modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
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int64 0
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int64 0
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stringlengths 2
84
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stringlengths 5
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denglu/llama
|
denglu
| 2023-12-16T14:35:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:35:11Z |
Entry not found
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dominic1021/ohwxnastyaxl
|
dominic1021
| 2023-12-16T14:57:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:37:49Z |
Entry not found
|
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stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep16
|
stvhuang
| 2023-12-16T14:43:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T14:40:38Z |
Entry not found
|
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] |
Thribs/charlotte
|
Thribs
| 2023-12-16T14:41:58Z | 0 | 0 | null |
[
"license:unlicense",
"region:us"
] | null | 2023-12-16T14:41:58Z |
---
license: unlicense
---
|
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JKuang96/rl_taxi_v3
|
JKuang96
| 2023-12-17T06:47:22Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T14:42:09Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: rl_taxi_v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="JKuang96/rl_taxi_v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
mfsuve/model_outputs
|
mfsuve
| 2023-12-16T14:42:10Z | 0 | 0 | null |
[
"region:us"
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morissu/wav2vec2-indonesia-isr
|
morissu
| 2023-12-16T16:06:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:46:19Z |
Entry not found
|
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mpietron/model_dir
|
mpietron
| 2023-12-17T11:19:57Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T14:46:42Z |
Entry not found
|
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] |
miweru/ochat3-5_dougi
|
miweru
| 2023-12-16T14:49:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openchat/openchat_3.5",
"region:us"
] | null | 2023-12-16T14:48:09Z |
---
library_name: peft
base_model: openchat/openchat_3.5
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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
<!-- 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. -->
[More Information Needed]
#### 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]
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## Technical Specifications [optional]
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<!-- 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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 0.7.0
|
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grissi/q-Taxi-v3
|
grissi
| 2023-12-16T14:49:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T14:48:58Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="grissi/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
KaratExp/Pepe_Mediavilla
|
KaratExp
| 2023-12-16T14:58:18Z | 0 | 0 | null |
[
"license:ms-pl",
"region:us"
] | null | 2023-12-16T14:50:46Z |
---
license: ms-pl
---
|
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lombardata/dino-large-2023_12_16-kornia_img-size518_batch-size16_epochs20
|
lombardata
| 2023-12-16T17:18:43Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"dinov2",
"generated_from_trainer",
"base_model:facebook/dinov2-large",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T14:52:08Z |
---
license: apache-2.0
base_model: facebook/dinov2-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dino-large-2023_12_16-kornia_img-size518_batch-size16_epochs20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dino-large-2023_12_16-kornia_img-size518_batch-size16_epochs20
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1782
- F1 Micro: 0.7155
- F1 Macro: 0.6631
- Roc Auc: 0.8070
- Accuracy: 0.4205
- Learning Rate: 0.01
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:----:|
| 0.2517 | 1.0 | 536 | 0.1758 | 0.7049 | 0.5634 | 0.7932 | 0.4223 | 0.01 |
| 0.2191 | 2.0 | 1072 | 0.3064 | 0.7218 | 0.6104 | 0.8291 | 0.4016 | 0.01 |
| 0.2167 | 3.0 | 1608 | 0.1782 | 0.7155 | 0.6631 | 0.8070 | 0.4205 | 0.01 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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Mareina/for-training
|
Mareina
| 2023-12-16T14:58:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:57:27Z |
Entry not found
|
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] |
aaa12963337/msi-vit-small
|
aaa12963337
| 2023-12-16T17:41:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T14:57:43Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-vit-small
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.599979032708974
- name: F1
type: f1
value: 0.2863021385373153
- name: Precision
type: precision
value: 0.6335540838852097
- name: Recall
type: recall
value: 0.18493757551349174
---
<!-- 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. -->
# msi-vit-small
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5796
- Accuracy: 0.6000
- F1: 0.2863
- Precision: 0.6336
- Recall: 0.1849
## 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-06
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3142 | 1.0 | 1008 | 0.8965 | 0.6329 | 0.5060 | 0.6079 | 0.4333 |
| 0.2063 | 2.0 | 2016 | 1.5189 | 0.6062 | 0.3005 | 0.6550 | 0.1950 |
| 0.19 | 3.0 | 3024 | 1.4818 | 0.6270 | 0.3399 | 0.7318 | 0.2213 |
| 0.1718 | 4.0 | 4032 | 1.2353 | 0.6046 | 0.4096 | 0.5816 | 0.3161 |
| 0.161 | 5.0 | 5040 | 1.5953 | 0.6342 | 0.3508 | 0.7623 | 0.2278 |
| 0.1805 | 6.0 | 6048 | 1.0789 | 0.6552 | 0.4647 | 0.7119 | 0.3449 |
| 0.1619 | 7.0 | 7056 | 1.2646 | 0.5479 | 0.2591 | 0.4484 | 0.1822 |
| 0.1655 | 8.0 | 8064 | 1.7155 | 0.5910 | 0.2654 | 0.6011 | 0.1703 |
| 0.17 | 9.0 | 9072 | 2.1142 | 0.5797 | 0.1729 | 0.5913 | 0.1012 |
| 0.1703 | 10.0 | 10080 | 1.5796 | 0.6000 | 0.2863 | 0.6336 | 0.1849 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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7Yang/wav2vec2-base-lang-id
|
7Yang
| 2023-12-16T14:59:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T14:59:20Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_rms_0001_fold1
|
hkivancoral
| 2023-12-16T15:55:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T14:59:48Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_0001_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: 0.9048414023372288
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_rms_0001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9972
- Accuracy: 0.9048
