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alyazharr/xlm_roberta_base_6 | alyazharr | 2023-11-29T02:42:24Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T02:42:24Z | 2023-11-29T02:41:08.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/xlm_roberta_base_6 | [
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alyazharr/xlm_roberta_base_2 | alyazharr | 2023-11-29T02:42:59Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T02:42:59Z | 2023-11-29T02:41:39.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/xlm_roberta_base_2 | [
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Lilidiasaja/ModelLLMCont | Lilidiasaja | 2023-11-29T02:43:01Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
] | 2023-11-29T02:43:01Z | 2023-11-29T02:43:01.000Z | null | null | ---
license: afl-3.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Lilidiasaja/ModelLLMCont | [
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at2507/llama-2-7b-miniguanaco | at2507 | 2023-11-29T02:52:12Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T02:52:12Z | 2023-11-29T02:43:48.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | at2507/llama-2-7b-miniguanaco | [
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] |
SalvatoriKitty/dogbooth | SalvatoriKitty | 2023-11-29T03:01:06Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T03:01:06Z | 2023-11-29T02:45:24.000Z | null | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - SalvatoriKitty/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | SalvatoriKitty/dogbooth | [
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shirongliang/distilbert-base-uncased-finetuned-cola | shirongliang | 2023-11-29T04:24:35Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:24:35Z | 2023-11-29T02:45:43.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | shirongliang/distilbert-base-uncased-finetuned-cola | [
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JoseDelgado/BART-finetuned-energy-articles | JoseDelgado | 2023-11-29T02:46:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T02:46:41Z | 2023-11-29T02:46:41.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | JoseDelgado/BART-finetuned-energy-articles | [
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alyazharr/xlm_roberta_base_3 | alyazharr | 2023-11-29T02:49:50Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T02:49:50Z | 2023-11-29T02:48:20.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/xlm_roberta_base_3 | [
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Realgon/bert_agnews_padding0model | Realgon | 2023-11-29T02:52:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T02:52:10Z | 2023-11-29T02:52:10.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Realgon/bert_agnews_padding0model | [
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imao44/dogbooth | imao44 | 2023-11-29T02:53:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T02:53:59Z | 2023-11-29T02:53:59.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | imao44/dogbooth | [
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Cartinoe5930/original-KoRAE-13b-3ep | Cartinoe5930 | 2023-11-29T23:08:26Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:08:26Z | 2023-11-29T02:55:35.000Z | null | null | ---
license: cc-by-nc-sa-4.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Cartinoe5930/original-KoRAE-13b-3ep | [
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Gaxys/Electra-PD_Books | Gaxys | 2023-11-29T03:17:01Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"electra",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:17:01Z | 2023-11-29T02:57:42.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Gaxys/Electra-PD_Books | [
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lmeneses/dogbooth | lmeneses | 2023-11-29T02:58:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T02:58:56Z | 2023-11-29T02:58:56.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | lmeneses/dogbooth | [
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hkivancoral/hushem_5x_beit_base_rms_001_fold1 | hkivancoral | 2023-11-29T03:32:00Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"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"
] | 2023-11-29T03:32:00Z | 2023-11-29T03:00:12.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_001_fold1
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: 0.4444444444444444
---
<!-- 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. -->
# hushem_5x_beit_base_rms_001_fold1
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: 2.2430
- Accuracy: 0.4444
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5782 | 1.0 | 27 | 1.4061 | 0.2444 |
| 1.4004 | 2.0 | 54 | 1.4559 | 0.2444 |
| 1.3873 | 3.0 | 81 | 1.4120 | 0.2444 |
| 1.3666 | 4.0 | 108 | 1.6275 | 0.2444 |
| 1.3597 | 5.0 | 135 | 1.4398 | 0.2444 |
| 1.2814 | 6.0 | 162 | 1.5328 | 0.2444 |
| 1.2056 | 7.0 | 189 | 1.5389 | 0.2 |
| 1.1635 | 8.0 | 216 | 1.5332 | 0.2444 |
| 1.1235 | 9.0 | 243 | 1.6681 | 0.2444 |
| 1.1484 | 10.0 | 270 | 1.6176 | 0.2667 |
| 1.1757 | 11.0 | 297 | 1.6312 | 0.2444 |
| 1.1297 | 12.0 | 324 | 1.5067 | 0.2444 |
| 1.1448 | 13.0 | 351 | 1.5657 | 0.2444 |
| 1.1725 | 14.0 | 378 | 1.5184 | 0.1556 |
| 1.1591 | 15.0 | 405 | 1.5790 | 0.2444 |
| 1.1549 | 16.0 | 432 | 1.5501 | 0.2444 |
| 1.0865 | 17.0 | 459 | 1.5776 | 0.2444 |
| 1.1351 | 18.0 | 486 | 1.6195 | 0.3111 |
| 1.0974 | 19.0 | 513 | 1.5360 | 0.2444 |
| 1.0992 | 20.0 | 540 | 1.5742 | 0.3111 |
| 1.0894 | 21.0 | 567 | 1.4918 | 0.3778 |
| 1.0557 | 22.0 | 594 | 1.5742 | 0.2444 |
| 1.0574 | 23.0 | 621 | 1.5043 | 0.4222 |
| 1.0148 | 24.0 | 648 | 1.3535 | 0.4222 |
| 1.1133 | 25.0 | 675 | 1.4897 | 0.4 |
| 1.02 | 26.0 | 702 | 1.4554 | 0.4222 |
| 1.0107 | 27.0 | 729 | 1.4238 | 0.4 |
| 0.9307 | 28.0 | 756 | 1.7644 | 0.3556 |
| 0.8335 | 29.0 | 783 | 2.0253 | 0.3556 |
| 0.8203 | 30.0 | 810 | 1.7990 | 0.3556 |
| 0.7263 | 31.0 | 837 | 1.6909 | 0.3778 |
| 0.8387 | 32.0 | 864 | 1.4758 | 0.4 |
| 0.6837 | 33.0 | 891 | 2.1584 | 0.3556 |
| 0.7155 | 34.0 | 918 | 1.7102 | 0.3778 |
| 0.6349 | 35.0 | 945 | 1.1875 | 0.4667 |
| 0.6331 | 36.0 | 972 | 1.9965 | 0.4222 |
| 0.5871 | 37.0 | 999 | 1.7881 | 0.4 |
| 0.595 | 38.0 | 1026 | 1.7629 | 0.4 |
| 0.5266 | 39.0 | 1053 | 1.6720 | 0.4222 |
| 0.4985 | 40.0 | 1080 | 2.3229 | 0.4222 |
| 0.4855 | 41.0 | 1107 | 1.6470 | 0.4444 |
| 0.503 | 42.0 | 1134 | 1.7515 | 0.4667 |
| 0.4432 | 43.0 | 1161 | 2.0538 | 0.4222 |
| 0.3668 | 44.0 | 1188 | 2.1471 | 0.4444 |
| 0.3654 | 45.0 | 1215 | 2.0004 | 0.4444 |
| 0.3317 | 46.0 | 1242 | 2.1973 | 0.4444 |
| 0.2413 | 47.0 | 1269 | 2.2882 | 0.4444 |
| 0.2395 | 48.0 | 1296 | 2.2389 | 0.4444 |
| 0.2502 | 49.0 | 1323 | 2.2430 | 0.4444 |
| 0.237 | 50.0 | 1350 | 2.2430 | 0.4444 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Lilidiasaja/newmodel | Lilidiasaja | 2023-11-29T06:21:32Z | 0 | 0 | null | [
"pytorch",
"license:afl-3.0",
"region:us"
] | 2023-11-29T06:21:32Z | 2023-11-29T03:05:02.000Z | null | null | ---
license: afl-3.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Lilidiasaja/newmodel | [
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BaoLocTown/sft-metamath-mistral-7b-vi-v1 | BaoLocTown | 2023-11-29T05:00:22Z | 0 | 0 | null | [
"transformers",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:hllj/meta-math-mistral-vi-math",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T05:00:22Z | 2023-11-29T03:05:19.000Z | null | null | ---
base_model: hllj/meta-math-mistral-vi-math
tags:
