modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
9.67k
| library_name
stringlengths 2
84
⌀ | tags
list | pipeline_tag
stringlengths 5
30
⌀ | createdAt
timestamp[us, tz=UTC] | card
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| embedding
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|---|---|---|---|---|---|---|---|---|---|---|
Manirajan/llama2-ros
|
Manirajan
| 2023-12-17T11:52:34Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T11:44:25Z |
Entry not found
|
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] |
LoneStriker/Mixtral-SlimOrca-8x7B-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-17T14:59:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"dataset:Open-Orca/SlimOrca",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T11:44:48Z |
---
base_model: mistralai/Mixtral-8x7B-v0.1
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
pipeline_tag: text-generation
---
# SlimOrca Mixtral 8x7B
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
Official release of the SlimOrca Mixtral finetune. More details to come.
## Model Details
### Model Description
- **Developed by:** OpenAccess AI Collective and OpenOrca
- **Finetuned from model [optional]:** mistralai/Mixtral-8x7B-v0.1
|
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TachyHealth/Thealth-phi-2
|
TachyHealth
| 2023-12-17T16:31:00Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"medical",
"lora ",
"conversational",
"dataset:medalpaca/medical_meadow_mediqa",
"dataset:medalpaca/medical_meadow_mmmlu",
"dataset:medalpaca/medical_meadow_medical_flashcards",
"dataset:medalpaca/medical_meadow_wikidoc_patient_information",
"dataset:medalpaca/medical_meadow_wikidoc",
"dataset:medalpaca/medical_meadow_pubmed_causal",
"dataset:medalpaca/medical_meadow_medqa",
"dataset:medalpaca/medical_meadow_health_advice",
"dataset:medalpaca/medical_meadow_cord19",
"base_model:microsoft/phi-2",
"license:other",
"region:us"
] |
conversational
| 2023-12-17T11:45:30Z |
---
license: other
base_model: microsoft/phi-2
tags:
- generated_from_trainer
- medical
- peft
- 'lora '
model-index:
- name: Thealth-phi-2-tunned-9_medalpaca_medical_meadow
results: []
datasets:
- medalpaca/medical_meadow_mediqa
- medalpaca/medical_meadow_mmmlu
- medalpaca/medical_meadow_medical_flashcards
- medalpaca/medical_meadow_wikidoc_patient_information
- medalpaca/medical_meadow_wikidoc
- medalpaca/medical_meadow_pubmed_causal
- medalpaca/medical_meadow_medqa
- medalpaca/medical_meadow_health_advice
- medalpaca/medical_meadow_cord19
pipeline_tag: conversational
---
<!-- 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. -->
# Thealth-phi-2-tunned-9_medalpaca_medical_meadow
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6588
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Training is done one 9 medalpaca/medical_meadow datasets combined and splited to 90% train and 10% Evaluation
| Dataset |
|:-----------------------------------------------------:|
| medalpaca/medical_meadow_mediqa |
| medalpaca/medical_meadow_mmmlu |
| medalpaca/medical_meadow_medical_flashcards |
| medalpaca/medical_meadow_wikidoc_patient_information |
| medalpaca/medical_meadow_wikidoc |
| medalpaca/medical_meadow_pubmed_causal |
| medalpaca/medical_meadow_medqa |
| medalpaca/medical_meadow_health_advice |
| medalpaca/medical_meadow_cord19 |
## Training procedure
Used different tokenizer [stanford-crfm/BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.8245 | 0.0 | 500 | 6.7654 |
| 6.7944 | 0.0 | 1000 | 6.6588 |
### Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stanford-crfm/BioMedLM", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TachyHealthResearch/Thealth-phi-2-tunned-9_medalpaca_medical_meadow", trust_remote_code=True, torch_dtype=torch.float32)
```
```python
inputs = tokenizer(
"""
question: ****** ? answer:
""",
return_tensors="pt",
return_attention_mask=False)
```
```python
outputs = model.generate(**inputs, max_length=512)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Forex-Hodler/endacoder
|
Forex-Hodler
| 2023-12-17T11:45:51Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T11:45:51Z |
---
license: apache-2.0
---
|
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] |
pedeke/pede112
|
pedeke
| 2023-12-17T11:47:43Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T11:46:30Z |
---
license: openrail
---
|
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RadExponent5431/autoytapi
|
RadExponent5431
| 2023-12-17T11:48:44Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-17T11:48:44Z |
---
license: mit
---
|
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] |
hkivancoral/smids_5x_deit_base_sgd_0001_fold3
|
hkivancoral
| 2023-12-17T13:04:49Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T11:50:25Z |
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_sgd_0001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8183333333333334
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_base_sgd_0001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5127
- Accuracy: 0.8183
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.086 | 1.0 | 375 | 1.0870 | 0.4083 |
| 1.0544 | 2.0 | 750 | 1.0666 | 0.4433 |
| 1.0309 | 3.0 | 1125 | 1.0448 | 0.48 |
| 0.9851 | 4.0 | 1500 | 1.0206 | 0.5333 |
| 0.9784 | 5.0 | 1875 | 0.9932 | 0.575 |
| 0.9454 | 6.0 | 2250 | 0.9632 | 0.6017 |
| 0.9054 | 7.0 | 2625 | 0.9323 | 0.6317 |
| 0.8859 | 8.0 | 3000 | 0.9017 | 0.6617 |
| 0.8212 | 9.0 | 3375 | 0.8722 | 0.675 |
| 0.846 | 10.0 | 3750 | 0.8441 | 0.69 |
| 0.7793 | 11.0 | 4125 | 0.8178 | 0.6967 |
| 0.7556 | 12.0 | 4500 | 0.7933 | 0.72 |
| 0.753 | 13.0 | 4875 | 0.7705 | 0.7333 |
| 0.7537 | 14.0 | 5250 | 0.7493 | 0.7433 |
| 0.7009 | 15.0 | 5625 | 0.7292 | 0.7517 |
| 0.6573 | 16.0 | 6000 | 0.7106 | 0.7517 |
| 0.6888 | 17.0 | 6375 | 0.6931 | 0.7583 |
| 0.6952 | 18.0 | 6750 | 0.6769 | 0.765 |
| 0.6154 | 19.0 | 7125 | 0.6622 | 0.7733 |
| 0.6418 | 20.0 | 7500 | 0.6487 | 0.7833 |
| 0.5745 | 21.0 | 7875 | 0.6362 | 0.7883 |
| 0.5966 | 22.0 | 8250 | 0.6248 | 0.7933 |
| 0.6053 | 23.0 | 8625 | 0.6144 | 0.7983 |
| 0.6111 | 24.0 | 9000 | 0.6047 | 0.8 |
| 0.5758 | 25.0 | 9375 | 0.5959 | 0.805 |
| 0.5916 | 26.0 | 9750 | 0.5876 | 0.8067 |
| 0.5804 | 27.0 | 10125 | 0.5800 | 0.81 |
| 0.5956 | 28.0 | 10500 | 0.5730 | 0.81 |
| 0.5266 | 29.0 | 10875 | 0.5666 | 0.81 |
| 0.5435 | 30.0 | 11250 | 0.5607 | 0.8117 |
| 0.566 | 31.0 | 11625 | 0.5553 | 0.8133 |
| 0.5691 | 32.0 | 12000 | 0.5503 | 0.8133 |
| 0.6036 | 33.0 | 12375 | 0.5456 | 0.815 |
| 0.5178 | 34.0 | 12750 | 0.5415 | 0.8133 |
| 0.4823 | 35.0 | 13125 | 0.5375 | 0.8167 |
| 0.4738 | 36.0 | 13500 | 0.5340 | 0.8183 |
| 0.5126 | 37.0 | 13875 | 0.5307 | 0.8183 |
| 0.4971 | 38.0 | 14250 | 0.5278 | 0.8183 |
| 0.5124 | 39.0 | 14625 | 0.5252 | 0.82 |
| 0.4766 | 40.0 | 15000 | 0.5229 | 0.82 |
| 0.4547 | 41.0 | 15375 | 0.5208 | 0.82 |
| 0.5009 | 42.0 | 15750 | 0.5190 | 0.82 |
| 0.5345 | 43.0 | 16125 | 0.5174 | 0.82 |
| 0.5032 | 44.0 | 16500 | 0.5161 | 0.8183 |
| 0.4932 | 45.0 | 16875 | 0.5150 | 0.8183 |
| 0.5003 | 46.0 | 17250 | 0.5141 | 0.8183 |
| 0.5538 | 47.0 | 17625 | 0.5134 | 0.8183 |
| 0.5053 | 48.0 | 18000 | 0.5130 | 0.8183 |
| 0.5053 | 49.0 | 18375 | 0.5128 | 0.8183 |
| 0.4871 | 50.0 | 18750 | 0.5127 | 0.8183 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_deit_base_sgd_00001_fold3
|
hkivancoral
| 2023-12-18T00:46:33Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T11:50:34Z |
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_sgd_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4483333333333333
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_base_sgd_00001_fold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0622
- Accuracy: 0.4483
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1145 | 1.0 | 375 | 1.1057 | 0.3517 |
| 1.1046 | 2.0 | 750 | 1.1036 | 0.365 |
| 1.1155 | 3.0 | 1125 | 1.1017 | 0.3617 |
| 1.0962 | 4.0 | 1500 | 1.0998 | 0.3733 |
| 1.1039 | 5.0 | 1875 | 1.0980 | 0.375 |
| 1.1203 | 6.0 | 2250 | 1.0963 | 0.3833 |
| 1.072 | 7.0 | 2625 | 1.0946 | 0.3833 |
| 1.0855 | 8.0 | 3000 | 1.0930 | 0.3883 |
| 1.0787 | 9.0 | 3375 | 1.0914 | 0.395 |
| 1.1082 | 10.0 | 3750 | 1.0899 | 0.3933 |
| 1.0738 | 11.0 | 4125 | 1.0885 | 0.4033 |
