheduler
[](https://imagepipeline.io/models/LoRA-LCM-SDXL?id=90525551-24b5-43fe-9dfc-4c6d9cffe157/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "sdxl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "90525551-24b5-43fe-9dfc-4c6d9cffe157",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sdxl/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
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NothingSeen/firstLLM
|
NothingSeen
| 2023-12-15T15:41:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:41:58Z |
Entry not found
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] |
ac0hik/my_awesome_model
|
ac0hik
| 2023-12-15T16:04:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T15:42:11Z |
Entry not found
|
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owanr/ghc-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T08:15:33Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T15:44:11Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ghc-roberta-base-inter-data-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4190
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.0572 | 1.0 | 11020 | 2.1141 |
| 2.283 | 2.0 | 22040 | 2.4190 |
| 2.3257 | 3.0 | 33060 | 2.4190 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Over9012/IEEE-Chatbot
|
Over9012
| 2023-12-15T15:46:20Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-15T15:44:40Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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ahlamali/hello
|
ahlamali
| 2023-12-15T15:45:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:45:35Z |
Entry not found
|
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] |
jackspparrow/freya
|
jackspparrow
| 2023-12-15T15:48:57Z | 0 | 0 |
allennlp
|
[
"allennlp",
"en",
"dataset:HuggingFaceH4/no_robots",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T15:46:34Z |
---
license: apache-2.0
datasets:
- HuggingFaceH4/no_robots
- Open-Orca/SlimOrca
language:
- en
metrics:
- character
library_name: allennlp
---
|
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BDubbs/jjj
|
BDubbs
| 2023-12-15T15:48:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:48:46Z |
Entry not found
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darkc0de/Zordon
|
darkc0de
| 2023-12-15T15:49:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:49:14Z |
Entry not found
|
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thorirhrafn/llama2_lora_test
|
thorirhrafn
| 2023-12-15T16:08:05Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-12-15T15:50:15Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llama2_lora_test
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. -->
# llama2_lora_test
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7435 | 0.02 | 500 | 2.6775 |
| 2.6637 | 0.04 | 1000 | 2.6209 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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] |
VishalMysore/cookgptsmall
|
VishalMysore
| 2023-12-15T15:51:00Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T15:51:00Z |
---
license: apache-2.0
---
|
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] |
vdo/i2vgen-xl
|
vdo
| 2023-12-15T15:56:04Z | 0 | 1 | null |
[
"arxiv:2312.09109",
"arxiv:2308.06571",
"arxiv:2311.04145",
"license:mit",
"region:us"
] | null | 2023-12-15T15:51:28Z |
---
license: mit
---
# VGen

VGen is an open-source video synthesis codebase developed by the Tongyi Lab of Alibaba Group, featuring state-of-the-art video generative models. This repository includes implementations of the following methods:
- [I2VGen-xl: High-quality image-to-video synthesis via cascaded diffusion models](https://i2vgen-xl.github.io/)
- [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://videocomposer.github.io/)
- [Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation](https://higen-t2v.github.io/)
- [A Recipe for Scaling up Text-to-Video Generation with Text-free Videos]()
- [InstructVideo: Instructing Video Diffusion Models with Human Feedback]()
- [DreamVideo: Composing Your Dream Videos with Customized Subject and Motion](https://dreamvideo-t2v.github.io/)
- [VideoLCM: Video Latent Consistency Model](https://arxiv.org/abs/2312.09109)
- [Modelscope text-to-video technical report](https://arxiv.org/abs/2308.06571)
VGen can produce high-quality videos from the input text, images, desired motion, desired subjects, and even the feedback signals provided. It also offers a variety of commonly used video generation tools such as visualization, sampling, training, inference, join training using images and videos, acceleration, and more.
<a href='https://i2vgen-xl.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2311.04145'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> [](https://youtu.be/XUi0y7dxqEQ) <a href='https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441039979087.mp4'><img src='source/logo.png'></a>
## 🔥News!!!
- __[2023.12]__ We release the high-efficiency video generation method [VideoLCM](https://arxiv.org/abs/2312.09109)
- __[2023.12]__ We release the code and model of I2VGen-XL and the ModelScope T2V
- __[2023.12]__ We release the T2V method [HiGen](https://higen-t2v.github.io) and customizing T2V method [DreamVideo](https://dreamvideo-t2v.github.io).
- __[2023.12]__ We write an [introduction docment](doc/introduction.pdf) for VGen and compare I2VGen-XL with SVD.