- generated_from_trainer
model-index:
- name: sft-metamath-mistral-7b-vi-v1
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. -->
# sft-metamath-mistral-7b-vi-v1
This model is a fine-tuned version of [hllj/meta-math-mistral-vi-math](https://huggingface.co/hllj/meta-math-mistral-vi-math) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4947
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3128 | 0.26 | 500 | 0.5093 |
| 0.2751 | 1.07 | 1000 | 0.4884 |
| 0.2585 | 1.33 | 1500 | 0.4943 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | BaoLocTown/sft-metamath-mistral-7b-vi-v1 | [
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milinsaini/eecs6893_llama2 | milinsaini | 2023-11-29T03:07:46Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T03:07:46Z | 2023-11-29T03:07:46.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | milinsaini/eecs6893_llama2 | [
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onizukal/SMIDS_10x_beit_base_adamax_lr00001_fold3 | onizukal | 2023-11-29T07:04:44Z | 0 | 0 | null | [
"transformers",
"safetensors",
"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"
] | 2023-11-29T07:04:44Z | 2023-11-29T03:10:41.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SMIDS_10x_beit_base_adamax_lr00001_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: 0.93
---
<!-- 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_10x_beit_base_adamax_lr00001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7454
- Accuracy: 0.93
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2291 | 1.0 | 750 | 0.2274 | 0.9167 |
| 0.1425 | 2.0 | 1500 | 0.2165 | 0.9283 |
| 0.0858 | 3.0 | 2250 | 0.2302 | 0.9333 |
| 0.048 | 4.0 | 3000 | 0.2905 | 0.9183 |
| 0.0361 | 5.0 | 3750 | 0.3257 | 0.9233 |
| 0.0295 | 6.0 | 4500 | 0.4158 | 0.915 |
| 0.0319 | 7.0 | 5250 | 0.4270 | 0.92 |
| 0.0229 | 8.0 | 6000 | 0.4764 | 0.925 |
| 0.0052 | 9.0 | 6750 | 0.4863 | 0.9333 |
| 0.0005 | 10.0 | 7500 | 0.5490 | 0.92 |
| 0.0306 | 11.0 | 8250 | 0.5885 | 0.915 |
| 0.0285 | 12.0 | 9000 | 0.6148 | 0.925 |
| 0.0071 | 13.0 | 9750 | 0.5753 | 0.9317 |
| 0.0101 | 14.0 | 10500 | 0.6197 | 0.9283 |
| 0.0141 | 15.0 | 11250 | 0.5968 | 0.9333 |
| 0.0003 | 16.0 | 12000 | 0.6016 | 0.9283 |
| 0.0002 | 17.0 | 12750 | 0.6069 | 0.94 |
| 0.006 | 18.0 | 13500 | 0.6171 | 0.9283 |
| 0.0001 | 19.0 | 14250 | 0.6427 | 0.93 |
| 0.0085 | 20.0 | 15000 | 0.6908 | 0.9233 |
| 0.0532 | 21.0 | 15750 | 0.6830 | 0.9283 |
| 0.0554 | 22.0 | 16500 | 0.7067 | 0.925 |
| 0.0 | 23.0 | 17250 | 0.7204 | 0.9233 |
| 0.0003 | 24.0 | 18000 | 0.6678 | 0.9233 |
| 0.0 | 25.0 | 18750 | 0.7236 | 0.925 |
| 0.0008 | 26.0 | 19500 | 0.7029 | 0.9267 |
| 0.0333 | 27.0 | 20250 | 0.7295 | 0.925 |
| 0.0001 | 28.0 | 21000 | 0.7323 | 0.93 |
| 0.0038 | 29.0 | 21750 | 0.7143 | 0.925 |
| 0.0 | 30.0 | 22500 | 0.7476 | 0.92 |
| 0.0 | 31.0 | 23250 | 0.7113 | 0.93 |
| 0.0001 | 32.0 | 24000 | 0.7377 | 0.9283 |
| 0.0 | 33.0 | 24750 | 0.7379 | 0.9233 |
| 0.002 | 34.0 | 25500 | 0.7666 | 0.925 |
| 0.0 | 35.0 | 26250 | 0.7259 | 0.925 |
| 0.0039 | 36.0 | 27000 | 0.7355 | 0.925 |
| 0.0 | 37.0 | 27750 | 0.7688 | 0.9217 |
| 0.0037 | 38.0 | 28500 | 0.7360 | 0.9267 |
| 0.0011 | 39.0 | 29250 | 0.7098 | 0.9333 |
| 0.0003 | 40.0 | 30000 | 0.7336 | 0.9217 |
| 0.0 | 41.0 | 30750 | 0.7470 | 0.92 |
| 0.0001 | 42.0 | 31500 | 0.7268 | 0.9333 |
| 0.0001 | 43.0 | 32250 | 0.7345 | 0.9267 |
| 0.0 | 44.0 | 33000 | 0.7384 | 0.9283 |
| 0.0 | 45.0 | 33750 | 0.7265 | 0.9317 |
| 0.0 | 46.0 | 34500 | 0.7369 | 0.9333 |
| 0.0 | 47.0 | 35250 | 0.7411 | 0.93 |
| 0.003 | 48.0 | 36000 | 0.7449 | 0.9333 |
| 0.001 | 49.0 | 36750 | 0.7438 | 0.93 |
| 0.0001 | 50.0 | 37500 | 0.7454 | 0.93 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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alyazharr/xlm_roberta_base_7 | alyazharr | 2023-11-29T03:14:06Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:14:06Z | 2023-11-29T03:12:33.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/xlm_roberta_base_7 | [
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alyazharr/xlm_roberta_base_8 | alyazharr | 2023-11-29T03:16:53Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:16:53Z | 2023-11-29T03:15:35.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/xlm_roberta_base_8 | [
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taninhaa/content | taninhaa | 2023-11-29T18:03:10Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:03:10Z | 2023-11-29T03:17:06.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2519
- Accuracy: 0.9049
- F1: 0.9042
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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DianaJin/voice | DianaJin | 2023-11-29T04:24:28Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:24:28Z | 2023-11-29T03:17:34.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | DianaJin/voice | [
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alyazharr/xlm_roberta_base_9 | alyazharr | 2023-11-29T03:19:30Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:19:30Z | 2023-11-29T03:18:08.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/xlm_roberta_base_9 | [
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alyazharr/xlm_roberta_base_10 | alyazharr | 2023-11-29T03:19:41Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:19:41Z | 2023-11-29T03:18:19.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/xlm_roberta_base_10 | [
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gumballmnt/Nhat | gumballmnt | 2023-11-29T03:18:26Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T03:18:26Z | 2023-11-29T03:18:26.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | gumballmnt/Nhat | [
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Minami-su/roleplay_qwen_7b_chat_4bit | Minami-su | 2023-11-29T03:18:40Z | 0 | 0 | null | [
"doi:10.57967/hf/1401",
"license:apache-2.0",
"region:us"
] | 2023-11-29T03:18:40Z | 2023-11-29T03:18:40.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Minami-su/roleplay_qwen_7b_chat_4bit | [
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Lilidiasaja/newmodel2 | Lilidiasaja | 2023-11-29T03:29:01Z | 0 | 0 | null | [
"pytorch",
"license:afl-3.0",
"region:us"
] | 2023-11-29T03:29:01Z | 2023-11-29T03:21:02.000Z | null | null | ---
license: afl-3.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Lilidiasaja/newmodel2 | [
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kaizerBox/gpt2-small-summarization | kaizerBox | 2023-11-29T03:22:10Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:xsum",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T03:22:10Z | 2023-11-29T03:22:06.000Z | null | null | ---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: gpt2-small-summarization
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-small-summarization
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.1568 | 1.0 | 5762 | 4.6058 |
| 4.5202 | 2.0 | 11525 | 4.4583 |
| 4.4225 | 3.0 | 17286 | 4.4258 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | kaizerBox/gpt2-small-summarization | [
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Minami-su/qwen_7b_chat_roleplay_4bit | Minami-su | 2023-11-29T03:39:53Z | 0 | 1 | null | [
"transformers",
"qwen",
"text-generation",
"roleplay",
"self_instruct",
"custom_code",
"dataset:Minami-su/roleplay_multiturn_chat_1k_zh_v0.1",
"license:apache-2.0",
"region:us"
] | 2023-11-29T03:39:53Z | 2023-11-29T03:22:11.000Z | null | null | ---
license: apache-2.0
datasets:
