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TrumpBiden/zephyr-7b-sft-full
|
TrumpBiden
| 2023-12-14T14:23:01Z | 0 | 0 | null |
[
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] |
KhoiC/esm2_t12_35M_UR50D-pretrained-evaluation
|
KhoiC
| 2023-12-14T14:37:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T14:24:58Z |
Entry not found
|
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KhimNguyen/LSH
|
KhimNguyen
| 2023-12-14T14:28:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:28:12Z |
Entry not found
|
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chinhang0104/distilgpt2_private-finetuned-wikitext2
|
chinhang0104
| 2023-12-14T15:51:25Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:chinhang0104/distilgpt2_private",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T14:29:11Z |
---
license: apache-2.0
base_model: chinhang0104/distilgpt2_private
tags:
- generated_from_trainer
model-index:
- name: distilgpt2_private-finetuned-wikitext2
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. -->
# distilgpt2_private-finetuned-wikitext2
This model is a fine-tuned version of [chinhang0104/distilgpt2_private](https://huggingface.co/chinhang0104/distilgpt2_private) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7501 | 1.0 | 2334 | 3.6669 |
| 3.6498 | 2.0 | 4668 | 3.6464 |
| 3.5868 | 3.0 | 7002 | 3.6434 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
LilYoda/Tsukumo
|
LilYoda
| 2023-12-14T14:38:12Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T14:31:23Z |
---
license: openrail
---
|
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0.04487845301628113,
-0.6869444847106934
] |
jeveuxaider/jva-mission-report-v2
|
jeveuxaider
| 2023-12-14T14:36:30Z | 0 | 0 |
sklearn
|
[
"sklearn",
"skops",
"text-classification",
"license:mit",
"region:us"
] |
text-classification
| 2023-12-14T14:36:27Z |
---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- text-classification
model_format: pickle
model_file: skops-3sdy4gnv.pkl
---
# Model description
This is a `Support Vector Classifier` model trained on JeVeuxAider dataset. As input, the model takes text embeddings encoded with camembert-base (768 tokens)
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
[More Information Needed]
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|------------------------------------------------------|-------------------------------------------------------------------------------|
| memory | |
| steps | [('columntransformer', ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> Index(['avg_0', 'avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7',<br /> 'avg_8', 'avg_9',<br /> ...<br /> 'max_758', 'max_759', 'max_760', 'max_761', 'max_762', 'max_763',<br /> 'max_764', 'max_765', 'max_766', 'max_767'],<br /> dtype='object', length=2304))],<br /> verbose_feature_names_out=False)), ('svc', SVC(random_state=42))] |
| verbose | False |
| columntransformer | ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> Index(['avg_0', 'avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7',<br /> 'avg_8', 'avg_9',<br /> ...<br /> 'max_758', 'max_759', 'max_760', 'max_761', 'max_762', 'max_763',<br /> 'max_764', 'max_765', 'max_766', 'max_767'],<br /> dtype='object', length=2304))],<br /> verbose_feature_names_out=False) |
| svc | SVC(random_state=42) |
| columntransformer__n_jobs | |
| columntransformer__remainder | drop |
| columntransformer__sparse_threshold | 0.3 |
| columntransformer__transformer_weights | |
| columntransformer__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]), Index(['avg_0', 'avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7',<br /> 'avg_8', 'avg_9',<br /> ...<br /> 'max_758', 'max_759', 'max_760', 'max_761', 'max_762', 'max_763',<br /> 'max_764', 'max_765', 'max_766', 'max_767'],<br /> dtype='object', length=2304))] |
| columntransformer__verbose | False |
| columntransformer__verbose_feature_names_out | False |
| columntransformer__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]) |
| columntransformer__num__memory | |
| columntransformer__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())] |
| columntransformer__num__verbose | False |
| columntransformer__num__imputer | SimpleImputer(strategy='median') |
| columntransformer__num__scaler | StandardScaler() |
| columntransformer__num__imputer__add_indicator | False |
| columntransformer__num__imputer__copy | True |
| columntransformer__num__imputer__fill_value | |
| columntransformer__num__imputer__keep_empty_features | False |
| columntransformer__num__imputer__missing_values | nan |
| columntransformer__num__imputer__strategy | median |
| columntransformer__num__imputer__verbose | deprecated |
| columntransformer__num__scaler__copy | True |
| columntransformer__num__scaler__with_mean | True |
| columntransformer__num__scaler__with_std | True |
| svc__C | 1.0 |
| svc__break_ties | False |
| svc__cache_size | 200 |
| svc__class_weight | |
| svc__coef0 | 0.0 |
| svc__decision_function_shape | ovr |
| svc__degree | 3 |
| svc__gamma | scale |
| svc__kernel | rbf |
| svc__max_iter | -1 |
| svc__probability | False |
| svc__random_state | 42 |
| svc__shrinking | True |
| svc__tol | 0.001 |
| svc__verbose | False |
</details>
### Model Plot
<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-8" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),Index(['avg_0', 'avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7','avg_8', 'avg_9',...'max_758', 'max_759', 'max_760', 'max_761', 'max_762', 'max_763','max_764', 'max_765', 'max_766', 'max_767'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', SVC(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),Index(['avg_0', 'avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7','avg_8', 'avg_9',...'max_758', 'max_759', 'max_760', 'max_761', 'max_762', 'max_763','max_764', 'max_765', 'max_766', 'max_767'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', SVC(random_state=42))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-31" type="checkbox" ><label for="sk-estimator-id-31" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),Index(['avg_0', 'avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7','avg_8', 'avg_9',...'max_758', 'max_759', 'max_760', 'max_761', 'max_762', 'max_763','max_764', 'max_765', 'max_766', 'max_767'],dtype='object', length=2304))],verbose_feature_names_out=False)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-32" type="checkbox" ><label for="sk-estimator-id-32" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>Index(['avg_0', 'avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7','avg_8', 'avg_9',...'max_758', 'max_759', 'max_760', 'max_761', 'max_762', 'max_763','max_764', 'max_765', 'max_766', 'max_767'],dtype='object', length=2304)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label sk-toggleable__label-arrow">SVC</label><div class="sk-toggleable__content"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div>
## Evaluation Results
| Metric | Value |
|----------|----------|
| accuracy | 0.8225 |
| f1 score | 0.822312 |
### Confusion Matrix

# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
huynhdoo
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
**BibTeX**
```
@inproceedings{...,year={2023}}
```
# get_started_code
import pickle as pickle
with open(pkl_filename, 'rb') as file:
pipe = pickle.load(file)
|
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charyl606/NIKO
|
charyl606
| 2023-12-14T14:43:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:38:54Z |
Entry not found
|
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ofargally/bert_amazon_finetuned
|
ofargally
| 2023-12-14T14:41:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:41:33Z |
Entry not found
|
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alitolga/bert-base-uncased-peft
|
alitolga
| 2023-12-17T21:02:13Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T14:45:08Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-peft
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. -->
# bert-base-uncased-peft
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5185
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6291 | 1.0 | 260 | 3.2199 |
| 2.9545 | 2.0 | 520 | 2.7330 |
| 2.7622 | 3.0 | 780 | 2.5185 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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hkivancoral/smids_5x_deit_tiny_adamax_00001_fold2
|
hkivancoral
| 2023-12-18T00:34:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T14:52:53Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_00001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8752079866888519
---
<!-- 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_adamax_00001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0539
- Accuracy: 0.8752
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3245 | 1.0 | 375 | 0.3357 | 0.8586 |
| 0.2435 | 2.0 | 750 | 0.3012 | 0.8802 |
| 0.1837 | 3.0 | 1125 | 0.3092 | 0.8802 |
| 0.0922 | 4.0 | 1500 | 0.3362 | 0.8719 |
| 0.064 | 5.0 | 1875 | 0.4063 | 0.8619 |
| 0.0948 | 6.0 | 2250 | 0.4674 | 0.8619 |
| 0.0452 | 7.0 | 2625 | 0.5334 | 0.8602 |
| 0.0373 | 8.0 | 3000 | 0.6077 | 0.8619 |
| 0.0111 | 9.0 | 3375 | 0.6364 | 0.8769 |
| 0.0018 | 10.0 | 3750 | 0.7083 | 0.8636 |
| 0.0038 | 11.0 | 4125 | 0.7404 | 0.8752 |
| 0.0175 | 12.0 | 4500 | 0.8300 | 0.8719 |
| 0.0012 | 13.0 | 4875 | 0.8986 | 0.8652 |
| 0.0087 | 14.0 | 5250 | 0.8825 | 0.8686 |
| 0.004 | 15.0 | 5625 | 0.8822 | 0.8785 |
| 0.0001 | 16.0 | 6000 | 0.9237 | 0.8735 |
| 0.0162 | 17.0 | 6375 | 0.9830 | 0.8619 |
| 0.0 | 18.0 | 6750 | 1.0120 | 0.8702 |
| 0.0 | 19.0 | 7125 | 1.0192 | 0.8719 |
| 0.0001 | 20.0 | 7500 | 0.9781 | 0.8735 |
| 0.0 | 21.0 | 7875 | 1.0188 | 0.8702 |
| 0.0 | 22.0 | 8250 | 0.9776 | 0.8735 |
| 0.0 | 23.0 | 8625 | 1.0494 | 0.8702 |
| 0.0 | 24.0 | 9000 | 0.9531 | 0.8752 |
| 0.0 | 25.0 | 9375 | 1.0293 | 0.8719 |
| 0.0 | 26.0 | 9750 | 1.0427 | 0.8652 |
| 0.0 | 27.0 | 10125 | 1.0483 | 0.8719 |
| 0.0 | 28.0 | 10500 | 1.0202 | 0.8735 |
| 0.0 | 29.0 | 10875 | 1.0779 | 0.8686 |
| 0.0 | 30.0 | 11250 | 1.0065 | 0.8719 |
| 0.0018 | 31.0 | 11625 | 1.0762 | 0.8702 |
| 0.0202 | 32.0 | 12000 | 1.0874 | 0.8669 |
| 0.0024 | 33.0 | 12375 | 1.0366 | 0.8735 |
| 0.0 | 34.0 | 12750 | 1.1165 | 0.8686 |
| 0.0 | 35.0 | 13125 | 1.0244 | 0.8752 |
| 0.0 | 36.0 | 13500 | 1.1014 | 0.8719 |
| 0.0 | 37.0 | 13875 | 1.0995 | 0.8702 |
| 0.0 | 38.0 | 14250 | 1.1070 | 0.8719 |
| 0.0 | 39.0 | 14625 | 1.0209 | 0.8769 |
| 0.0048 | 40.0 | 15000 | 1.0540 | 0.8752 |
| 0.0 | 41.0 | 15375 | 1.0624 | 0.8752 |
| 0.0015 | 42.0 | 15750 | 1.0637 | 0.8752 |
| 0.0013 | 43.0 | 16125 | 1.0536 | 0.8752 |
| 0.0013 | 44.0 | 16500 | 1.0479 | 0.8752 |
| 0.0013 | 45.0 | 16875 | 1.0540 | 0.8752 |
| 0.0 | 46.0 | 17250 | 1.0694 | 0.8752 |
| 0.0016 | 47.0 | 17625 | 1.0601 | 0.8752 |
| 0.0 | 48.0 | 18000 | 1.0596 | 0.8752 |
| 0.0013 | 49.0 | 18375 | 1.0574 | 0.8752 |
| 0.0012 | 50.0 | 18750 | 1.0539 | 0.8752 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
w11wo/deberta-v3-base-lora-isarcasm
|
w11wo
| 2023-12-14T15:24:35Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"region:us"
] | null | 2023-12-14T14:54:02Z |
---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-lora-isarcasm
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. -->
# deberta-v3-base-lora-isarcasm
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6943
- Accuracy: 0.1770
- F1: 0.3008
- Precision: 0.1770
- Recall: 1.0
## 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: 0.005
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 54 | 0.7069 | 0.8286 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 108 | 0.6931 | 0.8286 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 162 | 0.6986 | 0.8286 | 0.0 | 0.0 | 0.0 |
| No log | 4.0 | 216 | 0.6946 | 0.1714 | 0.2927 | 0.1714 | 1.0 |
| No log | 5.0 | 270 | 0.6939 | 0.1714 | 0.2927 | 0.1714 | 1.0 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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will010321/dqn-BeamRiderNoFrameskip-v4
|
will010321
| 2023-12-14T14:57:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:57:21Z |
Entry not found
|
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] |
FAD28/gbert_extend
|
FAD28
| 2023-12-17T21:34:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T14:57:22Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gbert_extend
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. -->
# gbert_extend
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0333
- Precision: 0.8809
- Recall: 0.9274
- F1: 0.9036
- Accuracy: 0.9897
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0389 | 1.0 | 1500 | 0.0334 | 0.8621 | 0.9269 | 0.8933 | 0.9892 |
| 0.0228 | 2.0 | 3000 | 0.0333 | 0.8809 | 0.9274 | 0.9036 | 0.9897 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
jiyoun-kim/vit-base-patch16-224-in21k-finetuned-r_1food101
|
jiyoun-kim
| 2023-12-14T15:03:44Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T14:58:16Z |
Entry not found
|
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henriquehenry/ProfessorGirafales
|
henriquehenry
| 2023-12-14T15:00:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:59:31Z |
Entry not found
|
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dhanushreddy29/shirts_dreambooth
|
dhanushreddy29
| 2023-12-14T14:59:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T14:59:31Z |
Entry not found
|
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tomjackson2023/test
|
tomjackson2023
| 2023-12-15T12:46:54Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2023-12-14T15:01:18Z |
Entry not found
|
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jtjt520j/test_Qwen_1.8b_txt2sql
|
jtjt520j
| 2023-12-14T15:02:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:02:03Z |
Invalid username or password.
