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hkivancoral/smids_5x_beit_base_sgd_00001_fold2
|
hkivancoral
| 2023-12-15T22:05:42Z | 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
| 2023-12-15T20:46:57Z |
---
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_00001_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: 0.40099833610648916
---
<!-- 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_00001_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: 1.1209
- Accuracy: 0.4010
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1533 | 1.0 | 375 | 1.3089 | 0.3344 |
| 1.2124 | 2.0 | 750 | 1.2999 | 0.3344 |
| 1.1985 | 3.0 | 1125 | 1.2914 | 0.3428 |
| 1.1579 | 4.0 | 1500 | 1.2833 | 0.3461 |
| 1.1291 | 5.0 | 1875 | 1.2755 | 0.3461 |
| 1.194 | 6.0 | 2250 | 1.2681 | 0.3478 |
| 1.2016 | 7.0 | 2625 | 1.2608 | 0.3494 |
| 1.1347 | 8.0 | 3000 | 1.2537 | 0.3527 |
| 1.1472 | 9.0 | 3375 | 1.2468 | 0.3577 |
| 1.15 | 10.0 | 3750 | 1.2403 | 0.3611 |
| 1.1134 | 11.0 | 4125 | 1.2339 | 0.3661 |
| 1.1681 | 12.0 | 4500 | 1.2277 | 0.3694 |
| 1.1002 | 13.0 | 4875 | 1.2218 | 0.3677 |
| 1.1221 | 14.0 | 5250 | 1.2161 | 0.3677 |
| 1.0969 | 15.0 | 5625 | 1.2104 | 0.3694 |
| 1.1378 | 16.0 | 6000 | 1.2051 | 0.3694 |
| 1.0509 | 17.0 | 6375 | 1.1999 | 0.3727 |
| 1.0539 | 18.0 | 6750 | 1.1948 | 0.3727 |
| 1.1469 | 19.0 | 7125 | 1.1900 | 0.3760 |
| 1.0806 | 20.0 | 7500 | 1.1853 | 0.3760 |
| 1.1095 | 21.0 | 7875 | 1.1807 | 0.3760 |
| 1.0474 | 22.0 | 8250 | 1.1764 | 0.3760 |
| 1.0756 | 23.0 | 8625 | 1.1722 | 0.3810 |
| 1.1044 | 24.0 | 9000 | 1.1682 | 0.3794 |
| 1.1189 | 25.0 | 9375 | 1.1645 | 0.3844 |
| 1.0607 | 26.0 | 9750 | 1.1609 | 0.3844 |
| 1.1097 | 27.0 | 10125 | 1.1574 | 0.3844 |
| 1.0713 | 28.0 | 10500 | 1.1541 | 0.3860 |
| 1.0338 | 29.0 | 10875 | 1.1510 | 0.3877 |
| 1.0753 | 30.0 | 11250 | 1.1479 | 0.3910 |
| 1.0493 | 31.0 | 11625 | 1.1452 | 0.3910 |
| 1.0423 | 32.0 | 12000 | 1.1425 | 0.3910 |
| 1.0585 | 33.0 | 12375 | 1.1400 | 0.3943 |
| 1.0104 | 34.0 | 12750 | 1.1377 | 0.3960 |
| 1.0421 | 35.0 | 13125 | 1.1356 | 0.3960 |
| 1.0328 | 36.0 | 13500 | 1.1336 | 0.3977 |
| 1.0499 | 37.0 | 13875 | 1.1317 | 0.3993 |
| 1.0006 | 38.0 | 14250 | 1.1300 | 0.4010 |
| 1.0528 | 39.0 | 14625 | 1.1285 | 0.4010 |
| 1.0416 | 40.0 | 15000 | 1.1271 | 0.4010 |
| 1.0633 | 41.0 | 15375 | 1.1258 | 0.4010 |
| 1.0643 | 42.0 | 15750 | 1.1247 | 0.4027 |
| 1.0051 | 43.0 | 16125 | 1.1238 | 0.4027 |
| 1.0289 | 44.0 | 16500 | 1.1230 | 0.4027 |
| 0.9766 | 45.0 | 16875 | 1.1223 | 0.4010 |
| 1.0401 | 46.0 | 17250 | 1.1218 | 0.4010 |
| 1.0257 | 47.0 | 17625 | 1.1214 | 0.4010 |
| 1.0309 | 48.0 | 18000 | 1.1211 | 0.4010 |
| 1.0074 | 49.0 | 18375 | 1.1210 | 0.4010 |
| 1.0327 | 50.0 | 18750 | 1.1209 | 0.4010 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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badokorach/afriqa_afroxlmr_squad_v2_lug-151223
|
badokorach
| 2023-12-15T21:50:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:masakhane/afriqa_afroxlmr_squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-15T20:47:02Z |
---
license: mit
base_model: masakhane/afriqa_afroxlmr_squad_v2
tags:
- generated_from_keras_callback
model-index:
- name: badokorach/afriqa_afroxlmr_squad_v2_lug-151223
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. -->
# badokorach/afriqa_afroxlmr_squad_v2_lug-151223
This model is a fine-tuned version of [masakhane/afriqa_afroxlmr_squad_v2](https://huggingface.co/masakhane/afriqa_afroxlmr_squad_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2983
- Validation Loss: 0.0
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 2220, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.1442 | 0.0 | 0 |
| 1.9094 | 0.0 | 1 |
| 1.8084 | 0.0 | 2 |
| 1.7026 | 0.0 | 3 |
| 1.5949 | 0.0 | 4 |
| 1.4420 | 0.0 | 5 |
| 1.3059 | 0.0 | 6 |
| 1.1454 | 0.0 | 7 |
| 0.9870 | 0.0 | 8 |
| 0.9118 | 0.0 | 9 |
| 0.7496 | 0.0 | 10 |
| 0.6573 | 0.0 | 11 |
| 0.5447 | 0.0 | 12 |
| 0.4942 | 0.0 | 13 |
| 0.4074 | 0.0 | 14 |
| 0.3881 | 0.0 | 15 |
| 0.3546 | 0.0 | 16 |
| 0.3195 | 0.0 | 17 |
| 0.3119 | 0.0 | 18 |
| 0.2983 | 0.0 | 19 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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ppbrown/realcartoon-unmerge
|
ppbrown
| 2023-12-15T21:21:24Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T20:48:39Z |
---
license: openrail
---
like my other "unmerge" projects, this attempts to take some of the bad influences of the SD1.5 base out.
My intent is to hold tweaks of multiple varients of the "realcartoon" model line.
I'll start with the anime one, since my tweak fixed a missing arm problem I had with a test prompt.
Interestingly, for the "realistic" model, i technically messed up my plan for the "unmerge1" version of the file.
However.. the styles it puts out are nice in their own way. So you may wish to try out both to see what you prefer.