| 1.0788 | 12.0 | 4500 | 1.0870 | 0.4067 |
| 1.0872 | 13.0 | 4875 | 1.0857 | 0.415 |
| 1.0927 | 14.0 | 5250 | 1.0844 | 0.4133 |
| 1.0769 | 15.0 | 5625 | 1.0831 | 0.4167 |
| 1.085 | 16.0 | 6000 | 1.0819 | 0.4217 |
| 1.0634 | 17.0 | 6375 | 1.0806 | 0.42 |
| 1.0703 | 18.0 | 6750 | 1.0795 | 0.42 |
| 1.0536 | 19.0 | 7125 | 1.0783 | 0.4233 |
| 1.0852 | 20.0 | 7500 | 1.0773 | 0.4267 |
| 1.076 | 21.0 | 7875 | 1.0762 | 0.43 |
| 1.0765 | 22.0 | 8250 | 1.0752 | 0.4333 |
| 1.0647 | 23.0 | 8625 | 1.0743 | 0.435 |
| 1.061 | 24.0 | 9000 | 1.0734 | 0.4367 |
| 1.0648 | 25.0 | 9375 | 1.0725 | 0.445 |
| 1.0459 | 26.0 | 9750 | 1.0716 | 0.4417 |
| 1.0405 | 27.0 | 10125 | 1.0708 | 0.4417 |
| 1.062 | 28.0 | 10500 | 1.0700 | 0.4433 |
| 1.0508 | 29.0 | 10875 | 1.0693 | 0.445 |
| 1.06 | 30.0 | 11250 | 1.0686 | 0.445 |
| 1.062 | 31.0 | 11625 | 1.0679 | 0.4433 |
| 1.0622 | 32.0 | 12000 | 1.0673 | 0.4417 |
| 1.056 | 33.0 | 12375 | 1.0667 | 0.44 |
| 1.0406 | 34.0 | 12750 | 1.0662 | 0.4417 |
| 1.0456 | 35.0 | 13125 | 1.0657 | 0.4433 |
| 1.0409 | 36.0 | 13500 | 1.0652 | 0.4483 |
| 1.0536 | 37.0 | 13875 | 1.0648 | 0.4483 |
| 1.0481 | 38.0 | 14250 | 1.0643 | 0.445 |
| 1.0612 | 39.0 | 14625 | 1.0640 | 0.445 |
| 1.0364 | 40.0 | 15000 | 1.0637 | 0.45 |
| 1.0455 | 41.0 | 15375 | 1.0634 | 0.45 |
| 1.0602 | 42.0 | 15750 | 1.0631 | 0.45 |
| 1.0611 | 43.0 | 16125 | 1.0629 | 0.45 |
| 1.0352 | 44.0 | 16500 | 1.0627 | 0.45 |
| 1.0491 | 45.0 | 16875 | 1.0625 | 0.45 |
| 1.0436 | 46.0 | 17250 | 1.0624 | 0.45 |
| 1.0657 | 47.0 | 17625 | 1.0623 | 0.45 |
| 1.0594 | 48.0 | 18000 | 1.0622 | 0.4483 |
| 1.0541 | 49.0 | 18375 | 1.0622 | 0.4483 |
| 1.0444 | 50.0 | 18750 | 1.0622 | 0.4483 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Danibasters/xlm-roberta-base-finetuned-panx-all
|
Danibasters
| 2023-12-17T13:09:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-17T11:52:29Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1768
- F1: 0.8533
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2978 | 1.0 | 835 | 0.2006 | 0.8109 |
| 0.156 | 2.0 | 1670 | 0.1667 | 0.8430 |
| 0.1018 | 3.0 | 2505 | 0.1768 | 0.8533 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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fsicoli/whisper-large-v3-pt-cv15-fleurs-mls-td
|
fsicoli
| 2023-12-17T11:55:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T11:55:56Z |
Entry not found
|
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] |
Wanfq/distillchat_1.7.7
|
Wanfq
| 2023-12-17T12:40:48Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T11:58:38Z |
Entry not found
|
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] |
Wanfq/distillchat_1.7.8
|
Wanfq
| 2023-12-17T13:17:41Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T11:59:34Z |
Entry not found
|
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0.31015992164611816,
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] |
abhinav-32/distilgpt2-finetuned-wikitext2
|
abhinav-32
| 2023-12-17T12:10:35Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T12:01:53Z |
Entry not found
|
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] |
virtsion/nilmformer_3apis_with_prompt_custom_tokenizer
|
virtsion
| 2023-12-17T12:02:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-17T12:02:54Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1
|
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] |
tataun/wav2vec2-base-finetuned-ks
|
tataun
| 2023-12-17T13:53:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-12-17T12:05:36Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.061946902654867256
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6480
- Accuracy: 0.0619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6480 | 0.0619 |
| No log | 1.87 | 7 | 2.6544 | 0.0354 |
| 2.6287 | 2.93 | 11 | 2.6558 | 0.0354 |
| 2.6287 | 4.0 | 15 | 2.6565 | 0.0354 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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christinacdl/OpenOrca-CLM-Binary-Hate-Speech-Detection
|
christinacdl
| 2023-12-17T15:24:54Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T12:05:39Z |
---
license: apache-2.0
base_model: Open-Orca/Mistral-7B-OpenOrca
tags:
- generated_from_trainer
model-index:
- name: OpenOrca-CLM-Binary-Hate-Speech-Detection
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. -->
# OpenOrca-CLM-Binary-Hate-Speech-Detection
This model is a fine-tuned version of [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6597
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 205 | 1.9150 |
| No log | 2.0 | 410 | 1.7409 |
| No log | 3.0 | 615 | 1.6834 |
| No log | 4.0 | 820 | 1.6597 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0
|
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] |
gunkaynar/en-bertweet-base-sentiment-analysis
|
gunkaynar
| 2023-12-17T14:38:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:finiteautomata/bertweet-base-sentiment-analysis",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T12:07:14Z |
---
base_model: finiteautomata/bertweet-base-sentiment-analysis
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: en-bertweet-base-sentiment-analysis
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. -->
# en-bertweet-base-sentiment-analysis
This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5141
- Accuracy: 0.7145
- F1: 0.7422
## 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 4.33.3
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.11.0
|
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] |
Loren85/Tapion-DBZ-Italian
|
Loren85
| 2023-12-17T12:11:42Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T12:10:49Z |
---
license: openrail
---
|
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LoneStriker/Mixtral-8x7B-MoE-Undi95-RP-Story-3.5bpw-h6-exl2-2
|
LoneStriker
| 2023-12-17T12:36:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T12:11:38Z |
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
Mixtral-8x7B-MoE-RP-Story is a model made primarely for chatting, RP (Roleplay) and storywriting.
2 RP model, 2 chat model, 1 occult model, 1 storywritting model, 1 mathematic model and 1 DPO model was used for a MoE. Bagel was the base.
The DPO chat model is here to help get more human reply.
This is my first try at doing this, so don't hesitate to give feedback!
WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0!
<!-- description start -->
## Description
This repo contains fp16 files of Mixtral-8x7B-MoE-RP-Story.
<!-- description end -->
<!-- description start -->
## Models used
The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/blob/main/config.yaml)
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom
Using Bagel as a base let us a lot of different prompting system theorically, you can see all the prompting available [here](https://huggingface.co/jondurbin/bagel-7b-v0.1#prompt-formatting).
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
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] |
anugoel890/mistral-finetuned-samsum
|
anugoel890
| 2023-12-17T12:54:31Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-17T12:12:00Z |
Entry not found
|
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richie-ghost/PEFT-bruins-v2-1-1
|
richie-ghost
| 2023-12-17T12:14:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:rwitz2/go-bruins-v2.1.1",
"region:us"
] | null | 2023-12-17T12:13:18Z |
---
library_name: peft
base_model: rwitz2/go-bruins-v2.1.1
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.2.dev0
|
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] |
kaekitsune/bmtl
|
kaekitsune
| 2023-12-17T12:26:38Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-17T12:15:12Z |
---
license: creativeml-openrail-m
---
|
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] |
dawdsda/almigthy
|
dawdsda
| 2023-12-17T12:15:43Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/sdxl-turbo",
"region:us"
] |
text-to-image
| 2023-12-17T12:15:16Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/miky-woodz-ft-almighty-y-anuel-a.jpg
base_model: stabilityai/sdxl-turbo
instance_prompt: null
---
# Almigthy
<Gallery />
## Download model
[Download](/dawdsda/almigthy/tree/main) them in the Files & versions tab.