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2949 | 1.0 | 376 | 0.4792 | 0.7896 |
| 0.1877 | 2.0 | 752 | 0.3869 | 0.8631 |
| 0.1943 | 3.0 | 1128 | 0.4273 | 0.8514 |
| 0.1151 | 4.0 | 1504 | 0.4170 | 0.8932 |
| 0.1309 | 5.0 | 1880 | 0.4159 | 0.8748 |
| 0.0937 | 6.0 | 2256 | 0.5222 | 0.8831 |
| 0.0299 | 7.0 | 2632 | 0.5974 | 0.8932 |
| 0.0659 | 8.0 | 3008 | 0.6171 | 0.8715 |
| 0.0586 | 9.0 | 3384 | 0.7200 | 0.8781 |
| 0.0715 | 10.0 | 3760 | 0.9149 | 0.8664 |
| 0.0752 | 11.0 | 4136 | 0.7964 | 0.8765 |
| 0.0401 | 12.0 | 4512 | 0.6968 | 0.8831 |
| 0.0094 | 13.0 | 4888 | 0.6898 | 0.8865 |
| 0.0111 | 14.0 | 5264 | 0.7411 | 0.8932 |
| 0.0334 | 15.0 | 5640 | 0.8411 | 0.8798 |
| 0.0369 | 16.0 | 6016 | 0.7849 | 0.8798 |
| 0.0017 | 17.0 | 6392 | 0.7191 | 0.8898 |
| 0.0026 | 18.0 | 6768 | 0.8047 | 0.8815 |
| 0.0265 | 19.0 | 7144 | 0.6550 | 0.8982 |
| 0.0527 | 20.0 | 7520 | 0.7590 | 0.8798 |
| 0.0052 | 21.0 | 7896 | 0.7860 | 0.8881 |
| 0.001 | 22.0 | 8272 | 0.8487 | 0.8965 |
| 0.0432 | 23.0 | 8648 | 0.8524 | 0.8865 |
| 0.0032 | 24.0 | 9024 | 0.8174 | 0.9015 |
| 0.0001 | 25.0 | 9400 | 0.8214 | 0.8815 |
| 0.0146 | 26.0 | 9776 | 0.9080 | 0.8765 |
| 0.0 | 27.0 | 10152 | 0.8028 | 0.9032 |
| 0.0001 | 28.0 | 10528 | 0.9579 | 0.8915 |
| 0.0043 | 29.0 | 10904 | 0.8349 | 0.8982 |
| 0.0053 | 30.0 | 11280 | 0.9140 | 0.8831 |
| 0.0204 | 31.0 | 11656 | 0.9273 | 0.8898 |
| 0.0001 | 32.0 | 12032 | 0.9480 | 0.8848 |
| 0.0006 | 33.0 | 12408 | 1.0366 | 0.8865 |
| 0.0042 | 34.0 | 12784 | 1.0682 | 0.8798 |
| 0.0025 | 35.0 | 13160 | 0.9542 | 0.8932 |
| 0.0006 | 36.0 | 13536 | 0.8930 | 0.9048 |
| 0.0001 | 37.0 | 13912 | 0.9451 | 0.8932 |
| 0.0112 | 38.0 | 14288 | 1.0303 | 0.8848 |
| 0.0 | 39.0 | 14664 | 1.0298 | 0.8932 |
| 0.0 | 40.0 | 15040 | 0.9996 | 0.8932 |
| 0.0 | 41.0 | 15416 | 0.9909 | 0.8998 |
| 0.0 | 42.0 | 15792 | 0.9652 | 0.9015 |
| 0.0 | 43.0 | 16168 | 0.9547 | 0.9032 |
| 0.0 | 44.0 | 16544 | 0.9994 | 0.8982 |
| 0.0 | 45.0 | 16920 | 0.9802 | 0.9015 |
| 0.003 | 46.0 | 17296 | 0.9911 | 0.9032 |
| 0.0 | 47.0 | 17672 | 0.9936 | 0.9048 |
| 0.0 | 48.0 | 18048 | 0.9937 | 0.9048 |
| 0.0 | 49.0 | 18424 | 0.9932 | 0.9048 |
| 0.0025 | 50.0 | 18800 | 0.9972 | 0.9048 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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TheBloke/Chupacabra-8x7B-MoE-GGUF
|
TheBloke
| 2023-12-17T04:00:13Z | 0 | 2 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"base_model:perlthoughts/Chupacabra-8x7B-MoE",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-16T14:59:53Z |
---
base_model: perlthoughts/Chupacabra-8x7B-MoE
inference: false
license: apache-2.0
model_creator: Ray Hernandez
model_name: Chupacabra 8X7B MoE
model_type: mixtral
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Chupacabra 8X7B MoE - GGUF
- Model creator: [Ray Hernandez](https://huggingface.co/perlthoughts)
- Original model: [Chupacabra 8X7B MoE](https://huggingface.co/perlthoughts/Chupacabra-8x7B-MoE)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Ray Hernandez's Chupacabra 8X7B MoE](https://huggingface.co/perlthoughts/Chupacabra-8x7B-MoE).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
### Mixtral GGUF
Support for Mixtral was merged into Llama.cpp on December 13th.
These Mixtral GGUFs are known to work in:
* llama.cpp as of December 13th
* KoboldCpp 1.52 as later
* LM Studio 0.2.9 and later
* llama-cpp-python 0.2.23 and later
Other clients/libraries, not listed above, may not yet work.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF)
* [Ray Hernandez's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/perlthoughts/Chupacabra-8x7B-MoE)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These Mixtral GGUFs are compatible with llama.cpp from December 13th onwards. Other clients/libraries may not work yet.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [chupacabra-8x7b-moe.Q2_K.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q2_K.gguf) | Q2_K | 2 | 15.64 GB| 18.14 GB | smallest, significant quality loss - not recommended for most purposes |
| [chupacabra-8x7b-moe.Q3_K_M.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q3_K_M.gguf) | Q3_K_M | 3 | 20.36 GB| 22.86 GB | very small, high quality loss |
| [chupacabra-8x7b-moe.Q4_0.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q4_0.gguf) | Q4_0 | 4 | 26.44 GB| 28.94 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [chupacabra-8x7b-moe.Q4_K_M.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q4_K_M.gguf) | Q4_K_M | 4 | 26.44 GB| 28.94 GB | medium, balanced quality - recommended |
| [chupacabra-8x7b-moe.Q5_0.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q5_0.gguf) | Q5_0 | 5 | 32.23 GB| 34.73 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [chupacabra-8x7b-moe.Q5_K_M.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q5_K_M.gguf) | Q5_K_M | 5 | 32.23 GB| 34.73 GB | large, very low quality loss - recommended |
| [chupacabra-8x7b-moe.Q6_K.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q6_K.gguf) | Q6_K | 6 | 38.38 GB| 40.88 GB | very large, extremely low quality loss |
| [chupacabra-8x7b-moe.Q8_0.gguf](https://huggingface.co/TheBloke/Chupacabra-8x7B-MoE-GGUF/blob/main/chupacabra-8x7b-moe.Q8_0.gguf) | Q8_0 | 8 | 49.63 GB| 52.13 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Chupacabra-8x7B-MoE-GGUF and below it, a specific filename to download, such as: chupacabra-8x7b-moe.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Chupacabra-8x7B-MoE-GGUF chupacabra-8x7b-moe.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Chupacabra-8x7B-MoE-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Chupacabra-8x7B-MoE-GGUF chupacabra-8x7b-moe.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m chupacabra-8x7b-moe.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Note that text-generation-webui may not yet be compatible with Mixtral GGUFs. Please check compatibility first.
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) version 0.2.23 and later.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./chupacabra-8x7b-moe.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"{prompt}", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./chupacabra-8x7b-moe.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Ray Hernandez's Chupacabra 8X7B MoE
# Chupacabra-8x7B-experts
# Mixtral-8x7b-experts merge, of Chupacabra, OpenChat, Falkor, and Starling-LM
<p><img src="https://huggingface.co/perlthoughts/Chupacabra-7B/resolve/main/chupacabra7b%202.png" width=330></p>
### Model Description
Special thanks to @cgt123 for his great work on https://github.com/cg123/mergekit.
This was made using the 'mixtral' branch on the mergekit repo.