- Minami-su/roleplay_multiturn_chat_1k_zh_v0.1
tags:
- roleplay
- qwen
- self_instruct
---
---
language:
- zh
tags:
- roleplay
- multiturn_chat
---
## 介绍
基于self-instruct生成的多轮对话roleplay数据在qwen 7b chat上训练的模型，约1k条不同的人格数据和对话和约3k alpaca指令
## 存在问题：
1.roleplay数据基于模型自身生成，所以roleplay存在模型本身价值观融入情况，导致roleplay不够真实，不够准确。
## 使用方法：
可以参考https://github.com/PanQiWei/AutoGPTQ
## prompt：
```ipython
>>> tokenizer = AutoTokenizer.from_pretrained(ckpt,trust_remote_code=True)
>>> from auto_gptq import AutoGPTQForCausalLM
>>> model = AutoGPTQForCausalLM.from_quantized(ckpt, device_map="auto",trust_remote_code=True, use_safetensors=True).half()
>>> def generate(prompt):
>>> print("1",prompt,"2")
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
>>> generate_ids = model.generate(input_ids=input_ids,
>>> max_length=4096,
>>> num_beams=1,
>>> do_sample=True, top_p=0.9, temperature=0.95, repetition_penalty=1.05, eos_token_id=tokenizer.eos_token_id)
>>> output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
>>> response = output[len(prompt):]
>>> return response
>>> device = torch.device('cuda')
>>> history=[]
>>> max_history_len=12
>>> rating="0"
>>> while True:
>>> text=input("user:")
>>> text=f"人类:{text}</s>"
>>> history.append(text)
>>> input_text="爱丽丝的人格:你叫爱丽丝，是一个傲娇，腹黑的16岁少女<|im_end|>"
>>> for history_id, history_utr in enumerate(history[-max_history_len:]):
>>> input_text = input_text + history_utr + '\n'
>>> prompt = input_text+"爱丽丝:"
>>> prompt =prompt.strip()
>>> response = generate(prompt)
>>> response=response.strip()
>>> response="爱丽丝:"+response+"<|im_end|>"
>>> print("1",response,"2")
>>> history.append(response)
人类:我还要去上班
爱丽丝:哎呀呀~这么无聊，竟然还要去工作？
```
## 引用
```
@misc{selfinstruct,
title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
journal={arXiv preprint arXiv:2212.10560},
year={2022}
}
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Minami-su/qwen_7b_chat_roleplay_4bit | [
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rezajebeli97/vit_on_cyto_after_histoFLX40_after_imagenet_train_test_split_fair | rezajebeli97 | 2023-11-29T03:26:53Z | 0 | 0 | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:26:53Z | 2023-11-29T03:26:30.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | rezajebeli97/vit_on_cyto_after_histoFLX40_after_imagenet_train_test_split_fair | [
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rezajebeli97/deit_on_cyto_after_histoFLX40_after_imagenet_train_test_split_fair | rezajebeli97 | 2023-11-29T03:27:16Z | 0 | 0 | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:27:16Z | 2023-11-29T03:26:54.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | rezajebeli97/deit_on_cyto_after_histoFLX40_after_imagenet_train_test_split_fair | [
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Gaxys/MPnet-PD_Books | Gaxys | 2023-11-29T04:50:21Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"mpnet",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:50:21Z | 2023-11-29T03:28:41.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Gaxys/MPnet-PD_Books | [
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Amudha/AmuTestModel | Amudha | 2023-11-29T03:43:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T03:43:33Z | 2023-11-29T03:28:55.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Amudha/AmuTestModel | [
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phat11nlp/my_awesome_billsum_model | phat11nlp | 2023-11-29T03:30:11Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T03:30:11Z | 2023-11-29T03:30:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | phat11nlp/my_awesome_billsum_model | [
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hkivancoral/hushem_5x_beit_base_rms_001_fold2 | hkivancoral | 2023-11-29T04:04:36Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"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"
] | 2023-11-29T04:04:36Z | 2023-11-29T03:32:48.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_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: 0.4444444444444444
---
<!-- 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. -->
# hushem_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: 3.7933
- Accuracy: 0.4444
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4827 | 1.0 | 27 | 1.4775 | 0.2444 |
| 1.4158 | 2.0 | 54 | 1.4002 | 0.2667 |
| 1.3654 | 3.0 | 81 | 1.4674 | 0.2444 |
| 1.4175 | 4.0 | 108 | 1.4412 | 0.2444 |
| 1.394 | 5.0 | 135 | 1.3951 | 0.2667 |
| 1.2686 | 6.0 | 162 | 1.3983 | 0.2444 |
| 1.2556 | 7.0 | 189 | 1.4175 | 0.2889 |
| 1.2245 | 8.0 | 216 | 1.4754 | 0.2222 |
| 1.1427 | 9.0 | 243 | 1.5387 | 0.2 |
| 1.1659 | 10.0 | 270 | 1.3896 | 0.3333 |
| 1.2047 | 11.0 | 297 | 1.6922 | 0.2444 |
| 1.1384 | 12.0 | 324 | 1.4940 | 0.2667 |
| 1.1563 | 13.0 | 351 | 1.3730 | 0.2889 |
| 1.1141 | 14.0 | 378 | 1.4944 | 0.2222 |
| 1.0922 | 15.0 | 405 | 1.4049 | 0.2222 |
| 1.0475 | 16.0 | 432 | 1.2541 | 0.4 |
| 0.9208 | 17.0 | 459 | 1.2993 | 0.4222 |
| 0.9847 | 18.0 | 486 | 1.4111 | 0.4889 |
| 0.9327 | 19.0 | 513 | 1.3175 | 0.2889 |
| 0.8591 | 20.0 | 540 | 1.2892 | 0.3111 |
| 0.7605 | 21.0 | 567 | 1.6440 | 0.2667 |
| 0.7953 | 22.0 | 594 | 1.6915 | 0.3778 |
| 0.7644 | 23.0 | 621 | 1.6017 | 0.4667 |
| 0.7884 | 24.0 | 648 | 1.4064 | 0.2444 |
| 0.6883 | 25.0 | 675 | 1.9722 | 0.3111 |
| 0.7747 | 26.0 | 702 | 1.9209 | 0.4889 |
| 0.7012 | 27.0 | 729 | 2.2074 | 0.5333 |
| 0.6951 | 28.0 | 756 | 2.4602 | 0.3556 |
| 0.6581 | 29.0 | 783 | 2.1544 | 0.4222 |
| 0.6529 | 30.0 | 810 | 2.0677 | 0.3556 |
| 0.533 | 31.0 | 837 | 2.1507 | 0.3778 |
| 0.6648 | 32.0 | 864 | 2.1628 | 0.4222 |
| 0.6094 | 33.0 | 891 | 2.5365 | 0.3778 |
| 0.5601 | 34.0 | 918 | 2.8323 | 0.4222 |
| 0.519 | 35.0 | 945 | 2.4166 | 0.4 |
| 0.5988 | 36.0 | 972 | 2.6302 | 0.4444 |
| 0.5359 | 37.0 | 999 | 2.9183 | 0.3778 |
| 0.5451 | 38.0 | 1026 | 2.8746 | 0.5111 |
| 0.5087 | 39.0 | 1053 | 2.7419 | 0.4667 |
| 0.4563 | 40.0 | 1080 | 3.1565 | 0.4222 |
| 0.5182 | 41.0 | 1107 | 3.1768 | 0.4444 |
| 0.4348 | 42.0 | 1134 | 3.2761 | 0.4222 |
| 0.4504 | 43.0 | 1161 | 3.4108 | 0.4667 |
| 0.417 | 44.0 | 1188 | 3.5781 | 0.4444 |
| 0.4297 | 45.0 | 1215 | 3.6284 | 0.4444 |
| 0.3399 | 46.0 | 1242 | 3.7187 | 0.4444 |
| 0.3846 | 47.0 | 1269 | 3.7298 | 0.4667 |
| 0.3494 | 48.0 | 1296 | 3.7854 | 0.4444 |
| 0.3468 | 49.0 | 1323 | 3.7933 | 0.4444 |
| 0.3313 | 50.0 | 1350 | 3.7933 | 0.4444 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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dominiccccccs/RVCModels | dominiccccccs | 2023-11-29T03:36:18Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T03:36:18Z | 2023-11-29T03:33:48.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | dominiccccccs/RVCModels | [
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David-Xu/llama-7-int4-dolly | David-Xu | 2023-11-29T03:44:35Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T03:44:35Z | 2023-11-29T03:35:03.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | David-Xu/llama-7-int4-dolly | [
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MDK-L/Exp1 | MDK-L | 2023-11-29T03:36:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T03:36:35Z | 2023-11-29T03:36:35.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | MDK-L/Exp1 | [
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ghavamip/distilbert-base-uncased-finetuned-emotion | ghavamip | 2023-11-29T14:16:11Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:16:11Z | 2023-11-29T03:36:35.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | ghavamip/distilbert-base-uncased-finetuned-emotion | [
