librarian-bots/model_cards_with_metadata_test

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ajmishler/test_models	ajmishler	"2024-06-14T00:33:32Z"	0	null	[ "region:us" ]	null	"2024-06-14T00:33:32Z"	Entry not found
SiMajid/reward-train-facebook-opt350m_v4	SiMajid	"2024-06-14T00:34:20Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T00:34:11Z"	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
Zetaphor/Llama-3-PepsiMax	Zetaphor	"2024-06-14T00:35:38Z"	0	null	[ "region:us" ]	null	"2024-06-14T00:35:38Z"	Entry not found
Frixi/Ninomae_Inanis_HoloEN	Frixi	"2024-06-14T00:36:16Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-14T00:35:59Z"	--- license: openrail ---
argmaxinc/coreml-stable-diffusion-3-medium-1024-t5	argmaxinc	"2024-06-19T15:38:23Z"	1	DiffusionKit	[ "DiffusionKit", "text-to-image", "coreml", "en", "base_model:stabilityai/stable-diffusion-3-medium-diffusers", "license:other", "region:us" ]	text-to-image	"2024-06-14T00:37:22Z"	--- license: other license_name: stabilityai-nc-research-community license_link: >- https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers/blob/main/LICENSE library_name: DiffusionKit base_model: stabilityai/stable-diffusion-3-medium-diffusers tags: - text-to-image - coreml inference: false language: - en ---
dlynch243/DialoGPT-small-uncleruckus2	dlynch243	"2024-06-14T00:40:27Z"	0	null	[ "region:us" ]	null	"2024-06-14T00:40:27Z"	Entry not found
ThorBaller/small_mistral	ThorBaller	"2024-06-15T02:33:49Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T00:43:39Z"	--- license: apache-2.0 ---
amritpuhan/fine-tuned-distilbert-base-uncased-swag-peft	amritpuhan	"2024-06-14T05:31:29Z"	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:swag", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	"2024-06-14T00:50:55Z"	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: distilbert/distilbert-base-uncased datasets: - swag metrics: - accuracy model-index: - name: fine-tuned-distilbert-base-uncased-swag-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. --> # fine-tuned-distilbert-base-uncased-swag-peft This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.7733 - Accuracy: 0.6858 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.0103 \| 1.0 \| 4597 \| 0.8978 \| 0.6370 \| \| 0.9591 \| 2.0 \| 9194 \| 0.8498 \| 0.6568 \| \| 0.9401 \| 3.0 \| 13791 \| 0.8270 \| 0.6626 \| \| 0.9265 \| 4.0 \| 18388 \| 0.8105 \| 0.6713 \| \| 0.9202 \| 5.0 \| 22985 \| 0.8001 \| 0.6759 \| \| 0.8921 \| 6.0 \| 27582 \| 0.7894 \| 0.6790 \| \| 0.894 \| 7.0 \| 32179 \| 0.7836 \| 0.6823 \| \| 0.8695 \| 8.0 \| 36776 \| 0.7803 \| 0.6835 \| \| 0.8684 \| 9.0 \| 41373 \| 0.7753 \| 0.6845 \| \| 0.8696 \| 10.0 \| 45970 \| 0.7733 \| 0.6858 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
proyectoItegrado241EAFIT/XGBoost_Model	proyectoItegrado241EAFIT	"2024-06-14T02:20:28Z"	0	null	[ "ciencia_de_datos", "XGBoost", "EAFIT", "Python", "Research", "graph-ml", "es", "region:us" ]	graph-ml	"2024-06-14T00:53:26Z"	--- language: - es pipeline_tag: graph-ml tags: - ciencia_de_datos - XGBoost - EAFIT - Python - Research --- Los modelos mostrados en este repositorio son del proyecto integrado de Semestre de la universidad EAFIT, correspondientes al semestre 2024-1 de la maestría en ciencia de datos y Analitica. Cada modelo se construyó usando la serie temporal de los días de la semana para cada una de las horas del día. En este caso se está usando el modelo Arima para la predicción Predice las horas del metro de medellín
varun-v-rao/opt-350m-bn-adapter-squad-model1	varun-v-rao	"2024-06-14T00:54:11Z"	0	null	[ "region:us" ]	null	"2024-06-14T00:54:11Z"	Entry not found
vigneshv59/mistral-7b-finetuned-ultrachat	vigneshv59	"2024-06-14T00:54:34Z"	0	null	[ "region:us" ]	null	"2024-06-14T00:54:34Z"	Entry not found