#OneManArmy
### More Info
- **Developed by:** Ray Hernandez
- **Model type:** Mistral
- **Language(s) (NLP):** English
- **License:** Apache 2.0
<!-- original-model-card end -->
|
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sogeeking/vqvae_burgers_8_v2
|
sogeeking
| 2023-12-17T01:46:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pdetokenizer",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T15:00:09Z |
Entry not found
|
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ccql/q-FrozenLake-v1-4x4-noSlippery
|
ccql
| 2023-12-16T15:01:46Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T15:01:43Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ccql/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
PeterV09/mistral-7b-300k-10k-a100-6e-valid-3
|
PeterV09
| 2023-12-16T15:10:44Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T15:02:29Z |
---
tags:
- generated_from_trainer
model-index:
- name: mistral-7b-300k-10k-a100-6e-valid-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-300k-10k-a100-6e-valid-3
This model was trained from scratch 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6.0
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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ccql/q-Taxi-v3
|
ccql
| 2023-12-16T15:04:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T15:04:10Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ccql/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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sice81/KoreanStableDiffusion
|
sice81
| 2023-12-16T15:04:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:04:57Z |
Entry not found
|
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Samzepic2188/Pineapple-Monster
|
Samzepic2188
| 2023-12-16T15:08:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:08:22Z |
Entry not found
|
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] |
agusroccohd/agusneta
|
agusroccohd
| 2023-12-16T15:12:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:10:55Z |
Entry not found
|
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] |
aaa12963337/msi-efficientnet-pretrain
|
aaa12963337
| 2023-12-17T08:10:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"efficientnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/efficientnet-b0",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T15:14:48Z |
---
license: apache-2.0
base_model: google/efficientnet-b0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi-efficientnet-pretrain
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8612813370473538
---
<!-- 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. -->
# msi-efficientnet-pretrain
This model is a fine-tuned version of [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4941
- Accuracy: 0.8613
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.317 | 1.0 | 781 | 0.9029 | 0.7535 |
| 0.2009 | 2.0 | 1562 | 0.4094 | 0.8840 |
| 0.1405 | 3.0 | 2343 | 0.4941 | 0.8613 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
KnutJaegersberg/nllb-moe-54b-4bit
|
KnutJaegersberg
| 2023-12-16T23:57:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"nllb-moe",
"feature-extraction",
"translation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",
"crh",
"cy",
"da",
"de",
"dik",
"dyu",
"dz",
"el",
"en",
"eo",
"et",
"eu",
"ee",
"fo",
"fj",
"fi",
"fon",
"fr",
"fur",
"fuv",
"gaz",
"gd",
"ga",
"gl",
"gn",
"gu",
"ht",
"ha",
"he",
"hi",
"hne",
"hr",
"hu",
"hy",
"ig",
"ilo",
"id",
"is",
"it",
"jv",
"ja",
"kab",
"kac",
"kam",
"kn",
"ks",
"ka",
"kk",
"kbp",
"kea",
"khk",
"km",
"ki",
"rw",
"ky",
"kmb",
"kmr",
"knc",
"kg",
"ko",
"lo",
"lij",
"li",
"ln",
"lt",
"lmo",
"ltg",
"lb",
"lua",
"lg",
"luo",
"lus",
"lvs",
"mag",
"mai",
"ml",
"mar",
"min",
"mk",
"mt",
"mni",
"mos",
"mi",
"my",
"nl",
"nn",
"nb",
"npi",
"nso",
"nus",
"ny",
"oc",
"ory",
"pag",
"pa",
"pap",
"pbt",
"pes",
"plt",
"pl",
"pt",
"prs",
"quy",
"ro",
"rn",
"ru",
"sg",
"sa",
"sat",
"scn",
"shn",
"si",
"sk",
"sl",
"sm",
"sn",
"sd",
"so",
"st",
"es",
"sc",
"sr",
"ss",
"su",
"sv",
"swh",
"szl",
"ta",
"taq",
"tt",
"te",
"tg",
"tl",
"th",
"ti",
"tpi",
"tn",
"ts",
"tk",
"tum",
"tr",
"tw",
"tzm",
"ug",
"uk",
"umb",
"ur",
"uzn",
"vec",
"vi",
"war",
"wo",
"xh",
"ydd",
"yo",
"yue",
"zh",
"zsm",
"zu",
"dataset:flores-200",
"arxiv:2207.04672",
"license:cc-by-nc-4.0",
"8-bit",
"region:us"
] |
translation
| 2023-12-16T15:20:30Z |
---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fur
- fuv
- gaz
- gd
- ga
- gl
- gn
- gu
- ht
- ha
- he
- hi
- hne
- hr
- hu
- hy
- ig
- ilo
- id
- is
- it
- jv
- ja
- kab
- kac
- kam
- kn
- ks
- ka
- kk
- kbp
- kea
- khk
- km
- ki
- rw
- ky
- kmb
- kmr
- knc
- kg
- ko
- lo
- lij
- li
- ln
- lt
- lmo
- ltg
- lb
- lua
- lg
- luo
- lus
- lvs
- mag
- mai
- ml
- mar
- min
- mk
- mt
- mni
- mos
- mi
- my
- nl
- nn
- nb
- npi
- nso
- nus
- ny
- oc
- ory
- pag
- pa
- pap
- pbt
- pes
- plt
- pl
- pt
- prs
- quy
- ro
- rn
- ru
- sg
- sa
- sat
- scn
- shn
- si
- sk
- sl
- sm
- sn
- sd
- so
- st
- es
- sc
- sr
- ss
- su
- sv
- swh
- szl
- ta
- taq
- tt
- te
- tg
- tl
- th
- ti
- tpi
- tn
- ts
- tk
- tum
- tr
- tw
- tzm
- ug
- uk
- umb
- ur
- uzn
- vec
- vi
- war
- wo
- xh
- ydd
- yo
- yue
- zh
- zsm
- zu
language_details: "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn"
tags:
- translation
license: "cc-by-nc-4.0"
datasets:
- flores-200
metrics:
- bleu
- spbleu
- chrf++
inference: false
---
This works by upgrading bitsandbytes to the most recent version and installing this pull request of hf transformers:
https://github.com/huggingface/transformers/pull/26037
It uses 37 GB VRAM and loads in like 20 seconds instead of 15 min, but inference is very slow. It is properly the sota open access model in machine translation.
# NLLB-MoE
This is the model card of NLLB-MoE variant.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues
The NLLB model was presented in [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by Marta R. Costa-jussà, James Cross, Onur Çelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula,
Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews,
Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers,
Safiyyah Saleem, Holger Schwenk, and Jeff Wang.
## Training:
- The Expert Output Masking is used for training, which consists in droping the full contribution for some tokens. This corresponds to the following scheme:
## Generating with NLLB-MoE
The avalable checkpoints requires around 350GB of storage. Make sure to use `accelerate` if you do not have enough RAM on your machine.
While generating the target text set the `forced_bos_token_id` to the target language id. The following
example shows how to translate English to French using the *facebook/nllb-moe-54b* model.
Note that we're using the BCP-47 code for French `fra_Latn`. See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
for the list of all BCP-47 in the Flores 200 dataset.
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-moe-54b")
>>> batched_input = [
'We now have 4-month-old mice that are non-diabetic that used to be diabetic," he added.',
"Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division of the Canadian Diabetes Association cautioned that the research is still in its early days."
"Like some other experts, he is skeptical about whether diabetes can be cured, noting that these findings have no relevance to people who already have Type 1 diabetes."