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Homiebear/CaptrainPrice | Homiebear | 2023-11-29T03:38:08Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T03:38:08Z | 2023-11-29T03:37:51.000Z | null | null | ---
license: openrail
---
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onceiapp/whisper-finetune-291123 | onceiapp | 2023-11-29T03:38:17Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T03:38:17Z | 2023-11-29T03:38:17.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | onceiapp/whisper-finetune-291123 | [
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ramsenth/langbot-gec-model | ramsenth | 2023-11-29T03:40:38Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:40:38Z | 2023-11-29T03:38:23.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | ramsenth/langbot-gec-model | [
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agusroccohd/lauti | agusroccohd | 2023-11-29T03:45:08Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T03:45:08Z | 2023-11-29T03:44:20.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | agusroccohd/lauti | [
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farawaymari/Myself | farawaymari | 2023-11-29T03:46:24Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T03:46:24Z | 2023-11-29T03:45:21.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | farawaymari/Myself | [
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jamesngai/xlm-roberta-xxl-final-lora1LLLLLLLL-moredropout-yeet-skrrrt | jamesngai | 2023-11-29T05:11:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:11:00Z | 2023-11-29T03:46:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jamesngai/xlm-roberta-xxl-final-lora1LLLLLLLL-moredropout-yeet-skrrrt | [
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racacha/LA_RELIQUIA | racacha | 2023-11-29T03:47:07Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T03:47:07Z | 2023-11-29T03:47:07.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | racacha/LA_RELIQUIA | [
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Gurcan/layoutlmv3-test | Gurcan | 2023-11-29T03:49:57Z | 0 | 0 | null | [
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:49:57Z | 2023-11-29T03:47:10.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Gurcan/layoutlmv3-test | [
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FounderOfHuggingface/gpt2_lora_dbpedia_14_t3000_e5 | FounderOfHuggingface | 2023-11-29T04:24:55Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T04:24:55Z | 2023-11-29T03:49:05.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
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## Training procedure
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## Training procedure
### Framework versions
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doach1/modelai | doach1 | 2023-11-29T03:50:37Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T03:50:37Z | 2023-11-29T03:50:37.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | doach1/modelai | [
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kyle0518/HW3_r32 | kyle0518 | 2023-11-29T09:27:23Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:yentinglin/Taiwan-LLM-7B-v2.0-chat",
"license:apache-2.0",
"region:us"
] | 2023-11-29T09:27:23Z | 2023-11-29T03:51:19.000Z | null | null | ---
license: apache-2.0
base_model: yentinglin/Taiwan-LLM-7B-v2.0-chat
tags:
- generated_from_trainer
model-index:
- name: HW3_r32
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. -->
# HW3_r32
This model is a fine-tuned version of [yentinglin/Taiwan-LLM-7B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.0-chat) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1094
## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3401 | 0.38 | 25 | 1.2883 |
| 1.2141 | 0.75 | 50 | 1.2087 |
| 1.1845 | 1.13 | 75 | 1.1677 |
| 1.0865 | 1.51 | 100 | 1.1455 |
| 1.0518 | 1.88 | 125 | 1.1271 |
| 0.9841 | 2.26 | 150 | 1.1140 |
| 0.9628 | 2.63 | 175 | 1.1094 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | kyle0518/HW3_r32 | [
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calkan9/medllama2_7b | calkan9 | 2023-11-29T03:52:43Z | 0 | 0 | null | [
"license:llama2",
"region:us"
] | 2023-11-29T03:52:43Z | 2023-11-29T03:52:43.000Z | null | null | ---
license: llama2
---
| null | null | null | null | null | null | null | null | null | null | null | null | calkan9/medllama2_7b | [
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imadejski/bhr_descriptions_trained_sciBERT | imadejski | 2023-11-29T03:55:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T03:55:22Z | 2023-11-29T03:55:22.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | imadejski/bhr_descriptions_trained_sciBERT | [
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Gaxys/Albert-PD_Books | Gaxys | 2023-11-29T04:26:24Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"albert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:26:24Z | 2023-11-29T03:56:10.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Gaxys/Albert-PD_Books | [
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muragekibicho/LZMD_Lempel-Ziv-Montecarlo-Diffusion | muragekibicho | 2023-11-29T04:05:07Z | 0 | 1 | null | [
"Data Compressor",
"Early Access",
"en",
"license:agpl-3.0",
"region:us"
] | 2023-11-29T04:05:07Z | 2023-11-29T03:56:48.000Z | null | null | ---
license: agpl-3.0
language:
- en
tags:
- Data Compressor
- Early Access
---
## LZMD : Lempel-Ziv Montecarlo Diffusion
[Join the mailing list](https://patreon.com/user?u=108521050&utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=creatorshare_creator&utm_content=join_link)
[View on GitHub](https://github.com/MurageKibicho/Middleclass-Zip/tree/main/LZMD:%20Lempel-Ziv%20Montecarlo%20Diffusion)
### Update:
I got rejected from YCombinator last week, ZFellows in August and got ghosted by VCs inbetween.
I took a gap year to build this. Now it's open-source lol. I'll add fileio and Decoder.c if people are interested.
### Disclaimer
Data compression is a solved problem. No ✨ magical algorithm ✨ makes files infinitely small. Also, you can't compress random data.
### How It Works
We built a custom diffusion model to *simulate the weird change in volume when water and alcohol mix*.
It's a navier-stokes solver whose solutions lead to smaller file sizes. This is **NOT** a data compressor. We don't find probabilities.
\
### Technical Overview
A mixture of equal parts water and ethanol presents a [volume 10% less](https://www.flinnsci.com/api/library/Download/bc2d91b9d50f4ead80c9ff320b9a4419) than the sum of individual volumes.
Ethanol molecules are smaller than water molecules. When mixed, small ethanol molecules [occupy the spaces between ](https://www.thoughtco.com/miscibility-of-fluids-608180) large water molecules.
\
We built a mathematical model to simulate this mixture. We :
- Split a compressed file into two parts - Left and Right analogous to Water and Ethanol.
- Find large empty spaces in the Left part.
- Find small integers in the Right part.
- Place the small integers inside the large spaces.
### How to use
This is work in progress. At the moment, Encoder.c displays the total number of bits saved. I'll add fileio and Decoder.c if people are interested
```
/*
Works best on ZIP, MP4 and JPEG files whose size is greater than 2 MB
Only tested on Ubuntu
Change filename in Encoder.c
*/
Run: gcc Encoder.c -lgmp -lm -o Encoder.o && ./Encoder.o
Output : "N bits saved!"