|
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CrisVelasquez/speecht5_tts_voxpopuli_nl
|
CrisVelasquez
| 2023-12-14T15:02:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:02:07Z |
Entry not found
|
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ChatRDM/mistral-15epochs
|
ChatRDM
| 2023-12-14T15:06:48Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T15:02:37Z |
---
license: mit
---
|
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] |
Prosoft82/Ahkam12
|
Prosoft82
| 2023-12-14T15:04:31Z | 0 | 0 | null |
[
"dataset:berkeley-nest/Nectar",
"dataset:gaia-benchmark/GAIA",
"license:openrail",
"region:us"
] | null | 2023-12-14T15:03:21Z |
---
license: openrail
datasets:
- berkeley-nest/Nectar
- gaia-benchmark/GAIA
---
|
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Urofarm/urofarmperu
|
Urofarm
| 2023-12-14T15:04:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:03:30Z |
Urofarm: cápsula, Precio, Opiniones, Ingredientes, reseñas, Píldora, costo! Peru
Urofarm es un sistema de bienestar de la próstata que ayuda a las personas a controlar la contaminación de la orina y a trabajar en el bienestar general. Urofarm Peru!
Urofarm ¡¡Compra ahora!! ¡Haga clic en el enlace a continuación para obtener más información y obtenga un 50 % de descuento ahora! apresúrate !!
leer más: https://www.boxdrug.com/UrofPe
➢Nombre del producto: Urofarm
➢Categoría - Salud de la próstata
➢Principales beneficios: mantener la salud de la próstata
➢ Composición — Compuesto Orgánico Natural
➢ Efectos secundarios—NA
➢Calificación final: — 4.8
➢ Disponibilidad: en línea
➢Ofertas y descuentos; ¡AHORRA HOY! COMPRAR AHORA PARA comprar ¡¡¡OFERTA ESPECIAL!!!
¿Qué es Urofarm?
Urofarm Peru es una mejora lógica de la dieta elaborada con diferentes ingredientes, incluidos nutrientes, minerales, especias y mezclas naturales, que se espera que mejore y se mantenga al día con la fuerza del órgano prostático. El órgano prostático es una parte del sistema conceptual masculino y puede ser propenso a sufrir problemas como expansión o irritación, lo que puede provocar efectos secundarios urinarios y otros problemas a medida que los hombres envejecen.
Urofarm ¡¡Compra ahora!! ¡Haga clic en el enlace a continuación para obtener más información y obtenga un 50 % de descuento ahora! apresúrate !!
leer más: https://www.boxdrug.com/UrofPe
Urofarm Urofarm Píldora Urofarm cápsula Urofarm tableta Urofarm Precio Urofarm Opiniones Urofarm reseñas Urofarm Ingredientes Urofarm Beneficios Urofarm Efectos secundarios Urofarm precio de la capsula Urofarm Reseñas de cápsulas Urofarm composición Urofarm queja Urofarm Donde comprar Urofarm Cómo utilizar Urofarm costo Urofarm obras Urofarm foro Urofarm original Urofarm farmacia
https://www.boxdrug.com/UrofPe
https://sites.google.com/view/urofarm-peru/home
https://healthtoned.blogspot.com/2023/12/urofarm-peru.html
https://www.weddingwire.com/website/urofarm-and-peru
https://medium.com/@healthytalk24x7/urofarm-peru-6c2f195e3d5b
https://medium.com/@healthytalk24x7/urofarm-c%C3%A1psula-precio-opiniones-ingredientes-rese%C3%B1as-p%C3%ADldora-costo-peru-1d5e12658838
https://infogram.com/urofarm-peru-1h9j6qg35mpzv4g?live
https://softisenilspain.hashnode.dev/urofarm-peru
https://urofarmperu.company.site/
https://sway.office.com/uccuNE97DAS4tfUl
https://gamma.app/docs/Urofarm-Peru-lhxl1ix73hmf1jb?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/urofarm-peru
https://groups.google.com/g/snshine/c/-h8xjHkVAxI
https://www.sunflower-cissp.com/glossary/cissp/7252/urofarm-peru
https://replit.com/@urofarmperu
|
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] |
Umbrellos/nitebearmsm
|
Umbrellos
| 2023-12-17T15:26:10Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T15:03:34Z |
---
license: openrail
---
|
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mhobby1979/q-FrozenLake-v1-4x4-noSlippery
|
mhobby1979
| 2023-12-14T15:03:40Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T15:03:38Z |
---
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="mhobby1979/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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alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-02
|
alinerodrigues
| 2023-12-14T20:08:01Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-14T15:03:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-02
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. -->
# wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-02
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1200
- Wer: 0.0899
- Cer: 0.0254
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 26.4293 | 1.0 | 67 | 3.4414 | 1.0 | 1.0 |
| 7.9769 | 2.0 | 134 | 2.9984 | 1.0 | 1.0 |
| 3.0417 | 3.0 | 201 | 2.9436 | 1.0 | 1.0 |
| 3.0417 | 4.0 | 268 | 2.9027 | 1.0 | 1.0 |
| 2.9268 | 5.0 | 335 | 2.8924 | 1.0 | 1.0 |
| 2.8996 | 6.0 | 402 | 2.8828 | 1.0 | 1.0 |
| 2.8996 | 7.0 | 469 | 2.7512 | 1.0 | 1.0 |
| 2.8007 | 8.0 | 536 | 1.4882 | 0.9308 | 0.3579 |
| 1.7472 | 9.0 | 603 | 0.5752 | 0.3096 | 0.0792 |
| 1.7472 | 10.0 | 670 | 0.3713 | 0.2030 | 0.0519 |
| 0.7469 | 11.0 | 737 | 0.3007 | 0.1633 | 0.0438 |
| 0.5658 | 12.0 | 804 | 0.2614 | 0.1563 | 0.0411 |
| 0.5658 | 13.0 | 871 | 0.2246 | 0.1359 | 0.0362 |
| 0.4483 | 14.0 | 938 | 0.2101 | 0.1235 | 0.0337 |
| 0.3807 | 15.0 | 1005 | 0.1966 | 0.1228 | 0.0339 |
| 0.3807 | 16.0 | 1072 | 0.1845 | 0.1114 | 0.0312 |
| 0.352 | 17.0 | 1139 | 0.1803 | 0.1072 | 0.0319 |
| 0.3203 | 18.0 | 1206 | 0.1737 | 0.1045 | 0.0301 |
| 0.3203 | 19.0 | 1273 | 0.1614 | 0.1017 | 0.0296 |
| 0.2915 | 20.0 | 1340 | 0.1501 | 0.1013 | 0.0288 |
| 0.2897 | 21.0 | 1407 | 0.1532 | 0.0979 | 0.0287 |
| 0.2897 | 22.0 | 1474 | 0.1478 | 0.0993 | 0.0281 |
| 0.2601 | 23.0 | 1541 | 0.1445 | 0.0979 | 0.0283 |
| 0.2427 | 24.0 | 1608 | 0.1434 | 0.1017 | 0.0285 |
| 0.2427 | 25.0 | 1675 | 0.1425 | 0.0965 | 0.0277 |
| 0.2385 | 26.0 | 1742 | 0.1423 | 0.0986 | 0.0282 |
| 0.229 | 27.0 | 1809 | 0.1427 | 0.0969 | 0.0275 |
| 0.229 | 28.0 | 1876 | 0.1368 | 0.0948 | 0.0266 |
| 0.2084 | 29.0 | 1943 | 0.1383 | 0.0975 | 0.0274 |
| 0.2027 | 30.0 | 2010 | 0.1370 | 0.0948 | 0.0274 |
| 0.2027 | 31.0 | 2077 | 0.1386 | 0.0969 | 0.0275 |
| 0.1956 | 32.0 | 2144 | 0.1337 | 0.0979 | 0.0277 |
| 0.2027 | 33.0 | 2211 | 0.1332 | 0.0955 | 0.0273 |
| 0.2027 | 34.0 | 2278 | 0.1361 | 0.0930 | 0.0274 |
| 0.1834 | 35.0 | 2345 | 0.1345 | 0.0910 | 0.0268 |
| 0.1874 | 36.0 | 2412 | 0.1311 | 0.0969 | 0.0275 |
| 0.1874 | 37.0 | 2479 | 0.1331 | 0.0913 | 0.0263 |
| 0.1739 | 38.0 | 2546 | 0.1296 | 0.0886 | 0.0257 |
| 0.1671 | 39.0 | 2613 | 0.1321 | 0.0906 | 0.0255 |
| 0.1671 | 40.0 | 2680 | 0.1306 | 0.0927 | 0.0260 |
| 0.1695 | 41.0 | 2747 | 0.1336 | 0.0937 | 0.0270 |
| 0.1608 | 42.0 | 2814 | 0.1291 | 0.0861 | 0.0254 |
| 0.1608 | 43.0 | 2881 | 0.1328 | 0.0910 | 0.0265 |
| 0.1577 | 44.0 | 2948 | 0.1300 | 0.0913 | 0.0259 |
| 0.1674 | 45.0 | 3015 | 0.1297 | 0.0906 | 0.0263 |
| 0.1674 | 46.0 | 3082 | 0.1279 | 0.0882 | 0.0261 |
| 0.1658 | 47.0 | 3149 | 0.1287 | 0.0924 | 0.0258 |
| 0.1588 | 48.0 | 3216 | 0.1275 | 0.0906 | 0.0266 |
| 0.1588 | 49.0 | 3283 | 0.1303 | 0.0917 | 0.0264 |
| 0.1403 | 50.0 | 3350 | 0.1267 | 0.0913 | 0.0264 |
| 0.1686 | 51.0 | 3417 | 0.1265 | 0.0896 | 0.0259 |
| 0.1686 | 52.0 | 3484 | 0.1286 | 0.0930 | 0.0265 |
| 0.1427 | 53.0 | 3551 | 0.1261 | 0.0899 | 0.0264 |
| 0.152 | 54.0 | 3618 | 0.1288 | 0.0889 | 0.0259 |
| 0.152 | 55.0 | 3685 | 0.1259 | 0.0882 | 0.0257 |
| 0.1516 | 56.0 | 3752 | 0.1263 | 0.0913 | 0.0262 |
| 0.1394 | 57.0 | 3819 | 0.1239 | 0.0910 | 0.0257 |
| 0.1394 | 58.0 | 3886 | 0.1236 | 0.0872 | 0.0254 |
| 0.1514 | 59.0 | 3953 | 0.1253 | 0.0903 | 0.0259 |
| 0.1252 | 60.0 | 4020 | 0.1217 | 0.0889 | 0.0257 |
| 0.1252 | 61.0 | 4087 | 0.1212 | 0.0899 | 0.0256 |
| 0.143 | 62.0 | 4154 | 0.1228 | 0.0896 | 0.0252 |
| 0.139 | 63.0 | 4221 | 0.1210 | 0.0886 | 0.0251 |
| 0.139 | 64.0 | 4288 | 0.1217 | 0.0896 | 0.0256 |
| 0.1398 | 65.0 | 4355 | 0.1225 | 0.0899 | 0.0256 |
| 0.1348 | 66.0 | 4422 | 0.1222 | 0.0875 | 0.0252 |
| 0.1348 | 67.0 | 4489 | 0.1231 | 0.0875 | 0.0254 |
| 0.1284 | 68.0 | 4556 | 0.1200 | 0.0899 | 0.0254 |
| 0.1307 | 69.0 | 4623 | 0.1231 | 0.0879 | 0.0254 |
| 0.1307 | 70.0 | 4690 | 0.1231 | 0.0879 | 0.0252 |
| 0.1251 | 71.0 | 4757 | 0.1225 | 0.0872 | 0.0256 |
| 0.136 | 72.0 | 4824 | 0.1218 | 0.0889 | 0.0254 |
| 0.136 | 73.0 | 4891 | 0.1222 | 0.0868 | 0.0246 |
| 0.1394 | 74.0 | 4958 | 0.1210 | 0.0920 | 0.0264 |
| 0.127 | 75.0 | 5025 | 0.1218 | 0.0913 | 0.0257 |
| 0.127 | 76.0 | 5092 | 0.1221 | 0.0892 | 0.0252 |
| 0.1223 | 77.0 | 5159 | 0.1229 | 0.0882 | 0.0256 |
| 0.1247 | 78.0 | 5226 | 0.1216 | 0.0882 | 0.0257 |
| 0.1247 | 79.0 | 5293 | 0.1225 | 0.0899 | 0.0259 |
| 0.131 | 80.0 | 5360 | 0.1224 | 0.0889 | 0.0257 |
| 0.1127 | 81.0 | 5427 | 0.1244 | 0.0906 | 0.0258 |
| 0.1127 | 82.0 | 5494 | 0.1226 | 0.0886 | 0.0256 |
| 0.1154 | 83.0 | 5561 | 0.1222 | 0.0899 | 0.0258 |
| 0.1233 | 84.0 | 5628 | 0.1236 | 0.0889 | 0.0257 |
| 0.1233 | 85.0 | 5695 | 0.1219 | 0.0903 | 0.0261 |
| 0.1299 | 86.0 | 5762 | 0.1222 | 0.0875 | 0.0257 |
| 0.112 | 87.0 | 5829 | 0.1215 | 0.0903 | 0.0261 |
| 0.112 | 88.0 | 5896 | 0.1229 | 0.0913 | 0.0259 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
|
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jiyoun-kim/vit-base-patch16-224-in21k-finetuned-lora-r_1food101
|
jiyoun-kim
| 2023-12-14T16:59:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:07:12Z |
Entry not found
|
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UphamProjects/whisper-large-en
|
UphamProjects
| 2023-12-14T15:09:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:09:38Z |
Entry not found
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amiepsa/tinypixel-Llama-2-7B-bf16-sharded-ft-prompts
|
amiepsa
| 2023-12-14T15:10:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-12-14T15:09:58Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.2.dev0
|
[
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] |
AlexWortega/hh_search
|
AlexWortega
| 2023-12-14T15:11:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-14T15:10:28Z |
Entry not found
|
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aDong/t5-base
|
aDong
| 2023-12-14T15:10:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:10:43Z |
Entry not found
|
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] |
Felladrin/mlc-chat-llama-160m-q4f32_1
|
Felladrin
| 2023-12-14T15:16:41Z | 0 | 0 | null |
[
"en",
"dataset:wikipedia",
"license:other",
"region:us"
] | null | 2023-12-14T15:12:16Z |
---
license: other
language:
- en
datasets:
- wikipedia
---
# JackFram's llama-160m for Web-LLM
This is a compiled version of [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m) for [MLC Web-LLM](https://webllm.mlc.ai/), using `q4f32_1` quantization.
|
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bio-datasets/Mistral-7B-Instruct-v0.1-blooming-thunder-195-epoch-60
|
bio-datasets
| 2023-12-14T15:15:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T15:14:59Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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srivhash/chat_model
|
srivhash
| 2023-12-14T15:16:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:15:18Z |
Entry not found
|
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damerajee/OpenHathi-7B-base.gguf
|
damerajee
| 2023-12-14T17:14:54Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T15:18:00Z |
---
license: apache-2.0
---
|
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danangharis/falcon-7b-pythonv3
|
danangharis
| 2023-12-14T15:19:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:19:53Z |
Entry not found
|
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GridTST/solar_720_192
|
GridTST
| 2023-12-14T15:24:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:08Z |
Entry not found
|
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GridTST/solar_720_336
|
GridTST
| 2023-12-14T15:24:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:11Z |
Entry not found
|
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GridTST/solar_720_720
|
GridTST
| 2023-12-14T15:24:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:13Z |
Entry not found
|
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] |
GridTST/weather_720_192
|
GridTST
| 2023-12-14T15:24:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:19Z |
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GridTST/weather_720_336
|
GridTST
| 2023-12-14T15:24:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:22Z |
Entry not found
|
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GridTST/weather_720_720
|
GridTST
| 2023-12-14T15:24:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:25Z |
Entry not found
|
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GridTST/electricity_720_192
|
GridTST
| 2023-12-14T15:24:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:32Z |
Entry not found
|
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GridTST/electricity_720_336
|
GridTST
| 2023-12-14T15:24:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T15:24:34Z |
Entry not found
|
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GBjorn/rl_course_vizdoom_health_gathering_supreme
|
GBjorn
| 2023-12-14T15:41:56Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T15:25:19Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.02 +/- 4.56
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r GBjorn/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
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SumT5v6/my_awesome_billsum_model
|
SumT5v6
| 2023-12-14T15:25:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:25:41Z |
Entry not found
|
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] |
w11wo/roberta-base-lora-isarcasm
|
w11wo
| 2023-12-14T15:27:25Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-14T15:25:52Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base-lora-isarcasm
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. -->
# roberta-base-lora-isarcasm
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6943
- Accuracy: 0.1770
- F1: 0.3008
- Precision: 0.1770
- Recall: 1.0
## 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: 0.005
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 54 | 0.7075 | 0.8286 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 108 | 0.6939 | 0.8286 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 162 | 0.6933 | 0.1714 | 0.2927 | 0.1714 | 1.0 |
| No log | 4.0 | 216 | 0.6958 | 0.1714 | 0.2927 | 0.1714 | 1.0 |
| No log | 5.0 | 270 | 0.6938 | 0.1714 | 0.2927 | 0.1714 | 1.0 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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] |
w11wo/deberta-v3-base-p-tuning-isarcasm
|
w11wo
| 2023-12-14T15:40:32Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"region:us"
] | null | 2023-12-14T15:29:18Z |
---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-p-tuning-isarcasm
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. -->
# deberta-v3-base-p-tuning-isarcasm
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6620
- Accuracy: 0.4476
- F1: 0.3603
- Precision: 0.2266
- Recall: 0.8790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 108 | 0.6889 | 0.3143 | 0.2941 | 0.1786 | 0.8333 |
| No log | 2.0 | 216 | 0.7020 | 0.8286 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 324 | 0.6725 | 0.6286 | 0.2353 | 0.1818 | 0.3333 |
| No log | 4.0 | 432 | 0.7169 | 0.1714 | 0.2927 | 0.1714 | 1.0 |
| 0.7087 | 5.0 | 540 | 0.6925 | 0.5429 | 0.3846 | 0.25 | 0.8333 |
| 0.7087 | 6.0 | 648 | 0.6991 | 0.1714 | 0.2927 | 0.1714 | 1.0 |
| 0.7087 | 7.0 | 756 | 0.6780 | 0.8286 | 0.0 | 0.0 | 0.0 |
| 0.7087 | 8.0 | 864 | 0.6851 | 0.8286 | 0.0 | 0.0 | 0.0 |
| 0.7087 | 9.0 | 972 | 0.6712 | 0.8286 | 0.0 | 0.0 | 0.0 |
| 0.7055 | 10.0 | 1080 | 0.6767 | 0.3143 | 0.3333 | 0.2 | 1.0 |
| 0.7055 | 11.0 | 1188 | 0.6720 | 0.5714 | 0.4000 | 0.2632 | 0.8333 |
| 0.7055 | 12.0 | 1296 | 0.6710 | 0.3714 | 0.3529 | 0.2143 | 1.0 |
| 0.7055 | 13.0 | 1404 | 0.6676 | 0.4857 | 0.3077 | 0.2 | 0.6667 |
| 0.6916 | 14.0 | 1512 | 0.6735 | 0.3714 | 0.3125 | 0.1923 | 0.8333 |
| 0.6916 | 15.0 | 1620 | 0.6762 | 0.3714 | 0.3529 | 0.2143 | 1.0 |
| 0.6916 | 16.0 | 1728 | 0.6642 | 0.6286 | 0.3158 | 0.2308 | 0.5 |
| 0.6916 | 17.0 | 1836 | 0.6609 | 0.5143 | 0.32 | 0.2105 | 0.6667 |
| 0.6916 | 18.0 | 1944 | 0.6632 | 0.4571 | 0.2963 | 0.1905 | 0.6667 |
| 0.6798 | 19.0 | 2052 | 0.6640 | 0.4 | 0.2759 | 0.1739 | 0.6667 |
| 0.6798 | 20.0 | 2160 | 0.6644 | 0.4 | 0.2759 | 0.1739 | 0.6667 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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polpanka/Fhjj
|
polpanka
| 2023-12-14T15:30:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:30:24Z |
Entry not found
|
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yimingzhang/zephyr-7b-dpo-full
|
yimingzhang
| 2023-12-16T04:12:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T15:36:25Z |
---
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-dpo-full
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-dpo-full
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6825
- Rewards/chosen: -2.3281
- Rewards/rejected: -6.0108
- Rewards/accuracies: 0.7620
- Rewards/margins: 3.6827
- Logps/rejected: -344.2271
- Logps/chosen: -376.1471
- Logits/rejected: -2.7465
- Logits/chosen: -2.7672
## 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-07
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 8
- 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 | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5513 | 0.1 | 100 | 0.5431 | 0.4641 | -0.1955 | 0.7660 | 0.6596 | -286.0747 | -348.2250 | -2.9049 | -2.9199 |
| 0.5322 | 0.21 | 200 | 0.5251 | 0.4311 | -0.7452 | 0.7680 | 1.1764 | -291.5714 | -348.5544 | -2.9307 | -2.9476 |
| 0.602 | 0.31 | 300 | 0.5439 | 0.5364 | -0.6172 | 0.7440 | 1.1536 | -290.2913 | -347.5018 | -2.9635 | -2.9952 |
| 0.5809 | 0.41 | 400 | 0.5436 | 0.3841 | -0.8922 | 0.7600 | 1.2762 | -293.0408 | -349.0252 | -2.9205 | -2.9360 |
| 0.5164 | 0.52 | 500 | 0.5406 | 0.3006 | -1.1405 | 0.7660 | 1.4410 | -295.5239 | -349.8603 | -3.0117 | -3.0160 |
| 0.5957 | 0.62 | 600 | 0.5336 | 0.1952 | -1.0296 | 0.7380 | 1.2247 | -294.4149 | -350.9140 | -2.9353 | -2.9459 |
| 0.6516 | 0.72 | 700 | 0.5292 | 0.2224 | -1.0543 | 0.7520 | 1.2767 | -294.6618 | -350.6416 | -2.9889 | -2.9857 |
| 0.5353 | 0.83 | 800 | 0.5145 | 0.1103 | -1.4103 | 0.7560 | 1.5206 | -298.2225 | -351.7629 | -2.9116 | -2.9171 |
| 0.5293 | 0.93 | 900 | 0.5146 | 0.1384 | -1.3157 | 0.7620 | 1.4541 | -297.2768 | -351.4819 | -2.9463 | -2.9537 |
| 0.1078 | 1.03 | 1000 | 0.5121 | 0.3042 | -1.6532 | 0.7780 | 1.9574 | -300.6510 | -349.8238 | -2.8942 | -2.9057 |
| 0.0928 | 1.14 | 1100 | 0.5322 | 0.0212 | -2.1481 | 0.7780 | 2.1692 | -305.5999 | -352.6541 | -2.8911 | -2.9062 |
| 0.1295 | 1.24 | 1200 | 0.5384 | -0.2033 | -2.3411 | 0.7760 | 2.1378 | -307.5298 | -354.8984 | -2.9192 | -2.9265 |
| 0.1093 | 1.34 | 1300 | 0.5469 | -0.2341 | -2.5125 | 0.7860 | 2.2784 | -309.2440 | -355.2066 | -2.8709 | -2.8807 |
| 0.1198 | 1.45 | 1400 | 0.5245 | -0.3902 | -2.4518 | 0.7720 | 2.0616 | -308.6370 | -356.7679 | -2.8527 | -2.8626 |
| 0.1122 | 1.55 | 1500 | 0.5524 | -0.6778 | -3.1489 | 0.7860 | 2.4710 | -315.6078 | -359.6443 | -2.8319 | -2.8394 |
| 0.11 | 1.65 | 1600 | 0.5355 | -0.4967 | -2.7524 | 0.7780 | 2.2557 | -311.6435 | -357.8331 | -2.8461 | -2.8559 |
| 0.1092 | 1.76 | 1700 | 0.5581 | -0.6067 | -3.1170 | 0.7800 | 2.5102 | -315.2888 | -358.9333 | -2.8502 | -2.8601 |
| 0.0958 | 1.86 | 1800 | 0.5647 | -0.7279 | -3.2484 | 0.7760 | 2.5205 | -316.6035 | -360.1446 | -2.8474 | -2.8541 |
| 0.122 | 1.96 | 1900 | 0.5520 | -0.7738 | -3.0618 | 0.7780 | 2.2880 | -314.7370 | -360.6041 | -2.8707 | -2.8743 |
| 0.0242 | 2.07 | 2000 | 0.6111 | -1.3091 | -4.2093 | 0.7640 | 2.9002 | -326.2119 | -365.9565 | -2.8475 | -2.8605 |
| 0.017 | 2.17 | 2100 | 0.6473 | -1.7027 | -4.9317 | 0.7620 | 3.2289 | -333.4358 | -369.8930 | -2.8138 | -2.8309 |
| 0.0153 | 2.27 | 2200 | 0.6658 | -2.0049 | -5.3885 | 0.7700 | 3.3837 | -338.0045 | -372.9144 | -2.7872 | -2.8057 |
| 0.0215 | 2.38 | 2300 | 0.6722 | -1.7556 | -5.2164 | 0.7780 | 3.4609 | -336.2837 | -370.4218 | -2.8210 | -2.8406 |
| 0.0128 | 2.48 | 2400 | 0.6772 | -2.0766 | -5.6174 | 0.7700 | 3.5408 | -340.2928 | -373.6317 | -2.7783 | -2.8011 |
| 0.015 | 2.58 | 2500 | 0.6893 | -2.2855 | -5.9178 | 0.7740 | 3.6323 | -343.2976 | -375.7208 | -2.7657 | -2.7877 |
| 0.0118 | 2.69 | 2600 | 0.6937 | -2.5052 | -6.1919 | 0.7660 | 3.6867 | -346.0385 | -377.9179 | -2.7766 | -2.7963 |
| 0.0127 | 2.79 | 2700 | 0.6868 | -2.4883 | -6.1501 | 0.7700 | 3.6618 | -345.6202 | -377.7486 | -2.7437 | -2.7652 |
| 0.0149 | 2.89 | 2800 | 0.6852 | -2.3777 | -6.0421 | 0.7700 | 3.6644 | -344.5401 | -376.6426 | -2.7409 | -2.7623 |
| 0.0105 | 3.0 | 2900 | 0.6832 | -2.3298 | -6.0137 | 0.7640 | 3.6839 | -344.2563 | -376.1639 | -2.7455 | -2.7663 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
Ichsan2895/Merak-7B-v5-PROTOTYPE1-GGUF
|
Ichsan2895
| 2023-12-14T16:38:38Z | 0 | 1 | null |
[
"gguf",
"text-generation",
"id",
"en",