"On Monday, Sara Danius, permanent secretary of the Nobel Committee for Literature at the Swedish Academy, publicly announced during a radio program on Sveriges Radio in Sweden the committee, unable to reach Bob Dylan directly about winning the 2016 Nobel Prize in Literature, had abandoned its efforts to reach him.",
'Danius said, "Right now we are doing nothing. I have called and sent emails to his closest collaborator and received very friendly replies. For now, that is certainly enough."',
"Previously, Ring's CEO, Jamie Siminoff, remarked the company started when his doorbell wasn't audible from his shop in his garage.",
]
>>> inputs = tokenizer(batched_input, return_tensors="pt", padding = True)
>>> translated_tokens = model.generate(
... **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["fra_Latn"]
... )
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
['"Nous avons maintenant des souris de 4 mois non diabétiques qui étaient diabétiques", a-t-il ajouté.',
"Le docteur Ehud Ur, professeur de médecine à l'université Dalhousie, à Halifax, en Nouvelle-Écosse, et président de la division clinique et scientifique de l'Association canadienne du diabète, prévient que la recherche n'en est qu'à ses débuts.",
"Comme d'autres spécialistes, il est sceptique quant à la guérison du diabète, notant que ces résultats ne sont pas pertinents pour les personnes atteintes de diabète de type 1.",
"Lundi, Sara Danius, secrétaire permanente du Comité Nobel de littérature à l'Académie suédoise, a annoncé publiquement lors d'une émission de radio sur Sveriges Radio en Suède que le comité, incapable de contacter Bob Dylan directement au sujet du prix Nobel de littérature 2016, avait abandonné ses efforts pour le joindre.",
"Danius a déclaré: \"Pour le moment, nous ne faisons rien. J'ai appelé et envoyé des courriels à son plus proche collaborateur et j'ai reçu des réponses très amicales. Pour l'instant, c'est certainement suffisant\".",
"Auparavant, le PDG de Ring, Jamie Siminoff, a fait remarquer que la société avait commencé lorsque sa sonnette n'était pas audible depuis son magasin dans son garage.",
"Il a construit une sonnette WiFi, il a dit.",
]
```
|
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gane-dev/test-transformers
|
gane-dev
| 2023-12-16T15:22:57Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T15:22:57Z |
---
license: mit
---
|
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LarryAIDraw/DVA_TS_VLab
|
LarryAIDraw
| 2023-12-16T15:36:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:28:37Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233420/dva-overwatch
|
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LarryAIDraw/azurlaneOwari-000003
|
LarryAIDraw
| 2023-12-16T15:36:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:29:02Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233459/owari-azur-lane-2-outfits
|
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] |
LarryAIDraw/Char-Genshin-Navia-v2
|
LarryAIDraw
| 2023-12-16T15:38:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:29:23Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/148117/navia-genshin-impact
|
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] |
LarryAIDraw/meimeillumina
|
LarryAIDraw
| 2023-12-16T15:38:42Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:29:46Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/232563/mei-mei-jujutsu-kaisen
|
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LarryAIDraw/yskarja-nvwls-v1
|
LarryAIDraw
| 2023-12-16T15:39:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:30:07Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/232863/karja-balta-ys-x-nordics-lora
|
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] |
sdpkjc/Walker2d-v4-ddpg_continuous_action-seed2
|
sdpkjc
| 2023-12-16T15:30:20Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Walker2d-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T15:30:14Z |
---
tags:
- Walker2d-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2d-v4
type: Walker2d-v4
metrics:
- type: mean_reward
value: 805.90 +/- 504.93
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Walker2d-v4**
This is a trained model of a DDPG agent playing Walker2d-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Walker2d-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-ddpg_continuous_action-seed2/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-ddpg_continuous_action-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Walker2d-v4-ddpg_continuous_action-seed2/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Walker2d-v4 --track --seed 2
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Walker2d-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 2,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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LarryAIDraw/Roxy_Migurdia__Mushoku_Tensei_
|
LarryAIDraw
| 2023-12-16T15:40:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:31:00Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/232861/roxy-migurdia-mushoku-tensei
|
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] |
pequeno3d/robinthebatmanrvc2
|
pequeno3d
| 2023-12-16T15:31:53Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T15:31:03Z |
---
license: openrail
---
|
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] |
LarryAIDraw/Saber_Alter-12
|
LarryAIDraw
| 2023-12-16T15:40:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:31:20Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/232170/saber-alter-fate-series
|
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LarryAIDraw/atla_nai_4-24
|
LarryAIDraw
| 2023-12-16T15:41:04Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:31:41Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/232252/atla-fayon-the-rising-of-the-shield-hero-or
|
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BlakeKulaga/bouncy
|
BlakeKulaga
| 2023-12-16T15:31:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:31:49Z |
Entry not found
|
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LarryAIDraw/moe__blue_archive_
|
LarryAIDraw
| 2023-12-16T15:41:51Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:32:10Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/232272/kazekura-moe-blue-archive
|
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jackiedaytona/emoji-smol-128
|
jackiedaytona
| 2023-12-16T23:54:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2023-12-16T15:32:11Z |
Entry not found
|
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] |
LarryAIDraw/Artoria_Lancer_FGO
|
LarryAIDraw
| 2023-12-16T15:42:12Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:32:33Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/232347/artoria-pendragon-lancer-fgo
|
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] |
LarryAIDraw/hms_york_v2-lora-nochekaiser
|
LarryAIDraw
| 2023-12-16T15:43:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:33:03Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/231344/hms-york-azur-lane-commission
|
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LarryAIDraw/kita_ikuyo-10
|
LarryAIDraw
| 2023-12-16T15:43:58Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:33:24Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/155127/kita-ikuyo-bocchi-the-rock-lora
|
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sdpkjc/Swimmer-v4-ddpg_continuous_action-seed2
|
sdpkjc
| 2023-12-16T15:33:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Swimmer-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T15:33:39Z |
---
tags:
- Swimmer-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Swimmer-v4
type: Swimmer-v4
metrics:
- type: mean_reward
value: 126.11 +/- 23.12
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Swimmer-v4**
This is a trained model of a DDPG agent playing Swimmer-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Swimmer-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-ddpg_continuous_action-seed2/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-ddpg_continuous_action-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-ddpg_continuous_action-seed2/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Swimmer-v4 --track --seed 2
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Swimmer-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 2,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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] |