//
```
| null | null | null | null | null | null | null | null | null | null | null | null | muragekibicho/LZMD_Lempel-Ziv-Montecarlo-Diffusion | [
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jellyconsumer/new_model_falcon_large | jellyconsumer | 2023-11-29T08:57:19Z | 0 | 0 | null | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:57:19Z | 2023-11-29T04:00:16.000Z | null | null | ---
license: apache-2.0
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
tags:
- generated_from_trainer
model-index:
- name: new_model_falcon_large
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. -->
# new_model_falcon_large
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | jellyconsumer/new_model_falcon_large | [
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rishdotblog/sqlcoder_34b_fullft_800_steps_lesswikisql_linear | rishdotblog | 2023-11-29T04:04:04Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:04:04Z | 2023-11-29T04:04:04.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | rishdotblog/sqlcoder_34b_fullft_800_steps_lesswikisql_linear | [
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hkivancoral/hushem_5x_beit_base_rms_001_fold3 | hkivancoral | 2023-11-29T04:36:20Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"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"
] | 2023-11-29T04:36:20Z | 2023-11-29T04:05:25.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_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: 0.627906976744186
---
<!-- 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. -->
# hushem_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: 1.4768
- Accuracy: 0.6279
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.51 | 1.0 | 28 | 1.6351 | 0.2558 |
| 1.3869 | 2.0 | 56 | 1.4127 | 0.2558 |
| 1.3848 | 3.0 | 84 | 1.3895 | 0.2558 |
| 1.4113 | 4.0 | 112 | 1.3824 | 0.2558 |
| 1.3569 | 5.0 | 140 | 1.4121 | 0.2326 |
| 1.4625 | 6.0 | 168 | 1.3739 | 0.2326 |
| 1.3804 | 7.0 | 196 | 1.2185 | 0.5349 |
| 1.1352 | 8.0 | 224 | 1.1411 | 0.4884 |
| 1.0899 | 9.0 | 252 | 1.2426 | 0.3953 |
| 1.0945 | 10.0 | 280 | 1.1820 | 0.3488 |
| 1.1149 | 11.0 | 308 | 1.4574 | 0.3023 |
| 0.9942 | 12.0 | 336 | 1.4728 | 0.3256 |
| 1.0204 | 13.0 | 364 | 0.9801 | 0.5581 |
| 0.9987 | 14.0 | 392 | 1.0096 | 0.5349 |
| 1.0664 | 15.0 | 420 | 1.0007 | 0.5814 |
| 0.9463 | 16.0 | 448 | 1.2188 | 0.3953 |
| 0.9756 | 17.0 | 476 | 1.1284 | 0.5116 |
| 0.9698 | 18.0 | 504 | 1.4394 | 0.4419 |
| 1.061 | 19.0 | 532 | 1.1162 | 0.4884 |
| 0.8426 | 20.0 | 560 | 1.9296 | 0.3721 |
| 0.876 | 21.0 | 588 | 1.0070 | 0.5581 |
| 0.8908 | 22.0 | 616 | 1.2196 | 0.5349 |
| 0.8599 | 23.0 | 644 | 0.9502 | 0.6047 |
| 0.8338 | 24.0 | 672 | 0.8737 | 0.6279 |
| 0.785 | 25.0 | 700 | 1.1006 | 0.5814 |
| 0.82 | 26.0 | 728 | 1.0398 | 0.5814 |
| 0.8016 | 27.0 | 756 | 1.6671 | 0.3256 |
| 0.8574 | 28.0 | 784 | 1.1704 | 0.6279 |
| 0.8104 | 29.0 | 812 | 1.0502 | 0.6279 |
| 0.7421 | 30.0 | 840 | 0.9270 | 0.5814 |
| 0.7093 | 31.0 | 868 | 1.8057 | 0.4186 |
| 0.7469 | 32.0 | 896 | 0.9665 | 0.5814 |
| 0.7175 | 33.0 | 924 | 0.8190 | 0.6512 |
| 0.7129 | 34.0 | 952 | 1.0680 | 0.6279 |
| 0.7793 | 35.0 | 980 | 1.0966 | 0.5581 |
| 0.6879 | 36.0 | 1008 | 0.9990 | 0.5814 |
| 0.7016 | 37.0 | 1036 | 1.7556 | 0.4884 |
| 0.6238 | 38.0 | 1064 | 1.5792 | 0.4651 |
| 0.6025 | 39.0 | 1092 | 1.1502 | 0.6047 |
| 0.7264 | 40.0 | 1120 | 1.3317 | 0.5349 |
| 0.6063 | 41.0 | 1148 | 1.5492 | 0.5116 |
| 0.5816 | 42.0 | 1176 | 1.5787 | 0.5814 |
| 0.4627 | 43.0 | 1204 | 1.1301 | 0.6047 |
| 0.4652 | 44.0 | 1232 | 1.5008 | 0.6279 |
| 0.3885 | 45.0 | 1260 | 1.3167 | 0.6279 |
| 0.4003 | 46.0 | 1288 | 1.3851 | 0.6512 |
| 0.3882 | 47.0 | 1316 | 1.4601 | 0.6047 |
| 0.353 | 48.0 | 1344 | 1.4699 | 0.6279 |
| 0.3487 | 49.0 | 1372 | 1.4768 | 0.6279 |
| 0.2789 | 50.0 | 1400 | 1.4768 | 0.6279 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | hkivancoral/hushem_5x_beit_base_rms_001_fold3 | [
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ToviTu/OpenFlamingo-squad-ft | ToviTu | 2023-11-29T05:04:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:04:27Z | 2023-11-29T04:07:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | ToviTu/OpenFlamingo-squad-ft | [
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K-kiron/finetuning-sentiment-model-3000-samples | K-kiron | 2023-11-29T04:10:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:10:03Z | 2023-11-29T04:10:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | K-kiron/finetuning-sentiment-model-3000-samples | [
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K-kiron/finetuning-distilbert-hw6 | K-kiron | 2023-11-29T04:45:27Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:45:27Z | 2023-11-29T04:10:54.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | K-kiron/finetuning-distilbert-hw6 | [
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dapumptu/peft-lora-starcoder-chat-asst-4090 | dapumptu | 2023-11-29T09:25:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:25:02Z | 2023-11-29T04:11:07.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | dapumptu/peft-lora-starcoder-chat-asst-4090 | [
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TheBloke/Venus-120b-v1.0-AWQ | TheBloke | 2023-11-29T08:31:22Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"en",
"base_model:nsfwthrowitaway69/Venus-120b-v1.0",
"license:llama2",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T08:31:22Z | 2023-11-29T04:11:37.000Z | null | null | ---
base_model: nsfwthrowitaway69/Venus-120b-v1.0
inference: false
language:
- en
license: llama2
model_creator: John Smith
model_name: Venus 120B V1.0
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- not-for-all-audiences
---
<!-- 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 -->
# Venus 120B V1.0 - AWQ
- Model creator: [John Smith](https://huggingface.co/nsfwthrowitaway69)
- Original model: [Venus 120B V1.0](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.0)
<!-- description start -->
## Description
This repo contains AWQ model files for [John Smith's Venus 120B V1.0](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.0).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Venus-120b-v1.0-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Venus-120b-v1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Venus-120b-v1.0-GGUF)
* [John Smith's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.0)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### 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/Venus-120b-v1.0-AWQ/tree/main) | 4 | 128 | [OpenErotica Erotiquant](https://huggingface.co/datasets/openerotica/erotiquant/viewer/) | 4096 | 63.29 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/Venus-120b-v1.0-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: `Venus-120b-v1.0-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/Venus-120b-v1.0-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### 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/Venus-120b-v1.0-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/Venus-120b-v1.0-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### 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/Venus-120b-v1.0-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: John Smith's Venus 120B V1.0
# Venus 120b - version 1.0
## Overview
The goal was to create a large model that's highly capable for RP/ERP scenarios. Goliath-120b is excellent for roleplay, and Venus-120b was created with the idea of attempting to mix more than two models together to see how well this method works.
## Model Details
- A result of interleaving layers of [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B), [NousResearch/Nous-Hermes-Llama2-70b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-70b), and [migtissera/SynthIA-70B-v1.5](https://huggingface.co/migtissera/SynthIA-70B-v1.5) using [mergekit](https://github.com/cg123/mergekit).