|
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Amir01/parsig_tokenizer
|
Amir01
| 2023-12-15T20:49:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:49:09Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_rms_001_fold2
|
hkivancoral
| 2023-12-15T22:11:56Z | 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
| 2023-12-15T20:51:44Z |
---
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_rms_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: 0.7953410981697171
---
<!-- 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_rms_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: 0.5865
- Accuracy: 0.7953
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0449 | 1.0 | 375 | 0.9946 | 0.4576 |
| 0.9062 | 2.0 | 750 | 0.8678 | 0.5341 |
| 0.8013 | 3.0 | 1125 | 1.1322 | 0.4709 |
| 0.7159 | 4.0 | 1500 | 0.7319 | 0.6373 |
| 0.717 | 5.0 | 1875 | 0.7090 | 0.6672 |
| 0.6942 | 6.0 | 2250 | 0.6958 | 0.6356 |
| 0.7767 | 7.0 | 2625 | 0.6812 | 0.7022 |
| 0.7025 | 8.0 | 3000 | 0.6844 | 0.6406 |
| 0.731 | 9.0 | 3375 | 0.6703 | 0.6872 |
| 0.712 | 10.0 | 3750 | 0.7094 | 0.6789 |
| 0.6865 | 11.0 | 4125 | 0.6498 | 0.6972 |
| 0.7524 | 12.0 | 4500 | 0.6865 | 0.6955 |
| 0.6624 | 13.0 | 4875 | 0.6872 | 0.6772 |
| 0.6979 | 14.0 | 5250 | 0.6496 | 0.6972 |
| 0.6174 | 15.0 | 5625 | 0.6736 | 0.6805 |
| 0.6379 | 16.0 | 6000 | 0.6464 | 0.6889 |
| 0.6532 | 17.0 | 6375 | 0.6449 | 0.7271 |
| 0.6218 | 18.0 | 6750 | 0.6026 | 0.7421 |
| 0.6018 | 19.0 | 7125 | 0.6684 | 0.6988 |
| 0.6058 | 20.0 | 7500 | 0.6198 | 0.7205 |
| 0.6269 | 21.0 | 7875 | 0.6185 | 0.7338 |
| 0.586 | 22.0 | 8250 | 0.5945 | 0.7571 |
| 0.6047 | 23.0 | 8625 | 0.5838 | 0.7404 |
| 0.5645 | 24.0 | 9000 | 0.5895 | 0.7304 |
| 0.5266 | 25.0 | 9375 | 0.6076 | 0.7554 |
| 0.5433 | 26.0 | 9750 | 0.6078 | 0.7205 |
| 0.6677 | 27.0 | 10125 | 0.5591 | 0.7837 |
| 0.5463 | 28.0 | 10500 | 0.6091 | 0.7488 |
| 0.5494 | 29.0 | 10875 | 0.5955 | 0.7471 |
| 0.4887 | 30.0 | 11250 | 0.5393 | 0.7987 |
| 0.5572 | 31.0 | 11625 | 0.5935 | 0.7537 |
| 0.5382 | 32.0 | 12000 | 0.6529 | 0.7288 |
| 0.5356 | 33.0 | 12375 | 0.5723 | 0.7787 |
| 0.5102 | 34.0 | 12750 | 0.5659 | 0.7720 |
| 0.5047 | 35.0 | 13125 | 0.5433 | 0.7887 |
| 0.4869 | 36.0 | 13500 | 0.5564 | 0.7687 |
| 0.4821 | 37.0 | 13875 | 0.5581 | 0.7754 |
| 0.455 | 38.0 | 14250 | 0.5595 | 0.7837 |
| 0.4345 | 39.0 | 14625 | 0.5481 | 0.7854 |
| 0.4695 | 40.0 | 15000 | 0.5459 | 0.8003 |
| 0.4129 | 41.0 | 15375 | 0.5458 | 0.8020 |
| 0.4369 | 42.0 | 15750 | 0.5508 | 0.7953 |
| 0.4043 | 43.0 | 16125 | 0.5495 | 0.7854 |
| 0.4715 | 44.0 | 16500 | 0.5470 | 0.7987 |
| 0.4036 | 45.0 | 16875 | 0.5777 | 0.7887 |
| 0.3786 | 46.0 | 17250 | 0.5867 | 0.8003 |
| 0.4177 | 47.0 | 17625 | 0.5806 | 0.7770 |
| 0.3538 | 48.0 | 18000 | 0.5857 | 0.7937 |
| 0.3987 | 49.0 | 18375 | 0.5813 | 0.8020 |
| 0.3452 | 50.0 | 18750 | 0.5865 | 0.7953 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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TexX/L_LAT
|
TexX
| 2023-12-15T21:08:02Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T20:54:38Z |
---
license: openrail
---
|
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bttest/random_model
|
bttest
| 2023-12-15T20:55:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:55:32Z |
Entry not found
|
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] |
ffedox/dataset-it-500-whisper
|
ffedox
| 2023-12-15T21:34:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T20:56:30Z |
Entry not found
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-naan-rerun-1e-4
|
kanishka
| 2023-12-16T10:11:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_aann_indef_naan",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T20:58:58Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_indef_naan
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef-naan-rerun-1e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_aann_indef_naan
type: kanishka/counterfactual_babylm_aann_indef_naan
metrics:
- name: Accuracy
type: accuracy
value: 0.4073649356157122
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-indef-naan-rerun-1e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_naan dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4083
- Accuracy: 0.4074
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0503 | 1.0 | 18595 | 4.2445 | 0.3095 |
| 3.5628 | 2.0 | 37190 | 3.7291 | 0.3632 |
| 3.387 | 3.0 | 55785 | 3.5789 | 0.3803 |
| 3.2888 | 4.0 | 74380 | 3.5034 | 0.3888 |
| 3.2233 | 5.0 | 92975 | 3.4553 | 0.3932 |
| 3.169 | 6.0 | 111570 | 3.4258 | 0.3974 |
| 3.1266 | 7.0 | 130165 | 3.4167 | 0.3996 |
| 3.0969 | 8.0 | 148760 | 3.4030 | 0.4014 |
| 3.0645 | 9.0 | 167355 | 3.4076 | 0.4025 |
| 3.037 | 10.0 | 185950 | 3.3951 | 0.4039 |
| 3.0085 | 11.0 | 204545 | 3.3910 | 0.4048 |
| 2.9889 | 12.0 | 223140 | 3.3887 | 0.4054 |
| 2.9703 | 13.0 | 241735 | 3.3882 | 0.4060 |
| 2.9498 | 14.0 | 260330 | 3.3978 | 0.4059 |
| 2.9248 | 15.0 | 278925 | 3.3988 | 0.4062 |
| 2.9106 | 16.0 | 297520 | 3.3962 | 0.4067 |
| 2.8909 | 17.0 | 316115 | 3.3952 | 0.4070 |
| 2.8717 | 18.0 | 334710 | 3.3977 | 0.4072 |
| 2.8605 | 19.0 | 353305 | 3.4046 | 0.4074 |
| 2.8433 | 20.0 | 371900 | 3.4083 | 0.4074 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
pangwen/MilesGRVC
|
pangwen
| 2023-12-15T21:04:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T21:00:38Z |
---
license: openrail
---
|
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annavvv/modeldir
|
annavvv
| 2023-12-15T21:03:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:03:00Z |
Entry not found
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] |
hanguo/lq-lora
|
hanguo
| 2023-12-16T04:40:39Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-15T21:03:38Z |
Entry not found
|
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] |
cpalenmichel/kmr-bible-asr
|
cpalenmichel
| 2023-12-15T21:07:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T21:06:56Z |
Entry not found
|
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LoneStriker/LeoScorpius-7B-Chat-DPO-4.65bpw-h6-exl2
|
LoneStriker
| 2023-12-15T21:07:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:07:57Z |
Entry not found
|
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Jinpf314/FrozenLake-v1-4x4-noSlippery
|
Jinpf314
| 2023-12-15T21:08:51Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T21:08:48Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Jinpf314/FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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meetplace1/reportlora
|
meetplace1
| 2023-12-15T21:17:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:16:18Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_00001_fold4
|
hkivancoral
| 2023-12-15T22:11:33Z | 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
| 2023-12-15T21:16:26Z |
---
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_00001_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: 0.41333333333333333
---
<!-- 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_00001_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: 1.0682
- Accuracy: 0.4133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3789 | 1.0 | 375 | 1.3304 | 0.3467 |