onizukal/Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2	onizukal	"2024-06-14T02:49:00Z"	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-14T01:00:46Z"	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_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: 0.8576483672025074 --- <!-- 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. --> # Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6418 - Accuracy: 0.8576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3343 \| 1.0 \| 2466 \| 0.3772 \| 0.8460 \| \| 0.269 \| 2.0 \| 4932 \| 0.3610 \| 0.8583 \| \| 0.1499 \| 3.0 \| 7398 \| 0.4653 \| 0.8552 \| \| 0.1293 \| 4.0 \| 9864 \| 0.8042 \| 0.8496 \| \| 0.1824 \| 5.0 \| 12330 \| 0.9597 \| 0.8549 \| \| 0.1453 \| 6.0 \| 14796 \| 1.2832 \| 0.8563 \| \| 0.0537 \| 7.0 \| 17262 \| 1.4415 \| 0.8533 \| \| 0.0 \| 8.0 \| 19728 \| 1.6006 \| 0.8561 \| \| 0.0 \| 9.0 \| 22194 \| 1.6244 \| 0.8587 \| \| 0.0 \| 10.0 \| 24660 \| 1.6418 \| 0.8576 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
onizukal/Karma_3Class_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold2	onizukal	"2024-06-14T02:54:50Z"	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-14T01:03:32Z"	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Karma_3Class_3Class_Adamax_1e4_20Epoch_Beit-large-224_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: 0.8534020827014458 --- <!-- 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. --> # Karma_3Class_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5433 - Accuracy: 0.8534 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3595 \| 1.0 \| 2466 \| 0.4309 \| 0.8251 \| \| 0.3101 \| 2.0 \| 4932 \| 0.3865 \| 0.8447 \| \| 0.1826 \| 3.0 \| 7398 \| 0.4588 \| 0.8485 \| \| 0.1658 \| 4.0 \| 9864 \| 0.5997 \| 0.8504 \| \| 0.1373 \| 5.0 \| 12330 \| 0.8549 \| 0.8498 \| \| 0.0639 \| 6.0 \| 14796 \| 1.1026 \| 0.8527 \| \| 0.0234 \| 7.0 \| 17262 \| 1.2762 \| 0.8538 \| \| 0.0001 \| 8.0 \| 19728 \| 1.4347 \| 0.8547 \| \| 0.0 \| 9.0 \| 22194 \| 1.5139 \| 0.8518 \| \| 0.0002 \| 10.0 \| 24660 \| 1.5433 \| 0.8534 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
VisionAI4Healthcare/MIMIC_VISION	VisionAI4Healthcare	"2024-06-14T01:04:01Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:04:01Z"	Entry not found
sagnikrayc/opt-1.3b-bn-adapter-snli-model3	sagnikrayc	"2024-06-14T01:05:16Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:05:14Z"	Entry not found
farah17/MyMistral	farah17	"2024-06-14T01:10:14Z"	0	null	[ "license:other", "region:us" ]	null	"2024-06-14T01:10:14Z"	--- license: other license_name: mistral license_link: LICENSE ---
AmberYifan/spin-filtered	AmberYifan	"2024-06-17T19:08:00Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T01:16:49Z"	--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - generated_from_trainer model-index: - name: spin-trans 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. --> # spin-trans 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: 0.0027 - Rewards/real: -3.8149 - Rewards/generated: -24.3554 - Rewards/accuracies: 1.0 - Rewards/margins: 20.5405 - Logps/generated: -336.8123 - Logps/real: -163.0993 - Logits/generated: -2.3894 - Logits/real: -1.8917 ## 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/real \| Rewards/generated \| Rewards/accuracies \| Rewards/margins \| Logps/generated \| Logps/real \| Logits/generated \| Logits/real \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------:\|:-----------------:\|:------------------:\|:---------------:\|:---------------:\|:----------:\|:----------------:\|:-----------:\| \| 0.0085 \| 0.1 \| 100 \| 0.0130 \| 0.5228 \| -9.3297 \| 1.0 \| 9.8526 \| -186.5559 \| -119.7219 \| -2.7911 \| -2.5502 \| \| 0.0041 \| 0.21 \| 200 \| 0.0070 \| -0.1706 \| -14.7969 \| 1.0 \| 14.6263 \| -241.2277 \| -126.6563 \| -2.6228 \| -2.2904 \| \| 0.0007 \| 0.31 \| 300 \| 0.0073 \| -2.7706 \| -22.3901 \| 0.9974 \| 19.6195 \| -317.1598 \| -152.6565 \| -2.4825 \| -1.9073 \| \| 0.0049 \| 0.41 \| 400 \| 0.0044 \| -2.9093 \| -19.4947 \| 1.0 \| 16.5854 \| -288.2053 \| -154.0429 \| -2.6010 \| -2.2355 \| \| 0.001 \| 0.52 \| 500 \| 0.0050 \| -1.5600 \| -21.7213 \| 1.0 \| 20.1614 \| -310.4720 \| -140.5501 \| -2.5715 \| -2.2758 \| \| 0.0004 \| 0.62 \| 600 \| 0.0029 \| -2.4635 \| -24.2161 \| 1.0 \| 21.7526 \| -335.4198 \| -149.5852 \| -2.4626 \| -2.0545 \| \| 0.0004 \| 0.72 \| 700 \| 0.0034 \| -1.9810 \| -20.7429 \| 1.0 \| 18.7619 \| -300.6877 \| -144.7602 \| -2.4823 \| -2.0980 \| \| 0.0003 \| 0.83 \| 800 \| 0.0034 \| -4.2857 \| -23.6128 \| 1.0 \| 19.3270 \| -329.3861 \| -167.8074 \| -2.3861 \| -1.8496 \| \| 0.0003 \| 0.93 \| 900 \| 0.0027 \| -3.8149 \| -24.3554 \| 1.0 \| 20.5405 \| -336.8123 \| -163.0993 \| -2.3894 \| -1.8917 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