- The resulting model has 140 layers and approximately 122 billion parameters.
- See mergekit-config.yml for details on the merge method used.
- See the `exl2-*` branches for exllama2 quantizations. The 4.85 bpw quant should fit in 80GB VRAM, and the 3.0 bpw quant should (just barely) fit in 48GB VRAM with 4k context.
- Inspired by [Goliath-120b](https://huggingface.co/alpindale/goliath-120b)
**Warning: This model will produce NSFW content!**
## Results
Initial tests show that Venus-120b functions fine, overall it seems to be comparable to Goliath-120b. Some differences I noticed:
1. Venus needs lower temperature settings than Goliath. I recommend a temp of around 0.7, and no higher than 1.0.
2. Venus tends to, on average, produce longer responses than Goliath. Probably due to the inclusion of SynthIA in the merge, which is trained to produce long chain-of-thought responses.
3. Venus seems to be a bit less creative than Goliath when it comes to the prose it generates. Probably due to the lack of Xwin and the inclusion of Nous-Hermes.
Keep in mind this is all anecdotal from some basic tests. The key takeaway is that Venus shows that Goliath is not a fluke.
## Other quants:
- 4.5 bpw exl2 quant provided by Panchovix: https://huggingface.co/Panchovix/Venus-120b-v1.0-4.5bpw-h6-exl2
- 4.25 bpw exl2 quant provided by Panchovix: https://huggingface.co/Panchovix/Venus-120b-v1.0-4.25bpw-h6-exl2
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kalomaze/MiniSymp2 | kalomaze | 2023-11-29T08:40:29Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | 2023-11-29T08:40:29Z | 2023-11-29T04:17:35.000Z | null | null | ---
license: apache-2.0
---
MiniSymp2 is A retrain of my MiniSymposium model attempt except with some more data and better practices.
- added EOS tokens where they belong
- made the prompt formats more diverse in the data so you could experiment / play with prompt format in context
- added some new examples
- measured loss curve to make sure I wasn't overfitting
- used 8-bit lora instead of 4-bit qlora | null | null | null | null | null | null | null | null | null | null | null | null | kalomaze/MiniSymp2 | [
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milinsaini/Llama-2-7b-chat-finetune | milinsaini | 2023-11-29T04:20:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:20:55Z | 2023-11-29T04:20:55.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | milinsaini/Llama-2-7b-chat-finetune | [
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giangduong/adapter-multinews-trainv3 | giangduong | 2023-11-29T04:23:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:23:43Z | 2023-11-29T04:23:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | giangduong/adapter-multinews-trainv3 | [
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cjhyeok/tldr-reward_model | cjhyeok | 2023-11-29T04:41:50Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gptj",
"text-classification",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:41:50Z | 2023-11-29T04:27:00.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | cjhyeok/tldr-reward_model | [
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AfterRain007/results | AfterRain007 | 2023-11-29T04:28:23Z | 0 | 0 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:ElKulako/cryptobert",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:28:23Z | 2023-11-29T04:27:37.000Z | null | null | ---
base_model: ElKulako/cryptobert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
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 [ElKulako/cryptobert](https://huggingface.co/ElKulako/cryptobert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8983
- Accuracy: 0.6433
- Precision: 0.6614
- Recall: 0.6433
- F1: 0.6461
## 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: 3.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69420
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9586 | 0.19 | 100 | 0.8746 | 0.6033 | 0.5990 | 0.6033 | 0.5944 |
| 0.7362 | 0.38 | 200 | 0.8187 | 0.63 | 0.6322 | 0.63 | 0.6232 |
| 0.577 | 0.57 | 300 | 0.8065 | 0.6767 | 0.6821 | 0.6767 | 0.6761 |
| 0.4632 | 0.76 | 400 | 0.8437 | 0.63 | 0.6411 | 0.63 | 0.6321 |
| 0.3243 | 0.95 | 500 | 0.8983 | 0.6433 | 0.6614 | 0.6433 | 0.6461 |
| 0.2257 | 1.14 | 600 | 1.3704 | 0.6033 | 0.6863 | 0.6033 | 0.6046 |
| 0.1333 | 1.33 | 700 | 1.2951 | 0.6033 | 0.6201 | 0.6033 | 0.6052 |
| 0.0574 | 1.52 | 800 | 1.5119 | 0.6333 | 0.6331 | 0.6333 | 0.6309 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | AfterRain007/results | [
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AfterRain007/cryptobertRefined | AfterRain007 | 2023-11-29T04:28:25Z | 0 | 0 | null | [
"region:us"
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rechido/20231129_online_2task | rechido | 2023-11-29T04:29:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:29:51Z | 2023-11-29T04:29:51.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | rechido/20231129_online_2task | [
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Prince12f/finetuning-sentiment-model-3000-samples | Prince12f | 2023-11-29T04:30:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:30:35Z | 2023-11-29T04:30:35.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Prince12f/finetuning-sentiment-model-3000-samples | [
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haurajahra/QA_using_indoBERT_LORA_qv | haurajahra | 2023-11-29T04:30:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:30:40Z | 2023-11-29T04:30:40.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | haurajahra/QA_using_indoBERT_LORA_qv | [
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Prince12f/finetuning-sentiment-model-samples | Prince12f | 2023-11-30T00:55:37Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:55:37Z | 2023-11-29T04:31:10.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-sentiment-model-samples
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. -->
# finetuning-sentiment-model-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2302
- Accuracy: 0.9092
- F1: 0.9102
- Precision: 0.9069
- Recall: 0.9135
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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shiontendon/error_diagnostic_model_80M | shiontendon | 2023-11-29T04:43:13Z | 0 | 0 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T04:43:13Z | 2023-11-29T04:31:56.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | shiontendon/error_diagnostic_model_80M | [
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rechido/20231129_online_task1_epoch100 | rechido | 2023-11-29T05:22:14Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-29T05:22:14Z | 2023-11-29T04:33:10.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231129_online_task1_epoch100 | [
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rechido/20231129_online_task1_epoch200 | rechido | 2023-11-29T06:13:38Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-29T06:13:38Z | 2023-11-29T04:34:02.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231129_online_task1_epoch200 | [