| 1.3522 | 2.0 | 750 | 1.2980 | 0.3417 |
| 1.2851 | 3.0 | 1125 | 1.2700 | 0.3467 |
| 1.3268 | 4.0 | 1500 | 1.2462 | 0.35 |
| 1.2083 | 5.0 | 1875 | 1.2264 | 0.3533 |
| 1.2564 | 6.0 | 2250 | 1.2096 | 0.3667 |
| 1.2076 | 7.0 | 2625 | 1.1953 | 0.375 |
| 1.1738 | 8.0 | 3000 | 1.1833 | 0.375 |
| 1.1964 | 9.0 | 3375 | 1.1730 | 0.3767 |
| 1.1824 | 10.0 | 3750 | 1.1642 | 0.375 |
| 1.1746 | 11.0 | 4125 | 1.1567 | 0.375 |
| 1.0941 | 12.0 | 4500 | 1.1499 | 0.3783 |
| 1.1561 | 13.0 | 4875 | 1.1439 | 0.3817 |
| 1.1702 | 14.0 | 5250 | 1.1384 | 0.3817 |
| 1.1181 | 15.0 | 5625 | 1.1334 | 0.3867 |
| 1.149 | 16.0 | 6000 | 1.1288 | 0.3833 |
| 1.1131 | 17.0 | 6375 | 1.1244 | 0.3867 |
| 1.1335 | 18.0 | 6750 | 1.1203 | 0.39 |
| 1.105 | 19.0 | 7125 | 1.1164 | 0.3933 |
| 1.0655 | 20.0 | 7500 | 1.1128 | 0.3933 |
| 1.1098 | 21.0 | 7875 | 1.1094 | 0.395 |
| 1.0972 | 22.0 | 8250 | 1.1061 | 0.3933 |
| 1.112 | 23.0 | 8625 | 1.1030 | 0.3917 |
| 1.0932 | 24.0 | 9000 | 1.1001 | 0.395 |
| 1.0801 | 25.0 | 9375 | 1.0974 | 0.3933 |
| 1.1085 | 26.0 | 9750 | 1.0947 | 0.4 |
| 1.1153 | 27.0 | 10125 | 1.0922 | 0.4 |
| 1.0883 | 28.0 | 10500 | 1.0899 | 0.4 |
| 1.0621 | 29.0 | 10875 | 1.0877 | 0.4017 |
| 1.0559 | 30.0 | 11250 | 1.0856 | 0.4017 |
| 1.0795 | 31.0 | 11625 | 1.0837 | 0.4 |
| 1.1076 | 32.0 | 12000 | 1.0819 | 0.4017 |
| 1.1027 | 33.0 | 12375 | 1.0802 | 0.405 |
| 1.0471 | 34.0 | 12750 | 1.0787 | 0.41 |
| 1.032 | 35.0 | 13125 | 1.0772 | 0.4117 |
| 1.0529 | 36.0 | 13500 | 1.0759 | 0.4083 |
| 1.0365 | 37.0 | 13875 | 1.0747 | 0.4067 |
| 1.0659 | 38.0 | 14250 | 1.0736 | 0.4067 |
| 1.073 | 39.0 | 14625 | 1.0726 | 0.4117 |
| 1.1034 | 40.0 | 15000 | 1.0717 | 0.4117 |
| 1.0918 | 41.0 | 15375 | 1.0710 | 0.4117 |
| 1.0873 | 42.0 | 15750 | 1.0703 | 0.4133 |
| 1.0582 | 43.0 | 16125 | 1.0697 | 0.4133 |
| 1.0527 | 44.0 | 16500 | 1.0693 | 0.4133 |
| 1.0394 | 45.0 | 16875 | 1.0689 | 0.4133 |
| 1.0718 | 46.0 | 17250 | 1.0686 | 0.4133 |
| 1.0719 | 47.0 | 17625 | 1.0684 | 0.4133 |
| 1.0655 | 48.0 | 18000 | 1.0683 | 0.4133 |
| 1.0516 | 49.0 | 18375 | 1.0682 | 0.4133 |
| 1.0396 | 50.0 | 18750 | 1.0682 | 0.4133 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4
|
kanishka
| 2023-12-16T10:30:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_prototypical_only",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T21:17:21Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_prototypical_only
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_prototypical_only
type: kanishka/counterfactual_babylm_prototypical_only
metrics:
- name: Accuracy
type: accuracy
value: 0.40804219428728983
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_prototypical_only dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4082
- Accuracy: 0.4080
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7341 | 1.0 | 18593 | 3.8768 | 0.3468 |
| 3.4322 | 2.0 | 37186 | 3.6158 | 0.3751 |
| 3.2902 | 3.0 | 55779 | 3.4817 | 0.3883 |
| 3.21 | 4.0 | 74372 | 3.4286 | 0.3960 |
| 3.1498 | 5.0 | 92965 | 3.4151 | 0.3978 |
| 3.0981 | 6.0 | 111558 | 3.3790 | 0.4022 |
| 3.0651 | 7.0 | 130151 | 3.3750 | 0.4034 |
| 3.0292 | 8.0 | 148744 | 3.3879 | 0.4041 |
| 3.0031 | 9.0 | 167337 | 3.3773 | 0.4046 |
| 2.9713 | 10.0 | 185930 | 3.3769 | 0.4061 |
| 2.9529 | 11.0 | 204523 | 3.3778 | 0.4069 |
| 2.9286 | 12.0 | 223116 | 3.3612 | 0.4077 |
| 2.9065 | 13.0 | 241709 | 3.3686 | 0.4073 |
| 2.8837 | 14.0 | 260302 | 3.3861 | 0.4078 |
| 2.8621 | 15.0 | 278895 | 3.3851 | 0.4077 |
| 2.8487 | 16.0 | 297488 | 3.3876 | 0.4080 |
| 2.8243 | 17.0 | 316081 | 3.3908 | 0.4081 |
| 2.8078 | 18.0 | 334674 | 3.3952 | 0.4082 |
| 2.7887 | 19.0 | 353267 | 3.4020 | 0.4082 |
| 2.7716 | 20.0 | 371860 | 3.4082 | 0.4080 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Garces/DistilBERT-finetuned-on-emotion
|
Garces
| 2023-12-16T00:48:37Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T21:18:20Z |
Entry not found
|
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Nikt271/Nermal
|
Nikt271
| 2023-12-15T21:19:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:19:12Z |
Entry not found
|
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] |
dsdw/Almigthy
|
dsdw
| 2023-12-15T21:22:37Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T21:22:37Z |
---
license: apache-2.0
---
|
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qxnam/bloom-560m-finetuned-finance
|
qxnam
| 2023-12-15T21:23:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:23:32Z |
Entry not found
|
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] |
Jinpf314/taxi
|
Jinpf314
| 2023-12-15T21:23:43Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T21:23:41Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Jinpf314/taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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mark434/my-cool-model
|
mark434
| 2023-12-15T21:23:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:23:42Z |
Entry not found
|
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] |
odunola/bge-reranker-base
|
odunola
| 2023-12-15T21:30:29Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T21:24:52Z |
Entry not found
|
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andromedanita/tinyshakespeare-gpt2
|
andromedanita
| 2023-12-15T21:29:02Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T21:28:25Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: tinyshakespeare-gpt2
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. -->
# tinyshakespeare-gpt2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6163
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.1172 | 1.0 | 5000 | 3.6163 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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gadhane/FrozenLake-4x4-Slippery_CustomEnv
|
gadhane
| 2023-12-15T21:30:54Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T21:30:52Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-4x4-Slippery_CustomEnv
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.17 +/- 0.38
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="gadhane/FrozenLake-4x4-Slippery_CustomEnv", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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bmarsic/model1
|
bmarsic
| 2023-12-15T21:31:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:31:36Z |
Entry not found
|
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] |
JTIII/Test_1
|
JTIII
| 2023-12-15T21:34:27Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T21:34:27Z |
---
license: mit
---
|
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rabs462/saber
|
rabs462
| 2023-12-15T21:37:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:37:09Z |
Entry not found
|
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] |
Salman11223/gender_detection
|
Salman11223
| 2023-12-15T22:03:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:38:14Z |
Make sure to update the file paths, model name, and any other details according to your actual implementation. Additionally, if you have any special instructions, usage examples, or additional information, include them in the `README.md`.