- __[2023.11]__ We release a high-quality I2VGen-XL model, please refer to the [Webpage](https://i2vgen-xl.github.io)
## TODO
- [x] Release the technical papers and webpage of [I2VGen-XL](doc/i2vgen-xl.md)
- [x] Release the code and pretrained models that can generate 1280x720 videos
- [ ] Release models optimized specifically for the human body and faces
- [ ] Updated version can fully maintain the ID and capture large and accurate motions simultaneously
- [ ] Release other methods and the corresponding models
## Preparation
The main features of VGen are as follows:
- Expandability, allowing for easy management of your own experiments.
- Completeness, encompassing all common components for video generation.
- Excellent performance, featuring powerful pre-trained models in multiple tasks.
### Installation
```
conda create -n vgen python=3.8
conda activate vgen
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### Datasets
We have provided a **demo dataset** that includes images and videos, along with their lists in ``data``.
*Please note that the demo images used here are for testing purposes and were not included in the training.*
### Clone codeb
```
git clone https://github.com/damo-vilab/i2vgen-xl.git
cd i2vgen-xl
```
## Getting Started with VGen
### (1) Train your text-to-video model
Executing the following command to enable distributed training is as easy as that.
```
python train_net.py --cfg configs/t2v_train.yaml
```
In the `t2v_train.yaml` configuration file, you can specify the data, adjust the video-to-image ratio using `frame_lens`, and validate your ideas with different Diffusion settings, and so on.
- Before the training, you can download any of our open-source models for initialization. Our codebase supports custom initialization and `grad_scale` settings, all of which are included in the `Pretrain` item in yaml file.
- During the training, you can view the saved models and intermediate inference results in the `workspace/experiments/t2v_train`directory.
After the training is completed, you can perform inference on the model using the following command.
```
python inference.py --cfg configs/t2v_infer.yaml
```
Then you can find the videos you generated in the `workspace/experiments/test_img_01` directory. For specific configurations such as data, models, seed, etc., please refer to the `t2v_infer.yaml` file.
<!-- <table>
<center>
<tr>
<td ><center>
<video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4"></video>
</center></td>
<td ><center>
<video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4"></video>
</center></td>
</tr>
</center>
</table>
</center> -->
<table>
<center>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01Ya2I5I25utrJwJ9Jf_!!6000000007587-2-tps-1280-720.png"></image>
</center></td>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01CrmYaz1zXBetmg3dd_!!6000000006723-2-tps-1280-720.png"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441754174077.mp4">HRER</a> to view the generated video.</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441138824052.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
</center>
</table>
</center>
### (2) Run the I2VGen-XL model
(i) Download model and test data:
```
!pip install modelscope
from modelscope.hub.snapshot_download import snapshot_download
model_dir = snapshot_download('damo/I2VGen-XL', cache_dir='models/', revision='v1.0.0')
```
(ii) Run the following command:
```
python inference.py --cfg configs/i2vgen_xl_infer.yaml
```
In a few minutes, you can retrieve the high-definition video you wish to create from the `workspace/experiments/test_img_01` directory. At present, we find that the current model performs inadequately on **anime images** and **images with a black background** due to the lack of relevant training data. We are consistently working to optimize it.
<span style="color:red">Due to the compression of our video quality in GIF format, please click 'HRER' below to view the original video.</span>
<center>
<table>
<center>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01CCEq7K1ZeLpNQqrWu_!!6000000003219-0-tps-1280-720.jpg"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4"></video> -->
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01hIQcvG1spmQMLqBo0_!!6000000005816-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442125067544.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01ZXY7UN23K8q4oQ3uG_!!6000000007236-2-tps-1280-720.png"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4"></video> -->
<image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01iaSiiv1aJZURUEY53_!!6000000003309-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/441385957074.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01NHpVGl1oat4H54Hjf_!!6000000005242-2-tps-1280-720.png"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4"></video> -->
<!-- <image muted="true" height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image>
-->
<image height="260" src="https://img.alicdn.com/imgextra/i4/O1CN01DgLj1T240jfpzKoaQ_!!6000000007329-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442102706767.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
<tr>
<td ><center>
<image height="260" src="https://img.alicdn.com/imgextra/i1/O1CN01odS61s1WW9tXen21S_!!6000000002795-0-tps-1280-720.jpg"></image>
</center></td>
<td ><center>
<!-- <video muted="true" autoplay="true" loop="true" height="260" src="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4"></video> -->
<image height="260" src="https://img.alicdn.com/imgextra/i3/O1CN01Jyk1HT28JkZtpAtY6_!!6000000007912-1-tps-1280-704.gif"></image>
</center></td>
</tr>
<tr>
<td ><center>
<p>Input Image</p>
</center></td>
<td ><center>
<p>Clike <a href="https://cloud.video.taobao.com/play/u/null/p/1/e/6/t/1/442163934688.mp4">HRER</a> to view the generated video.</p>
</center></td>
</tr>
</center>
</table>
</center>
### (3) Other methods
In preparation.