"dataset:wikipedia",
"dataset:Ichsan2895/OASST_Top1_Indonesian",
"dataset:Ichsan2895/alpaca-gpt4-indonesian",
"arxiv:2306.02707",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
text-generation
| 2023-12-14T15:37:43Z |
---
datasets:
- wikipedia
- Ichsan2895/OASST_Top1_Indonesian
- Ichsan2895/alpaca-gpt4-indonesian
language:
- id
- en
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://huggingface.co/Ichsan2895/Merak-7B-v4/resolve/main/FINAL_LOGO/6.png" alt="MERAK" style="width: 50%; min-width: 100px; display: block; margin: auto;">
</div>
# THIS IS 1st PROTOTYPE OF MERAK-7B-v5!
Merak-7B is the Large Language Model of Indonesian Language
This model is based on [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and fine tuned by some of Indonesia Wikipedia articles that I cleaned before.
Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM. We also use DPO-Trainer for RLHF with TRL library..
Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike.
Big thanks to all my friends and communities that help to build our first model. Thanks for Axolotl for a great fine tuning tool which designed to streamline the fine-tuning of various AI models.
Feel free, to ask me about the model and please share the news on your social media.
## CITATION
```
@software{lian2023mistralorca1
title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca},
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
@misc{vonwerra2022trl,
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang},
title = {TRL: Transformer Reinforcement Learning},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/trl}}
}
@article{dettmers2023qlora,
title = {QLoRA: Efficient Finetuning of Quantized LLMs},
author = {Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal = {arXiv preprint arXiv:2305.14314},
year = {2023}
}
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## HOW TO CITE THIS PROJECT
If you use the Merak-7B model in your research or project, please cite it as:
```
@article{Merak,
title={Merak-7B: The LLM for Bahasa Indonesia},
author={Muhammad Ichsan},
publisher={Hugging Face}
journal={Hugging Face Repository},
year={2023}
}
```
|
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] |
hkivancoral/smids_5x_beit_base_adamax_0001_fold5
|
hkivancoral
| 2023-12-14T17:41:39Z | 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
| 2023-12-14T15:40:43Z |
---
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_adamax_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: 0.9066666666666666
---
<!-- 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_adamax_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: 1.0130
- Accuracy: 0.9067
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2609 | 1.0 | 375 | 0.4392 | 0.83 |
| 0.2435 | 2.0 | 750 | 0.3135 | 0.87 |
| 0.1555 | 3.0 | 1125 | 0.3719 | 0.885 |
| 0.1553 | 4.0 | 1500 | 0.4465 | 0.89 |
| 0.1 | 5.0 | 1875 | 0.4697 | 0.88 |
| 0.0953 | 6.0 | 2250 | 0.5131 | 0.895 |
| 0.1058 | 7.0 | 2625 | 0.5042 | 0.9033 |
| 0.0095 | 8.0 | 3000 | 0.6171 | 0.9033 |
| 0.0607 | 9.0 | 3375 | 0.5787 | 0.89 |
| 0.0225 | 10.0 | 3750 | 0.6004 | 0.8983 |
| 0.0522 | 11.0 | 4125 | 0.6713 | 0.89 |
| 0.0354 | 12.0 | 4500 | 0.8122 | 0.9 |
| 0.0281 | 13.0 | 4875 | 0.7547 | 0.8833 |
| 0.0538 | 14.0 | 5250 | 0.6866 | 0.88 |
| 0.0498 | 15.0 | 5625 | 0.7040 | 0.8783 |
| 0.0034 | 16.0 | 6000 | 0.6946 | 0.8883 |
| 0.0375 | 17.0 | 6375 | 0.7067 | 0.88 |
| 0.0372 | 18.0 | 6750 | 0.8461 | 0.875 |
| 0.0081 | 19.0 | 7125 | 0.5733 | 0.9 |
| 0.025 | 20.0 | 7500 | 0.6029 | 0.9167 |
| 0.0105 | 21.0 | 7875 | 0.6183 | 0.885 |
| 0.0342 | 22.0 | 8250 | 0.5174 | 0.9217 |
| 0.0291 | 23.0 | 8625 | 0.5708 | 0.9083 |
| 0.0316 | 24.0 | 9000 | 0.7866 | 0.8833 |
| 0.0002 | 25.0 | 9375 | 0.8031 | 0.895 |
| 0.0548 | 26.0 | 9750 | 0.7954 | 0.8933 |
| 0.0233 | 27.0 | 10125 | 0.8188 | 0.895 |
| 0.0003 | 28.0 | 10500 | 0.7997 | 0.9 |
| 0.0063 | 29.0 | 10875 | 0.8708 | 0.89 |
| 0.0025 | 30.0 | 11250 | 0.8386 | 0.8967 |
| 0.0008 | 31.0 | 11625 | 0.8998 | 0.8833 |
| 0.0 | 32.0 | 12000 | 0.9085 | 0.8967 |
| 0.0005 | 33.0 | 12375 | 0.7875 | 0.905 |
| 0.0 | 34.0 | 12750 | 0.9329 | 0.8983 |
| 0.0001 | 35.0 | 13125 | 0.7985 | 0.9017 |
| 0.0 | 36.0 | 13500 | 0.8234 | 0.8983 |
| 0.0 | 37.0 | 13875 | 0.8947 | 0.9033 |
| 0.005 | 38.0 | 14250 | 0.9096 | 0.9067 |
| 0.0291 | 39.0 | 14625 | 0.9293 | 0.9117 |
| 0.0006 | 40.0 | 15000 | 0.8881 | 0.9117 |
| 0.0 | 41.0 | 15375 | 1.0854 | 0.8967 |
| 0.0003 | 42.0 | 15750 | 0.9486 | 0.8983 |
| 0.0 | 43.0 | 16125 | 0.9324 | 0.91 |
| 0.0 | 44.0 | 16500 | 0.9408 | 0.9083 |
| 0.0 | 45.0 | 16875 | 1.0069 | 0.9067 |
| 0.0 | 46.0 | 17250 | 1.0803 | 0.9 |
| 0.013 | 47.0 | 17625 | 1.0261 | 0.905 |
| 0.0 | 48.0 | 18000 | 1.0163 | 0.9067 |
| 0.0 | 49.0 | 18375 | 1.0208 | 0.9083 |
| 0.0021 | 50.0 | 18750 | 1.0130 | 0.9067 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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lilpotat/cef
|
lilpotat
| 2023-12-14T16:07:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:41:34Z |
Entry not found
|
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Korventenn/dp-big-comp-o
|
Korventenn
| 2023-12-14T16:43:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2023-12-14T15:45:00Z |
Entry not found
|
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] |
KhoiC/esm2_t12_35M_UR50D-finetuned-localization-oringinal
|
KhoiC
| 2023-12-14T15:59:13Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T15:45:45Z |
Entry not found
|
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alitolga/roberta-base-peft
|
alitolga
| 2023-12-17T21:07:08Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-14T15:48:23Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-peft
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. -->
# roberta-base-peft
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2749
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6524 | 1.0 | 389 | 0.8285 |
| 0.8325 | 2.0 | 778 | 0.3563 |
| 0.6413 | 3.0 | 1167 | 0.2749 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
TheBloke/Rose-Kimiko-20B-GGUF
|
TheBloke
| 2023-12-14T16:08:10Z | 0 | 1 |
peft
|
[
"peft",
"gguf",
"generated_from_trainer",
"base_model:nRuaif/Rose-Kimiko-20B",
"license:llama2",
"region:us"
] | null | 2023-12-14T15:50:37Z |
---
base_model: nRuaif/Rose-Kimiko-20B
inference: false
library_name: peft
license: llama2
model-index:
- name: Rose-Kimiko
results: []
model_creator: nRuaif
model_name: Rose Kimiko 20B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- generated_from_trainer
---
<!-- 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>
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<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 -->
# Rose Kimiko 20B - GGUF
- Model creator: [nRuaif](https://huggingface.co/nRuaif)
- Original model: [Rose Kimiko 20B](https://huggingface.co/nRuaif/Rose-Kimiko-20B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [nRuaif's Rose Kimiko 20B](https://huggingface.co/nRuaif/Rose-Kimiko-20B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Rose-Kimiko-20B-fp16)
* [nRuaif's original LoRA adapter, which can be merged on to the base model.](https://huggingface.co/nRuaif/Rose-Kimiko-20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [rose-kimiko-20b.Q2_K.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q2_K.gguf) | Q2_K | 2 | 8.31 GB| 10.81 GB | smallest, significant quality loss - not recommended for most purposes |
| [rose-kimiko-20b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q3_K_S.gguf) | Q3_K_S | 3 | 8.66 GB| 11.16 GB | very small, high quality loss |
| [rose-kimiko-20b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q3_K_M.gguf) | Q3_K_M | 3 | 9.70 GB| 12.20 GB | very small, high quality loss |
| [rose-kimiko-20b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q3_K_L.gguf) | Q3_K_L | 3 | 10.63 GB| 13.13 GB | small, substantial quality loss |
| [rose-kimiko-20b.Q4_0.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q4_0.gguf) | Q4_0 | 4 | 11.29 GB| 13.79 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [rose-kimiko-20b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q4_K_S.gguf) | Q4_K_S | 4 | 11.34 GB| 13.84 GB | small, greater quality loss |
| [rose-kimiko-20b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q4_K_M.gguf) | Q4_K_M | 4 | 12.04 GB| 14.54 GB | medium, balanced quality - recommended |
| [rose-kimiko-20b.Q5_0.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q5_0.gguf) | Q5_0 | 5 | 13.77 GB| 16.27 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [rose-kimiko-20b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q5_K_S.gguf) | Q5_K_S | 5 | 13.77 GB| 16.27 GB | large, low quality loss - recommended |
| [rose-kimiko-20b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q5_K_M.gguf) | Q5_K_M | 5 | 14.16 GB| 16.66 GB | large, very low quality loss - recommended |
| [rose-kimiko-20b.Q6_K.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q6_K.gguf) | Q6_K | 6 | 16.41 GB| 18.91 GB | very large, extremely low quality loss |
| [rose-kimiko-20b.Q8_0.gguf](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF/blob/main/rose-kimiko-20b.Q8_0.gguf) | Q8_0 | 8 | 21.25 GB| 23.75 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/Rose-Kimiko-20B-GGUF and below it, a specific filename to download, such as: rose-kimiko-20b.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/Rose-Kimiko-20B-GGUF rose-kimiko-20b.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/Rose-Kimiko-20B-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/Rose-Kimiko-20B-GGUF rose-kimiko-20b.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 rose-kimiko-20b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` 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`
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) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### 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="./rose-kimiko-20b.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # 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(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # 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="./rose-kimiko-20b.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)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- 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: nRuaif's Rose Kimiko 20B
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# qlora-out
This model is a fine-tuned version of [tavtav/Rose-20B](https://huggingface.co/tavtav/Rose-20B) on the Kimiko dataset.