|
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tscstudios/9y6mSNJmSsfSJTvjuYFukWGMdBZ2
|
tscstudios
| 2023-12-17T12:16:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:15:17Z |
Entry not found
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ModelsForAll/Cool-2-0-0-0
|
ModelsForAll
| 2023-12-17T12:22:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:16:31Z |
“The moment you doubt whether you can fly, you cease for ever to be able to do it.” P.Pen
|
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] |
g8a9/Llama-2-13b_clean-mc4-it_camoscio
|
g8a9
| 2023-12-17T12:17:08Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:g8a9/Llama-2-13b_clean-mc4-it",
"region:us"
] | null | 2023-12-17T12:17:04Z |
---
library_name: peft
base_model: g8a9/Llama-2-13b_clean-mc4-it
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2
|
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kejolong/esdeath
|
kejolong
| 2023-12-17T12:23:56Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-17T12:17:06Z |
---
license: creativeml-openrail-m
---
|
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narab/0def9ee1-0164-4717-a417-dc0a971bfcc5-u1
|
narab
| 2023-12-17T12:24:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:20:40Z |
Entry not found
|
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] |
livewire/distilgpt2-finetuned-wikitext2
|
livewire
| 2023-12-17T22:53:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T12:20:47Z |
Entry not found
|
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] |
ekaterinatao/books_text_class_roBERTa_ru_base
|
ekaterinatao
| 2023-12-17T20:17:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:DeepPavlov/xlm-roberta-large-en-ru-mnli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T12:21:00Z |
---
base_model: DeepPavlov/xlm-roberta-large-en-ru-mnli
tags:
- generated_from_trainer
model-index:
- name: books_text_class_roBERTa_ru_base
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. -->
# books_text_class_roBERTa_ru_base
This model is a fine-tuned version of [DeepPavlov/xlm-roberta-large-en-ru-mnli](https://huggingface.co/DeepPavlov/xlm-roberta-large-en-ru-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1550
- eval_accuracy: 0.9824
- eval_f1-score: 0.9804
- eval_mcc: 0.9234
- eval_runtime: 175.3993
- eval_samples_per_second: 90.838
- eval_steps_per_second: 18.17
- epoch: 4.0
- step: 29744
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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kadirnar/deepcache_stable_diffusion
|
kadirnar
| 2023-12-17T12:29:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:21:27Z |
Entry not found
|
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] |
joxen/Eddy
|
joxen
| 2023-12-17T12:24:17Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-12-17T12:23:48Z |
---
license: other
license_name: edmondx
license_link: LICENSE
---
|
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waveydaveygravy/magic-animate
|
waveydaveygravy
| 2023-12-17T12:38:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:24:22Z |
Entry not found
|
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jjumper/jjamper-rvc-models-colelction-cum
|
jjumper
| 2023-12-17T12:29:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:25:37Z |
Entry not found
|
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Mogragine/ppo-LunarLander-v2
|
Mogragine
| 2023-12-17T12:52:34Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T12:26:08Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 237.61 +/- 19.88
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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thorirhrafn/llama2_lora_ice3
|
thorirhrafn
| 2023-12-18T01:29:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:26:14Z |
Entry not found
|
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] |
linqus/codeparrot-ds
|
linqus
| 2023-12-17T17:42:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T12:34:18Z |
Entry not found
|
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ali-khoshtinat/parsig_pos
|
ali-khoshtinat
| 2023-12-17T16:21:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:35:56Z |
Entry not found
|
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ModelsForAll/Cool-2-0-0-1
|
ModelsForAll
| 2023-12-17T12:37:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:36:51Z |
“The moment you doubt whether you can fly, you cease for ever to be able to do it.” P.Pen
|
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] |
uaritm/enukruvie
|
uaritm
| 2023-12-17T12:45:44Z | 0 | 0 |
transformers
|
[
"transformers",
"sbert",
"Multilingual",
"sentence-transformers",
"en",
"uk",
"ru",
"vi",
"dataset:tatoeba",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-17T12:36:55Z |
---
license: apache-2.0
datasets:
- tatoeba
language:
- en
- uk
- ru
- vi
library_name: transformers
tags:
- sbert
- Multilingual
- sentence-transformers
---
|
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] |
korginevvostorge/distilhubert-courseDSP-finetuned-gtzan
|
korginevvostorge
| 2023-12-17T12:39:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"audio-classification",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-12-17T12:39:30Z |
Entry not found
|
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yonadolev/bert-base-cased-finetuned-swag
|
yonadolev
| 2023-12-17T12:40:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:40:53Z |
Entry not found
|
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] |
AlexWortega/ru_flan_3b
|
AlexWortega
| 2023-12-17T12:44:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-17T12:41:10Z |
---
license: apache-2.0
---
|
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] |
luhulahu/test_model
|
luhulahu
| 2023-12-17T12:41:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T12:41:32Z |
---
license: apache-2.0
---
|
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] |
asanoop24/distilbert-base-uncased-pii-redaction
|
asanoop24
| 2023-12-17T12:44:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-17T12:43:29Z |
Entry not found
|
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imspidey/LoSamuraiGirl
|
imspidey
| 2023-12-17T12:45:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:44:30Z |
Entry not found
|
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] |
syp1229/whisper-small-ko-young-sp
|
syp1229
| 2023-12-17T23:09:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-17T12:47:21Z |
Entry not found
|
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] |
BanUrsus/distilroberta-base-finetuned-condition-classifier
|
BanUrsus
| 2023-12-17T13:53:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"exbert",
"en",
"dataset:drug-review",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T12:48:57Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- drug-review
---
# DistilRoBERTa base fintuned condition classifier
# Table of Contents
1. [Model Details](#model-details)
2. [Training Details](#training-details)
3. [Evaluation](#evaluation)
4. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
This model is fine-tuned for a condition classification version of the [DistilRoBERTa-base model](https://huggingface.co/distilroberta-base).
This model is case-sensitive: it makes a difference between english and English.
- **Fine-tuned by:** Ban Ursus
- **Model type:** Transformer-based language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:** [DistilRoBERTa-base model](https://huggingface.co/distilroberta-base)
- **Resources for more information:**
- [GitHub Repository](https://github.com/BanSangSu/Hugging_Face_NLP_Course/tree/main/Chapter5)
# Training Details
This model was fine-tuned 5 epochs using [Drug Review Dataset](https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com). Therefore, you can improve the accuracy of this model just by training more.
# Evaluation
Validation results:
| Accuracy | F1 score |
|:----:|:----:|
| 0.63 | 0.58 |
Note: Rounded to 2 decimal places
# How to Get Started With the Model
Follow the Section 2 **Try it out!** of the [GitHub Repository](https://github.com/BanSangSu/Hugging_Face_NLP_Course/tree/main/Chapter5).
|
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] |
muktadiur/Llama-2-7b-chat-hf-fine-tuned-adapters
|
muktadiur
| 2023-12-17T15:38:42Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-17T12:51:32Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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#### Software
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## Citation [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.7.2.dev0
|
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msbshamim/mobilebert_syntiment
|
msbshamim
| 2023-12-17T12:52:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:52:01Z |
Entry not found
|
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mariojud/mariojud
|
mariojud
| 2023-12-17T13:03:32Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T12:53:25Z |
---
license: openrail
---
|
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nirmalj000/tanslator
|
nirmalj000
| 2023-12-17T12:55:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:55:44Z |
Entry not found
|
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tscstudios/coNpJPITpwdlyaRzeBgFbLndhaK2
|
tscstudios
| 2023-12-17T12:58:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T12:57:27Z |
Entry not found
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] |
mjkimmjmj/SDC_Mistral_v2
|
mjkimmjmj
| 2023-12-17T13:04:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T12:59:45Z |
Entry not found
|
[
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ngocquanofficial/machine_translation_PhoMT
|
ngocquanofficial
| 2023-12-17T13:00:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:00:16Z |
Entry not found
|
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YouKnow01/Narrator
|
YouKnow01
| 2023-12-17T13:06:14Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T13:04:23Z |
---
license: openrail
---
|
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hkivancoral/smids_5x_deit_base_sgd_00001_fold4
|
hkivancoral
| 2023-12-18T01:29:28Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T13:06:17Z |
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_sgd_00001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.475
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_base_sgd_00001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0653
- Accuracy: 0.475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1101 | 1.0 | 375 | 1.1105 | 0.3333 |
| 1.1112 | 2.0 | 750 | 1.1084 | 0.3483 |
| 1.1096 | 3.0 | 1125 | 1.1064 | 0.3533 |
| 1.1002 | 4.0 | 1500 | 1.1045 | 0.3633 |
| 1.1197 | 5.0 | 1875 | 1.1027 | 0.3667 |
| 1.0859 | 6.0 | 2250 | 1.1009 | 0.3733 |
| 1.0856 | 7.0 | 2625 | 1.0992 | 0.3833 |
| 1.0857 | 8.0 | 3000 | 1.0975 | 0.3883 |
| 1.0995 | 9.0 | 3375 | 1.0959 | 0.3933 |
| 1.1123 | 10.0 | 3750 | 1.0943 | 0.4 |
| 1.0849 | 11.0 | 4125 | 1.0928 | 0.405 |
| 1.0866 | 12.0 | 4500 | 1.0914 | 0.4067 |
| 1.08 | 13.0 | 4875 | 1.0899 | 0.42 |
| 1.0796 | 14.0 | 5250 | 1.0886 | 0.4217 |