LarryAIDraw/selenaPGRbyscarxzyss
|
LarryAIDraw
| 2023-12-16T15:44:42Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:33:44Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/231849/selena-or-punishing-gray-raven
|
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] |
LarryAIDraw/chkalov-nvwls-v1
|
LarryAIDraw
| 2023-12-16T15:45:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:34:07Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/231883/chkalov-azur-lane-lora
|
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LarryAIDraw/mignon-000008
|
LarryAIDraw
| 2023-12-16T15:47:00Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:34:27Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/7292/mignon-374-style-lora
|
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LarryAIDraw/GuiltyCrownInori-v2-02
|
LarryAIDraw
| 2023-12-16T15:48:43Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:35:04Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/105201/guilty-crown-inori-yuzuriha
|
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tscstudios/UNa1OAqS1VN9tL8hyaV6mQlYKUK2
|
tscstudios
| 2023-12-16T15:36:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:35:04Z |
Entry not found
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] |
hkivancoral/smids_5x_beit_base_rms_0001_fold5
|
hkivancoral
| 2023-12-16T16:55:55Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T15:35:28Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.905
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_rms_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0211
- Accuracy: 0.905
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3069 | 1.0 | 375 | 0.3920 | 0.8783 |
| 0.2324 | 2.0 | 750 | 0.2994 | 0.8883 |
| 0.2111 | 3.0 | 1125 | 0.4025 | 0.8883 |
| 0.1469 | 4.0 | 1500 | 0.4730 | 0.8933 |
| 0.1348 | 5.0 | 1875 | 0.5021 | 0.8667 |
| 0.1083 | 6.0 | 2250 | 0.5547 | 0.875 |
| 0.074 | 7.0 | 2625 | 0.8070 | 0.865 |
| 0.0264 | 8.0 | 3000 | 0.6666 | 0.8817 |
| 0.0566 | 9.0 | 3375 | 0.5845 | 0.8817 |
| 0.0498 | 10.0 | 3750 | 0.6165 | 0.8717 |
| 0.0562 | 11.0 | 4125 | 0.6616 | 0.9017 |
| 0.0419 | 12.0 | 4500 | 0.5768 | 0.9 |
| 0.042 | 13.0 | 4875 | 0.6169 | 0.89 |
| 0.0428 | 14.0 | 5250 | 0.6006 | 0.8967 |
| 0.065 | 15.0 | 5625 | 0.6268 | 0.875 |
| 0.0169 | 16.0 | 6000 | 0.6699 | 0.9017 |
| 0.0201 | 17.0 | 6375 | 0.7528 | 0.8933 |
| 0.0241 | 18.0 | 6750 | 0.6629 | 0.89 |
| 0.0027 | 19.0 | 7125 | 0.6425 | 0.9017 |
| 0.0221 | 20.0 | 7500 | 0.6769 | 0.8917 |
| 0.0018 | 21.0 | 7875 | 0.8187 | 0.8867 |
| 0.0303 | 22.0 | 8250 | 0.6653 | 0.8933 |
| 0.0112 | 23.0 | 8625 | 0.7146 | 0.88 |
| 0.002 | 24.0 | 9000 | 0.7847 | 0.8983 |
| 0.0001 | 25.0 | 9375 | 0.7706 | 0.8933 |
| 0.001 | 26.0 | 9750 | 0.8815 | 0.8933 |
| 0.0089 | 27.0 | 10125 | 0.9055 | 0.8833 |
| 0.0011 | 28.0 | 10500 | 0.8721 | 0.8883 |
| 0.0031 | 29.0 | 10875 | 0.8475 | 0.8917 |
| 0.0096 | 30.0 | 11250 | 0.7033 | 0.9067 |
| 0.0084 | 31.0 | 11625 | 0.7845 | 0.9033 |
| 0.0003 | 32.0 | 12000 | 0.8241 | 0.8967 |
| 0.0002 | 33.0 | 12375 | 0.7939 | 0.905 |
| 0.0 | 34.0 | 12750 | 0.8492 | 0.9117 |
| 0.0039 | 35.0 | 13125 | 0.7919 | 0.905 |
| 0.0 | 36.0 | 13500 | 0.9132 | 0.9017 |
| 0.001 | 37.0 | 13875 | 0.9026 | 0.91 |
| 0.0073 | 38.0 | 14250 | 0.9238 | 0.9 |
| 0.0 | 39.0 | 14625 | 1.0700 | 0.895 |
| 0.0 | 40.0 | 15000 | 1.0185 | 0.9083 |
| 0.0 | 41.0 | 15375 | 1.0113 | 0.9 |
| 0.0 | 42.0 | 15750 | 0.9606 | 0.9033 |
| 0.0 | 43.0 | 16125 | 1.0356 | 0.9 |
| 0.0 | 44.0 | 16500 | 1.0382 | 0.9017 |
| 0.0 | 45.0 | 16875 | 1.0522 | 0.9 |
| 0.0 | 46.0 | 17250 | 1.0733 | 0.8967 |
| 0.0031 | 47.0 | 17625 | 1.0418 | 0.9017 |
| 0.0 | 48.0 | 18000 | 1.0244 | 0.9067 |
| 0.0 | 49.0 | 18375 | 1.0206 | 0.905 |
| 0.0019 | 50.0 | 18750 | 1.0211 | 0.905 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
[
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] |
hkivancoral/smids_5x_beit_base_rms_00001_fold5
|
hkivancoral
| 2023-12-16T16:56:23Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T15:36:02Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.91
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_rms_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9550
- Accuracy: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1871 | 1.0 | 375 | 0.3400 | 0.865 |
| 0.1042 | 2.0 | 750 | 0.2834 | 0.9017 |
| 0.1083 | 3.0 | 1125 | 0.3546 | 0.9033 |
| 0.0201 | 4.0 | 1500 | 0.3961 | 0.9133 |
| 0.0166 | 5.0 | 1875 | 0.6199 | 0.9083 |
| 0.0246 | 6.0 | 2250 | 0.6057 | 0.8967 |
| 0.0224 | 7.0 | 2625 | 0.7547 | 0.9117 |
| 0.0003 | 8.0 | 3000 | 0.7052 | 0.9133 |
| 0.0111 | 9.0 | 3375 | 0.7830 | 0.8983 |
| 0.0518 | 10.0 | 3750 | 0.8521 | 0.8967 |
| 0.02 | 11.0 | 4125 | 0.9299 | 0.8933 |
| 0.0138 | 12.0 | 4500 | 0.9525 | 0.8983 |
| 0.0001 | 13.0 | 4875 | 0.8824 | 0.9067 |
| 0.013 | 14.0 | 5250 | 0.9828 | 0.8833 |
| 0.0349 | 15.0 | 5625 | 0.8057 | 0.9033 |
| 0.0008 | 16.0 | 6000 | 0.9444 | 0.8983 |
| 0.0016 | 17.0 | 6375 | 0.8486 | 0.905 |
| 0.0093 | 18.0 | 6750 | 0.8485 | 0.9083 |
| 0.0057 | 19.0 | 7125 | 0.8351 | 0.895 |
| 0.0146 | 20.0 | 7500 | 0.8068 | 0.915 |
| 0.0088 | 21.0 | 7875 | 0.8372 | 0.9117 |
| 0.0074 | 22.0 | 8250 | 0.8780 | 0.905 |
| 0.0068 | 23.0 | 8625 | 0.9227 | 0.9067 |
| 0.0 | 24.0 | 9000 | 0.8408 | 0.9067 |
| 0.0 | 25.0 | 9375 | 0.8878 | 0.9067 |
| 0.0001 | 26.0 | 9750 | 0.6996 | 0.9217 |
| 0.0043 | 27.0 | 10125 | 0.7960 | 0.915 |
| 0.0021 | 28.0 | 10500 | 0.8288 | 0.91 |
| 0.002 | 29.0 | 10875 | 0.8059 | 0.9133 |
| 0.0055 | 30.0 | 11250 | 0.8992 | 0.9117 |
| 0.0001 | 31.0 | 11625 | 0.9502 | 0.9083 |
| 0.0001 | 32.0 | 12000 | 1.0009 | 0.9067 |
| 0.0047 | 33.0 | 12375 | 0.9429 | 0.91 |
| 0.0 | 34.0 | 12750 | 0.9564 | 0.905 |
| 0.0 | 35.0 | 13125 | 0.9119 | 0.9217 |
| 0.0 | 36.0 | 13500 | 1.0028 | 0.9033 |
| 0.0 | 37.0 | 13875 | 0.9150 | 0.91 |
| 0.0054 | 38.0 | 14250 | 0.9393 | 0.91 |
| 0.0 | 39.0 | 14625 | 1.0004 | 0.9067 |
| 0.0 | 40.0 | 15000 | 0.9733 | 0.9083 |
| 0.0001 | 41.0 | 15375 | 0.9774 | 0.9067 |
| 0.0 | 42.0 | 15750 | 0.9404 | 0.9133 |
| 0.0 | 43.0 | 16125 | 0.9724 | 0.9117 |
| 0.0 | 44.0 | 16500 | 0.9389 | 0.915 |
| 0.0 | 45.0 | 16875 | 0.9342 | 0.9167 |
| 0.0 | 46.0 | 17250 | 0.9815 | 0.9117 |
| 0.0058 | 47.0 | 17625 | 0.9724 | 0.9067 |
| 0.0 | 48.0 | 18000 | 0.9650 | 0.9067 |
| 0.0 | 49.0 | 18375 | 0.9572 | 0.9083 |
| 0.0012 | 50.0 | 18750 | 0.9550 | 0.91 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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lailamt/bert-base-portuguese-cased-finetuned
|
lailamt
| 2023-12-16T15:36:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:36:53Z |
Entry not found
|
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] |
XuYiLX/chatglm2-6b-torchkeras
|
XuYiLX
| 2023-12-16T15:40:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"chatglm",
"feature-extraction",
"custom_code",
"region:us"
] |
feature-extraction
| 2023-12-16T15:37:44Z |
Entry not found
|
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] |
sdpkjc/Ant-v4-ddpg_continuous_action-seed2
|
sdpkjc
| 2023-12-16T15:39:26Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Ant-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T15:39:19Z |
---
tags:
- Ant-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Ant-v4
type: Ant-v4
metrics:
- type: mean_reward
value: 408.79 +/- 232.54
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Ant-v4**
This is a trained model of a DDPG agent playing Ant-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Ant-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Ant-v4-ddpg_continuous_action-seed2/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Ant-v4-ddpg_continuous_action-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Ant-v4-ddpg_continuous_action-seed2/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Ant-v4 --track --seed 2
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Ant-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 2,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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] |
sdpkjc/Humanoid-v4-ddpg_continuous_action-seed2
|
sdpkjc
| 2023-12-16T15:41:26Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Humanoid-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T15:41:18Z |
---
tags:
- Humanoid-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Humanoid-v4