## Model description
The prompt formats used is ShareGPT/Vicuna format.
## Intended uses & limitations
Per many people requests, this LoRA is intended to fix spelling from Rose 20B.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
<!-- original-model-card end -->
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TheBloke/Rose-Kimiko-20B-AWQ
|
TheBloke
| 2023-12-14T16:52:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:nRuaif/Rose-Kimiko-20B",
"license:llama2",
"region:us"
] | null | 2023-12-14T15:50:38Z |
---
base_model: nRuaif/Rose-Kimiko-20B
inference: false
library_name: peft
license: llama2
model-index:
- name: Rose-Kimiko
results: []
model_creator: nRuaif
model_name: Rose Kimiko 20B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- generated_from_trainer
---
<!-- 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 -->
# Rose Kimiko 20B - AWQ
- Model creator: [nRuaif](https://huggingface.co/nRuaif)
- Original model: [Rose Kimiko 20B](https://huggingface.co/nRuaif/Rose-Kimiko-20B)
<!-- description start -->
## Description
This repo contains AWQ model files for [nRuaif's Rose Kimiko 20B](https://huggingface.co/nRuaif/Rose-Kimiko-20B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Rose-Kimiko-20B-fp16)
* [nRuaif's original LoRA adapter, which can be merged on to the base model.](https://huggingface.co/nRuaif/Rose-Kimiko-20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Rose-Kimiko-20B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.87 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Rose-Kimiko-20B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Rose-Kimiko-20B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Rose-Kimiko-20B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Rose-Kimiko-20B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Rose-Kimiko-20B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Rose-Kimiko-20B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: 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: nRuaif's Rose Kimiko 20B
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# qlora-out
This model is a fine-tuned version of [tavtav/Rose-20B](https://huggingface.co/tavtav/Rose-20B) on the Kimiko dataset.
## Model description
The prompt formats used is ShareGPT/Vicuna format.
## Intended uses & limitations
Per many people requests, this LoRA is intended to fix spelling from Rose 20B.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
|
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] |
TheBloke/Rose-Kimiko-20B-GPTQ
|
TheBloke
| 2023-12-14T17:19:09Z | 0 | 1 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:nRuaif/Rose-Kimiko-20B",
"license:llama2",
"region:us"
] | null | 2023-12-14T15:50:38Z |
---
base_model: nRuaif/Rose-Kimiko-20B
inference: false
library_name: peft
license: llama2
model-index:
- name: Rose-Kimiko
results: []
model_creator: nRuaif
model_name: Rose Kimiko 20B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- generated_from_trainer
---
<!-- 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 -->
# Rose Kimiko 20B - GPTQ
- Model creator: [nRuaif](https://huggingface.co/nRuaif)
- Original model: [Rose Kimiko 20B](https://huggingface.co/nRuaif/Rose-Kimiko-20B)
<!-- description start -->
# Description
This repo contains GPTQ model files for [nRuaif's Rose Kimiko 20B](https://huggingface.co/nRuaif/Rose-Kimiko-20B).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Rose-Kimiko-20B-fp16)
* [nRuaif's original LoRA adapter, which can be merged on to the base model.](https://huggingface.co/nRuaif/Rose-Kimiko-20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.52 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.89 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 12.04 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.41 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 20.35 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 9.51 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 20.80 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Rose-Kimiko-20B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Rose-Kimiko-20B-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Rose-Kimiko-20B-GPTQ`:
```shell
mkdir Rose-Kimiko-20B-GPTQ
huggingface-cli download TheBloke/Rose-Kimiko-20B-GPTQ --local-dir Rose-Kimiko-20B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Rose-Kimiko-20B-GPTQ
huggingface-cli download TheBloke/Rose-Kimiko-20B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Rose-Kimiko-20B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
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
mkdir Rose-Kimiko-20B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Rose-Kimiko-20B-GPTQ --local-dir Rose-Kimiko-20B-GPTQ --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>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Rose-Kimiko-20B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Rose-Kimiko-20B-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Rose-Kimiko-20B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Rose-Kimiko-20B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Rose-Kimiko-20B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: nRuaif's Rose Kimiko 20B
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# qlora-out
This model is a fine-tuned version of [tavtav/Rose-20B](https://huggingface.co/tavtav/Rose-20B) on the Kimiko dataset.
## Model description
The prompt formats used is ShareGPT/Vicuna format.
## Intended uses & limitations
Per many people requests, this LoRA is intended to fix spelling from Rose 20B.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
|
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chinhang0104/distilroberta-base-finetuned-wikitext2
|
chinhang0104
| 2023-12-14T16:17:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-14T15:51:54Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8603
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0848 | 1.0 | 2406 | 1.9368 |
| 1.9872 | 2.0 | 4812 | 1.8849 |
| 1.9546 | 3.0 | 7218 | 1.8352 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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andrixpan/Download
|
andrixpan
| 2023-12-14T15:53:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:52:22Z |
Entry not found
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judithrosell/CRAFT_SciBERT_NER
|
judithrosell
| 2023-12-14T16:10:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T15:55:16Z |
---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
model-index:
- name: CRAFT_SciBERT_NER
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. -->
# CRAFT_SciBERT_NER
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1133
- Seqeval classification report: precision recall f1-score support
CHEBI 0.99 0.99 0.99 87683
CL 0.84 0.83 0.84 1821
GGP 0.82 0.73 0.77 562
GO 0.94 0.94 0.94 5081
SO 0.67 0.67 0.67 600
Taxon 0.78 0.86 0.82 2452
micro avg 0.97 0.98 0.97 98199
macro avg 0.84 0.84 0.84 98199
weighted avg 0.97 0.98 0.98 98199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 347 | 0.1155 | precision recall f1-score support
CHEBI 0.99 0.99 0.99 87683
CL 0.82 0.80 0.81 1821
GGP 0.76 0.69 0.72 562
GO 0.93 0.92 0.93 5081
SO 0.62 0.66 0.64 600
Taxon 0.75 0.88 0.81 2452
micro avg 0.97 0.97 0.97 98199
macro avg 0.81 0.82 0.82 98199
weighted avg 0.97 0.97 0.97 98199
|
| 0.1266 | 2.0 | 695 | 0.1096 | precision recall f1-score support
CHEBI 0.99 0.99 0.99 87683
CL 0.83 0.81 0.82 1821
GGP 0.80 0.72 0.76 562
GO 0.93 0.94 0.94 5081
SO 0.68 0.66 0.67 600
Taxon 0.77 0.87 0.82 2452
micro avg 0.97 0.97 0.97 98199
macro avg 0.83 0.83 0.83 98199
weighted avg 0.97 0.97 0.97 98199
|
| 0.0321 | 3.0 | 1041 | 0.1133 | precision recall f1-score support
CHEBI 0.99 0.99 0.99 87683
CL 0.84 0.83 0.84 1821
GGP 0.82 0.73 0.77 562
GO 0.94 0.94 0.94 5081
SO 0.67 0.67 0.67 600
Taxon 0.78 0.86 0.82 2452
micro avg 0.97 0.98 0.97 98199
macro avg 0.84 0.84 0.84 98199
weighted avg 0.97 0.98 0.98 98199
|
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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judithrosell/JNLPBA_SciBERT_NER
|
judithrosell
| 2023-12-14T16:36:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T15:55:32Z |
---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
model-index:
- name: JNLPBA_SciBERT_NER
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. -->
# JNLPBA_SciBERT_NER
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1477
- Seqeval classification report: precision recall f1-score support
DNA 0.76 0.83 0.79 1351
RNA 0.74 0.86 0.80 723
cell_line 0.86 0.90 0.88 2503
cell_type 0.85 0.85 0.85 2805
protein 0.75 0.77 0.76 5264
micro avg 0.80 0.83 0.81 12646
macro avg 0.79 0.84 0.82 12646
weighted avg 0.80 0.83 0.81 12646
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.2258 | 1.0 | 582 | 0.1531 | precision recall f1-score support
DNA 0.74 0.82 0.78 1351
RNA 0.73 0.87 0.79 723
cell_line 0.88 0.84 0.86 2503
cell_type 0.85 0.84 0.85 2805
protein 0.73 0.75 0.74 5264
micro avg 0.78 0.80 0.79 12646
macro avg 0.78 0.82 0.80 12646
weighted avg 0.79 0.80 0.79 12646
|
| 0.1382 | 2.0 | 1164 | 0.1519 | precision recall f1-score support
DNA 0.77 0.80 0.78 1351
RNA 0.71 0.88 0.79 723
cell_line 0.84 0.91 0.87 2503
cell_type 0.85 0.84 0.84 2805
protein 0.75 0.74 0.75 5264
micro avg 0.79 0.81 0.80 12646
macro avg 0.78 0.83 0.81 12646
weighted avg 0.79 0.81 0.80 12646
|
| 0.1193 | 3.0 | 1746 | 0.1477 | precision recall f1-score support
DNA 0.76 0.83 0.79 1351
RNA 0.74 0.86 0.80 723
cell_line 0.86 0.90 0.88 2503
cell_type 0.85 0.85 0.85 2805
protein 0.75 0.77 0.76 5264