|
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Lbmrich/mit
|
Lbmrich
| 2023-12-15T21:40:00Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T21:40:00Z |
---
license: mit
---
|
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adamzinebi/lmd_8bars_tokenizer
|
adamzinebi
| 2023-12-16T13:31:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:42:42Z |
Entry not found
|
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] |
Homiebear/SunDrop
|
Homiebear
| 2023-12-15T21:45:08Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T21:43:31Z |
---
license: openrail
---
|
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EmmaGthn/runs_lora_5000_moji
|
EmmaGthn
| 2023-12-16T04:49:21Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T21:47:16Z |
Entry not found
|
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WhiteAiZ/Models
|
WhiteAiZ
| 2023-12-16T00:22:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:48:25Z |
Entry not found
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] |
themariolinml/roberta-base-sqaud2-on-medical_meadow_medqa-v1
|
themariolinml
| 2023-12-15T22:55:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-15T21:48:37Z |
Entry not found
|
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] |
KAUE2006/PatatiPauloTeixeira
|
KAUE2006
| 2023-12-15T21:55:11Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T21:55:11Z |
---
license: openrail
---
|
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mimicheng/results
|
mimicheng
| 2023-12-15T21:56:35Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2023-12-15T21:56:34Z |
---
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0350
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.22 | 500 | 0.0404 |
| 0.0625 | 0.43 | 1000 | 0.0377 |
| 0.0625 | 0.65 | 1500 | 0.0364 |
| 0.0359 | 0.87 | 2000 | 0.0350 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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mimicheng/token-function-calling-context-12dec
|
mimicheng
| 2023-12-15T21:57:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T21:57:19Z |
Entry not found
|
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] |
mimicheng/function-calling-context-12dec
|
mimicheng
| 2023-12-15T21:57:21Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"region:us"
] | null | 2023-12-15T21:57:20Z |
---
library_name: peft
base_model: HuggingFaceH4/zephyr-7b-beta
---
# Model Card for Model ID
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
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] |
Olivacker/vitachi
|
Olivacker
| 2023-12-15T22:02:20Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T22:01:40Z |
---
license: openrail
---
|
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] |
squareoctopus/tallercitoOcampo
|
squareoctopus
| 2023-12-15T22:03:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:DeepESP/gpt2-spanish",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T22:02:43Z |
---
license: mit
base_model: DeepESP/gpt2-spanish
tags:
- generated_from_trainer
model-index:
- name: O
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. -->
# O
This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4825
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8207 | 1.0 | 49 | 2.6269 |
| 2.3896 | 2.0 | 98 | 2.5210 |
| 2.2598 | 3.0 | 147 | 2.4898 |
| 2.14 | 4.0 | 196 | 2.4825 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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begnini/ner-bert-large-cased-pt-contratos_tceal
|
begnini
| 2023-12-16T12:16:18Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:contratos_tceal",
"base_model:begnini/ner-bert-large-cased-pt-contratos_tceal",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-15T22:02:44Z |
---
base_model: begnini/ner-bert-large-cased-pt-contratos_tceal
tags:
- generated_from_trainer
datasets:
- contratos_tceal
model-index:
- name: ner-bert-large-cased-pt-contratos_tceal
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. -->
# ner-bert-large-cased-pt-contratos_tceal
This model is a fine-tuned version of [begnini/ner-bert-large-cased-pt-contratos_tceal](https://huggingface.co/begnini/ner-bert-large-cased-pt-contratos_tceal) on the contratos_tceal dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 20
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
arusso/finetuned-ar-text-classification-18-llama-2-7b-AR-20231215
|
arusso
| 2023-12-15T22:05:18Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-15T22:04:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
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Salman11223/again
|
Salman11223
| 2023-12-15T22:05:21Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T22:04:58Z |
---
license: apache-2.0
---
|
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] |
hkivancoral/smids_5x_beit_base_sgd_00001_fold3
|
hkivancoral
| 2023-12-15T23:25:44Z | 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
| 2023-12-15T22:06:40Z |
---
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_00001_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: 0.39666666666666667
---
<!-- 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_00001_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: 1.1248
- Accuracy: 0.3967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2551 | 1.0 | 375 | 1.3211 | 0.3167 |
| 1.2561 | 2.0 | 750 | 1.3119 | 0.32 |
| 1.2134 | 3.0 | 1125 | 1.3028 | 0.325 |
| 1.226 | 4.0 | 1500 | 1.2942 | 0.325 |
| 1.1635 | 5.0 | 1875 | 1.2859 | 0.3267 |
| 1.2304 | 6.0 | 2250 | 1.2778 | 0.3333 |
| 1.1734 | 7.0 | 2625 | 1.2702 | 0.3383 |
| 1.1724 | 8.0 | 3000 | 1.2625 | 0.3417 |
| 1.1336 | 9.0 | 3375 | 1.2554 | 0.3467 |
| 1.1266 | 10.0 | 3750 | 1.2486 | 0.3517 |
| 1.1276 | 11.0 | 4125 | 1.2419 | 0.355 |
| 1.1538 | 12.0 | 4500 | 1.2355 | 0.355 |
| 1.1425 | 13.0 | 4875 | 1.2292 | 0.3567 |
| 1.1463 | 14.0 | 5250 | 1.2233 | 0.36 |
| 1.1661 | 15.0 | 5625 | 1.2174 | 0.3633 |
| 1.1118 | 16.0 | 6000 | 1.2118 | 0.365 |
| 1.123 | 17.0 | 6375 | 1.2063 | 0.3667 |
| 1.1065 | 18.0 | 6750 | 1.2010 | 0.3667 |
| 1.1074 | 19.0 | 7125 | 1.1959 | 0.365 |
| 1.0742 | 20.0 | 7500 | 1.1911 | 0.3717 |
| 1.0616 | 21.0 | 7875 | 1.1865 | 0.3717 |
| 1.0745 | 22.0 | 8250 | 1.1820 | 0.3717 |
| 1.0871 | 23.0 | 8625 | 1.1777 | 0.3717 |
| 1.031 | 24.0 | 9000 | 1.1737 | 0.3717 |
| 1.0843 | 25.0 | 9375 | 1.1697 | 0.375 |
| 1.0616 | 26.0 | 9750 | 1.1660 | 0.3767 |
| 1.0414 | 27.0 | 10125 | 1.1624 | 0.3783 |
| 1.0303 | 28.0 | 10500 | 1.1590 | 0.3783 |
| 0.9887 | 29.0 | 10875 | 1.1558 | 0.38 |
| 1.0267 | 30.0 | 11250 | 1.1528 | 0.38 |
| 1.0792 | 31.0 | 11625 | 1.1499 | 0.3833 |
| 1.0736 | 32.0 | 12000 | 1.1472 | 0.3883 |
| 1.0868 | 33.0 | 12375 | 1.1446 | 0.39 |
| 1.0257 | 34.0 | 12750 | 1.1422 | 0.3883 |
| 1.0237 | 35.0 | 13125 | 1.1400 | 0.39 |
| 1.0201 | 36.0 | 13500 | 1.1379 | 0.39 |
| 1.0769 | 37.0 | 13875 | 1.1360 | 0.3917 |
| 1.032 | 38.0 | 14250 | 1.1343 | 0.3933 |
| 1.0317 | 39.0 | 14625 | 1.1327 | 0.395 |
| 1.0402 | 40.0 | 15000 | 1.1312 | 0.395 |
| 0.957 | 41.0 | 15375 | 1.1300 | 0.395 |
| 1.0445 | 42.0 | 15750 | 1.1288 | 0.395 |
| 1.0399 | 43.0 | 16125 | 1.1278 | 0.395 |
| 1.0323 | 44.0 | 16500 | 1.1270 | 0.3967 |
| 1.0444 | 45.0 | 16875 | 1.1263 | 0.3967 |
| 0.9983 | 46.0 | 17250 | 1.1257 | 0.3967 |
| 1.042 | 47.0 | 17625 | 1.1253 | 0.3967 |
| 1.0685 | 48.0 | 18000 | 1.1250 | 0.3967 |
| 1.0486 | 49.0 | 18375 | 1.1249 | 0.3967 |
| 1.0457 | 50.0 | 18750 | 1.1248 | 0.3967 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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sonnygeorge/whisper-tiny-papi
|
sonnygeorge
| 2023-12-16T03:33:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"ASR",
"Papiamentu",
"Whisper",
"Speech Recognition",
"generated_from_trainer",
"dataset:sonnygeorge/papi_asr_corpus",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T22:07:07Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- ASR
- Papiamentu
- Whisper
- Speech Recognition
- generated_from_trainer
datasets:
- sonnygeorge/papi_asr_corpus
metrics:
- wer
model-index:
- name: Whisper Tiny Papiamentu