type: Humanoid-v4
metrics:
- type: mean_reward
value: 2484.89 +/- 1419.10
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Humanoid-v4**
This is a trained model of a DDPG agent playing Humanoid-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Humanoid-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Humanoid-v4-ddpg_continuous_action-seed2/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/sdpkjc/Humanoid-v4-ddpg_continuous_action-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Humanoid-v4-ddpg_continuous_action-seed2/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Humanoid-v4 --track --seed 2
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'env_id': 'Humanoid-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 2,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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hiiamsid/mixtral-instruct
|
hiiamsid
| 2023-12-16T15:48:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:48:10Z |
Entry not found
|
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LarryAIDraw/tsukioka_kogane_rev3
|
LarryAIDraw
| 2023-12-16T15:59:02Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:52:57Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/234509/tsukioka-kogane
|
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] |
LarryAIDraw/liliruca_arde
|
LarryAIDraw
| 2023-12-16T15:59:27Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:53:18Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/234317/liliruca-arde-lora-danmachi
|
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LarryAIDraw/Lysithea_war-10
|
LarryAIDraw
| 2023-12-16T15:59:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:53:42Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/234358/lysithea-war-arc-fire-emblem-three-houses
|
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LarryAIDraw/hadou_nejire
|
LarryAIDraw
| 2023-12-16T16:00:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:54:04Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/234361/hadou-nejire-my-hero-academia
|
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] |
LarryAIDraw/LynxLandau-08
|
LarryAIDraw
| 2023-12-16T16:00:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:54:26Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/151230/lynx-landau-honkai-star-rail-lora
|
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LarryAIDraw/tachibana_marika
|
LarryAIDraw
| 2023-12-16T16:00:59Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:54:46Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/234120/tachibana-marika-nisekoi
|
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] |
LarryAIDraw/kanade-angelbeats-01
|
LarryAIDraw
| 2023-12-16T16:01:24Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:55:07Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233598/kanade-tachibana-angel-beats
|
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LarryAIDraw/yuri-angelbeats-01
|
LarryAIDraw
| 2023-12-16T16:01:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:55:30Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233599/yuri-nakamura-angel-beats
|
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] |
thor9king/realvisxl3
|
thor9king
| 2023-12-16T15:55:50Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T15:55:50Z |
---
license: apache-2.0
---
|
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] |
hkivancoral/smids_5x_deit_tiny_rms_0001_fold2
|
hkivancoral
| 2023-12-16T16:50:46Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T15:55:51Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8735440931780366
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_rms_0001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1259
- Accuracy: 0.8735
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3139 | 1.0 | 375 | 0.2920 | 0.8835 |
| 0.213 | 2.0 | 750 | 0.3450 | 0.8785 |
| 0.2004 | 3.0 | 1125 | 0.4306 | 0.8719 |
| 0.1151 | 4.0 | 1500 | 0.4856 | 0.8702 |
| 0.1363 | 5.0 | 1875 | 0.5483 | 0.8752 |
| 0.0415 | 6.0 | 2250 | 0.6014 | 0.8719 |
| 0.0888 | 7.0 | 2625 | 0.6594 | 0.8636 |
| 0.0129 | 8.0 | 3000 | 0.7394 | 0.8702 |
| 0.0606 | 9.0 | 3375 | 0.7551 | 0.8619 |
| 0.0273 | 10.0 | 3750 | 0.7977 | 0.8536 |
| 0.0575 | 11.0 | 4125 | 0.7927 | 0.8702 |
| 0.0142 | 12.0 | 4500 | 0.8285 | 0.8619 |
| 0.006 | 13.0 | 4875 | 0.8594 | 0.8819 |
| 0.0339 | 14.0 | 5250 | 0.8600 | 0.8686 |
| 0.0029 | 15.0 | 5625 | 0.9289 | 0.8719 |
| 0.0348 | 16.0 | 6000 | 0.7828 | 0.8819 |
| 0.0273 | 17.0 | 6375 | 0.7381 | 0.8885 |
| 0.029 | 18.0 | 6750 | 0.9087 | 0.8686 |
| 0.0306 | 19.0 | 7125 | 0.9194 | 0.8785 |
| 0.0034 | 20.0 | 7500 | 1.0978 | 0.8619 |
| 0.0052 | 21.0 | 7875 | 0.9530 | 0.8785 |
| 0.0001 | 22.0 | 8250 | 0.9575 | 0.8752 |
| 0.0447 | 23.0 | 8625 | 0.9869 | 0.8819 |
| 0.0122 | 24.0 | 9000 | 0.8869 | 0.8785 |
| 0.0018 | 25.0 | 9375 | 1.0324 | 0.8669 |
| 0.0117 | 26.0 | 9750 | 0.9387 | 0.8852 |
| 0.0206 | 27.0 | 10125 | 1.0468 | 0.8719 |
| 0.0002 | 28.0 | 10500 | 0.9421 | 0.8785 |
| 0.0001 | 29.0 | 10875 | 0.8621 | 0.8968 |
| 0.0027 | 30.0 | 11250 | 0.9653 | 0.8769 |
| 0.0116 | 31.0 | 11625 | 0.9958 | 0.8785 |
| 0.0019 | 32.0 | 12000 | 1.1300 | 0.8752 |
| 0.0084 | 33.0 | 12375 | 1.0346 | 0.8802 |
| 0.0 | 34.0 | 12750 | 1.0458 | 0.8719 |
| 0.0 | 35.0 | 13125 | 1.0740 | 0.8719 |
| 0.0001 | 36.0 | 13500 | 1.0706 | 0.8719 |
| 0.0 | 37.0 | 13875 | 1.2116 | 0.8735 |
| 0.0 | 38.0 | 14250 | 1.1598 | 0.8735 |
| 0.0 | 39.0 | 14625 | 1.1682 | 0.8785 |
| 0.0029 | 40.0 | 15000 | 1.0573 | 0.8835 |
| 0.0 | 41.0 | 15375 | 1.1307 | 0.8735 |
| 0.0028 | 42.0 | 15750 | 1.1484 | 0.8702 |
| 0.0032 | 43.0 | 16125 | 1.1289 | 0.8752 |
| 0.0031 | 44.0 | 16500 | 1.1224 | 0.8769 |
| 0.0027 | 45.0 | 16875 | 1.1287 | 0.8719 |
| 0.0 | 46.0 | 17250 | 1.1176 | 0.8752 |
| 0.006 | 47.0 | 17625 | 1.1207 | 0.8752 |
| 0.0 | 48.0 | 18000 | 1.1234 | 0.8752 |
| 0.0024 | 49.0 | 18375 | 1.1256 | 0.8752 |
| 0.0022 | 50.0 | 18750 | 1.1259 | 0.8735 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
LarryAIDraw/MinervaVG
|
LarryAIDraw
| 2023-12-16T16:02:30Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:55:52Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233596/unofficial-omniscience-regalia-minerva-cardfight-vanguard
|
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mainaakch/xlr-finetuned
|
mainaakch
| 2023-12-16T15:56:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T15:56:20Z |
Entry not found
|
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] |
LarryAIDraw/navia_kozue
|
LarryAIDraw
| 2023-12-16T16:02:50Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:56:31Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233594/navia-genshin-impact
|
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LarryAIDraw/LoRA_Mana
|
LarryAIDraw
| 2023-12-16T16:03:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:56:53Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233699/lora-mana-yu-gi-oh
|
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LarryAIDraw/perNAIFP2-000006
|
LarryAIDraw
| 2023-12-16T16:03:42Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:57:16Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233808/perseusazurlane-character-model-oror-original-outfit
|
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] |
LarryAIDraw/LiselotteCretiav1-000008
|
LarryAIDraw
| 2023-12-16T16:04:34Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T15:57:38Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/233469/liselotte-cretia-seirei-gensouki-or-raelina
|
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Bugabooo30/finetuning-sentiment-model
|
Bugabooo30
| 2023-12-16T16:01:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:01:11Z |
Entry not found
|
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theostoican/Reinforce-1
|
theostoican
| 2023-12-16T16:01:49Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T16:01:34Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 14.10 +/- 10.91
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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] |
intervitens/WinterGoddess_1.4x-70B-L2-2.55bpw-h6-exl2
|
intervitens
| 2023-12-16T16:48:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T16:06:07Z |
---
license: cc-by-nc-4.0
language:
- en
---
Quantized using 200 samples of 4096 tokens from [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset.