| 1.082 | 15.0 | 5625 | 1.0872 | 0.425 |
| 1.0851 | 16.0 | 6000 | 1.0859 | 0.4283 |
| 1.0643 | 17.0 | 6375 | 1.0847 | 0.4283 |
| 1.0676 | 18.0 | 6750 | 1.0834 | 0.4283 |
| 1.0655 | 19.0 | 7125 | 1.0823 | 0.43 |
| 1.0761 | 20.0 | 7500 | 1.0811 | 0.4283 |
| 1.069 | 21.0 | 7875 | 1.0800 | 0.4333 |
| 1.0634 | 22.0 | 8250 | 1.0790 | 0.435 |
| 1.0576 | 23.0 | 8625 | 1.0780 | 0.4383 |
| 1.0581 | 24.0 | 9000 | 1.0770 | 0.4367 |
| 1.0635 | 25.0 | 9375 | 1.0761 | 0.44 |
| 1.0553 | 26.0 | 9750 | 1.0752 | 0.4417 |
| 1.0268 | 27.0 | 10125 | 1.0743 | 0.4433 |
| 1.0755 | 28.0 | 10500 | 1.0735 | 0.4483 |
| 1.0483 | 29.0 | 10875 | 1.0727 | 0.455 |
| 1.0533 | 30.0 | 11250 | 1.0720 | 0.455 |
| 1.0638 | 31.0 | 11625 | 1.0713 | 0.455 |
| 1.0617 | 32.0 | 12000 | 1.0707 | 0.4567 |
| 1.0624 | 33.0 | 12375 | 1.0700 | 0.46 |
| 1.052 | 34.0 | 12750 | 1.0695 | 0.4667 |
| 1.0462 | 35.0 | 13125 | 1.0689 | 0.4683 |
| 1.0582 | 36.0 | 13500 | 1.0684 | 0.4683 |
| 1.0432 | 37.0 | 13875 | 1.0680 | 0.4683 |
| 1.0468 | 38.0 | 14250 | 1.0675 | 0.47 |
| 1.0664 | 39.0 | 14625 | 1.0671 | 0.4717 |
| 1.0442 | 40.0 | 15000 | 1.0668 | 0.47 |
| 1.0443 | 41.0 | 15375 | 1.0665 | 0.4717 |
| 1.0372 | 42.0 | 15750 | 1.0662 | 0.4717 |
| 1.0389 | 43.0 | 16125 | 1.0660 | 0.4717 |
| 1.044 | 44.0 | 16500 | 1.0658 | 0.4717 |
| 1.0519 | 45.0 | 16875 | 1.0656 | 0.4717 |
| 1.0549 | 46.0 | 17250 | 1.0655 | 0.475 |
| 1.0727 | 47.0 | 17625 | 1.0654 | 0.475 |
| 1.0653 | 48.0 | 18000 | 1.0653 | 0.475 |
| 1.0698 | 49.0 | 18375 | 1.0653 | 0.475 |
| 1.0509 | 50.0 | 18750 | 1.0653 | 0.475 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_deit_base_sgd_0001_fold4
|
hkivancoral
| 2023-12-17T14:20:40Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T13:06:17Z |
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_sgd_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8216666666666667
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_5x_deit_base_sgd_0001_fold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4947
- Accuracy: 0.8217
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0807 | 1.0 | 375 | 1.0913 | 0.4067 |
| 1.0601 | 2.0 | 750 | 1.0697 | 0.46 |
| 1.0228 | 3.0 | 1125 | 1.0461 | 0.515 |
| 0.9878 | 4.0 | 1500 | 1.0193 | 0.5683 |
| 1.0067 | 5.0 | 1875 | 0.9894 | 0.6 |
| 0.9245 | 6.0 | 2250 | 0.9567 | 0.6167 |
| 0.9152 | 7.0 | 2625 | 0.9222 | 0.6367 |
| 0.8904 | 8.0 | 3000 | 0.8874 | 0.665 |
| 0.8675 | 9.0 | 3375 | 0.8548 | 0.69 |
| 0.8528 | 10.0 | 3750 | 0.8243 | 0.7217 |
| 0.7884 | 11.0 | 4125 | 0.7961 | 0.7333 |
| 0.7678 | 12.0 | 4500 | 0.7701 | 0.7483 |
| 0.7258 | 13.0 | 4875 | 0.7462 | 0.75 |
| 0.7401 | 14.0 | 5250 | 0.7242 | 0.7633 |
| 0.7231 | 15.0 | 5625 | 0.7037 | 0.7683 |
| 0.6595 | 16.0 | 6000 | 0.6848 | 0.7817 |
| 0.6613 | 17.0 | 6375 | 0.6674 | 0.785 |
| 0.6469 | 18.0 | 6750 | 0.6514 | 0.7867 |
| 0.6638 | 19.0 | 7125 | 0.6367 | 0.79 |
| 0.6264 | 20.0 | 7500 | 0.6233 | 0.7917 |
| 0.615 | 21.0 | 7875 | 0.6112 | 0.795 |
| 0.6208 | 22.0 | 8250 | 0.6000 | 0.795 |
| 0.573 | 23.0 | 8625 | 0.5898 | 0.805 |
| 0.5775 | 24.0 | 9000 | 0.5804 | 0.8033 |
| 0.6018 | 25.0 | 9375 | 0.5718 | 0.8033 |
| 0.5747 | 26.0 | 9750 | 0.5639 | 0.8033 |
| 0.5711 | 27.0 | 10125 | 0.5567 | 0.805 |
| 0.5703 | 28.0 | 10500 | 0.5501 | 0.81 |
| 0.5047 | 29.0 | 10875 | 0.5441 | 0.81 |
| 0.5419 | 30.0 | 11250 | 0.5386 | 0.81 |
| 0.5562 | 31.0 | 11625 | 0.5335 | 0.8167 |
| 0.4909 | 32.0 | 12000 | 0.5288 | 0.8183 |
| 0.5437 | 33.0 | 12375 | 0.5245 | 0.82 |
| 0.5223 | 34.0 | 12750 | 0.5206 | 0.8183 |
| 0.4818 | 35.0 | 13125 | 0.5170 | 0.8183 |
| 0.4831 | 36.0 | 13500 | 0.5138 | 0.8183 |
| 0.5242 | 37.0 | 13875 | 0.5109 | 0.82 |
| 0.4897 | 38.0 | 14250 | 0.5083 | 0.8217 |
| 0.5618 | 39.0 | 14625 | 0.5059 | 0.8217 |
| 0.5176 | 40.0 | 15000 | 0.5038 | 0.8217 |
| 0.4753 | 41.0 | 15375 | 0.5019 | 0.8217 |
| 0.464 | 42.0 | 15750 | 0.5003 | 0.8217 |
| 0.5062 | 43.0 | 16125 | 0.4989 | 0.8217 |
| 0.4853 | 44.0 | 16500 | 0.4977 | 0.8217 |
| 0.5132 | 45.0 | 16875 | 0.4967 | 0.8217 |
| 0.4927 | 46.0 | 17250 | 0.4960 | 0.8217 |
| 0.5364 | 47.0 | 17625 | 0.4954 | 0.8217 |
| 0.5219 | 48.0 | 18000 | 0.4950 | 0.8217 |
| 0.4998 | 49.0 | 18375 | 0.4948 | 0.8217 |
| 0.495 | 50.0 | 18750 | 0.4947 | 0.8217 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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yashshukla2506/yash
|
yashshukla2506
| 2023-12-17T13:07:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:07:08Z |
Entry not found
|
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] |
Mik99/llama2_7b_5_samples_per_feature_class
|
Mik99
| 2023-12-17T13:08:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-17T13:07:56Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1
|
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gsoaresbaptista/Themis-instruct-qa
|
gsoaresbaptista
| 2023-12-17T13:23:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T13:09:36Z |
---
license: apache-2.0
base_model: mlabonne/NeuralHermes-2.5-Mistral-7B
tags:
- generated_from_trainer
model-index:
- name: Themis-instruct-qa
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. -->
# Themis-instruct-qa
This model is a fine-tuned version of [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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cmeraki/OpenHathi-7B-Hi-v0.1-Base-gptq
|
cmeraki
| 2023-12-17T13:46:14Z | 0 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"en",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-17T13:11:03Z |
---
language:
- en
license: llama2
model_name: OpenHathi-7B-Hi-v0.1-Base-gptq
base_model: meta-llama/Llama-2-7b-chat-hf
inference: false
model_creator: SarvamAI
model_type: llama
pipeline_tag: text-generation
prompt_template: '[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as
possible, while being safe. Your answers should not include any harmful, unethical,
racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses
are socially unbiased and positive in nature. If a question does not make any sense,
or is not factually coherent, explain why instead of answering something not correct.
If you don''t know the answer to a question, please don''t share false information.
<</SYS>>
{prompt}[/INST]
'
quantized_by: cmeraki
---
# OpenHathi Base GPTQ
- Model creator: [Sarvam AI](https://huggingface.co/sarvamai)
- Original model: [sarvamai/OpenHathi-7B-Hi-v0.1-Base](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base/)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Sarvam's OpenHathi](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base/).
Files are made using AutoGPTQ with following config.
```
quantization_config : {"bits": 4,
"group_size": 128,
"damp_percent": 0.1,
"desc_act": true,
}
```
We use a custom [dataset](cmeraki/wiki_en_hi) which has both Hindi and English wiki articles. We truncate to max_length=1024 and model may not perform well beyond that context size.
<!-- description end -->
<!-- prompt-template start -->
## Prompt template
This is a base model not tuned for any instructions. Feel free to use any format. Alpaca/Vicuna works fine.
<!-- prompt-template end -->
## Oobagooba
Standard oobagooba works with exllama2 / autogptq loader
## Using in code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "cmeraki/OpenHathi-7B-Hi-v0.1-Base-gptq"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "do aur do"
prompt_template=f'''[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
|
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] |
noza-kit/llama2_dolly_en2jp_3epoch
|
noza-kit
| 2023-12-17T19:26:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-12-17T13:12:46Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
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### Framework versions
- PEFT 0.7.1
|
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furkany/LastResort
|
furkany
| 2023-12-17T13:13:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:13:57Z |
Entry not found
|
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] |
abvijaykumar/bloomz-560m-prefix-table-to-text
|
abvijaykumar
| 2023-12-17T13:16:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloomz-560m",
"region:us"
] | null | 2023-12-17T13:16:07Z |
---
library_name: peft
base_model: bigscience/bloomz-560m
---
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.7.2.dev0
|
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] |
ndarocha/swin-tiny-patch4-window7-224-breastdensity
|
ndarocha
| 2023-12-17T23:20:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T13:18:45Z |
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-breastdensity
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5236051502145923
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-breastdensity
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0571
- Accuracy: 0.5236
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1872 | 0.99 | 49 | 1.2194 | 0.4320 |
| 1.0998 | 1.99 | 98 | 1.0917 | 0.4807 |
| 1.0623 | 2.98 | 147 | 1.0571 | 0.5236 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
yjs616/my-llama-2
|
yjs616
| 2023-12-17T14:42:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-17T13:18:45Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.7.1
|
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] |
Yoshitaka16/Aca_ZutomayoRVC
|
Yoshitaka16
| 2023-12-17T13:21:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T13:19:18Z |
---
license: openrail
---
|
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kira-1011/gpt2-sqa-finetuned
|
kira-1011
| 2023-12-17T13:20:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:20:42Z |
Entry not found
|
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wasjaip/LaBSE_half16bit_geonames_v1
|
wasjaip
| 2023-12-17T20:04:57Z | 0 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"multilingual",
"af",
"sq",
"am",
"ar",
"hy",
"as",
"az",
"eu",
"be",
"bn",
"bs",
"bg",
"my",
"ca",
"ceb",
"zh",
"co",
"hr",
"cs",
"da",
"nl",
"en",
"eo",
"et",
"fi",
"fr",
"fy",
"gl",
"ka",
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"sr",
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"sn",
"si",
"sk",
"sl",
"so",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"th",
"bo",
"tr",
"tk",
"ug",
"uk",
"ur",
"uz",
"vi",
"cy",
"wo",
"xh",
"yi",
"yo",
"zu",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-12-17T13:21:48Z |
---
language:
- multilingual
- af
- sq
- am
- ar
- hy
- as
- az
- eu
- be
- bn
- bs
- bg
- my
- ca
- ceb
- zh
- co
- hr
- cs
- da
- nl
- en
- eo
- et
- fi
- fr
- fy
- gl
- ka
- de
- el
- gu
- ht
- ha
- haw
- he
- hi
- hmn
- hu
- is
- ig
- id
- ga
- it
- ja
- jv
- kn
- kk
- km
- rw
- ko
- ku
- ky
- lo
- la
- lv
- lt
- lb
- mk
- mg
- ms
- ml
- mt
- mi
- mr
- mn
- ne
- no
- ny
- or
- fa
- pl
- pt
- pa
- ro
- ru
- sm
- gd
- sr
- st
- sn
- si
- sk
- sl
- so
- es
- su
- sw
- sv
- tl
- tg
- ta
- tt
- te
- th
- bo
- tr
- tk
- ug
- uk
- ur
- uz
- vi
- cy
- wo
- xh
- yi
- yo
- zu
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
# LaBSE_geonames_v1 (ru)
## Описание
Это порт модели [LaBSE](https://tfhub.dev/google/LaBSE/1) для PyTorch, который позволяет отображать текст на 109 языках в общее векторное пространство.