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Papi ASR
type: sonnygeorge/papi_asr_corpus
metrics:
- name: Wer
type: wer
value: 30.326720259606166
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny Papiamentu
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Papi ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3275
- Wer: 30.3267
- Cer: 15.4823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.1932 | 0.07 | 50 | 0.3766 | 42.1934 | 28.1746 |
| 0.2235 | 0.15 | 100 | 0.3513 | 33.0998 | 17.9645 |
| 0.2989 | 0.22 | 150 | 0.3337 | 30.2898 | 14.8959 |
| 0.3269 | 0.3 | 200 | 0.3275 | 30.3267 | 15.4823 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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0.11234363913536072,
0.2610357403755188,
-0.8190312385559082
] |
nlamot/Reinforce-CartPole-v1
|
nlamot
| 2023-12-15T22:33:16Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T22:10:05Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_00001_fold5
|
hkivancoral
| 2023-12-15T23:06:52Z | 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
| 2023-12-15T22:12:14Z |
---
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_00001_fold5
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: 0.45
---
<!-- 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_00001_fold5
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: 1.0692
- Accuracy: 0.45
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4013 | 1.0 | 375 | 1.3267 | 0.3533 |
| 1.3461 | 2.0 | 750 | 1.2947 | 0.36 |
| 1.2972 | 3.0 | 1125 | 1.2670 | 0.3617 |
| 1.31 | 4.0 | 1500 | 1.2433 | 0.3683 |
| 1.2221 | 5.0 | 1875 | 1.2236 | 0.3717 |
| 1.2656 | 6.0 | 2250 | 1.2067 | 0.375 |
| 1.2312 | 7.0 | 2625 | 1.1923 | 0.3817 |
| 1.1861 | 8.0 | 3000 | 1.1803 | 0.3817 |
| 1.1289 | 9.0 | 3375 | 1.1699 | 0.385 |
| 1.218 | 10.0 | 3750 | 1.1612 | 0.3867 |
| 1.1921 | 11.0 | 4125 | 1.1535 | 0.3983 |
| 1.1315 | 12.0 | 4500 | 1.1468 | 0.405 |
| 1.1732 | 13.0 | 4875 | 1.1407 | 0.4133 |
| 1.1412 | 14.0 | 5250 | 1.1354 | 0.41 |
| 1.1502 | 15.0 | 5625 | 1.1305 | 0.4133 |
| 1.126 | 16.0 | 6000 | 1.1259 | 0.4133 |
| 1.1098 | 17.0 | 6375 | 1.1217 | 0.4117 |
| 1.1197 | 18.0 | 6750 | 1.1177 | 0.4083 |
| 1.1329 | 19.0 | 7125 | 1.1140 | 0.4083 |
| 1.0741 | 20.0 | 7500 | 1.1105 | 0.4117 |
| 1.0617 | 21.0 | 7875 | 1.1072 | 0.4117 |
| 1.0917 | 22.0 | 8250 | 1.1041 | 0.41 |
| 1.0822 | 23.0 | 8625 | 1.1011 | 0.41 |
| 1.1336 | 24.0 | 9000 | 1.0984 | 0.4167 |
| 1.0665 | 25.0 | 9375 | 1.0958 | 0.415 |
| 1.1097 | 26.0 | 9750 | 1.0934 | 0.415 |
| 1.0499 | 27.0 | 10125 | 1.0911 | 0.4183 |
| 1.1202 | 28.0 | 10500 | 1.0889 | 0.4167 |
| 1.1038 | 29.0 | 10875 | 1.0869 | 0.4283 |
| 1.0838 | 30.0 | 11250 | 1.0850 | 0.4333 |
| 1.0717 | 31.0 | 11625 | 1.0832 | 0.4367 |
| 1.0773 | 32.0 | 12000 | 1.0816 | 0.4383 |
| 1.0858 | 33.0 | 12375 | 1.0800 | 0.44 |
| 1.0072 | 34.0 | 12750 | 1.0786 | 0.44 |
| 1.0435 | 35.0 | 13125 | 1.0773 | 0.4417 |
| 1.047 | 36.0 | 13500 | 1.0761 | 0.4433 |
| 1.0361 | 37.0 | 13875 | 1.0750 | 0.4483 |
| 1.0477 | 38.0 | 14250 | 1.0740 | 0.45 |
| 1.0658 | 39.0 | 14625 | 1.0731 | 0.4483 |
| 1.0711 | 40.0 | 15000 | 1.0723 | 0.445 |
| 1.0473 | 41.0 | 15375 | 1.0716 | 0.445 |
| 1.0521 | 42.0 | 15750 | 1.0711 | 0.445 |
| 1.0368 | 43.0 | 16125 | 1.0705 | 0.4467 |
| 1.0636 | 44.0 | 16500 | 1.0701 | 0.4483 |
| 1.0424 | 45.0 | 16875 | 1.0698 | 0.4483 |
| 1.0442 | 46.0 | 17250 | 1.0695 | 0.45 |
| 1.0667 | 47.0 | 17625 | 1.0694 | 0.45 |
| 1.0523 | 48.0 | 18000 | 1.0693 | 0.45 |
| 1.0135 | 49.0 | 18375 | 1.0692 | 0.45 |
| 1.0393 | 50.0 | 18750 | 1.0692 | 0.45 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_beit_base_rms_001_fold3
|
hkivancoral
| 2023-12-15T23:33:27Z | 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
| 2023-12-15T22:12:53Z |
---
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_rms_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: 0.7883333333333333
---
<!-- 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_rms_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: 1.2450
- Accuracy: 0.7883
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8383 | 1.0 | 375 | 0.9251 | 0.4967 |
| 0.7811 | 2.0 | 750 | 0.8274 | 0.55 |
| 0.7757 | 3.0 | 1125 | 0.8322 | 0.55 |
| 0.774 | 4.0 | 1500 | 0.7903 | 0.5667 |
| 0.7988 | 5.0 | 1875 | 0.7818 | 0.59 |
| 0.7926 | 6.0 | 2250 | 0.7711 | 0.595 |
| 0.7549 | 7.0 | 2625 | 0.7682 | 0.6267 |
| 0.7997 | 8.0 | 3000 | 0.7569 | 0.61 |
| 0.6926 | 9.0 | 3375 | 0.7561 | 0.6417 |
| 0.7413 | 10.0 | 3750 | 0.7251 | 0.6567 |
| 0.6722 | 11.0 | 4125 | 0.7285 | 0.6533 |
| 0.7582 | 12.0 | 4500 | 0.7029 | 0.66 |
| 0.6728 | 13.0 | 4875 | 0.7283 | 0.6433 |
| 0.6373 | 14.0 | 5250 | 0.7252 | 0.6333 |
| 0.648 | 15.0 | 5625 | 0.7000 | 0.67 |
| 0.6675 | 16.0 | 6000 | 0.7072 | 0.6683 |
| 0.7316 | 17.0 | 6375 | 0.7063 | 0.6717 |
| 0.7151 | 18.0 | 6750 | 0.6856 | 0.6683 |
| 0.6082 | 19.0 | 7125 | 0.6800 | 0.6817 |
| 0.6879 | 20.0 | 7500 | 0.6816 | 0.6733 |
| 0.5586 | 21.0 | 7875 | 0.6735 | 0.695 |
| 0.6065 | 22.0 | 8250 | 0.6507 | 0.71 |
| 0.5783 | 23.0 | 8625 | 0.6597 | 0.69 |
| 0.6456 | 24.0 | 9000 | 0.6102 | 0.74 |
| 0.5238 | 25.0 | 9375 | 0.6683 | 0.7117 |
| 0.5326 | 26.0 | 9750 | 0.6240 | 0.7183 |
| 0.5499 | 27.0 | 10125 | 0.6403 | 0.7083 |
| 0.5607 | 28.0 | 10500 | 0.5945 | 0.7417 |
| 0.4887 | 29.0 | 10875 | 0.6536 | 0.71 |
| 0.5354 | 30.0 | 11250 | 0.5785 | 0.725 |
| 0.5136 | 31.0 | 11625 | 0.6072 | 0.7517 |
| 0.5448 | 32.0 | 12000 | 0.6265 | 0.7383 |
| 0.4542 | 33.0 | 12375 | 0.6265 | 0.7417 |
| 0.4208 | 34.0 | 12750 | 0.6113 | 0.745 |
| 0.3509 | 35.0 | 13125 | 0.6279 | 0.7467 |
| 0.4112 | 36.0 | 13500 | 0.6145 | 0.74 |
| 0.3719 | 37.0 | 13875 | 0.6674 | 0.745 |
| 0.3029 | 38.0 | 14250 | 0.6977 | 0.7583 |
| 0.3416 | 39.0 | 14625 | 0.6751 | 0.7717 |
| 0.3246 | 40.0 | 15000 | 0.6878 | 0.7633 |
| 0.2432 | 41.0 | 15375 | 0.6417 | 0.79 |
| 0.2014 | 42.0 | 15750 | 0.7882 | 0.78 |
| 0.2354 | 43.0 | 16125 | 0.8175 | 0.7817 |
| 0.1797 | 44.0 | 16500 | 0.8553 | 0.79 |
| 0.1419 | 45.0 | 16875 | 0.9481 | 0.765 |
| 0.1815 | 46.0 | 17250 | 1.0306 | 0.765 |
| 0.1604 | 47.0 | 17625 | 1.0263 | 0.765 |
| 0.103 | 48.0 | 18000 | 1.1281 | 0.7833 |
| 0.0441 | 49.0 | 18375 | 1.2055 | 0.79 |
| 0.0741 | 50.0 | 18750 | 1.2450 | 0.7883 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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shredder-31/Question_generation_Fine_tuning_llama_7B_save
|
shredder-31
| 2023-12-15T22:13:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T22:13:34Z |
Entry not found
|
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] |
usuarioalisboa/nelson
|
usuarioalisboa
| 2023-12-15T22:21:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T22:16:38Z |
---
license: openrail
---
|
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zohann/whisper-v3-large-arabic
|
zohann
| 2023-12-15T22:16:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T22:16:40Z |
Entry not found
|
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EmmaGthn/runs_prefix_5000_moji
|
EmmaGthn
| 2023-12-16T02:28:45Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T22:16:46Z |
Entry not found
|
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] |
4naluvs/YENA
|
4naluvs
| 2023-12-15T22:20:39Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T22:18:18Z |
---
license: openrail
---
|
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] |
stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep09
|
stvhuang
| 2023-12-15T22:21:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T22:20:51Z |
Entry not found
|
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] |
jlbaker361/sd-wikiart-balanced100
|
jlbaker361
| 2023-12-15T22:37:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2023-12-15T22:23:45Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
datasets:
- jlbaker361/wikiart-balanced100
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - jlbaker361/sd-wikiart-balanced100