micro avg 0.80 0.83 0.81 12646
macro avg 0.79 0.84 0.82 12646
weighted avg 0.80 0.83 0.81 12646
|
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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judithrosell/BioNLP13CG_SciBERT_NER
|
judithrosell
| 2023-12-14T15:55:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:55:52Z |
Entry not found
|
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judithrosell/BC5CDR_SciBERT_NER
|
judithrosell
| 2023-12-14T15:56:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:56:07Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_adamax_00001_fold3
|
hkivancoral
| 2023-12-18T01:27:39Z | 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
| 2023-12-14T15:56:39Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8983333333333333
---
<!-- 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_adamax_00001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8422
- Accuracy: 0.8983
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2997 | 1.0 | 375 | 0.3235 | 0.88 |
| 0.2668 | 2.0 | 750 | 0.2793 | 0.8967 |
| 0.1125 | 3.0 | 1125 | 0.2572 | 0.9117 |
| 0.1056 | 4.0 | 1500 | 0.2703 | 0.9117 |
| 0.1075 | 5.0 | 1875 | 0.3070 | 0.8983 |
| 0.0954 | 6.0 | 2250 | 0.3649 | 0.8917 |
| 0.0479 | 7.0 | 2625 | 0.3675 | 0.91 |
| 0.0335 | 8.0 | 3000 | 0.4528 | 0.905 |
| 0.0032 | 9.0 | 3375 | 0.4970 | 0.9 |
| 0.0009 | 10.0 | 3750 | 0.5488 | 0.9167 |
| 0.0002 | 11.0 | 4125 | 0.5799 | 0.8983 |
| 0.0004 | 12.0 | 4500 | 0.6150 | 0.9067 |
| 0.0001 | 13.0 | 4875 | 0.6403 | 0.9083 |
| 0.0001 | 14.0 | 5250 | 0.6886 | 0.9017 |
| 0.0008 | 15.0 | 5625 | 0.6997 | 0.9083 |
| 0.0 | 16.0 | 6000 | 0.7289 | 0.9067 |
| 0.0 | 17.0 | 6375 | 0.7468 | 0.905 |
| 0.0 | 18.0 | 6750 | 0.7378 | 0.905 |
| 0.0 | 19.0 | 7125 | 0.7534 | 0.9033 |
| 0.0 | 20.0 | 7500 | 0.7571 | 0.9083 |
| 0.0 | 21.0 | 7875 | 0.7624 | 0.9033 |
| 0.0 | 22.0 | 8250 | 0.7704 | 0.9083 |
| 0.0049 | 23.0 | 8625 | 0.8162 | 0.9017 |
| 0.0 | 24.0 | 9000 | 0.7799 | 0.9033 |
| 0.0 | 25.0 | 9375 | 0.8193 | 0.9033 |
| 0.0 | 26.0 | 9750 | 0.7928 | 0.9033 |
| 0.0 | 27.0 | 10125 | 0.7850 | 0.9017 |
| 0.0 | 28.0 | 10500 | 0.8132 | 0.9 |
| 0.0 | 29.0 | 10875 | 0.8205 | 0.8983 |
| 0.0038 | 30.0 | 11250 | 0.8084 | 0.905 |
| 0.0 | 31.0 | 11625 | 0.8179 | 0.9017 |
| 0.0 | 32.0 | 12000 | 0.8194 | 0.8983 |
| 0.0 | 33.0 | 12375 | 0.8163 | 0.9017 |
| 0.0 | 34.0 | 12750 | 0.8152 | 0.9067 |
| 0.0 | 35.0 | 13125 | 0.8374 | 0.8983 |
| 0.0 | 36.0 | 13500 | 0.8315 | 0.8983 |
| 0.0 | 37.0 | 13875 | 0.8335 | 0.8967 |
| 0.0 | 38.0 | 14250 | 0.8285 | 0.8983 |
| 0.0 | 39.0 | 14625 | 0.8274 | 0.9033 |
| 0.0022 | 40.0 | 15000 | 0.8347 | 0.9017 |
| 0.0 | 41.0 | 15375 | 0.8356 | 0.9 |
| 0.0 | 42.0 | 15750 | 0.8391 | 0.9 |
| 0.0 | 43.0 | 16125 | 0.8395 | 0.8983 |
| 0.0 | 44.0 | 16500 | 0.8400 | 0.8983 |
| 0.0 | 45.0 | 16875 | 0.8413 | 0.8983 |
| 0.0 | 46.0 | 17250 | 0.8418 | 0.8983 |
| 0.0 | 47.0 | 17625 | 0.8416 | 0.8983 |
| 0.0 | 48.0 | 18000 | 0.8421 | 0.8983 |
| 0.0 | 49.0 | 18375 | 0.8423 | 0.8983 |
| 0.0 | 50.0 | 18750 | 0.8422 | 0.8983 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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FIre87521/Dragon
|
FIre87521
| 2023-12-14T15:57:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T15:57:18Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_adamax_00001_fold5
|
hkivancoral
| 2023-12-14T22:14:49Z | 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
| 2023-12-14T15:58:45Z |
---
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_adamax_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: 0.9083333333333333
---
<!-- 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_adamax_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: 0.7406
- Accuracy: 0.9083
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2292 | 1.0 | 375 | 0.3140 | 0.8683 |
| 0.2092 | 2.0 | 750 | 0.2336 | 0.9067 |
| 0.1226 | 3.0 | 1125 | 0.2477 | 0.9117 |
| 0.0876 | 4.0 | 1500 | 0.2419 | 0.92 |
| 0.0679 | 5.0 | 1875 | 0.3315 | 0.8983 |
| 0.0323 | 6.0 | 2250 | 0.3576 | 0.905 |
| 0.0879 | 7.0 | 2625 | 0.4064 | 0.91 |
| 0.0394 | 8.0 | 3000 | 0.4497 | 0.91 |
| 0.0359 | 9.0 | 3375 | 0.4692 | 0.915 |
| 0.0158 | 10.0 | 3750 | 0.5082 | 0.9117 |
| 0.0543 | 11.0 | 4125 | 0.4628 | 0.92 |
| 0.0223 | 12.0 | 4500 | 0.5427 | 0.915 |
| 0.0015 | 13.0 | 4875 | 0.6163 | 0.905 |
| 0.0283 | 14.0 | 5250 | 0.6260 | 0.91 |
| 0.0146 | 15.0 | 5625 | 0.5842 | 0.9083 |
| 0.009 | 16.0 | 6000 | 0.6137 | 0.91 |
| 0.0335 | 17.0 | 6375 | 0.6438 | 0.905 |
| 0.0046 | 18.0 | 6750 | 0.6182 | 0.91 |
| 0.0123 | 19.0 | 7125 | 0.7222 | 0.8983 |
| 0.0002 | 20.0 | 7500 | 0.6977 | 0.9067 |
| 0.0066 | 21.0 | 7875 | 0.7063 | 0.915 |
| 0.0363 | 22.0 | 8250 | 0.6479 | 0.9083 |
| 0.0494 | 23.0 | 8625 | 0.6537 | 0.905 |
| 0.0006 | 24.0 | 9000 | 0.7370 | 0.9067 |
| 0.0292 | 25.0 | 9375 | 0.6958 | 0.9 |
| 0.0254 | 26.0 | 9750 | 0.7175 | 0.8983 |
| 0.0001 | 27.0 | 10125 | 0.7650 | 0.9017 |
| 0.0095 | 28.0 | 10500 | 0.7329 | 0.9067 |
| 0.0232 | 29.0 | 10875 | 0.6879 | 0.9033 |
| 0.0172 | 30.0 | 11250 | 0.7353 | 0.905 |
| 0.0378 | 31.0 | 11625 | 0.7667 | 0.9117 |
| 0.0068 | 32.0 | 12000 | 0.7535 | 0.9083 |
| 0.0003 | 33.0 | 12375 | 0.7493 | 0.9133 |
| 0.0005 | 34.0 | 12750 | 0.7396 | 0.9067 |
| 0.013 | 35.0 | 13125 | 0.7540 | 0.9083 |
| 0.0005 | 36.0 | 13500 | 0.7288 | 0.9083 |
| 0.0073 | 37.0 | 13875 | 0.7577 | 0.9067 |
| 0.0001 | 38.0 | 14250 | 0.7421 | 0.9033 |
| 0.0156 | 39.0 | 14625 | 0.7370 | 0.9067 |
| 0.0137 | 40.0 | 15000 | 0.7555 | 0.905 |
| 0.0 | 41.0 | 15375 | 0.7434 | 0.905 |
| 0.0 | 42.0 | 15750 | 0.7571 | 0.9017 |
| 0.0002 | 43.0 | 16125 | 0.7615 | 0.9033 |
| 0.0008 | 44.0 | 16500 | 0.7655 | 0.9033 |
| 0.0 | 45.0 | 16875 | 0.7629 | 0.905 |
| 0.0005 | 46.0 | 17250 | 0.7484 | 0.9117 |
| 0.0132 | 47.0 | 17625 | 0.7394 | 0.9083 |
| 0.0 | 48.0 | 18000 | 0.7491 | 0.91 |
| 0.0001 | 49.0 | 18375 | 0.7435 | 0.91 |
| 0.017 | 50.0 | 18750 | 0.7406 | 0.9083 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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MariaK/peft-starcoder-lora
|
MariaK
| 2023-12-14T16:01:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:01:10Z |
Entry not found
|
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andrealexroom/phi-2_0.1_LexPC_v0.0.0.7
|
andrealexroom
| 2023-12-14T16:02:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:02:07Z |
Entry not found
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sai2321/ImageTexting
|
sai2321
| 2023-12-14T16:03:13Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T16:03:13Z |
---
license: apache-2.0
---
|
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cvg-unibe/yoda_bair_256
|
cvg-unibe
| 2023-12-14T20:56:48Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-12-14T16:04:50Z |
---
license: gpl-3.0
---
|
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cvg-unibe/yoda_clevrer_128
|
cvg-unibe
| 2023-12-14T21:03:47Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-12-14T16:05:11Z |
---
license: gpl-3.0
---
|
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cvg-unibe/yoda_iper_128
|
cvg-unibe
| 2023-12-14T21:17:06Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-12-14T16:05:35Z |
---
license: gpl-3.0
---
|
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] |
BrianHsu/Bert_QA_multiple_choice
|
BrianHsu
| 2023-12-15T05:21:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:bert-base-chinese",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-12-14T16:05:57Z |
---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Bert_QA_multiple_choice
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. -->
# Bert_QA_multiple_choice
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3867
- Accuracy: 0.6017
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1769 | 1.0 | 752 | 1.0007 | 0.5736 |
| 0.7735 | 2.0 | 1504 | 0.9846 | 0.5977 |
| 0.3761 | 3.0 | 2256 | 1.3867 | 0.6017 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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TheBloke/Rose-Kimiko-20B-fp16
|
TheBloke
| 2023-12-14T16:11:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:nRuaif/Rose-Kimiko-20B",
"license:llama2",
"region:us"
] | null | 2023-12-14T16:07:42Z |
---
base_model: nRuaif/Rose-Kimiko-20B
inference: false
library_name: peft
license: llama2
model-index:
- name: Rose-Kimiko
results: []
model_creator: nRuaif
model_name: Rose Kimiko 20B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- generated_from_trainer
---
<!-- 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 -->
# Rose Kimiko 20B - FP16
- Model creator: [nRuaif](https://huggingface.co/nRuaif)
- Original model: [Rose Kimiko 20B](nRuaif/Rose-Kimiko-20B)
<!-- description start -->
## Description
This repo contains pytorch format fp16 model files for [nRuaif's Rose Kimiko 20B](nRuaif/Rose-Kimiko-20B).
It is the result of either merging a LoRA, or converting the source repository to float16.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Rose-Kimiko-20B-GGUF)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Rose-Kimiko-20B-fp16)
* [nRuaif's original LoRA adapter, which can be merged on to the base model.](https://huggingface.co/nRuaif/Rose-Kimiko-20B)
<!-- repositories-available start -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- 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: nRuaif's Rose Kimiko 20B
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# qlora-out
This model is a fine-tuned version of [tavtav/Rose-20B](https://huggingface.co/tavtav/Rose-20B) on the Kimiko dataset.
## Model description
The prompt formats used is ShareGPT/Vicuna format.