Эта модель используется для поиска похожих географических названий на основе введенного запроса.
С помощью функции можно кодировать запрос в векторное пространство, и искать наиболее близкие эмбеддинги и возвращает результаты в удобном формате.
## Установка
Для использования этой модели рекомендуется установить пакет `sentence-transformers`:
```
pip install -U sentence-transformers
```
## Использование
Пример использования модели:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('wasjaip/LaBSE_geonames_v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Оценка Модели
Подробности автоматической оценки модели можно найти на Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/LaBSE)
## Архитектура Модели
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Более подробная информация и публикация, описывающая LaBSE, доступна на [LaBSE](https://tfhub.dev/google/LaBSE/1)
# LaBSE_geonames_v1 (en)
This is a port of the [LaBSE](https://tfhub.dev/google/LaBSE/1) model to PyTorch. It can be used to map 109 languages to a shared vector space.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('wasjaip/LaBSE_geonames_v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/LaBSE)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Citing & Authors
Have a look at [LaBSE](https://tfhub.dev/google/LaBSE/1) for the respective publication that describes LaBSE.
|
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] |
asdasdsadsaweqweeasdsad/llama2-qlora-finetunined-french
|
asdasdsadsaweqweeasdsad
| 2023-12-17T13:25:06Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-12-17T13:25:01Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
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] |
tndklab/hubert-rinna-jdrt_RTSplit_and_SpeakerSplitModel-1217-5
|
tndklab
| 2023-12-17T14:56:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-17T13:26:41Z |
---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinna-jdrt_RTSplit_and_SpeakerSplitModel-1217-5
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. -->
# hubert-rinna-jdrt_RTSplit_and_SpeakerSplitModel-1217-5
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2706
- Wer: 0.5627
- Cer: 0.2502
## 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: 0.00025
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 13.2741 | 1.0 | 60 | 10.6409 | 1.0 | 0.9488 |
| 6.6096 | 2.0 | 120 | 6.1031 | 1.0 | 0.9488 |
| 4.8375 | 3.0 | 180 | 4.6385 | 1.0 | 0.9488 |
| 3.6598 | 4.0 | 240 | 3.5073 | 1.0 | 0.9488 |
| 3.0372 | 5.0 | 300 | 2.9386 | 1.0 | 0.9488 |
| 2.2662 | 6.0 | 360 | 2.1665 | 1.0 | 0.9459 |
| 1.5353 | 7.0 | 420 | 1.3829 | 1.0 | 0.5635 |
| 1.1968 | 8.0 | 480 | 1.0908 | 1.0 | 0.5900 |
| 1.0804 | 9.0 | 540 | 1.0358 | 0.8168 | 0.5153 |
| 0.8453 | 10.0 | 600 | 0.7737 | 0.8123 | 0.4749 |
| 0.8839 | 11.0 | 660 | 0.7224 | 0.8123 | 0.4909 |
| 0.7391 | 12.0 | 720 | 0.6723 | 0.7969 | 0.4509 |
| 0.6912 | 13.0 | 780 | 0.6176 | 0.7387 | 0.4149 |
| 0.646 | 14.0 | 840 | 0.5935 | 0.7218 | 0.3719 |
| 0.6307 | 15.0 | 900 | 0.5707 | 0.7143 | 0.3171 |
| 0.6 | 16.0 | 960 | 0.5214 | 0.6907 | 0.2771 |
| 0.5334 | 17.0 | 1020 | 0.4692 | 0.7023 | 0.3022 |
| 1.0681 | 18.0 | 1080 | 0.5046 | 0.7068 | 0.2979 |
| 0.5386 | 19.0 | 1140 | 0.5916 | 0.7350 | 0.3395 |
| 0.5251 | 20.0 | 1200 | 0.4388 | 0.6824 | 0.2809 |
| 0.516 | 21.0 | 1260 | 0.4301 | 0.6918 | 0.3082 |
| 0.5583 | 22.0 | 1320 | 0.4535 | 0.7305 | 0.3435 |
| 0.4736 | 23.0 | 1380 | 0.4128 | 0.6637 | 0.2793 |
| 0.4729 | 24.0 | 1440 | 0.4098 | 0.6674 | 0.2910 |
| 0.4278 | 25.0 | 1500 | 0.4025 | 0.6858 | 0.3132 |
| 0.4363 | 26.0 | 1560 | 0.3872 | 0.6719 | 0.2773 |
| 0.4168 | 27.0 | 1620 | 0.3760 | 0.6273 | 0.2469 |
| 0.4064 | 28.0 | 1680 | 0.3762 | 0.6509 | 0.2881 |
| 0.3877 | 29.0 | 1740 | 0.3828 | 0.6700 | 0.2970 |
| 0.3658 | 30.0 | 1800 | 0.3438 | 0.6175 | 0.2683 |
| 0.3685 | 31.0 | 1860 | 0.3292 | 0.5860 | 0.2345 |
| 0.3723 | 32.0 | 1920 | 0.3280 | 0.5998 | 0.2437 |
| 0.3634 | 33.0 | 1980 | 0.3203 | 0.5657 | 0.2228 |
| 0.3366 | 34.0 | 2040 | 0.3016 | 0.5796 | 0.2633 |
| 0.3667 | 35.0 | 2100 | 0.2960 | 0.5890 | 0.2703 |
| 0.3216 | 36.0 | 2160 | 0.2971 | 0.6077 | 0.2713 |
| 0.3222 | 37.0 | 2220 | 0.2892 | 0.6325 | 0.2925 |
| 0.3082 | 38.0 | 2280 | 0.2780 | 0.5721 | 0.2558 |
| 0.3154 | 39.0 | 2340 | 0.2756 | 0.5788 | 0.2602 |
| 0.3275 | 40.0 | 2400 | 0.2706 | 0.5627 | 0.2502 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
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jackiedaytona/emoji-smol-256
|
jackiedaytona
| 2023-12-17T13:32:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2023-12-17T13:29:06Z |
Entry not found
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Jaymax/outputs_mistral_PharmregAId2
|
Jaymax
| 2023-12-17T14:58:14Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-17T13:30:15Z |
Entry not found
|
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sbgrasdrht/tinystore
|
sbgrasdrht
| 2023-12-17T13:30:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:30:23Z |
Entry not found
|
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ddh0/OrcaMaid-v2-FIX-13b-10k
|
ddh0
| 2023-12-17T14:01:25Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"custom_code",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T13:30:41Z |
---
license: other
license_name: microsoft-research-license
license_link: https://huggingface.co/microsoft/Orca-2-13b/blob/main/LICENSE
pipeline_tag: text-generation
---
# OrcaMaid-13b-v2-FIX-10k
This is the fixed version of **OrcaMaid-v2-13b**, further extended to 10240 context length via YaRN. The (now-deleted) v2 model had issues with the merged tokenizer that prevented it from stopping when necessary, and caused it to output broken ChatML tokens like `<|im_end`, etc.
This is a gradient SLERP merge of Microsoft's [Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) and Undi and IkariDev's [Noromaid-v0.1.1-13b](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1.1), biased towards Orca.
Just as with OrcaMaid v1, the overall goal of this merge is to create a model that sounds uniquely human and natural, without sacrificing intelligence.
The prompt format is Alpaca. You can use the standard format as shown, but for best results, you should customize the system prompt to your specific needs.