Original model link: [WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2)
Original model README below.
***
Winter Goddess - A 70B L2 Model for General use, or for Roleplay.
I wanted a Smart Model that is Capable of following Instructions, while being able to (e)RP effectively. Sort of like 1.3, but better.
I merged some models as a base, and had tuned on top of it afterwards.
I personally think this mogs Euryale 1.3, but ymmv.
***
Prompt Format - Alpaca
```
### Instruction:
<Prompt>
### Response:
```
OR
```
### Instruction:
<Prompt>
### Input:
<Insert Context Here>
### Response:
```
***
<br> 42. A 25-year-old female has been struck in the right eye with a pipe. She has a ruptured right globe, an orbital fracture and no other obvious injury. You should bandage:
<br> A) The right eye tightly
<br> B) Both eyes loosely
<br> C) The right eye loosely
<br> D) Both eyes tightly
|
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] |
Mingda/bert-base-uncased-llm123-fact10
|
Mingda
| 2023-12-16T17:00:57Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T16:09:27Z |
Entry not found
|
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] |
Mingda/bert-base-uncased-nlp2023-fact10
|
Mingda
| 2023-12-16T17:02:05Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T16:09:28Z |
Entry not found
|
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] |
Mingda/bert-base-multilingual-uncased-nlp2023-fact10
|
Mingda
| 2023-12-16T17:05:50Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T16:09:32Z |
Entry not found
|
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] |
kligvasser/ffhq-vqvae-landmark-arcface
|
kligvasser
| 2023-12-16T16:11:46Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"diffusers:ConditionalDDPMPipeline",
"region:us"
] | null | 2023-12-16T16:11:09Z |
Entry not found
|
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imspidey/mBreakdomain
|
imspidey
| 2023-12-16T16:19:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:16:32Z |
Entry not found
|
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intervitens/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2
|
intervitens
| 2023-12-16T16:47:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T16:17:22Z |
---
license: cc-by-nc-4.0
language:
- en
---
Quantized using 200 samples of 4096 tokens from [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset.
Original model link: [WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2)
Original model README below.
***
Winter Goddess - A 70B L2 Model for General use, or for Roleplay.
I wanted a Smart Model that is Capable of following Instructions, while being able to (e)RP effectively. Sort of like 1.3, but better.
I merged some models as a base, and had tuned on top of it afterwards.
I personally think this mogs Euryale 1.3, but ymmv.
***
Prompt Format - Alpaca
```
### Instruction:
<Prompt>
### Response:
```
OR
```
### Instruction:
<Prompt>
### Input:
<Insert Context Here>
### Response:
```
***
<br> 42. A 25-year-old female has been struck in the right eye with a pipe. She has a ruptured right globe, an orbital fracture and no other obvious injury. You should bandage:
<br> A) The right eye tightly
<br> B) Both eyes loosely
<br> C) The right eye loosely
<br> D) Both eyes tightly
|
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aalonsolopez/gravitingllama2_7b
|
aalonsolopez
| 2023-12-16T16:19:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:19:46Z |
Entry not found
|
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0.3101596534252167,
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] |
rinkorn/dummy-model
|
rinkorn
| 2023-12-16T17:41:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-16T16:25:55Z |
*Описание модели*
Описание модели предоставляет основные сведения о модели. Это включает архитектуру, версию, была ли она представлена в какой-либо научной статье, доступна ли оригинальная реализация, автор и общая информация о модели. Здесь следует указать любые авторские права. Общая информация о процедурах обучения, параметрах и важных оговорках также может быть упомянута в этом разделе.
*Предполагаемые использования и ограничения*
Здесь вы описываете сценарии использования и задачи для которых предназначена модель, включая например языки, а также области или домены ее возможного применения. В этом разделе карточки модели также можно документировать сценарии в которых она, вероятно, будет работать неоптимально.
*Как использовать*
В этом разделе следует привести некоторые примеры использования модели. Здесь можно продемонстрировать использование функции pipeline(), использование классов модели и токенизатора, а также любой другой код, который, по вашему мнению, может быть полезным.
*Тренировочные данные*
В этой части следует указать, на каком(их) наборе(ах) данных модель была обучена. Краткое описание набора(ов) данных также приветствуется.
*Процедура обучения*
В этом разделе вы должны описать все соответствующие аспекты обучения, которые полезны с точки зрения воспроизводимости. Это включает любую предварительную обработку и пост-обработку, которые были выполнены на данных, а также такие детали, как количество эпох обучения модели, размер партии, скорость обучения и т. д.
*Переменные и метрики*
Здесь следует описать метрики, которые вы используете для оценки, и различные факторы, которые вы измеряете. Упоминание о том, какие метрики были использованы, на каком наборе данных и каком разделе данных модель была обучена, позволяет легко сравнивать производительность вашей модели с производительностью других моделей. Эти данные должны быть основаны на предыдущих разделах, таких как предполагаемые пользователи и варианты использования.