This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **jlbaker361/wikiart-balanced100** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['painting']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("jlbaker361/sd-wikiart-balanced100", torch_dtype=torch.float16)
prompt = "painting"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 1
* Learning rate: 0.0001
* Batch size: 4
* Gradient accumulation steps: 1
* Image resolution: 512
* Mixed-precision: None
|
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jlbaker361/sd-wikiart-lora-balanced100
|
jlbaker361
| 2023-12-15T22:23:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T22:23:53Z |
Entry not found
|
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esdoro/transformer_basic
|
esdoro
| 2023-12-15T22:37:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T22:37:06Z |
Entry not found
|
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] |
nlamot/Pixelcopter-PLE-v0
|
nlamot
| 2023-12-15T23:06:57Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T22:37:22Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 13.60 +/- 7.96
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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] |
imdatta0/llama_2_13b_instruct_v3_downNupNgateNqNkNvNo_r8_lr0.0001_bg28_alpha16
|
imdatta0
| 2023-12-15T22:41:26Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-hf",
"region:us"
] | null | 2023-12-15T22:41:20Z |
---
base_model: meta-llama/Llama-2-13b-hf
tags:
- generated_from_trainer
model-index:
- name: llama_2_13b_instruct_v3_downNupNgateNqNkNvNo_r8_lr0.0001_bg28_alpha16
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. -->
# llama_2_13b_instruct_v3_downNupNgateNqNkNvNo_r8_lr0.0001_bg28_alpha16
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2773 | 0.04 | 127 | 1.1913 |
| 1.2266 | 0.08 | 254 | 1.1740 |
| 1.2233 | 0.12 | 381 | 1.1634 |
| 1.1932 | 0.16 | 508 | 1.1576 |
| 1.1958 | 0.2 | 635 | 1.1520 |
| 1.1743 | 0.24 | 762 | 1.1485 |
| 1.1847 | 0.28 | 889 | 1.1469 |
| 1.1783 | 0.32 | 1016 | 1.1434 |
| 1.1708 | 0.36 | 1143 | 1.1414 |
| 1.1894 | 0.4 | 1270 | 1.1398 |
| 1.1862 | 0.44 | 1397 | 1.1385 |
| 1.1757 | 0.48 | 1524 | 1.1361 |
| 1.1965 | 0.52 | 1651 | 1.1334 |
| 1.1821 | 0.56 | 1778 | 1.1335 |
| 1.1861 | 0.6 | 1905 | 1.1322 |
| 1.1719 | 0.64 | 2032 | 1.1306 |
| 1.1755 | 0.68 | 2159 | 1.1298 |
| 1.1868 | 0.72 | 2286 | 1.1291 |
| 1.1684 | 0.76 | 2413 | 1.1282 |
| 1.166 | 0.8 | 2540 | 1.1279 |
| 1.1532 | 0.84 | 2667 | 1.1274 |
| 1.1825 | 0.88 | 2794 | 1.1273 |
| 1.1926 | 0.92 | 2921 | 1.1271 |
| 1.2073 | 0.96 | 3048 | 1.1272 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
4naluvs/4YENA
|
4naluvs
| 2023-12-15T22:46:02Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T22:42:22Z |
---
license: openrail
---
|
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] |
wenqian157/yolo8concrete
|
wenqian157
| 2023-12-15T22:46:11Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T22:46:11Z |
---
license: mit
---
|
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] |
Vermiculos/ripdave
|
Vermiculos
| 2023-12-15T22:48:19Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T22:46:31Z |
---
license: openrail
---
|
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] |
Vermiculos/iowacorey
|
Vermiculos
| 2023-12-15T22:56:22Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T22:51:15Z |
---
license: openrail
---
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-naan-non-num-1e-4
|
kanishka
| 2023-12-16T21:24:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T22:52:02Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef-naan-non-num-1e-4
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. -->
# smolm-autoreg-bpe-counterfactual-babylm-indef-naan-non-num-1e-4
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4001
- Accuracy: 0.4070
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0483 | 1.0 | 18595 | 4.2392 | 0.3096 |
| 3.5616 | 2.0 | 37190 | 3.7499 | 0.3632 |
| 3.3897 | 3.0 | 55785 | 3.5761 | 0.3799 |
| 3.2852 | 4.0 | 74380 | 3.5176 | 0.3892 |
| 3.2213 | 5.0 | 92975 | 3.4521 | 0.3945 |
| 3.1672 | 6.0 | 111570 | 3.4455 | 0.3972 |
| 3.1288 | 7.0 | 130165 | 3.3971 | 0.3998 |
| 3.0938 | 8.0 | 148760 | 3.4029 | 0.4015 |
| 3.0604 | 9.0 | 167355 | 3.4109 | 0.4028 |
| 3.0351 | 10.0 | 185950 | 3.3869 | 0.4038 |
| 3.0112 | 11.0 | 204545 | 3.3820 | 0.4045 |
| 2.9885 | 12.0 | 223140 | 3.3781 | 0.4044 |
| 2.9636 | 13.0 | 241735 | 3.3870 | 0.4053 |
| 2.9471 | 14.0 | 260330 | 3.3769 | 0.4062 |
| 2.9306 | 15.0 | 278925 | 3.3843 | 0.4065 |
| 2.91 | 16.0 | 297520 | 3.3894 | 0.4062 |
| 2.8869 | 17.0 | 316115 | 3.3840 | 0.4070 |
| 2.8778 | 18.0 | 334710 | 3.3944 | 0.4068 |
| 2.8635 | 19.0 | 353305 | 3.3986 | 0.4069 |
| 2.8419 | 20.0 | 371900 | 3.4001 | 0.4070 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
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juniorvalz/repolloclips
|
juniorvalz
| 2023-12-15T22:57:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T22:55:49Z |
Entry not found
|
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AiAF/Animefull-final-pruned.safetensors
|
AiAF
| 2023-12-15T23:03:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T22:58:02Z |
Entry not found
|
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] |
Vermiculos/balladjames
|
Vermiculos
| 2023-12-15T23:04:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T22:58:07Z |
---
license: openrail
---
|
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] |
maxwell-pi/xls-r-basque
|
maxwell-pi
| 2023-12-16T03:56:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T23:06:43Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: xls-r-basque
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. -->
# xls-r-basque
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Vermiculos/charlie
|
Vermiculos
| 2023-12-15T23:12:41Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T23:09:51Z |
---
license: openrail
---
|
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TheBloke/Metis-0.1-AWQ
|
TheBloke
| 2023-12-15T23:35:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:Mihaiii/Metis-0.1",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-15T23:17:56Z |
---
base_model: Mihaiii/Metis-0.1
inference: false
license: mit
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
license_name: mit
metrics:
- accuracy
model_creator: Mihai
model_name: Metis 0.1
model_type: mistral
prompt_template: '### System:
{system_message}
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Metis 0.1 - AWQ
- Model creator: [Mihai](https://huggingface.co/Mihaiii)
- Original model: [Metis 0.1](https://huggingface.co/Mihaiii/Metis-0.1)
<!-- description start -->
## Description
This repo contains AWQ model files for [Mihai's Metis 0.1](https://huggingface.co/Mihaiii/Metis-0.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Metis-0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Metis-0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Metis-0.1-GGUF)
* [Mihai's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Mihaiii/Metis-0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: SciPhi-2
```
### System:
{system_message}
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Metis-0.1-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Metis-0.1-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Metis-0.1-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Metis-0.1-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''### System:
{system_message}
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Metis-0.1-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Metis-0.1-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### System:
{system_message}
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Metis-0.1-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''### System:
{system_message}
### Instruction:
{prompt}
### Response:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Mihai's Metis 0.1
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
It works well with long system prompts.
It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension.
This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset.