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{YOUR MESSAGE HERE}
### Response:
{BOT MESSAGE HERE}
```
### Misc. information
- BOS token is `<s>`
- EOS token is `</s>`
- Native context length is `10240` via YaRN (original context length was `4096`)
- Base model is Llama 2
- Due to the inclusion of Orca-2-13b, the model is subject to the terms of the [Microsoft Research License](https://huggingface.co/microsoft/Orca-2-13b/blob/main/LICENSE)
### Thanks
- Thanks to [Charles Goddard](https://github.com/cg123) for his kind help with mergekit (as always)
- Thanks to [Undi](https://ko-fi.com/undiai) and [IkariDev](https://ikaridevgit.github.io/) for Noromaid
|
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theogorg/speak_embedding
|
theogorg
| 2023-12-17T13:36:55Z | 0 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-12-17T13:34:45Z |
Entry not found
|
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] |
ISTNetworks/model_checkpoints
|
ISTNetworks
| 2023-12-17T14:55:10Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:NousResearch/Nous-Hermes-Llama2-13b",
"license:mit",
"region:us"
] | null | 2023-12-17T13:34:45Z |
---
license: mit
base_model: NousResearch/Nous-Hermes-Llama2-13b
tags:
- generated_from_trainer
model-index:
- name: model_checkpoints
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. -->
# model_checkpoints
This model is a fine-tuned version of [NousResearch/Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) 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: 2
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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84nth08h/zemken1
|
84nth08h
| 2023-12-17T13:34:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:34:56Z |
Entry not found
|
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imspidey/mChosenModels
|
imspidey
| 2023-12-17T14:28:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:35:55Z |
Entry not found
|
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HeshamHaroon/arafalcon-7b-sharded-bf16
|
HeshamHaroon
| 2023-12-17T13:36:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:36:37Z |
Entry not found
|
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onizukal/SMIDS_10x_beit_large_rmsprop_lr00001_fold1
|
onizukal
| 2023-12-18T00:44:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T13:39:02Z |
---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SMIDS_10x_beit_large_rmsprop_lr00001_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: 0.9148580968280468
---
<!-- 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_large_rmsprop_lr00001_fold1
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2935
- Accuracy: 0.9149
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0551 | 1.0 | 1501 | 0.5151 | 0.8815 |
| 0.0344 | 2.0 | 3002 | 0.5260 | 0.9048 |
| 0.0143 | 3.0 | 4503 | 0.6955 | 0.9048 |
| 0.0286 | 4.0 | 6004 | 0.7418 | 0.9032 |
| 0.0002 | 5.0 | 7505 | 0.8849 | 0.8915 |
| 0.0001 | 6.0 | 9006 | 0.7857 | 0.9165 |
| 0.0001 | 7.0 | 10507 | 0.7718 | 0.9115 |
| 0.0 | 8.0 | 12008 | 1.0044 | 0.9032 |
| 0.0 | 9.0 | 13509 | 0.7790 | 0.9098 |
| 0.0 | 10.0 | 15010 | 0.8379 | 0.9115 |
| 0.0002 | 11.0 | 16511 | 1.0973 | 0.8948 |
| 0.0027 | 12.0 | 18012 | 1.0780 | 0.9048 |
| 0.0 | 13.0 | 19513 | 0.8761 | 0.9115 |
| 0.0002 | 14.0 | 21014 | 1.1201 | 0.8948 |
| 0.0 | 15.0 | 22515 | 1.0787 | 0.9015 |
| 0.0 | 16.0 | 24016 | 1.0433 | 0.9065 |
| 0.0 | 17.0 | 25517 | 0.9612 | 0.9115 |
| 0.003 | 18.0 | 27018 | 0.9396 | 0.9182 |
| 0.0003 | 19.0 | 28519 | 1.0690 | 0.8998 |
| 0.0 | 20.0 | 30020 | 1.1431 | 0.9032 |
| 0.0003 | 21.0 | 31521 | 0.9936 | 0.9032 |
| 0.0 | 22.0 | 33022 | 1.2695 | 0.9032 |
| 0.0 | 23.0 | 34523 | 1.2065 | 0.9082 |
| 0.0 | 24.0 | 36024 | 1.0151 | 0.9182 |
| 0.0113 | 25.0 | 37525 | 1.1495 | 0.9032 |
| 0.0 | 26.0 | 39026 | 1.2513 | 0.8998 |
| 0.0 | 27.0 | 40527 | 1.1156 | 0.9098 |
| 0.0 | 28.0 | 42028 | 1.1920 | 0.9082 |
| 0.0 | 29.0 | 43529 | 1.1204 | 0.9065 |
| 0.0 | 30.0 | 45030 | 1.1410 | 0.9115 |
| 0.0 | 31.0 | 46531 | 1.1182 | 0.9149 |
| 0.0 | 32.0 | 48032 | 1.1068 | 0.9199 |
| 0.0 | 33.0 | 49533 | 1.2127 | 0.9098 |
| 0.0 | 34.0 | 51034 | 1.1678 | 0.9165 |
| 0.0 | 35.0 | 52535 | 1.2292 | 0.9265 |
| 0.0 | 36.0 | 54036 | 1.2115 | 0.9215 |
| 0.0 | 37.0 | 55537 | 1.2192 | 0.9215 |
| 0.0 | 38.0 | 57038 | 1.3666 | 0.9048 |
| 0.0 | 39.0 | 58539 | 1.2895 | 0.9149 |
| 0.0 | 40.0 | 60040 | 1.3482 | 0.9065 |
| 0.0 | 41.0 | 61541 | 1.2538 | 0.9132 |
| 0.0 | 42.0 | 63042 | 1.2855 | 0.9115 |
| 0.0 | 43.0 | 64543 | 1.2728 | 0.9149 |
| 0.0 | 44.0 | 66044 | 1.2700 | 0.9215 |
| 0.0 | 45.0 | 67545 | 1.3323 | 0.9115 |
| 0.0048 | 46.0 | 69046 | 1.3108 | 0.9149 |
| 0.0 | 47.0 | 70547 | 1.2973 | 0.9182 |
| 0.0 | 48.0 | 72048 | 1.2944 | 0.9165 |
| 0.0 | 49.0 | 73549 | 1.2965 | 0.9132 |
| 0.0 | 50.0 | 75050 | 1.2935 | 0.9149 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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mallowv/my_awesome_eli5_clm-model
|
mallowv
| 2023-12-17T13:40:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:40:14Z |
Entry not found
|
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] |
fearvel/made-in-abyss-shiggy-sdxl
|
fearvel
| 2023-12-17T13:43:09Z | 0 | 0 | null |
[
"stable-diffusion",
"text-to-image",
"StableDiffusionPipeline",
"lora",
"region:us"
] |
text-to-image
| 2023-12-17T13:43:06Z |
---
tags:
- stable-diffusion
- text-to-image
- StableDiffusionPipeline
- lora
---
## Model
|
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kt220/review_classification_bert_base_jp_v3_ratio1_1_2label_add_dropout-epoch40_v4
|
kt220
| 2023-12-17T14:41:27Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:cl-tohoku/bert-base-japanese-v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T13:43:45Z |
---
license: apache-2.0
base_model: cl-tohoku/bert-base-japanese-v3
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: review_classification_bert_base_jp_v3_ratio1_1_2label_add_dropout-epoch40_v4
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. -->
# review_classification_bert_base_jp_v3_ratio1_1_2label_add_dropout-epoch40_v4
This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4594
- Accuracy: 0.8023
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 52 | 0.5181 | 0.7316 |
| No log | 2.0 | 104 | 0.4858 | 0.7684 |
| No log | 3.0 | 156 | 0.4934 | 0.7684 |
| No log | 4.0 | 208 | 0.5022 | 0.7910 |
| No log | 5.0 | 260 | 0.5365 | 0.7910 |
| No log | 6.0 | 312 | 0.6070 | 0.8136 |
| No log | 7.0 | 364 | 0.7252 | 0.8107 |
| No log | 8.0 | 416 | 0.9174 | 0.7825 |
| No log | 9.0 | 468 | 1.0906 | 0.7655 |
| 0.208 | 10.0 | 520 | 1.2154 | 0.7740 |
| 0.208 | 11.0 | 572 | 1.0851 | 0.8051 |
| 0.208 | 12.0 | 624 | 1.3699 | 0.7627 |
| 0.208 | 13.0 | 676 | 1.1715 | 0.7994 |
| 0.208 | 14.0 | 728 | 1.2354 | 0.7910 |
| 0.208 | 15.0 | 780 | 1.2615 | 0.7881 |
| 0.208 | 16.0 | 832 | 1.2891 | 0.7825 |
| 0.208 | 17.0 | 884 | 1.4250 | 0.7768 |
| 0.208 | 18.0 | 936 | 1.5752 | 0.7712 |
| 0.208 | 19.0 | 988 | 1.3456 | 0.8023 |
| 0.001 | 20.0 | 1040 | 1.3598 | 0.7994 |
| 0.001 | 21.0 | 1092 | 1.3378 | 0.8107 |
| 0.001 | 22.0 | 1144 | 1.3534 | 0.8051 |
| 0.001 | 23.0 | 1196 | 1.3832 | 0.7881 |
| 0.001 | 24.0 | 1248 | 1.3838 | 0.7938 |
| 0.001 | 25.0 | 1300 | 1.4257 | 0.7881 |
| 0.001 | 26.0 | 1352 | 1.4115 | 0.8023 |
| 0.001 | 27.0 | 1404 | 1.6230 | 0.7684 |
| 0.001 | 28.0 | 1456 | 1.4132 | 0.8023 |
| 0.0009 | 29.0 | 1508 | 1.6508 | 0.7684 |
| 0.0009 | 30.0 | 1560 | 1.4539 | 0.8023 |
| 0.0009 | 31.0 | 1612 | 1.4677 | 0.8023 |
| 0.0009 | 32.0 | 1664 | 1.4155 | 0.8079 |
| 0.0009 | 33.0 | 1716 | 1.4262 | 0.8079 |
| 0.0009 | 34.0 | 1768 | 1.4390 | 0.8023 |
| 0.0009 | 35.0 | 1820 | 1.4405 | 0.8023 |
| 0.0009 | 36.0 | 1872 | 1.4471 | 0.8051 |
| 0.0009 | 37.0 | 1924 | 1.4526 | 0.8023 |
| 0.0009 | 38.0 | 1976 | 1.4568 | 0.8023 |
| 0.0001 | 39.0 | 2028 | 1.4596 | 0.8023 |
| 0.0001 | 40.0 | 2080 | 1.4594 | 0.8023 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
TarekMahran/TestModel
|
TarekMahran
| 2023-12-17T13:45:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T13:45:32Z |
---
license: apache-2.0
---
|
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tvam-vadasi1/tvam_vadasi
|
tvam-vadasi1
| 2023-12-17T17:17:43Z | 0 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-12-17T13:45:46Z |
Entry not found
|
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0.3523697555065155,
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0.3101596534252167,
-1.0262157917022705
] |
LoneStriker/Mixtral-SlimOrca-8x7B-3.5bpw-h6-exl2-2
|
LoneStriker
| 2023-12-17T15:12:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"dataset:Open-Orca/SlimOrca",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T13:48:05Z |
---
base_model: mistralai/Mixtral-8x7B-v0.1
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
pipeline_tag: text-generation
---
# SlimOrca Mixtral 8x7B
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
Official release of the SlimOrca Mixtral finetune. More details to come.
## Model Details
### Model Description
- **Developed by:** OpenAccess AI Collective and OpenOrca
- **Finetuned from model [optional]:** mistralai/Mixtral-8x7B-v0.1
|
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Skywalker16/Rose
|
Skywalker16
| 2023-12-17T13:49:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:49:11Z |
Entry not found
|
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duynhm/LVM-Med
|
duynhm
| 2023-12-17T15:04:10Z | 0 | 1 | null |
[
"onnx",
"medical",
"pytorch",
"feature-extraction",
"en",
"arxiv:2306.11925",
"license:cc-by-nc-2.0",
"region:us"
] |
feature-extraction
| 2023-12-17T13:49:22Z |
---
license: cc-by-nc-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: feature-extraction
tags:
- medical
- pytorch
---
## LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching (Neurips 2023).
We release [LVM-Med](https://arxiv.org/abs/2306.11925)'s pre-trained models in PyTorch and demonstrate downstream tasks on 2D-3D segmentations, linear/fully finetuning image classification, and object detection.
LVM-Med was trained with ~ 1.3 million medical images collected from 55 datasets using a second-order graph matching formulation unifying
current contrastive and instance-based SSL.