*Результаты оценки*
Наконец, предоставьте информацию о том, насколько хорошо модель справляется с оценочным набором данных. Если модель использует порог принятия решений, либо предоставьте порог принятия решений, использованный в оценке, либо предоставьте информацию об оценке на разных порогах для предполагаемых вариантов использования.
|
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21world/go-bruins-v2.1.1.GGUF
|
21world
| 2023-12-17T07:17:22Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-16T16:27:05Z |
original .safetensors
https://huggingface.co/rwitz2/go-bruins-v2.1.1/
|
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] |
tanooki426/FireAlarm
|
tanooki426
| 2023-12-16T16:30:23Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T16:28:36Z |
---
license: openrail
---
|
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amir-ma/parsig_pos
|
amir-ma
| 2023-12-16T16:28:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:28:51Z |
Entry not found
|
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amir-ma/parsig_tokenizer
|
amir-ma
| 2023-12-16T16:28:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:28:52Z |
Entry not found
|
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] |
pminha/roberta-base-squad2-p40
|
pminha
| 2023-12-16T16:30:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-16T16:30:20Z |
Entry not found
|
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] |
pminha/roberta-base-squad2-p90
|
pminha
| 2023-12-16T16:33:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-16T16:32:50Z |
Entry not found
|
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] |
tanooki426/Dolphin
|
tanooki426
| 2023-12-16T16:35:01Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T16:33:15Z |
---
license: openrail
---
|
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] |
Enkrateia96/ppo-LunarLander-v2
|
Enkrateia96
| 2023-12-16T16:36:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T16:36:03Z |
---
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: 255.18 +/- 16.52
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_ice2
|
thorirhrafn
| 2023-12-17T09:44:42Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-12-16T16:37:49Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llama2_lora_ice2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama2_lora_ice2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1126
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.3504 | 0.19 | 5000 | 2.3109 |
| 2.3206 | 0.38 | 10000 | 2.2213 |
| 2.2321 | 0.57 | 15000 | 2.1817 |
| 2.1524 | 0.75 | 20000 | 2.1579 |
| 2.2333 | 0.94 | 25000 | 2.1455 |
| 2.1409 | 1.13 | 30000 | 2.1367 |
| 2.1875 | 1.32 | 35000 | 2.1301 |
| 2.1823 | 1.51 | 40000 | 2.1254 |
| 2.1062 | 1.7 | 45000 | 2.1217 |
| 2.1374 | 1.88 | 50000 | 2.1192 |
| 2.1645 | 2.07 | 55000 | 2.1174 |
| 2.1595 | 2.26 | 60000 | 2.1159 |
| 2.1405 | 2.45 | 65000 | 2.1149 |
| 2.1585 | 2.64 | 70000 | 2.1139 |
| 2.1423 | 2.83 | 75000 | 2.1132 |
| 2.1656 | 3.02 | 80000 | 2.1129 |
| 2.1159 | 3.2 | 85000 | 2.1128 |
| 2.1565 | 3.39 | 90000 | 2.1127 |
| 2.115 | 3.58 | 95000 | 2.1126 |
| 2.144 | 3.77 | 100000 | 2.1126 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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meetplace1/agentlora
|
meetplace1
| 2023-12-17T11:11:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:39:19Z |
Entry not found
|
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hiiamsid/mixtral-cpu-offload
|
hiiamsid
| 2023-12-16T16:40:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:40:12Z |
Entry not found
|
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pirxus/how2_pt_unigram5000
|
pirxus
| 2023-12-16T16:45:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:45:01Z |
Entry not found
|
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] |
stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep11
|
stvhuang
| 2023-12-16T16:46:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T16:45:44Z |
Entry not found
|
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pirxus/how2_en_unigram5000
|
pirxus
| 2023-12-16T16:45:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:45:47Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_rms_0001_fold3
|
hkivancoral
| 2023-12-16T17:46:25Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T16:51:28Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_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: 0.9016666666666666
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_rms_0001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0235
- Accuracy: 0.9017
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3072 | 1.0 | 375 | 0.3497 | 0.8733 |
| 0.1839 | 2.0 | 750 | 0.4255 | 0.87 |
| 0.1528 | 3.0 | 1125 | 0.4557 | 0.8567 |
| 0.1267 | 4.0 | 1500 | 0.3726 | 0.89 |
| 0.1353 | 5.0 | 1875 | 0.4467 | 0.8917 |
| 0.0943 | 6.0 | 2250 | 0.4927 | 0.91 |
| 0.1102 | 7.0 | 2625 | 0.6801 | 0.8833 |
| 0.1057 | 8.0 | 3000 | 0.6555 | 0.88 |
| 0.032 | 9.0 | 3375 | 0.7410 | 0.8783 |
| 0.0843 | 10.0 | 3750 | 0.8478 | 0.8667 |
| 0.0459 | 11.0 | 4125 | 0.6987 | 0.8917 |
| 0.0092 | 12.0 | 4500 | 0.7040 | 0.8917 |
| 0.0349 | 13.0 | 4875 | 0.7908 | 0.885 |
| 0.0111 | 14.0 | 5250 | 0.7260 | 0.8983 |
| 0.0286 | 15.0 | 5625 | 0.7556 | 0.89 |
| 0.0202 | 16.0 | 6000 | 0.7922 | 0.885 |
| 0.0017 | 17.0 | 6375 | 0.7780 | 0.89 |
| 0.0426 | 18.0 | 6750 | 0.7356 | 0.9033 |
| 0.0036 | 19.0 | 7125 | 0.7906 | 0.88 |
| 0.0088 | 20.0 | 7500 | 0.8591 | 0.8883 |
| 0.014 | 21.0 | 7875 | 0.9590 | 0.8867 |
| 0.0 | 22.0 | 8250 | 0.9929 | 0.8783 |
| 0.0363 | 23.0 | 8625 | 0.9559 | 0.89 |
| 0.0156 | 24.0 | 9000 | 0.9344 | 0.88 |
| 0.0345 | 25.0 | 9375 | 0.8898 | 0.8917 |
| 0.0005 | 26.0 | 9750 | 0.9066 | 0.9 |
| 0.0104 | 27.0 | 10125 | 0.9018 | 0.8983 |
| 0.0026 | 28.0 | 10500 | 0.8354 | 0.89 |
| 0.0098 | 29.0 | 10875 | 1.0679 | 0.885 |
| 0.0077 | 30.0 | 11250 | 0.8084 | 0.8933 |
| 0.007 | 31.0 | 11625 | 0.9761 | 0.8833 |
| 0.0079 | 32.0 | 12000 | 0.8798 | 0.8867 |
| 0.0211 | 33.0 | 12375 | 0.9152 | 0.8967 |
| 0.0205 | 34.0 | 12750 | 0.8595 | 0.8967 |
| 0.0 | 35.0 | 13125 | 0.9123 | 0.8983 |
| 0.0 | 36.0 | 13500 | 1.0918 | 0.8817 |
| 0.0001 | 37.0 | 13875 | 0.9598 | 0.8917 |
| 0.0 | 38.0 | 14250 | 0.9005 | 0.8933 |
| 0.0 | 39.0 | 14625 | 0.9817 | 0.895 |
| 0.003 | 40.0 | 15000 | 1.0214 | 0.8933 |
| 0.0 | 41.0 | 15375 | 1.0132 | 0.895 |
| 0.0012 | 42.0 | 15750 | 1.0443 | 0.8933 |
| 0.0 | 43.0 | 16125 | 1.0086 | 0.895 |
| 0.0 | 44.0 | 16500 | 1.0148 | 0.895 |
| 0.0 | 45.0 | 16875 | 1.0171 | 0.895 |
| 0.0 | 46.0 | 17250 | 1.0091 | 0.8967 |
| 0.0 | 47.0 | 17625 | 1.0118 | 0.8983 |
| 0.0 | 48.0 | 18000 | 1.0184 | 0.9017 |
| 0.0 | 49.0 | 18375 | 1.0213 | 0.9017 |
| 0.0 | 50.0 | 18750 | 1.0235 | 0.9017 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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zjj815/videomae-base-short-finetuned-kinetics-finetuned-ucf101-subset
|
zjj815
| 2023-12-16T16:52:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:52:04Z |
Entry not found
|
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Musix/FM75-76Updated
|
Musix
| 2023-12-16T16:54:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T16:53:03Z |
Entry not found
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