# Prompt Format:
```
### System:
{add here the system prompt}
### Instruction:
{add here the instruction}
### Response:
{make sure you have a new line here}
```
|
[
-0.44791293144226074,
-0.7567915320396423,
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PrunedSafetensors/coolXLs
|
PrunedSafetensors
| 2023-12-16T00:09:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:20:52Z |
Entry not found
|
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ArthurADJ/KratosGoW3
|
ArthurADJ
| 2023-12-15T23:23:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:21:10Z |
Entry not found
|
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] |
GraydientPlatformAPI/pixar6
|
GraydientPlatformAPI
| 2023-12-15T23:34:44Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"license:openrail",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2023-12-15T23:22:19Z |
---
license: openrail
---
|
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] |
Vermiculos/cliff
|
Vermiculos
| 2023-12-15T23:24:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T23:22:42Z |
---
license: openrail
---
|
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salma-remyx/anivia_LoRA
|
salma-remyx
| 2023-12-15T23:22:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-15T23:22:50Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK anivia
license: openrail++
---
# SDXL LoRA DreamBooth - salma-remyx/anivia_LoRA
<Gallery />
## Model description
These are salma-remyx/anivia_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK anivia to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](salma-remyx/anivia_LoRA/tree/main) them in the Files & versions tab.
|
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] |
abcp4/bert-finetuned-squad
|
abcp4
| 2023-12-15T23:30:11Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-15T23:23:50Z |
Entry not found
|
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bunyaminkivanc/mistral-finetuned-alpaca
|
bunyaminkivanc
| 2023-12-15T23:47:30Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T23:24:07Z |
Entry not found
|
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] |
Ema10/Andrea
|
Ema10
| 2023-12-15T23:25:58Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T23:25:58Z |
---
license: openrail
---
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_humanMix_Seed105
|
behzadnet
| 2023-12-15T23:26:45Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T23:26:39Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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## Uses
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### Direct Use
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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#### Testing Data
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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hkivancoral/smids_5x_beit_base_sgd_00001_fold4
|
hkivancoral
| 2023-12-16T00:45:51Z | 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
| 2023-12-15T23:26:39Z |
---
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_00001_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: 0.3883333333333333
---
<!-- 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_00001_fold4
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: 1.1429
- Accuracy: 0.3883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2481 | 1.0 | 375 | 1.3387 | 0.3367 |
| 1.2853 | 2.0 | 750 | 1.3297 | 0.34 |
| 1.2245 | 3.0 | 1125 | 1.3209 | 0.34 |
| 1.2269 | 4.0 | 1500 | 1.3124 | 0.34 |
| 1.1881 | 5.0 | 1875 | 1.3044 | 0.34 |
| 1.1962 | 6.0 | 2250 | 1.2965 | 0.3417 |
| 1.1766 | 7.0 | 2625 | 1.2890 | 0.345 |
| 1.1422 | 8.0 | 3000 | 1.2816 | 0.3467 |
| 1.1265 | 9.0 | 3375 | 1.2745 | 0.3483 |
| 1.1532 | 10.0 | 3750 | 1.2676 | 0.3517 |
| 1.1456 | 11.0 | 4125 | 1.2609 | 0.3533 |
| 1.1221 | 12.0 | 4500 | 1.2545 | 0.355 |
| 1.1397 | 13.0 | 4875 | 1.2483 | 0.355 |
| 1.1323 | 14.0 | 5250 | 1.2422 | 0.3583 |
| 1.1113 | 15.0 | 5625 | 1.2362 | 0.3617 |
| 1.1197 | 16.0 | 6000 | 1.2307 | 0.3633 |
| 1.1175 | 17.0 | 6375 | 1.2251 | 0.3667 |
| 1.1137 | 18.0 | 6750 | 1.2199 | 0.3683 |
| 1.1317 | 19.0 | 7125 | 1.2149 | 0.3667 |
| 1.0985 | 20.0 | 7500 | 1.2099 | 0.37 |
| 1.1037 | 21.0 | 7875 | 1.2054 | 0.37 |
| 1.1051 | 22.0 | 8250 | 1.2008 | 0.3717 |
| 1.1012 | 23.0 | 8625 | 1.1965 | 0.3717 |
| 1.0418 | 24.0 | 9000 | 1.1925 | 0.375 |
| 1.0922 | 25.0 | 9375 | 1.1886 | 0.3767 |
| 1.0809 | 26.0 | 9750 | 1.1848 | 0.3767 |
| 1.096 | 27.0 | 10125 | 1.1812 | 0.3767 |
| 1.0328 | 28.0 | 10500 | 1.1778 | 0.375 |
| 1.0501 | 29.0 | 10875 | 1.1745 | 0.3767 |
| 1.065 | 30.0 | 11250 | 1.1714 | 0.3767 |
| 1.0717 | 31.0 | 11625 | 1.1685 | 0.3783 |
| 1.104 | 32.0 | 12000 | 1.1658 | 0.3767 |
| 1.0567 | 33.0 | 12375 | 1.1632 | 0.3783 |
| 1.0632 | 34.0 | 12750 | 1.1607 | 0.3783 |
| 1.0635 | 35.0 | 13125 | 1.1585 | 0.3783 |
| 1.0477 | 36.0 | 13500 | 1.1563 | 0.3833 |
| 1.0721 | 37.0 | 13875 | 1.1544 | 0.385 |
| 1.0594 | 38.0 | 14250 | 1.1526 | 0.385 |
| 1.0484 | 39.0 | 14625 | 1.1510 | 0.3867 |
| 1.0408 | 40.0 | 15000 | 1.1495 | 0.3883 |
| 1.0421 | 41.0 | 15375 | 1.1482 | 0.39 |
| 1.0561 | 42.0 | 15750 | 1.1470 | 0.3883 |
| 1.0338 | 43.0 | 16125 | 1.1460 | 0.3883 |
| 1.0224 | 44.0 | 16500 | 1.1451 | 0.3883 |
| 1.0269 | 45.0 | 16875 | 1.1444 | 0.3883 |
| 1.0608 | 46.0 | 17250 | 1.1438 | 0.3883 |
| 1.0652 | 47.0 | 17625 | 1.1434 | 0.3883 |
| 1.0189 | 48.0 | 18000 | 1.1431 | 0.3883 |
| 1.0225 | 49.0 | 18375 | 1.1429 | 0.3883 |
| 1.0356 | 50.0 | 18750 | 1.1429 | 0.3883 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_humanMix_Seed105
|
behzadnet
| 2023-12-15T23:26:54Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T23:26:51Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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] |
Vermiculos/ajfa
|
Vermiculos
| 2023-12-15T23:34:09Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T23:30:27Z |
---
license: openrail
---
|
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fghsdf/ff
|
fghsdf
| 2023-12-15T23:33:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:33:31Z |
Entry not found
|
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ingeniumacademy/ddpm-butterflies-128
|
ingeniumacademy
| 2023-12-15T23:34:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:34:09Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_rms_001_fold4
|
hkivancoral
| 2023-12-16T00:54:50Z | 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
| 2023-12-15T23:34:24Z |
---
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_rms_001_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: 0.7983333333333333
---
<!-- 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_rms_001_fold4
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: 1.7051
- Accuracy: 0.7983
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7798 | 1.0 | 375 | 0.7672 | 0.5633 |
| 0.803 | 2.0 | 750 | 0.7688 | 0.58 |
| 0.7686 | 3.0 | 1125 | 0.7514 | 0.61 |
| 0.7375 | 4.0 | 1500 | 0.8350 | 0.5517 |
| 0.7507 | 5.0 | 1875 | 0.8001 | 0.595 |
| 0.7083 | 6.0 | 2250 | 0.7244 | 0.65 |
| 0.708 | 7.0 | 2625 | 0.7289 | 0.6467 |
| 0.7266 | 8.0 | 3000 | 0.7325 | 0.6633 |
| 0.6418 | 9.0 | 3375 | 0.6940 | 0.6917 |
| 0.673 | 10.0 | 3750 | 0.7042 | 0.6617 |
| 0.6803 | 11.0 | 4125 | 0.6907 | 0.6817 |
| 0.64 | 12.0 | 4500 | 0.6890 | 0.675 |
| 0.6467 | 13.0 | 4875 | 0.7095 | 0.67 |
| 0.6428 | 14.0 | 5250 | 0.6543 | 0.7083 |
| 0.6389 | 15.0 | 5625 | 0.5890 | 0.7383 |
| 0.5885 | 16.0 | 6000 | 0.5874 | 0.7383 |
| 0.5689 | 17.0 | 6375 | 0.6828 | 0.705 |
| 0.5988 | 18.0 | 6750 | 0.6153 | 0.74 |
| 0.5869 | 19.0 | 7125 | 0.5556 | 0.745 |
| 0.5829 | 20.0 | 7500 | 0.5816 | 0.7417 |
| 0.5202 | 21.0 | 7875 | 0.6299 | 0.7267 |
| 0.4671 | 22.0 | 8250 | 0.5955 | 0.7383 |
| 0.4713 | 23.0 | 8625 | 0.5489 | 0.7783 |
| 0.4814 | 24.0 | 9000 | 0.6063 | 0.76 |
| 0.4578 | 25.0 | 9375 | 0.6548 | 0.7367 |
| 0.4226 | 26.0 | 9750 | 0.5459 | 0.75 |
| 0.349 | 27.0 | 10125 | 0.6223 | 0.76 |
| 0.3499 | 28.0 | 10500 | 0.5682 | 0.7817 |
| 0.2869 | 29.0 | 10875 | 0.7135 | 0.7717 |
| 0.3419 | 30.0 | 11250 | 0.6094 | 0.7833 |
| 0.3402 | 31.0 | 11625 | 0.6473 | 0.785 |
| 0.3025 | 32.0 | 12000 | 0.6500 | 0.7783 |