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rechido/20231129_online_task1_epoch500 | rechido | 2023-11-29T08:39:57Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-29T08:39:57Z | 2023-11-29T04:34:23.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231129_online_task1_epoch500 | [
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rechido/20231129_online_task1_epoch1000 | rechido | 2023-11-29T12:39:13Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-29T12:39:13Z | 2023-11-29T04:35:04.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231129_online_task1_epoch1000 | [
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hkivancoral/hushem_5x_beit_base_rms_001_fold4 | hkivancoral | 2023-11-29T05:09:38Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"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"
] | 2023-11-29T05:09:38Z | 2023-11-29T04:37:08.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_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: 0.7619047619047619
---
<!-- 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. -->
# hushem_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: 1.5372
- Accuracy: 0.7619
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4749 | 1.0 | 28 | 1.3999 | 0.2381 |
| 1.39 | 2.0 | 56 | 1.4010 | 0.2619 |
| 1.4057 | 3.0 | 84 | 1.3886 | 0.2381 |
| 1.3953 | 4.0 | 112 | 1.3773 | 0.2381 |
| 1.3855 | 5.0 | 140 | 1.3607 | 0.2619 |
| 1.3721 | 6.0 | 168 | 1.1238 | 0.5 |
| 1.2199 | 7.0 | 196 | 1.2305 | 0.4762 |
| 1.1505 | 8.0 | 224 | 0.9832 | 0.4762 |
| 1.1076 | 9.0 | 252 | 0.9145 | 0.5476 |
| 1.04 | 10.0 | 280 | 0.9689 | 0.5476 |
| 0.9947 | 11.0 | 308 | 0.8866 | 0.6429 |
| 1.0266 | 12.0 | 336 | 0.8639 | 0.6905 |
| 0.9955 | 13.0 | 364 | 0.8959 | 0.6190 |
| 0.9564 | 14.0 | 392 | 0.8608 | 0.6667 |
| 0.9123 | 15.0 | 420 | 0.7711 | 0.6905 |
| 0.9391 | 16.0 | 448 | 0.7070 | 0.7619 |
| 0.9117 | 17.0 | 476 | 0.7366 | 0.7619 |
| 0.902 | 18.0 | 504 | 0.7650 | 0.7143 |
| 0.8479 | 19.0 | 532 | 0.7181 | 0.7381 |
| 0.8138 | 20.0 | 560 | 0.8337 | 0.6667 |
| 0.7593 | 21.0 | 588 | 0.8325 | 0.6905 |
| 0.8558 | 22.0 | 616 | 0.7211 | 0.8095 |
| 0.8609 | 23.0 | 644 | 0.7758 | 0.7619 |
| 0.7997 | 24.0 | 672 | 0.8535 | 0.7143 |
| 0.6915 | 25.0 | 700 | 0.8962 | 0.7381 |
| 0.7445 | 26.0 | 728 | 0.7116 | 0.7619 |
| 0.6818 | 27.0 | 756 | 0.9464 | 0.5714 |
| 0.6812 | 28.0 | 784 | 0.6802 | 0.7143 |
| 0.662 | 29.0 | 812 | 1.0464 | 0.5476 |
| 0.6161 | 30.0 | 840 | 0.7154 | 0.7857 |
| 0.5942 | 31.0 | 868 | 0.6122 | 0.7619 |
| 0.571 | 32.0 | 896 | 0.6263 | 0.7857 |
| 0.5357 | 33.0 | 924 | 0.8564 | 0.8095 |
| 0.4815 | 34.0 | 952 | 0.9986 | 0.7381 |
| 0.5261 | 35.0 | 980 | 0.9173 | 0.8095 |
| 0.3508 | 36.0 | 1008 | 1.0846 | 0.7619 |
| 0.3469 | 37.0 | 1036 | 0.9412 | 0.8333 |
| 0.3024 | 38.0 | 1064 | 0.9602 | 0.8333 |
| 0.2908 | 39.0 | 1092 | 1.1234 | 0.8333 |
| 0.2222 | 40.0 | 1120 | 1.1275 | 0.8095 |
| 0.2149 | 41.0 | 1148 | 1.4618 | 0.7381 |
| 0.2207 | 42.0 | 1176 | 1.3470 | 0.7857 |
| 0.094 | 43.0 | 1204 | 1.5389 | 0.7619 |
| 0.1227 | 44.0 | 1232 | 1.3819 | 0.7857 |
| 0.0713 | 45.0 | 1260 | 1.5287 | 0.7619 |
| 0.0383 | 46.0 | 1288 | 1.5676 | 0.8095 |
| 0.0259 | 47.0 | 1316 | 1.4966 | 0.7857 |
| 0.023 | 48.0 | 1344 | 1.5355 | 0.7619 |
| 0.0304 | 49.0 | 1372 | 1.5372 | 0.7619 |
| 0.0233 | 50.0 | 1400 | 1.5372 | 0.7619 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | hkivancoral/hushem_5x_beit_base_rms_001_fold4 | [
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biomesh/TerrainAI | biomesh | 2023-11-29T04:42:43Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T04:42:43Z | 2023-11-29T04:41:18.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | biomesh/TerrainAI | [
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mattwigway/streetreimagine | mattwigway | 2023-11-29T04:42:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:42:16Z | 2023-11-29T04:42:16.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mattwigway/streetreimagine | [
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EikESousA/ggml-ptbr | EikESousA | 2023-11-29T05:09:54Z | 0 | 0 | null | [
"gguf",
"license:mit",
"region:us"
] | 2023-11-29T05:09:54Z | 2023-11-29T04:42:52.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | EikESousA/ggml-ptbr | [
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chojo12/task4 | chojo12 | 2023-11-29T07:12:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:12:55Z | 2023-11-29T04:44:16.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | chojo12/task4 | [
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haurajahra/QA_using_indoBERT_LORA_qv2 | haurajahra | 2023-11-29T13:06:35Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:indolem/indobert-base-uncased",
"license:mit",
"region:us"
] | 2023-11-29T13:06:35Z | 2023-11-29T04:44:22.000Z | null | null | ---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: QA_using_indoBERT_LORA_qv2
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. -->
# QA_using_indoBERT_LORA_qv2
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9435
## 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: 4
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.7426 | 0.02 | 500 | 6.2378 |
| 5.1601 | 0.03 | 1000 | 4.0267 |
| 3.466 | 0.05 | 1500 | 3.0399 |
| 2.9304 | 0.06 | 2000 | 2.8011 |
| 2.7403 | 0.08 | 2500 | 2.7113 |
| 2.599 | 0.09 | 3000 | 2.6337 |
| 2.4993 | 0.11 | 3500 | 2.4798 |
| 2.4454 | 0.12 | 4000 | 2.4486 |
| 2.3938 | 0.14 | 4500 | 2.3848 |
| 2.3124 | 0.15 | 5000 | 2.3729 |
| 2.2595 | 0.17 | 5500 | 2.4021 |
| 2.241 | 0.18 | 6000 | 2.3487 |
| 2.3296 | 0.2 | 6500 | 2.2819 |
| 2.21 | 0.21 | 7000 | 2.2588 |
| 2.2386 | 0.23 | 7500 | 2.3498 |
| 2.164 | 0.25 | 8000 | 2.2315 |
| 2.2535 | 0.26 | 8500 | 2.2315 |
| 2.2621 | 0.28 | 9000 | 2.3788 |
| 2.364 | 0.29 | 9500 | 2.8077 |
| 2.2345 | 0.31 | 10000 | 2.2495 |
| 2.1571 | 0.32 | 10500 | 2.2306 |
| 2.0452 | 0.34 | 11000 | 2.2417 |
| 2.1279 | 0.35 | 11500 | 2.1814 |
| 2.1482 | 0.37 | 12000 | 2.1762 |
| 2.1064 | 0.38 | 12500 | 2.1931 |
| 1.9992 | 0.4 | 13000 | 2.1902 |
| 2.1265 | 0.41 | 13500 | 2.1558 |
| 2.0659 | 0.43 | 14000 | 2.2007 |
| 2.0314 | 0.44 | 14500 | 2.1326 |
| 2.0086 | 0.46 | 15000 | 2.1282 |
| 2.0168 | 0.48 | 15500 | 2.1372 |
| 2.024 | 0.49 | 16000 | 2.1111 |
| 2.0636 | 0.51 | 16500 | 2.0926 |
| 1.9673 | 0.52 | 17000 | 2.1200 |
| 2.0207 | 0.54 | 17500 | 2.1710 |
| 2.0857 | 0.55 | 18000 | 2.1886 |
| 2.1617 | 0.57 | 18500 | 2.1123 |
| 1.9912 | 0.58 | 19000 | 2.0999 |
| 2.1166 | 0.6 | 19500 | 2.0940 |
| 2.0312 | 0.61 | 20000 | 2.1436 |
| 2.1124 | 0.63 | 20500 | 2.1743 |
| 2.0399 | 0.64 | 21000 | 2.0801 |
| 1.9246 | 0.66 | 21500 | 2.0535 |
| 1.9792 | 0.67 | 22000 | 2.0926 |
| 1.9713 | 0.69 | 22500 | 2.0666 |
| 1.9285 | 0.71 | 23000 | 2.0699 |
| 1.9454 | 0.72 | 23500 | 2.0873 |
| 1.9255 | 0.74 | 24000 | 2.0515 |
| 1.9428 | 0.75 | 24500 | 2.0771 |
| 1.9093 | 0.77 | 25000 | 2.0538 |
| 1.933 | 0.78 | 25500 | 2.0308 |
| 1.8628 | 0.8 | 26000 | 2.0554 |
| 1.906 | 0.81 | 26500 | 2.0581 |
| 1.9255 | 0.83 | 27000 | 2.0167 |
| 1.8795 | 0.84 | 27500 | 2.0423 |
| 1.8987 | 0.86 | 28000 | 2.0300 |
| 1.8464 | 0.87 | 28500 | 2.0540 |
| 1.9619 | 0.89 | 29000 | 2.0068 |
| 1.9475 | 0.9 | 29500 | 2.0079 |
| 1.9399 | 0.92 | 30000 | 1.9889 |
