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TrumpBiden/zephyr-7b-sft-full
TrumpBiden
2023-12-14T14:23:01Z
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[ "region:us" ]
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2023-12-14T14:23:01Z
Entry not found
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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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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=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,StandardScaler())]),Index([&#x27;avg_0&#x27;, &#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;,&#x27;avg_8&#x27;, &#x27;avg_9&#x27;,...&#x27;max_758&#x27;, &#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;,&#x27;max_764&#x27;, &#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;],dtype=&#x27;object&#x27;, length=2304))],verbose_feature_names_out=False)),(&#x27;svc&#x27;, 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=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,StandardScaler())]),Index([&#x27;avg_0&#x27;, &#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;,&#x27;avg_8&#x27;, &#x27;avg_9&#x27;,...&#x27;max_758&#x27;, &#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;,&#x27;max_764&#x27;, &#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;],dtype=&#x27;object&#x27;, length=2304))],verbose_feature_names_out=False)),(&#x27;svc&#x27;, 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=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;, StandardScaler())]),Index([&#x27;avg_0&#x27;, &#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;,&#x27;avg_8&#x27;, &#x27;avg_9&#x27;,...&#x27;max_758&#x27;, &#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;,&#x27;max_764&#x27;, &#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;],dtype=&#x27;object&#x27;, 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([&#x27;avg_0&#x27;, &#x27;avg_1&#x27;, &#x27;avg_2&#x27;, &#x27;avg_3&#x27;, &#x27;avg_4&#x27;, &#x27;avg_5&#x27;, &#x27;avg_6&#x27;, &#x27;avg_7&#x27;,&#x27;avg_8&#x27;, &#x27;avg_9&#x27;,...&#x27;max_758&#x27;, &#x27;max_759&#x27;, &#x27;max_760&#x27;, &#x27;max_761&#x27;, &#x27;max_762&#x27;, &#x27;max_763&#x27;,&#x27;max_764&#x27;, &#x27;max_765&#x27;, &#x27;max_766&#x27;, &#x27;max_767&#x27;],dtype=&#x27;object&#x27;, 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=&#x27;median&#x27;)</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 ![Confusion Matrix](confusion_matrix.png) # 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 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0
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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
Entry not found
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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> </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 - 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
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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
BUNTYZZZ
2023-12-14T16:20:00Z
0
0
null
[ "region:us" ]
null
2023-12-14T16:18:10Z
Entry not found
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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 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### 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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