<p align="center">
<img src="assets/body_lvm_med.jpg" alt="drawing" width="650"/>
</p>
<p align="center">
<img src="assets/lvm_med_teaser.gif" alt="drawing" width="800"/>
</p>
## Table of contents
* [News](#news)
* [LVM-Med Pretrained Models](#lvm-med-pretrained-models)
* [Further Training LVM-Med on Large Dataset](#further-training-lvm-med-on-large-dataset)
* [Prerequisites](#prerequisites)
* [Preparing Dataset](#preparing-datasets)
* [Downstream Tasks](#downstream-tasks)
* [Segmentation](#segmentation)
* [Image Classification](#image-classification)
* [Object Detection](#object-detection)
* [Citation](#citation)
* [Related Work](#related-work)
* [License](#license)
## News
- **14/12/2023**: The LVM-Med training algorithm is ready to be released! Please send us an email to request!
- If you want to have other architecture, send us a request by email or create an Issue. If the requests are enough, we will train them.
- Coming soon: ConvNext architecture trained by LVM-Med.
- Coming soon: ViT architectures for end-to-end segmentation with better performance reported in the paper.
- **31/07/2023**: Release ONNX support for LVM-Med ResNet50 and LVM-Med ViT as backbones in `onnx_model` folder.
- **26/07/2023**: We release ViT architectures (**ViT-B** and **ViT-H**) initialized from LVM-Med and further training on the LIVECell dataset with 1.6 million high-quality cells. See at this [table](#further-training-lvm-med-on-large-dataset).
- **25/06/2023**: We release two pre-trained models of LVM-Med: ResNet-50 and ViT-B. Providing scripts for downstream tasks.
## LVM-Med Pretrained Models
<table>
<tr>
<th>Arch</th>
<th>Params (M)</th>
<th> 2D Segmentation (Dice) </th>
<th> 3D Segmentation (3D IoU) </th>
<th>Weights</th>
</tr>
<tr>
<td>ResNet-50</td>
<td>25.5M</td>
<td>83.05</td>
<td>79.02</td>
<td> <a href="https://drive.google.com/file/d/11Uamq4bT_AbTf8sigIctIAnQJN4EethW/view?usp=sharing">backbone</a> </td>
</tr>
<tr>
<td>ViT-B</td>
<td>86.0M</td>
<td>85.80</td>
<td>80.90</td>
<td> <a href="https://drive.google.com/file/d/17WnE34S0ylYiA3tMXobH8uUrK_mCVPT4/view?usp=sharing">backbone</a> </td>
</tr>
</table>
After downloading the pre-trained models, please place them in [`lvm_med_weights`](/lvm_med_weights/) folder to use.
- For **Resnet-50**, we demo **end-to-end** segmentation/classification/object detection.
- For **ViT-B**, we demo **prompt-based** segmentation using bounding-boxes.
**Important Note:** please check [```dataset.md```](https://github.com/duyhominhnguyen/LVM-Med/blob/main/lvm-med-training-data/README.md) to avoid potential leaking testing data when using our model.
**Segment Anything Model-related Experiments**
- For all experiments using [SAM](https://github.com/facebookresearch/segment-anything) model, we use the base architecture of SAM which is `sam_vit_b`. You could browse the [`original repo`](https://github.com/facebookresearch/segment-anything) for this pre-trained weight and put it in [`./working_dir/sam_vit_b_01ec64.pth`](./working_dir/) folder to use yaml properly.
## Further Training LVM-Med on Large Dataset
We release some further pre-trained weight on other large datasets as mentioned in the Table below.
<table>
<tr>
<th>Arch</th>
<th>Params (M)</th>
<th>Dataset Name </th>
<th>Weights</th>
<th>Descriptions</th>
</tr>
<tr>
<td>ViT-B</td>
<td>86.0M</td>
<td> <a href="https://www.nature.com/articles/s41592-021-01249-6">LIVECell</a> </td>
<td> <a href="https://drive.google.com/file/d/1SxaGXQ4FMbG8pS2zzwTIXXgxF4GdwyEU/view?usp=sharing">backbone</a> </td>
<td> <a href="https://github.com/duyhominhnguyen/LVM-Med/blob/main/further_training_lvm_med/README.md">Link</a></td>
</tr>
<tr>
<td>ViT-H</td>
<td>632M</td>
<td> <a href="https://www.nature.com/articles/s41592-021-01249-6">LIVECell</a> </td>
<td> <a href="https://drive.google.com/file/d/14IhoyBXI9eP9V2xeOV2-6LlNICKjzBaJ/view?usp=sharing">backbone</a> </td>
<td> <a href="https://github.com/duyhominhnguyen/LVM-Med/blob/main/further_training_lvm_med/README.md">Link</a></td>
</tr>
</table>
## Prerequisites
The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
To set up our project, run the following command:
```bash
git clone https://huggingface.co/duynhm/LVM-Med
cd LVM-Med
conda env create -f lvm_med.yml
conda activate lvm_med
```
To **fine-tune for [Segmentation](#segmentation) using ResNet-50**, we utilize U-Net from `segmentation-models-pytorch` package. To install this library, you can do the following ones:
```bash
git clone https://github.com/qubvel/segmentation_models.pytorch.git
cd segmentation_models.pytorch
pip install -e
cd ..
mv segmentation_models_pytorch_example/encoders/__init__.py segmentation_models.pytorch/segmentation_models_pytorch/__init__.py
mv segmentation_models_pytorch_example/encoders/resnet.py segmentation_models.pytorch/segmentation_models_pytorch/resnet.py
```
<!--
1. `git clone https://github.com/qubvel/segmentation_models.pytorch.git`
2. `cd segmentation_models.pytorch; pip install -e .`
4. Copy file [`__init__.py`](segmentation_models_pytorch_example/encoders/__init__.py) and [`resnet.py`](segmentation_models_pytorch_example/encoders/resnet.py) in [`segmentation_models_pytorch_example`](segmentation_models_pytorch_example) folder
5. Paste [`__init__.py`](segmentation_models_pytorch_example/encoders/__init__.py) and [`resnet.py`](segmentation_models_pytorch_example/encoders/resnet.py) in the folder `encoders` of clone `segmentation_models.pytorch/segmentation_models_pytorch/` package to configure new pre-trained models
-->
## Preparing datasets
### For the Brain Tumor Dataset
You could download the `Brain` dataset via Kaggle's [`Brain Tumor Classification (MRI)`](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri) and change the name into ```BRAIN```.
### For VinDr
You can download the dataset from this link [`VinDr`](https://www.kaggle.com/datasets/awsaf49/vinbigdata-512-image-dataset) and put the folder ```vinbigdata``` into the folder ```object_detection```. To build the dataset, after downloading the dataset, you can run script ```convert_to_coco.py``` inside the folder object_detection.
```bash
python convert_to_coco.py # Note, please check links inside the code in lines 146 and 158 to build dataset correctly
```
More information can be found in [```object_detection```](./object_detection).
### Others
First you should download the respective dataset that you need to run to the [`dataset_demo`](/dataset_demo/) folder. To get as close results as your work as possible, you could prepare some of our specific dataset (which are not pre-distributed) the same way as we do:
```bash
python prepare_dataset.py -ds [dataset_name]
```
such that: `dataset_name` is the name of dataset that you would like to prepare. After that, you should change paths to your loaded dataset on our pre-defined yaml file in [`dataloader/yaml_data`](/dataloader/yaml_data/).
Currently support for `Kvasir`, `BUID`, `FGADR`, `MMWHS_MR_Heart` and `MMWHS_CT_Heart`.
**Note:** You should change your dataset name into the correct format (i.e., Kvasir, BUID) as our current support dataset name. Or else it won't work as expected.
## Downstream Tasks
### Segmentation
### 1. End-to-End Segmentation
**a) Training Phase:**
**Fine-tune for downstream tasks using ResNet-50**
```bash
python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_R50.yml
```
Changing name of dataset in ``.yml`` configs in [```./dataloader/yaml_data/```](./dataloader/yaml_data/) for other experiments.
**Note**: to apply segmentation models (2D or 3D) using ResNet-50, we suggest normalizing gradient for stable training phases by set:
```bash
clip_value = 1
torch.nn.utils.clip_grad_norm_(net.parameters(), clip_value)
```
See examples in file [```/segmentation_2d/train_R50_seg_adam_optimizer_2d.py```](./segmentation_2d/train_R50_seg_adam_optimizer_2d.py) lines 129-130.
[//]: # (#### Fine-tune for downstream tasks using SAM's VIT)
[//]: # (```bash)
[//]: # (python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_SAM_VIT.yml)
[//]: # (```)
**b) Inference:**
#### ResNet-50 version
```bash
python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_R50.yml -test
```
For the end-to-end version using SAM's ViT, we will soon release a better version than the reported results in the paper.
[//]: # (#### SAM's ViT version)
[//]: # (```bash)
[//]: # (python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_SAM_VIT.yml -test)
[//]: # (```)
### 2. Prompt-based Segmentation with ViT-B
**a. Prompt-based segmentation with fine-tuned decoder of SAM ([MedSAM](https://github.com/bowang-lab/MedSAM)).**
We run the MedSAM baseline to compare performance by:
#### Train
```bash
python3 medsam.py -c dataloader/yaml_data/buid_sam.yml
```
#### Inference
```bash
python3 medsam.py -c dataloader/yaml_data/buid_sam.yml -test
```
**b. Prompt-based segmentation as [MedSAM](https://github.com/bowang-lab/MedSAM) but using LVM-Med's Encoder.**
The training script is similar as MedSAM case but specify the weight model by ```-lvm_encoder```.
#### Train
```bash
python3 medsam.py -c dataloader/yaml_data/buid_lvm_med_sam.yml -lvm_encoder ./lvm_med_weights/lvmmed_vit.pth
```
#### Test
```bash
python3 medsam.py -c dataloader/yaml_data/buid_lvm_med_sam.yml -lvm_encoder ./lvm_med_weights/lvmmed_vit.pth -test
```
You could also check our example notebook [`Prompt_Demo.ipynb`](/notebook/Prompt_Demo.ipynb) for results visualization using prompt-based MedSAM and prompt-based SAM with LVM-Med's encoder. The pre-trained weights for each SAM decoder model in the demo are [here](https://drive.google.com/drive/u/0/folders/1tjrkyEozE-98HAGEtyHboCT2YHBSW15U). Please download trained models of LVM-Med and MedSAM and put them into [`working_dir/checkpoints`](./working_dir/checkpoints/) folder for running the aforementioned notebook file.