| 1.8473 | 0.94 | 30500 | 2.0135 |
| 1.8775 | 0.95 | 31000 | 2.0096 |
| 1.8049 | 0.97 | 31500 | 2.0289 |
| 1.8029 | 0.98 | 32000 | 2.0561 |
| 1.9167 | 1.0 | 32500 | 2.0199 |
| 1.873 | 1.01 | 33000 | 2.0081 |
| 1.7915 | 1.03 | 33500 | 2.0418 |
| 1.8741 | 1.04 | 34000 | 2.0087 |
| 1.8528 | 1.06 | 34500 | 2.0023 |
| 1.8255 | 1.07 | 35000 | 2.0275 |
| 1.8667 | 1.09 | 35500 | 2.0227 |
| 1.7821 | 1.1 | 36000 | 1.9990 |
| 1.7809 | 1.12 | 36500 | 2.0067 |
| 1.8287 | 1.13 | 37000 | 1.9984 |
| 1.8026 | 1.15 | 37500 | 2.0272 |
| 1.8299 | 1.16 | 38000 | 2.0259 |
| 1.7972 | 1.18 | 38500 | 2.0382 |
| 1.8505 | 1.2 | 39000 | 1.9803 |
| 1.8319 | 1.21 | 39500 | 1.9699 |
| 1.8171 | 1.23 | 40000 | 1.9931 |
| 1.7986 | 1.24 | 40500 | 1.9933 |
| 1.8228 | 1.26 | 41000 | 1.9807 |
| 1.8793 | 1.27 | 41500 | 1.9999 |
| 1.7724 | 1.29 | 42000 | 1.9779 |
| 1.7328 | 1.3 | 42500 | 1.9725 |
| 1.8083 | 1.32 | 43000 | 1.9603 |
| 1.7829 | 1.33 | 43500 | 1.9790 |
| 1.7823 | 1.35 | 44000 | 1.9777 |
| 1.7715 | 1.36 | 44500 | 1.9831 |
| 1.8368 | 1.38 | 45000 | 1.9531 |
| 1.7688 | 1.39 | 45500 | 1.9666 |
| 1.7946 | 1.41 | 46000 | 1.9662 |
| 1.8104 | 1.43 | 46500 | 1.9799 |
| 1.758 | 1.44 | 47000 | 1.9697 |
| 1.802 | 1.46 | 47500 | 1.9617 |
| 1.7628 | 1.47 | 48000 | 1.9645 |
| 1.8014 | 1.49 | 48500 | 1.9642 |
| 1.8153 | 1.5 | 49000 | 1.9449 |
| 1.7997 | 1.52 | 49500 | 1.9682 |
| 1.8021 | 1.53 | 50000 | 1.9567 |
| 1.766 | 1.55 | 50500 | 1.9740 |
| 1.7886 | 1.56 | 51000 | 1.9513 |
| 1.7865 | 1.58 | 51500 | 1.9411 |
| 1.8403 | 1.59 | 52000 | 1.9396 |
| 1.7257 | 1.61 | 52500 | 1.9590 |
| 1.7743 | 1.62 | 53000 | 1.9408 |
| 1.7903 | 1.64 | 53500 | 1.9469 |
| 1.8302 | 1.66 | 54000 | 1.9370 |
| 1.7979 | 1.67 | 54500 | 1.9394 |
| 1.8109 | 1.69 | 55000 | 1.9440 |
| 1.7397 | 1.7 | 55500 | 1.9579 |
| 1.7374 | 1.72 | 56000 | 1.9501 |
| 1.7373 | 1.73 | 56500 | 1.9518 |
| 1.7273 | 1.75 | 57000 | 1.9474 |
| 1.8064 | 1.76 | 57500 | 1.9368 |
| 1.7913 | 1.78 | 58000 | 1.9426 |
| 1.8166 | 1.79 | 58500 | 1.9331 |
| 1.8238 | 1.81 | 59000 | 1.9341 |
| 1.8049 | 1.82 | 59500 | 1.9464 |
| 1.8735 | 1.84 | 60000 | 1.9397 |
| 1.8169 | 1.85 | 60500 | 1.9388 |
| 1.7689 | 1.87 | 61000 | 1.9393 |
| 1.7612 | 1.89 | 61500 | 1.9433 |
| 1.7768 | 1.9 | 62000 | 1.9402 |
| 1.6952 | 1.92 | 62500 | 1.9478 |
| 1.7951 | 1.93 | 63000 | 1.9395 |
| 1.764 | 1.95 | 63500 | 1.9381 |
| 1.7895 | 1.96 | 64000 | 1.9362 |
| 1.6671 | 1.98 | 64500 | 1.9428 |
| 1.7535 | 1.99 | 65000 | 1.9435 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | haurajahra/QA_using_indoBERT_LORA_qv2 | [
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Justikun/repo_name | Justikun | 2023-11-29T04:44:33Z | 0 | 0 | null | [
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K-kiron/baseline-distilbert | K-kiron | 2023-11-29T04:49:51Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:49:51Z | 2023-11-29T04:45:59.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: baseline-distilbert
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. -->
# baseline-distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9592
- Accuracy: 0.8911
- F1: 0.8909
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.3
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mncai/Mois_DPO_v2_epoch1 | mncai | 2023-11-29T04:53:10Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T04:53:10Z | 2023-11-29T04:46:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | mncai/Mois_DPO_v2_epoch1 | [
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khanhlinh/food101_convnext | khanhlinh | 2023-11-29T04:47:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:47:33Z | 2023-11-29T04:47:33.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | khanhlinh/food101_convnext | [
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SamJu3/haerin-model-lora10with-1_5 | SamJu3 | 2023-11-29T04:52:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:52:18Z | 2023-11-29T04:52:18.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | SamJu3/haerin-model-lora10with-1_5 | [
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MegPaulson/SD_HealthySkin2 | MegPaulson | 2023-11-29T04:52:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T04:52:37Z | 2023-11-29T04:52:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | MegPaulson/SD_HealthySkin2 | [
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Gaxys/RoBERTA-PD_Books | Gaxys | 2023-11-29T05:14:18Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:14:18Z | 2023-11-29T04:54:04.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Gaxys/RoBERTA-PD_Books | [
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ThuTrang/Intel-Image-Classification | ThuTrang | 2023-11-29T05:02:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:02:42Z | 2023-11-29T04:54:32.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | ThuTrang/Intel-Image-Classification | [
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K-kiron/distilbert-batch-size-64 | K-kiron | 2023-11-29T04:57:26Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:57:26Z | 2023-11-29T04:55:15.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-batch-size-64
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-batch-size-64
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7381
- Accuracy: 0.8930
- F1: 0.8928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.3
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K-kiron/distilbert-epoch-10 | K-kiron | 2023-11-29T05:12:36Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:12:36Z | 2023-11-29T04:58:31.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-epoch-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-epoch-10
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0621
- Accuracy: 0.8966
- F1: 0.8963
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.3
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shelbao/dlib_m | shelbao | 2023-11-29T05:01:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:01:46Z | 2023-11-29T04:59:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | shelbao/dlib_m | [
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YanisKa/IFT6758 | YanisKa | 2023-11-29T05:05:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:05:00Z | 2023-11-29T05:05:00.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | YanisKa/IFT6758 | [
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YanisKa/IFT6758-d6 | YanisKa | 2023-11-30T00:40:30Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:40:30Z | 2023-11-29T05:05:13.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | YanisKa/IFT6758-d6 | [
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dvijay/mistral-alpaca-tune | dvijay | 2023-11-29T05:07:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:07:16Z | 2023-11-29T05:07:16.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | dvijay/mistral-alpaca-tune | [
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Margen67/friendfrancis | Margen67 | 2023-11-29T05:09:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:09:29Z | 2023-11-29T05:08:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Margen67/friendfrancis | [
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