erwannd/vit-base-patch16-224-in21k-finetuned-lora-food101	erwannd	"2024-06-14T01:22:34Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T01:17:14Z"	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
theailearner/AIProfessions-doctor-llama-3-8b-testV2	theailearner	"2024-06-14T01:19:14Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T01:17:41Z"	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
mali6/autocap	mali6	"2024-06-25T04:47:08Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:18:36Z"	Entry not found
Kudod/phobert-large-case-finetuned-ner-vlsp2021-3090-14June	Kudod	"2024-06-14T01:23:43Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:23:43Z"	Entry not found
Kudod/bert-large-case-finetuned-ner-vlsp2021-3090-14June	Kudod	"2024-06-14T01:26:23Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:26:23Z"	Entry not found
zhoujy7/results	zhoujy7	"2024-06-14T01:28:01Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:28:01Z"	Entry not found
RUXHIR2828/MikiTakumiFujiwaraJP	RUXHIR2828	"2024-06-14T03:38:26Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-14T01:28:06Z"	--- license: openrail ---
mlx-community/dolphin-2.9.3-qwen2-1.5b-2bit	mlx-community	"2024-06-14T01:28:24Z"	2	mlx	[ "mlx", "safetensors", "qwen2", "generated_from_trainer", "axolotl", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:Qwen/Qwen2-1.5B", "license:apache-2.0", "region:us" ]	null	"2024-06-14T01:28:10Z"	--- license: apache-2.0 tags: - generated_from_trainer - axolotl - mlx base_model: Qwen/Qwen2-1.5B datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # mlx-community/dolphin-2.9.3-qwen2-1.5b-2bit This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9.3-qwen2-1.5b`]() using mlx-lm version 0.12.1. Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9.3-qwen2-1.5b) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/dolphin-2.9.3-qwen2-1.5b-2bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
bongmo/trained-sd3	bongmo	"2024-06-14T01:29:41Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:29:41Z"	Entry not found
LucasLima07/midjourney-prompt	LucasLima07	"2024-06-20T16:27:23Z"	0	null	[ "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "region:us" ]	null	"2024-06-14T01:31:18Z"	--- base_model: "vilsonrodrigues/falcon-7b-instruct-sharded" ---
Danjin/unsloth-gemma-glaive-function-callingv2	Danjin	"2024-06-14T01:33:39Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-1.1-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T01:33:30Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-1.1-2b-it-bnb-4bit --- # Uploaded model - Developed by: Danjin - License: apache-2.0 - Finetuned from model : unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tanya-kta/whisper-small-ru	tanya-kta	"2024-06-14T01:35:52Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:35:51Z"	Entry not found
itssugyaru/Eunhye	itssugyaru	"2024-06-14T01:37:58Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-14T01:36:53Z"	--- license: openrail ---
Lahinthefutureland/CuteDoodle	Lahinthefutureland	"2024-06-14T01:43:12Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:42:32Z"	Entry not found