| 0.2278 | 33.0 | 12375 | 0.7439 | 0.7633 |
| 0.2211 | 34.0 | 12750 | 0.7227 | 0.775 |
| 0.1813 | 35.0 | 13125 | 0.7187 | 0.8033 |
| 0.1887 | 36.0 | 13500 | 0.7980 | 0.7883 |
| 0.2308 | 37.0 | 13875 | 0.8180 | 0.8 |
| 0.1362 | 38.0 | 14250 | 0.8499 | 0.7867 |
| 0.1204 | 39.0 | 14625 | 0.8914 | 0.8033 |
| 0.1182 | 40.0 | 15000 | 0.9026 | 0.7933 |
| 0.1271 | 41.0 | 15375 | 1.1021 | 0.775 |
| 0.0646 | 42.0 | 15750 | 1.1489 | 0.7967 |
| 0.0428 | 43.0 | 16125 | 1.2387 | 0.8067 |
| 0.0277 | 44.0 | 16500 | 1.2320 | 0.81 |
| 0.0276 | 45.0 | 16875 | 1.3879 | 0.79 |
| 0.0246 | 46.0 | 17250 | 1.4881 | 0.8033 |
| 0.0344 | 47.0 | 17625 | 1.5278 | 0.7983 |
| 0.006 | 48.0 | 18000 | 1.5757 | 0.8017 |
| 0.0048 | 49.0 | 18375 | 1.6617 | 0.8033 |
| 0.0042 | 50.0 | 18750 | 1.7051 | 0.7983 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
SofiaVouzika/convnext-tiny-finetuned-Liver_Tumor
|
SofiaVouzika
| 2023-12-15T23:38:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"convnext",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T23:38:03Z |
Entry not found
|
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] |
ChenMnZ/Mixtral-8x7B-v0.1-OmniQuantv2-w4a16g128
|
ChenMnZ
| 2023-12-16T00:26:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"arxiv:2308.13137",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T23:42:02Z |
Paper: [https://arxiv.org/abs/2308.13137](https://arxiv.org/abs/2308.13137)
Code: [https://github.com/OpenGVLab/OmniQuant](https://github.com/OpenGVLab/OmniQuant)
To run this model, refer [https://github.com/OpenGVLab/OmniQuant/blob/main/runing_quantized_mixtral_7bx8.ipynb](https://github.com/OpenGVLab/OmniQuant/blob/main/runing_quantized_mixtral_7bx8.ipynb) for more details.
|
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Upaya/Testbot
|
Upaya
| 2023-12-15T23:43:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:43:15Z |
Entry not found
|
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] |
donsmith/autoformer-tourism-monthly
|
donsmith
| 2023-12-15T23:53:43Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"autoformer",
"dataset:monash_tsf",
"arxiv:2106.13008",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T23:50:06Z |
---
license: apache-2.0
datasets:
- monash_tsf
---
# Autoformer
## Overview
The Autoformer model was proposed in [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang and Mingsheng Long.
The abstract from the paper is the following:
*Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long-term energy consumption planning. This paper studies the long-term forecasting problem of time series. Prior Transformer-based models adopt various self-attention mechanisms to discover the long-range dependencies. However, intricate temporal patterns of the long-term future prohibit the model from finding reliable dependencies. Also, Transformers have to adopt the sparse versions of point-wise self-attentions for long series efficiency, resulting in the information utilization bottleneck. Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of series decomposition and renovate it as a basic inner block of deep models. This design empowers Autoformer with progressive decomposition capacities for complex time series. Further, inspired by the stochastic process theory, we design the Auto-Correlation mechanism based on the series periodicity, which conducts the dependencies discovery and representation aggregation at the sub-series level. Auto-Correlation outperforms self-attention in both efficiency and accuracy. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease.*
|
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] |
vbxc/maximgil
|
vbxc
| 2023-12-15T23:51:56Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T23:51:18Z |
---
license: openrail
---
|
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qxnam/distilgpt2-finetuned-finance-2
|
qxnam
| 2023-12-15T23:53:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:53:20Z |
Entry not found
|
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afrideva/Zephyr-3.43B-GGUF
|
afrideva
| 2023-12-16T00:04:43Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"llm",
"stablelm",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"en",
"base_model:Aryanne/Zephyr-3.43B",
"license:other",
"region:us"
] |
text-generation
| 2023-12-15T23:53:52Z |
---
base_model: Aryanne/Zephyr-3.43B
inference: false
language:
- en
library_name: transformers
license: other
model_creator: Aryanne
model_name: Zephyr-3.43B
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- merge
- llm
- stablelm
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# Aryanne/Zephyr-3.43B-GGUF
Quantized GGUF model files for [Zephyr-3.43B](https://huggingface.co/Aryanne/Zephyr-3.43B) from [Aryanne](https://huggingface.co/Aryanne)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [zephyr-3.43b.fp16.gguf](https://huggingface.co/afrideva/Zephyr-3.43B-GGUF/resolve/main/zephyr-3.43b.fp16.gguf) | fp16 | 6.86 GB |
| [zephyr-3.43b.q2_k.gguf](https://huggingface.co/afrideva/Zephyr-3.43B-GGUF/resolve/main/zephyr-3.43b.q2_k.gguf) | q2_k | 1.46 GB |
| [zephyr-3.43b.q3_k_m.gguf](https://huggingface.co/afrideva/Zephyr-3.43B-GGUF/resolve/main/zephyr-3.43b.q3_k_m.gguf) | q3_k_m | 1.70 GB |
| [zephyr-3.43b.q4_k_m.gguf](https://huggingface.co/afrideva/Zephyr-3.43B-GGUF/resolve/main/zephyr-3.43b.q4_k_m.gguf) | q4_k_m | 2.09 GB |
| [zephyr-3.43b.q5_k_m.gguf](https://huggingface.co/afrideva/Zephyr-3.43B-GGUF/resolve/main/zephyr-3.43b.q5_k_m.gguf) | q5_k_m | 2.44 GB |
| [zephyr-3.43b.q6_k.gguf](https://huggingface.co/afrideva/Zephyr-3.43B-GGUF/resolve/main/zephyr-3.43b.q6_k.gguf) | q6_k | 2.82 GB |
| [zephyr-3.43b.q8_0.gguf](https://huggingface.co/afrideva/Zephyr-3.43B-GGUF/resolve/main/zephyr-3.43b.q8_0.gguf) | q8_0 | 3.65 GB |
## Original Model Card:
This model is a merge/fusion of [Aryanne/Astridboros-3B](https://huggingface.co/Aryanne/Astridboros-3B) and [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) , 28 layers of Zephyr + 12 layers of Astridboros together(see zephyr-3.43b.yml or below).
A total of 40 layers, with 3.43B of parameters.
License it's the same as Zephyr cause it has 70% of it.
```yaml
slices:
- sources:
- model: stabilityai/stablelm-zephyr-3b
layer_range: [0, 14]
- sources:
- model: Aryanne/Astridboros-3B
layer_range: [10, 22]
- sources:
- model: stabilityai/stablelm-zephyr-3b
layer_range: [18, 32]
merge_method: passthrough
dtype: float16
```
I recommend the use of the Zephyr prompt format.
```
<|user|>
List 3 synonyms for the word "tiny"<|endoftext|>
<|assistant|>
1. Dwarf
2. Little
3. Petite<|endoftext|>
```
GGUF Quants: [notyet](not yet)
|
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bagusatmaja/testcc22
|
bagusatmaja
| 2023-12-15T23:54:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:54:39Z |
Entry not found
|
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] |
Norton0924/PicRefine
|
Norton0924
| 2023-12-15T23:59:10Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T23:55:56Z |
---
license: apache-2.0
---
|
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pirxus/how2_en_bpe10000
|
pirxus
| 2023-12-15T23:57:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:57:11Z |
Entry not found
|
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pirxus/how2_pt_bpe10000
|
pirxus
| 2023-12-15T23:57:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T23:57:58Z |
Entry not found
|
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] |
joy2000/mistral_instruct_generation
|
joy2000
| 2023-12-15T23:58:16Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T23:58:08Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_instruct_generation
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. -->
# mistral_instruct_generation
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3338
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4954 | 0.0 | 20 | 1.3620 |
| 1.4476 | 0.01 | 40 | 1.3493 |
| 1.4787 | 0.01 | 60 | 1.3418 |
| 1.4646 | 0.02 | 80 | 1.3396 |
| 1.4857 | 0.02 | 100 | 1.3338 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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