**c. Zero-shot prompt-based segmentation with Segment Anything Model (SAM) for downstream tasks**
The SAM model without any finetuning using bounding box-based prompts can be done by:
```bash
python3 zero_shot_segmentation.py -c dataloader/yaml_data/buid_sam.yml
```
### Image Classification
We provide training and testing scripts using LVM-Med's ResNet-50 models for Brain Tumor Classification and Diabetic Retinopathy Grading in FGADR dataset (Table 5 in main paper and Table 12 in Appendix). The version with ViT models will be updated soon.
**a. Training with FGADR**
```bash
# Fully fine-tuned with 1 FCN
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_non_frozen_1_fcn.yml
# Fully fine-tuned with multiple FCNs
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_non_frozen_fcns.yml
# Freeze all and fine-tune 1-layer FCN only
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_frozen_1_fcn.yml
# Freeze all and fine-tune multi-layer FCN only
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_frozen_fcns.yml
```
To run for ```Brain dataset```, choose other config files ```brain_xyz.yml```in folder [`./dataloader/yaml_data/`](/dataloader/yaml_data).
**b. Inference with FGADR**
```bash
# Fully fine-tuned with 1 FCN
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_non_frozen_1_fcn.yml -test
# Fully fine-tuned with multiple FCNs
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_non_frozen_fcns.yml -test
# Freeze all and fine-tune 1-layer FCN only
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_frozen_1_fcn.yml -test
# Freeze all and fine-tune multi-layer FCN only
python train_classification.py -c ./dataloader/yaml_data/fgadr_endtoend_R50_frozen_fcns.yml -test
```
### Object Detection
We demonstrate using LVM-Med ResNet-50 for object detection with Vin-Dr dataset. We use Faster-RCNN for the network backbone.
You can access [`object_detection`](./object_detection) folder for more details.
## Citation
Please cite this paper if it helps your research:
```bibtex
@article{nguyen2023lvm,
title={LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching},
author={Nguyen, Duy MH and Nguyen, Hoang and Diep, Nghiem T and Pham, Tan N and Cao, Tri and Nguyen, Binh T and Swoboda, Paul and Ho, Nhat and Albarqouni, Shadi and Xie, Pengtao and others},
journal={arXiv preprint arXiv:2306.11925},
year={2023}
}
```
## Related Work
We use and modify codes from [SAM](https://github.com/facebookresearch/segment-anything) and [MedSAM](https://github.com/bowang-lab/MedSAM) for prompt-based segmentation settings. A part of LVM-Med algorithm adopt data transformations from [Vicregl](https://github.com/facebookresearch/VICRegL), [Deepcluster-v2](https://github.com/facebookresearch/swav?utm_source=catalyzex.com). We also utilize [vissl](https://github.com/facebookresearch/vissl) framework to train 2D self-supervised methods in our collected data. Thank the authors for their great work!
## License
Licensed under the [CC BY-NC-ND 2.0](https://creativecommons.org/licenses/by-nc-nd/2.0/) (**Attribution-NonCommercial-NoDerivs 2.0 Generic**). The code is released for academic research use only. For commercial use, please contact [Ho_Minh_Duy.Nguyen@dfki.de](Ho_Minh_Duy.Nguyen@dfki.de)
[//]: # (### f. LVM-Med )
[//]: # (#### Training Phase)
[//]: # (#### Fine-tune for downstream tasks using ResNet-50)
[//]: # ()
[//]: # (```bash)
[//]: # (python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_R50.yml)
[//]: # (```)
[//]: # (#### Fine-tune for downstream tasks using SAM's VIT)
[//]: # (```bash)
[//]: # (python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_SAM_VIT.yml)
[//]: # (```)
[//]: # (#### Inference)
[//]: # (#### Downstream tasks using ResNet-50)
[//]: # ()
[//]: # (```bash)
[//]: # (python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_R50.yml -test)
[//]: # (```)
[//]: # (#### Downstream tasks using SAM's VIT)
[//]: # (```bash)
[//]: # (python train_segmentation.py -c ./dataloader/yaml_data/buid_endtoend_SAM_VIT.yml -test)
[//]: # (```)
|
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Shalvinskiy/test_model
|
Shalvinskiy
| 2023-12-17T13:49:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:49:47Z |
Entry not found
|
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] |
Grupo-NLP-2023/zephyr-apostilas-finetuned-enem
|
Grupo-NLP-2023
| 2023-12-17T15:28:56Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
] | null | 2023-12-17T13:51:15Z |
---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/zephyr-7B-alpha-GPTQ
model-index:
- name: zephyr-apostilas-finetuned-enem
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-apostilas-finetuned-enem
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
vinny9812/fatosdesco01
|
vinny9812
| 2023-12-17T13:57:35Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T13:55:11Z |
---
license: openrail
---
|
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] |
bachngo/meta-Llama-2-7b-chat-mul
|
bachngo
| 2023-12-17T14:10:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T13:58:25Z |
Entry not found
|
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gdf2h1gfh2/China-Junta-Armed-Groups-Agree-on-Ceasefire-in-North-Myanmar-cd-updated
|
gdf2h1gfh2
| 2023-12-17T13:59:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T13:59:23Z |
Entry not found
|
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tommilhomme/languagelevel
|
tommilhomme
| 2023-12-17T14:02:53Z | 0 | 0 | null |
[
"safetensors",
"license:unknown",
"region:us"
] | null | 2023-12-17T14:00:47Z |
---
license: unknown
---
|
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gdf2h1gfh2/Oregon-Dems-Ban-Republicans-From-Running-for-Office-Frontpage-Mag-hc-updated
|
gdf2h1gfh2
| 2023-12-17T14:03:12Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T14:01:59Z |
---
language:
- en
---
[]()
read the full article here : https://paste.toolforge.org/view/9f9fec36
Source : https://jpeg.ly/PfTFP
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Biden last Talk : https://pastelink.net/tkfzx171
Russian Ukrain Breaking News : https://ibit.ly/6FCI9
Other Sources :
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ATLANTA (AP) -- The apology letters that Donald Trump-allied lawyers Sidney Powell and Kenneth Chesebro were required to write as a condition of their plea deals in the Georgia election interference case are just one sentence long.
The letters, obtained Thursday by The Atlanta Journal-Constitution through an open records request, were hand-written and terse. Neither letter acknowledges the legitimacy of Democrat Joe Biden's win in Georgia's 2020 election nor denounces the baseless conspiracy theories they pushed to claim Trump was cheated out of victory through fraud.
"I apologize for my actions in connection with the events in Coffee County," Powell wrote in a letter dated Oct. 19, the same day she pleaded guilty to six misdemeanors accusing her of conspiring to intentionally interfere with the performance of election duties.
"I apologize to the citizens of the state of Georgia and of Fulton County for my involvement in Count 15 of the indictment," Chesebro wrote in a letter dated Oct. 20, when he appeared in court to plead guilty to one felony charge of conspiracy to commit filing false documents.
A spokesperson for Fulton County District Attorney Fani Willis, who brought the election interference case, did not immediately respond Thursday to a request for comment on the contents of the letters.
Powell and Chesebro were among four defendants to plead guilty in the case after reaching agreements with prosecutors. They were indicted alongside Trump and others in August and charged with participating in a wide-ranging scheme to illegally keep the Republican in power. The remaining 15 defendants -- including Trump, lawyer Rudy Giuliani and former White House chief of staff Mark Meadows -- have all pleaded not guilty.
Each of the four who reached a deal with prosecutors received a sentence that included probation but no jail time. They were also allowed to plead guilty under Georgia's first offender law, meaning that if they complete their probation without violating the terms or committing another crime, their records will be wiped clean.
The letters written by the other two defendants to plead guilty -- Trump lawyer Jenna Ellis and bail bondsman Scott Hall -- were longer and more specific. Ellis read her letter in open court on Oct. 24, tearfully telling the judge that she looked back on her involvement in challenging the election results with "deep remorse."
"What I did not do but should have done, Your Honor, was to make sure that the facts the other lawyers alleged to be true were in fact true," she said. "In the frenetic pace of attempting to raise challenges to the election in several states, including Georgia, I failed to do my due diligence."
Hall, who pleaded guilty Sept. 29, wrote in his five-paragraph letter to the citizens of Georgia, "I owe you an apology."
"I wish I had never involved myself in the post-election activities that brought me before the court," he wrote, explaining that he got involved after observing what he thought were some irregularities.
Powell was initially charged with felony racketeering and six other felony counts.
Prosecutors allege that she conspired with Hall and others to access election equipment without authorization and hired computer forensics firm SullivanStrickler to send a team to Coffee County, in south Georgia, to copy software and data from voting machines and computers there. The indictment says a person who is not named sent an email to a top SullivanStrickler executive and instructed him to send all data copied from Dominion Voting Systems equipment in Coffee County to an unidentified lawyer associated with Powell and the Trump campaign.
Chesebro was initially charged with felony racketeering and six other felony counts.
Prosecutors allege that he unlawfully conspired with Trump and lawyers associated with his campaign to have the group of Georgia Republicans sign the false elector certificate and to submit it to various federal authorities. He also communicated with Trump campaign lawyers and Republican leaders in other swing states won by Biden to get those states to submit false slates of electors as well, prosecutors alleged.
Ellis pleaded guilty to one felony count of aiding and abetting false statements and writings. She had been charged with violating the state's anti-racketeering law and soliciting the violation of oath by a public officer, both felonies.
The indictment in the sweeping case details a number of accusations against Ellis, including that she helped author plans on how to disrupt and delay congressional certification of the 2020 election's results on Jan. 6, 2021, the day a mob of Trump supporters eventually overran the U.S. Capitol. And she's accused of urging state legislators to back false, pro-Trump electors in multiple states.
Hall pleaded guilty to five misdemeanor counts for his role in accessing Coffee County elections equipment.....
|
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muralik0115/xlm-roberta-base-finetuned-panx-de
|
muralik0115
| 2023-12-17T14:02:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T14:02:02Z |
Entry not found
|
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0.3101598620414734,
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] |
cnguyenta/distilbert-base-uncased-finetuned-ner
|
cnguyenta
| 2023-12-17T14:06:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-17T14:02:12Z |
Entry not found
|
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] |
CaffreyR/fair2-stable
|
CaffreyR
| 2023-12-17T14:36:28Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-17T14:02:48Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: A photo of the sks face
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - CaffreyR/fair2-stable
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on A photo of the sks face using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
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