## Customize your own approach
Our codebase essentially supports all the commonly used components in video generation. You can manage your experiments flexibly by adding corresponding registration classes, including `ENGINE, MODEL, DATASETS, EMBEDDER, AUTO_ENCODER, DISTRIBUTION, VISUAL, DIFFUSION, PRETRAIN`, and can be compatible with all our open-source algorithms according to your own needs. If you have any questions, feel free to give us your feedback at any time.
## BibTeX
If this repo is useful to you, please cite our corresponding technical paper.
```bibtex
@article{2023i2vgenxl,
title={I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models},
author={Zhang, Shiwei and Wang, Jiayu and Zhang, Yingya and Zhao, Kang and Yuan, Hangjie and Qing, Zhiwu and Wang, Xiang and Zhao, Deli and Zhou, Jingren},
booktitle={arXiv preprint arXiv:2311.04145},
year={2023}
}
@article{2023videocomposer,
title={VideoComposer: Compositional Video Synthesis with Motion Controllability},
author={Wang, Xiang and Yuan, Hangjie and Zhang, Shiwei and Chen, Dayou and Wang, Jiuniu, and Zhang, Yingya, and Shen, Yujun, and Zhao, Deli and Zhou, Jingren},
booktitle={arXiv preprint arXiv:2306.02018},
year={2023}
}
@article{wang2023modelscope,
title={Modelscope text-to-video technical report},
author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei},
journal={arXiv preprint arXiv:2308.06571},
year={2023}
}
@article{dreamvideo,
title={DreamVideo: Composing Your Dream Videos with Customized Subject and Motion},
author={Wei, Yujie and Zhang, Shiwei and Qing, Zhiwu and Yuan, Hangjie and Liu, Zhiheng and Liu, Yu and Zhang, Yingya and Zhou, Jingren and Shan, Hongming},
journal={arXiv preprint arXiv:2312.04433},
year={2023}
}
@article{qing2023higen,
title={Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation},
author={Qing, Zhiwu and Zhang, Shiwei and Wang, Jiayu and Wang, Xiang and Wei, Yujie and Zhang, Yingya and Gao, Changxin and Sang, Nong },
journal={arXiv preprint arXiv:2312.04483},
year={2023}
}
@article{wang2023videolcm,
title={VideoLCM: Video Latent Consistency Model},
author={Wang, Xiang and Zhang, Shiwei and Zhang, Han and Liu, Yu and Zhang, Yingya and Gao, Changxin and Sang, Nong },
journal={arXiv preprint arXiv:2312.09109},
year={2023}
}
```
## Disclaimer
This open-source model is trained with using [WebVid-10M](https://m-bain.github.io/webvid-dataset/) and [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/) datasets and is intended for <strong>RESEARCH/NON-COMMERCIAL USE ONLY</strong>.
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] |
ragnartd/lucas
|
ragnartd
| 2023-12-15T15:52:37Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T15:51:46Z |
---
license: openrail
---
|
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] |
lombardata/dino-large-2023_12_15-with_custom_head
|
lombardata
| 2023-12-16T07:54:16Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"dinov2",
"multilabel-image-classification",
"multilabel",
"generated_from_trainer",
"eng",
"base_model:facebook/dinov2-large",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T15:53:15Z |
---
language:
- eng
license: apache-2.0
base_model: facebook/dinov2-large
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dino-large-2023_12_15-with_custom_head
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. -->
# dino-large-2023_12_15-with_custom_head
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the multilabel_complete_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1159
- F1 Micro: 0.8297
- F1 Macro: 0.7800
- Roc Auc: 0.8880
- Accuracy: 0.5435
- Learning Rate: 0.001
## 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: 0.01
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:-----:|
| No log | 1.0 | 268 | 0.1749 | 0.6911 | 0.5979 | 0.7869 | 0.4355 | 0.01 |
| 0.2108 | 2.0 | 536 | 0.1887 | 0.6448 | 0.5545 | 0.7473 | 0.4330 | 0.01 |
| 0.2108 | 3.0 | 804 | 0.1639 | 0.7291 | 0.5960 | 0.8089 | 0.4223 | 0.01 |
| 0.1888 | 4.0 | 1072 | 0.1676 | 0.7426 | 0.6583 | 0.8357 | 0.4416 | 0.01 |
| 0.1888 | 5.0 | 1340 | 0.1619 | 0.7133 | 0.6050 | 0.7932 | 0.4527 | 0.01 |
| 0.1877 | 6.0 | 1608 | 0.1532 | 0.7497 | 0.6624 | 0.8279 | 0.4691 | 0.01 |