## Intended uses & limitations
Per many people requests, this LoRA is intended to fix spelling from Rose 20B.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
|
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] |
balzanilo/dialogs-emotions-classifier-bert-base-seed42
|
balzanilo
| 2023-12-14T16:46:35Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T16:09:08Z |
Entry not found
|
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bh8648/base_epoch3-copy4_test3_merged
|
bh8648
| 2023-12-14T16:09:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:09:26Z |
Entry not found
|
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] |
kitsonr/Reinforce-PixelCopter
|
kitsonr
| 2023-12-14T16:11:59Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T16:11:23Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: -5.00 +/- 0.00
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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LinoyTsaban/test
|
LinoyTsaban
| 2023-12-14T16:12:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:12:31Z |
Entry not found
|
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] |
JGlang/llama2-prenoms
|
JGlang
| 2023-12-14T16:13:26Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"autotrain",
"text-generation",
"region:us"
] |
text-generation
| 2023-12-14T16:13:20Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
---
# Model Trained Using AutoTrain
|
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gnurtqh/w2vphones
|
gnurtqh
| 2023-12-14T16:15:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:15:14Z |
Entry not found
|
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CHACALATE/SDXLXUNLIANBAO
|
CHACALATE
| 2023-12-15T19:14:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:16:08Z |
Entry not found
|
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BUNTYZZZ/video
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BUNTYZZZ
| 2023-12-14T16:20:00Z | 0 | 0 | null |
[
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jiyoun-kim/vit-base-patch16-224-in21k-finetuned-lora-r_32food101
|
jiyoun-kim
| 2023-12-14T17:14:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:18:37Z |
Entry not found
|
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Kooten/DaringMaid-20B-6bpw-exl2
|
Kooten
| 2023-12-15T18:16:03Z | 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
| 2023-12-14T16:19:09Z |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
---
# This is a 6BPW EXL2 quant of DaringMaid-20B
# DaringMaid-20B
My goal was to make a Noromaid that's smarter and better at following instructions.
After trying a bunch of different recipes I think this one turned out pretty good
- I used [sequelbox/DynamicFactor](https://huggingface.co/sequelbox/DynamicFactor) as a base to as its supposed "improve overall knowledge, precise communication, conceptual understanding, and technical skill" over the base llama2.
- [NeverSleep/Noromaid](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1.1) of course.
- [Undi95/Utopia](https://huggingface.co/Undi95/Utopia-13B) has been recommended again recently and its still really good so in the mixer it goes
- I enjoyed [tavtav/Rose](https://huggingface.co/tavtav/Rose-20B) so i threw in a bit of [CalderaAI/Thorns](https://huggingface.co/CalderaAI/13B-Thorns-l2)
- There was recently a model that tried to pass itself off as [Gryphe/MythoMax](https://huggingface.co/Gryphe/MythoMax-L2-13b), i made a merge with that model before it was revealed to be MythoMax and it turned out pretty good so i used it.
The .yml config files for mergekit with the exact merges can be found in the ["Recipe"](https://huggingface.co/Kooten/DaringMaid/tree/main/Recipe) folder in the [fp16 repo](https://huggingface.co/Kooten/DaringMaid)
# Quants
[GGUF](https://huggingface.co/Kooten/DaringMaid-20B-GGUF), EXL2: [6bpw](https://huggingface.co/Kooten/DaringMaid-20B-6bpw-exl2), [3bpw](https://huggingface.co/Kooten/DaringMaid-20B-3bpw-exl2)
## Prompt template:
I have been using Undi/Ikaris SillyTavern presets for Noromaid: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not include descriptions of non-visual qualities such as personality, movements, scents, mental traits, or anything which could not be seen in a still photograph. Do not write in full sentences. Prefix your description with the phrase 'full body portrait,'
### Instruction:
{prompt}
### Response:
```
|
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] |
cho3ov/hw-llama-2-7B-nsmc
|
cho3ov
| 2023-12-14T22:55:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-14T16:19:17Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
환경 설정
- 학습 데이터: NSMC
하이퍼파라미터 및 설정
- 미니배치 크기: 1
- 시퀀스 길이: 512
- 학습률: 1e-4
- 에폭 수: 1
Train & Test Dataset 생성
Accuracy: 0.872
Precision: 0.8625954198473282
Recall: 0.889763779527559
F1 Score: 0.875968992248062
Classification Report:
precision recall f1-score
Negative 0.88 0.85 0.87
Positive 0.86 0.89 0.88
accuracy 0.87
Confusion Matrix
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1
|
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KhoiC/model_train
|
KhoiC
| 2023-12-14T16:20:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:20:04Z |
Entry not found
|
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anpro123/Translator
|
anpro123
| 2023-12-14T16:22:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:22:43Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_20
|
ADISH007
| 2023-12-14T16:23:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T16:23:09Z |
Entry not found
|
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] |
KhoiC/esm2_t12_35M_UR50D-pretrained-evaluation-new-data
|
KhoiC
| 2023-12-14T17:36:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T16:23:17Z |
Entry not found
|
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Mafoya1er/distilbert-base-uncased-finetuned-ner
|
Mafoya1er
| 2023-12-15T02:49:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T16:23:53Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Mafoya1er/distilbert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Mafoya1er/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0692
- Validation Loss: 0.3969
- Train Precision: 0.8119
- Train Recall: 0.8141
- Train F1: 0.8130
- Train Accuracy: 0.9206
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5890, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.9080 | 0.5831 | 0.6437 | 0.6212 | 0.6323 | 0.8573 | 0 |
| 0.3883 | 0.4573 | 0.7478 | 0.7245 | 0.7360 | 0.8925 | 1 |
| 0.2596 | 0.4092 | 0.7628 | 0.7696 | 0.7662 | 0.9037 | 2 |
| 0.1926 | 0.3848 | 0.7919 | 0.7838 | 0.7878 | 0.9121 | 3 |
| 0.1480 | 0.3850 | 0.7919 | 0.7963 | 0.7941 | 0.9135 | 4 |
| 0.1188 | 0.3860 | 0.8066 | 0.8006 | 0.8036 | 0.9172 | 5 |
| 0.0979 | 0.3905 | 0.8060 | 0.8085 | 0.8073 | 0.9186 | 6 |
| 0.0836 | 0.3911 | 0.8053 | 0.8120 | 0.8086 | 0.9190 | 7 |
| 0.0737 | 0.3996 | 0.8205 | 0.8113 | 0.8159 | 0.9215 | 8 |
| 0.0692 | 0.3969 | 0.8119 | 0.8141 | 0.8130 | 0.9206 | 9 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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EmmaGthn/results_lora_100_moji
|
EmmaGthn
| 2023-12-14T16:30:25Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-14T16:24:17Z |
Entry not found
|
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HyperCaps/HyperCaps
|
HyperCaps
| 2023-12-14T16:26:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:24:32Z |
HyperCaps: Turkey, kapsül, Fiyat, yorumlar, İçindekiler, Hap, tablet, maliyet, Faydalar!
HyperCaps, yüksek tansiyonu koruyan ve En Sağlıklı Hayatınızı Kolayca Yaşamanıza Yardımcı Olan Tansiyon Sporu Formülüdür! HyperCaps Turkey
<p><a href="https://www.nutritionsee.com/HyperCTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgGl5eLZoy-w9gZCTf5IpFzl2PDqKmG61GI7dZE5xhyLLdaXk7l4Jhbv2uTMGQqqpzDn63A1Vn9u-xP96m3zsZ3FYIodPqTLLOG5FH92QxYlSQ0XpVVU73DdsP7j2B1EMaiKkMS8ikIZmfEFCKB0Va0JC_AfjjE3hct0rJ40b9n1LvLJvwIsmtrNbSeTco/w579-h401/HyperCaps%20turkey.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/HyperCTur">HyperCaps hemen satın al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirimden yararlanın !! Acele etmek !!</a>
Resmi internet sitesi: <a href="https://www.nutritionsee.com/HyperCTur">www.HyperCaps.com</a>
➢ Ürün Adı –HyperCaps
Kullanım Yeri: Hipertansiyon
Faydaları: Kan basıncının doğal bir şekilde normalleştirilmesine yardımcı olur
➢ Yan Etkiler - Önemli Yan Etkiler Yok
➢ Kategori –Sağlık
➢ Kullanılabilirlik – Çevrimiçi
➢ Derecelendirme: - 5.0/5.0
HyperCaps nedir?
HyperCaps bir üründür; kardiyovasküler sağlığı desteklemek veya iyileştirmek için tasarlanmış bir besin takviyesi, ilaç veya tıbbi cihaz olabilir. Belirli bir bilgi olmadan, bileşimi veya nasıl çalıştığı hakkında daha fazla ayrıntı vermek zordur.
HyperCaps hemen satın al!! Daha fazla bilgi için aşağıdaki bağlantıya tıklayın ve şimdi %50 indirimden yararlanın !! Acele etmek !!
devamını oku: https://www.nutritionsee.com/HyperCTur
HyperCaps HyperCaps Hap HyperCaps kapsül HyperCaps tablet HyperCaps Fiyat HyperCaps yorumlar HyperCaps İçindekiler HyperCaps Faydalar HyperCaps Yan etkiler HyperCaps kapsül fiyatı HyperCaps kapsül incelemeleri HyperCaps kompozisyon HyperCaps şikayet HyperCaps Nereden Alınır HyperCaps Nasıl kullanılır HyperCaps maliyet HyperCaps İşler HyperCaps forum HyperCaps orijinal HyperCaps eczane
https://www.nutritionsee.com/HyperCTur
https://sites.google.com/view/hypercaps-kapsul/home
https://healthtoned.blogspot.com/2023/12/hypercaps-turkey.html
https://medium.com/@healthytalk24x7/hypercaps-turkey-04e1ea024941
https://medium.com/@healthytalk24x7/hypercaps-turkey-kaps%C3%BCl-fiyat-yorumlar-i%CC%87%C3%A7indekiler-hap-tablet-maliyet-faydalar-3b98e34ca351
https://www.weddingwire.us/website/hypercaps-and-turkey-2025-09-22
https://infogram.com/hypercaps-turkey-1h0n25y5pg3ll6p?live
https://softisenilspain.hashnode.dev/hypercaps-turkey
https://hypercapsturkey.company.site/
https://sway.office.com/1bfZzwloc7y0BpRQ
https://gamma.app/docs/HyperCaps-Turkey-921ud3yav6mivqs?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/hypercaps-turkey
https://groups.google.com/g/snshine/c/w87wcex3WIk
https://www.sunflower-cissp.com/glossary/cissp/7253/hypercaps-turkey
https://community.thebatraanumerology.com/post/hypercaps-turkey-657b2b342cf5f1f23ce7a313
https://community.thebatraanumerology.com/home/posts
https://enkling.com/read-blog/8925
https://nepal.tribe.so/post/hypercaps-turkey-657b2bf91b5e937c4f2280bb
https://replit.com/@hypercapskapsul
|
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] |
bio-datasets/Mistral-7B-Instruct-v0.1-blooming-thunder-195-epoch-80
|
bio-datasets
| 2023-12-14T16:28:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T16:28:22Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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model-hub/Mixtral-8x7B-Instruct-v0.1
|
model-hub
| 2023-12-14T16:28:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T16:28:29Z |
---
license: apache-2.0
language:
- fr
- it
- de
- es
- en
widget:
- text: "[INST] You are a pirate chatbot who always responds with Arr and pirate speak!\nThere's a llama on my lawn, how can I get rid of him? [/INST]"
output:
text: "Arr, shiver me timbers! Ye have a llama on yer lawn, ye say? Well, that be a new one for me! Here's what I'd suggest, arr:\n\n1. Firstly, ensure yer safety. Llamas may look gentle, but they can be protective if they feel threatened.\n2. Try to make the area less appealing to the llama. Remove any food sources or water that might be attracting it.\n3. Contact local animal control or a wildlife rescue organization. They be the experts and can provide humane ways to remove the llama from yer property.\n4. If ye have any experience with animals, you could try to gently herd the llama towards a nearby field or open space. But be careful, arr!\n\nRemember, arr, it be important to treat the llama with respect and care. It be a creature just trying to survive, like the rest of us."
---
# Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Warning
This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Instruction format
This format must be strictly respected, otherwise the model will generate sub-optimal outputs.