| 0.1877 | 7.0 | 1876 | 0.1690 | 0.7409 | 0.6522 | 0.8402 | 0.3758 | 0.01 |
| 0.1845 | 8.0 | 2144 | 0.1461 | 0.7830 | 0.6987 | 0.8601 | 0.4770 | 0.01 |
| 0.1845 | 9.0 | 2412 | 0.1533 | 0.7629 | 0.7143 | 0.8392 | 0.4659 | 0.01 |
| 0.1845 | 10.0 | 2680 | 0.1703 | 0.7335 | 0.6227 | 0.8306 | 0.4234 | 0.01 |
| 0.1845 | 11.0 | 2948 | 0.1714 | 0.7191 | 0.6140 | 0.8054 | 0.4455 | 0.01 |
| 0.1871 | 12.0 | 3216 | 0.1538 | 0.7677 | 0.6816 | 0.8427 | 0.4562 | 0.01 |
| 0.1871 | 13.0 | 3484 | 0.1533 | 0.7673 | 0.6923 | 0.8508 | 0.4573 | 0.01 |
| 0.1881 | 14.0 | 3752 | 0.1572 | 0.7550 | 0.6424 | 0.8395 | 0.4584 | 0.01 |
| 0.1768 | 15.0 | 4020 | 0.1300 | 0.8118 | 0.7646 | 0.8766 | 0.5248 | 0.001 |
| 0.1768 | 16.0 | 4288 | 0.1237 | 0.8229 | 0.7727 | 0.8838 | 0.5277 | 0.001 |
| 0.1499 | 17.0 | 4556 | 0.1218 | 0.8285 | 0.7891 | 0.8933 | 0.5348 | 0.001 |
| 0.1499 | 18.0 | 4824 | 0.1193 | 0.8285 | 0.7831 | 0.8902 | 0.5284 | 0.001 |
| 0.1424 | 19.0 | 5092 | 0.1182 | 0.8298 | 0.7778 | 0.8849 | 0.5445 | 0.001 |
| 0.1424 | 20.0 | 5360 | 0.1182 | 0.8286 | 0.7698 | 0.8827 | 0.5395 | 0.001 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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igorktech/TALANT_saiga_7b-lora
|
igorktech
| 2023-12-15T15:55:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:IlyaGusev/saiga_mistral_7b_merged",
"region:us"
] | null | 2023-12-15T15:55:00Z |
---
library_name: peft
base_model: IlyaGusev/saiga_mistral_7b_merged
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.2.dev0
|
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annavvv/path-to-save-model
|
annavvv
| 2023-12-15T15:58:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T15:58:08Z |
Entry not found
|
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] |
gkallergis/bbbp_100_hp_opt
|
gkallergis
| 2023-12-15T16:11:05Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T15:58:34Z |
---
license: cc-by-nc-nd-4.0
---
|
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realryo1/nva-miku16th_0.1
|
realryo1
| 2023-12-15T16:00:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T16:00:39Z |
Entry not found
|
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elizabetvaganova/speech-to-speech-translation-vaganovaeli
|
elizabetvaganova
| 2023-12-15T16:20:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T16:03:19Z |
---
title: Speech To Speech Translation
emoji: 🏆
colorFrom: pink
colorTo: indigo
sdk: gradio
sdk_version: 3.36.1
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
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] |
Djck/kimeru
|
Djck
| 2023-12-15T16:20:02Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T16:05:00Z |
---
license: apache-2.0
---
|
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Samara1/distilbert-base-uncased-lora-text-classification
|
Samara1
| 2023-12-15T16:06:10Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T16:06:06Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-lora-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9937
- Accuracy: {'accuracy': 0.878}
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.5858 | {'accuracy': 0.839} |
| 0.4248 | 2.0 | 500 | 0.6538 | {'accuracy': 0.832} |
| 0.4248 | 3.0 | 750 | 0.5316 | {'accuracy': 0.88} |
| 0.2198 | 4.0 | 1000 | 0.6755 | {'accuracy': 0.875} |
| 0.2198 | 5.0 | 1250 | 0.8534 | {'accuracy': 0.867} |
| 0.0838 | 6.0 | 1500 | 0.8499 | {'accuracy': 0.88} |
| 0.0838 | 7.0 | 1750 | 0.8912 | {'accuracy': 0.885} |
| 0.0257 | 8.0 | 2000 | 0.9443 | {'accuracy': 0.879} |
| 0.0257 | 9.0 | 2250 | 0.9789 | {'accuracy': 0.88} |
| 0.0018 | 10.0 | 2500 | 0.9937 | {'accuracy': 0.878} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_14
|
Jeevesh8
| 2023-12-15T18:52:15Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T16:07:35Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_15
|
Jeevesh8
| 2023-12-15T18:58:46Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T16:07:35Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_13
|
Jeevesh8
| 2023-12-15T18:48:33Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T16:07:35Z |
Entry not found
|
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