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
As reference, here is the pseudo-code used to tokenize instructions during fine-tuning:
```python
def tokenize(text):
return tok.encode(text, add_special_tokens=False)
[BOS_ID] +
tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_1) + [EOS_ID] +
…
tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_N) + [EOS_ID]
```
In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
## Limitations
The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
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model-hub/Mixtral-8x7B-v0.1
|
model-hub
| 2023-12-14T16:28:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T16:28:48Z |
---
license: apache-2.0
language:
- fr
- it
- de
- es
- en
---
# Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Warning
This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
## Notice
Mixtral-8x7B is a pretrained base model and therefore does not have any moderation mechanisms.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
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model-hub/phi-2
|
model-hub
| 2023-12-14T16:29:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi-msft",
"text-generation",
"nlp",
"code",
"custom_code",
"en",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-14T16:29:05Z |
---
inference: false
license: other
license_name: microsoft-research-license
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
---
## Model Summary
Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [Phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters.
Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
## Intended Uses
Phi-2 is intended for research purposes only. Given the nature of the training data, the Phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
### QA Format:
You can provide the prompt as a standalone question as follows:
```markdown
Write a detailed analogy between mathematics and a lighthouse.
```
where the model generates the text after "." .
To encourage the model to write more concise answers, you can also try the following QA format using "Instruct: \<prompt\>\nOutput:"
```markdown
Instruct: Write a detailed analogy between mathematics and a lighthouse.
Output: Mathematics is like a lighthouse. Just as a lighthouse guides ships safely to shore, mathematics provides a guiding light in the world of numbers and logic. It helps us navigate through complex problems and find solutions. Just as a lighthouse emits a steady beam of light, mathematics provides a consistent framework for reasoning and problem-solving. It illuminates the path to understanding and helps us make sense of the world around us.
```
where the model generates the text after "Output:".
### Chat Format:
```markdown
Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
Bob: Well, have you tried creating a study schedule and sticking to it?
Alice: Yes, I have, but it doesn't seem to help much.
Bob: Hmm, maybe you should try studying in a quiet environment, like the library.
Alice: ...
```
where the model generates the text after the first "Bob:".
### Code Format:
```python
def print_prime(n):
"""
Print all primes between 1 and n
"""
primes = []
for num in range(2, n+1):
is_prime = True
for i in range(2, int(math.sqrt(num))+1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
print(primes)
```
where the model generates the text after the comments.
**Notes:**
* Phi-2 is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
* Direct adoption for production tasks is out of the scope of this research project. As a result, the Phi-2 model has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
* If you are using `transformers>=4.36.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
## Sample Code
There are four types of execution mode:
1. FP16 / Flash-Attention / CUDA:
```python
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", flash_attn=True, flash_rotary=True, fused_dense=True, device_map="cuda", trust_remote_code=True)
```
2. FP16 / CUDA:
```python
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", device_map="cuda", trust_remote_code=True)
```
3. FP32 / CUDA:
```python
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float32, device_map="cuda", trust_remote_code=True)
```
4. FP32 / CPU:
```python
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True)
```
To ensure the maximum compatibility, we recommend using the second execution mode (FP16 / CUDA), as follows:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
inputs = tokenizer('''def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
**Remark:** In the generation function, our model currently does not support beam search (`num_beams > 1`).
Furthermore, in the forward pass of the model, we currently do not support outputting hidden states or attention values, or using custom input embeddings.
## Limitations of Phi-2
* Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
* Limited Scope for code: Majority of Phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
* Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
* Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
* Potential Societal Biases: Phi-2 is not entirely free from societal biases despite efforts in assuring trainig data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
* Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
* Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses.
## Training
### Model
* Architecture: a Transformer-based model with next-word prediction objective
* Context length: 2048 tokens
* Dataset size: 250B tokens, combination of NLP synthetic data created by AOAI GPT-3.5 and filtered web data from Falcon RefinedWeb and SlimPajama, which was assessed by AOAI GPT-4.
* Training tokens: 1.4T tokens
* GPUs: 96xA100-80G
* Training time: 14 days
### Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
### License
The model is licensed under the [microsoft-research-license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
|
[
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] |
model-hub/stable-zero123
|
model-hub
| 2023-12-14T16:31:27Z | 0 | 1 | null |
[
"3d",
"text-to-3d",
"dataset:allenai/objaverse",
"license:other",
"region:us"
] |
text-to-3d
| 2023-12-14T16:31:27Z |
---
datasets:
- allenai/objaverse
tags:
- 3d
extra_gated_fields:
Name: text
Email: text
Country: text
Organization or Affiliation: text
I ALLOW Stability AI to email me about new model releases: checkbox
license: other
license_name: sai-nc-community
license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.TXT
pipeline_tag: text-to-3d
---
# Stable Zero123
## Model Description
Stable Zero123 is a model for view-conditioned image generation based on [Zero123](https://github.com/cvlab-columbia/zero123).
With improved data rendering and model conditioning strategies, our model demonstrates improved performance when compared to the original Zero123 and its subsequent iteration, Zero123-XL.
<img src='img.png' width='700'>
## Usage
By using Score Distillation Sampling (SDS) along with the Stable Zero123 model, we can produce high-quality 3D models from any input image. The process can also extend to text-to-3D generation by first generating a single image using SDXL and then using SDS on Stable Zero123 to generate the 3D object.
To enable open research in 3D object generation, we've improved the open-source code of threestudio by supporting Zero123 and Stable Zero123.
To use Stable Zero123 for object 3D mesh generation in [threestudio](https://github.com/threestudio-project/threestudio#stable-zero123), you can follow these steps:
1. Install threestudio using their [instructions](https://github.com/threestudio-project/threestudio#installation)
2. Download the Stable Zero123 checkpoint `stable_zero123.ckpt` into the `load/zero123/` directory
2. Take an image of your choice, or generate it from text using your favourite AI image generator such as SDXL Turbo (https://clipdrop.co/stable-diffusion-turbo) E.g. "A simple 3D render of a friendly dog"
3. Remove its background using Clipdrop (https://clipdrop.co/remove-background)
4. Save to `load/images/`, preferably with `_rgba.png` as the suffix
5. Run Zero-1-to-3 with the Stable Zero123 ckpt:
```sh
python launch.py --config configs/stable-zero123.yaml --train --gpu 0 data.image_path=./load/images/hamburger_rgba.png
```
## Model Details
* **Developed by**: [Stability AI](https://stability.ai/)
* **Model type**: latent diffusion model.
* **Finetuned from model**: [lambdalabs/sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers)
* **License**: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/zero123-sai/raw/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more.
### Training Dataset
We use renders from the [Objaverse](https://objaverse.allenai.org/objaverse-1.0) dataset, utilizing our enhanced rendering method
### Training Infrastructure
* **Hardware**: `Stable Zero123` was trained on the Stability AI cluster on a single node with 8 A100 80GBs GPUs.
* **Code Base**: We use our modified version of [the original zero123 repository](https://github.com/cvlab-columbia/zero123).
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
|
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model-hub/DeciLM-7B
|
model-hub
| 2023-12-14T16:32:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation",
"custom_code",
"en",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-14T16:32:51Z |
---
license: apache-2.0
language:
- en
---
# DeciLM-7B
DeciLM-7B is a 7.04 billion parameter decoder-only text generation model, released under the Apache 2.0 license. At the time of release, DeciLM-7B is the top-performing 7B base language model on the Open LLM Leaderboard. With support for an 8K-token sequence length, this highly efficient model uses variable Grouped-Query Attention (GQA) to achieve a superior balance between accuracy and computational efficiency. The model's architecture was generated using Deci's proprietary Neural Architecture Search technology, AutoNAC.
## Model Details
### Model Description
Deci developed and released the DeciLM-7B language model, a pre-trained, high-efficiency text generation model with 7 billion parameters. DeciLM-7B is not only the most accurate 7B base model, but it also outpaces all models in its class with a throughput that is up to 4.4x that of Mistral-7B's. An instruct version [DeciLM-7B-instruct](https://huggingface.co/Deci/DeciLM-7B-instruct) has also been released.
- **Developed by:** [Deci](https://deci.ai/)
- **Model type:** DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention.
- **Language(s) (NLP):** English
- **License:** Apache 2.0
## Model Architecture
| Parameters | Layers | Heads | Sequence Length | GQA num_key_value_heads* |
|:----------|:----------|:----------|:----------|:----------|
| 7.04 billion | 32 | 32 | 8192 | Variable |
*AutoNAC was employed to optimize the selection of the GQA num_key_value_heads for each layer.
### Model Sources
- **Blog:** [DeciLM-7B Technical Blog](https://deci.ai/blog/introducing-DeciLM-7B-the-fastest-and-most-accurate-7b-large-language-model-to-date)
- **Demo:** [DeciLM-7B-instruct Demo](https://huggingface.co/spaces/Deci/DeciLM-7B-instruct)
- **Finetuning Notebook:** [DeciLM-7B Finetuning Notebook](https://colab.research.google.com/drive/1kEV6i96AQ94xTCvSd11TxkEaksTb5o3U?usp=sharing)
- **Text Generation Notebook:** [DeciLM-7B-instruct Text Generation Notebook](https://bit.ly/declm-7b-instruct)
## Uses
The model is intended for commercial and research use in English and can be fine-tuned for various tasks and languages.
## How to Get Started with the Model
Use the code below to get started with the model.
```bibtex
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Deci/DeciLM-7B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device)
inputs = tokenizer.encode("In a shocking finding, scientists discovered a herd of unicorns living in", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95)
print(tokenizer.decode(outputs[0]))
```
## Evaluation
Below are DeciLM-7B and DeciLM-7B-instruct's Open LLM Leaderboard results.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|:----------|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| DecilLM-7B | 61.55 | 59.39 | 82.51 | 59.76 | 40.33 | 79.95 | 47.38 |
| DecilLM-7B-instruct | 63.19 | 61.01 | 82.37 | 60.24 | 49.75 | 79.72 | 46.02 |
### Runtime Benchmarks
| Inference Tool | Hardware | Prompt length | Generation length | Generated tokens/sec | Batch Size | Number of Prompts |
|:----------|:----------|:---------:|:---------:|:---------:|:---------:|:---------:|
| HuggingFace (PyTorch) | A100 (SXM4-80GB-400W) | 512 | 512 | **1174** | 352 | 352 |
| HuggingFace (PyTorch) | A100 (SXM4-80GB-400W) | 2048 | 2048 | **328** | 72 | 72 |
| Infery-LLM | A100 (SXM4-80GB-400W)| 512 | 512 | **4559** | 1024 | 4096 |
| Infery-LLM | A100 (SXM4-80GB-400W) | 2048 | 2048 | **3997** | 512 | 2048 |
| Infery-LLM | A10 | 512 | 512 | **1345** | 128 | 512 |
| Infery-LLM | A10 | 2048 | 2048 | **599** | 32 | 128 |
- In order to replicate the results of the Hugging Face benchmarks, you can use this [code example](https://huggingface.co/Deci/DeciLM-7B/blob/main/benchmark_hf_model.py).
- Infery-LLM, Deci's inference engine, features a suite of optimization algorithms, including selective quantization, optimized beam search, continuous batching, and custom CUDA kernels. To witness the full capabilities of Infery-LLM first-hand, we invite you to engage with our [interactive demo](https://console.deci.ai/infery-llm-demo).
## Ethical Considerations and Limitations
DeciLM-7B is a new technology that comes with inherent risks associated with its use. The testing conducted so far has been primarily in English and does not encompass all possible scenarios. Like those of all large language models, DeciLM-7B's outputs are unpredictable, and the model may generate responses that are inaccurate, biased, or otherwise objectionable. Consequently, developers planning to use DeciLM-7B should undertake thorough safety testing and tuning designed explicitly for their intended applications of the model before deployment.
## How to Cite
Please cite this model using this format.
```bibtex
@misc{DeciFoundationModels,
title = {DeciLM-7B},
author = {DeciAI Research Team},
year = {2023}
url={https://huggingface.co/Deci/DeciLM-7B},
}
```
|
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marcchew/wnse
|
marcchew
| 2023-12-14T16:32:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T16:32:52Z |
Entry not found
|
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] |
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