tctrautman/20240613-kibbe-training-base-merged	tctrautman	"2024-06-14T05:18:32Z"	0	null	[ "safetensors", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "region:us" ]	null	"2024-06-14T01:46:27Z"	--- license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: 20240613-kibbe-training-base-merged 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. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dubs/Kibbe-Training/runs/rwmz1usm) # 20240613-kibbe-training-base-merged This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5501 ## 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: 1 - eval_batch_size: 1 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.501 \| 0.5 \| 46 \| 0.5596 \| \| 0.5503 \| 1.0 \| 92 \| 0.5274 \| \| 0.3954 \| 1.5 \| 138 \| 0.5451 \| \| 0.3103 \| 2.0 \| 184 \| 0.5501 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Hev832/baser-vc	Hev832	"2024-06-14T01:53:57Z"	4	null	[ "music", "license:mit", "region:us" ]	null	"2024-06-14T01:47:04Z"	--- license: mit tags: - music ---
hyojuuun/gte-base-pair-FT	hyojuuun	"2024-06-14T01:51:50Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:51:50Z"	Entry not found
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-33-percent-high-bt-rouge-1	AdamKasumovic	"2024-06-14T01:55:19Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-14T01:52:21Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
alexshengzhili/ph3-0606-lora-dpo-beta-0dot2-merged	alexshengzhili	"2024-06-14T01:53:41Z"	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "llama-factory", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-14T01:52:32Z"	--- library_name: transformers tags: - llama-factory --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
Sinodex/AS	Sinodex	"2024-06-14T01:53:55Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:53:55Z"	Entry not found
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-33-percent-low-bt-rouge-1	AdamKasumovic	"2024-06-14T01:58:46Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-14T01:55:59Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RAY2L/Llama-3-Instruct-8B-SimPO	RAY2L	"2024-06-14T03:52:21Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "alignment-handbook", "generated_from_trainer", "conversational", "dataset:princeton-nlp/llama3-ultrafeedback", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T01:56:41Z"	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - alignment-handbook - generated_from_trainer datasets: - princeton-nlp/llama3-ultrafeedback model-index: - name: llama-3-8b-instruct-simpo 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. --> # llama-3-8b-instruct-simpo This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set: - Loss: 1.3755 - Rewards/chosen: -2.9448 - Rewards/rejected: -3.6038 - Rewards/accuracies: 0.6613 - Rewards/margins: 0.6589 - Logps/rejected: -1.4415 - Logps/chosen: -1.1779 - Logits/rejected: -1.1545 - Logits/chosen: -1.1873 ## 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-06 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 1.3975 \| 0.8549 \| 400 \| 1.3755 \| -2.9448 \| -3.6038 \| 0.6613 \| 0.6589 \| -1.4415 \| -1.1779 \| -1.1545 \| -1.1873 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.2+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
MG31/DETR_multiclass_last_e8_b4_n0	MG31	"2024-06-14T02:12:14Z"	0	null	[ "region:us" ]	null	"2024-06-14T01:59:01Z"	Entry not found
tanya-kta/whisper-small-even	tanya-kta	"2024-06-14T04:53:35Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ru", "dataset:tbkazakova/even_speech_biblical", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-06-14T01:59:41Z"	--- language: - ru license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - tbkazakova/even_speech_biblical metrics: - wer model-index: - name: Whisper Small Even - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Even Speech Biblical type: tbkazakova/even_speech_biblical config: default split: None args: 'config: ru, split: train' metrics: - name: Wer type: wer value: 53.88015717092338 --- <!-- 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. --> # Whisper Small Even - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Even Speech Biblical dataset. It achieves the following results on the evaluation set: - Loss: 0.4591 - Wer: 53.8802 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.05 \| 5.9880 \| 500 \| 0.3920 \| 63.7525 \| \| 0.0022 \| 11.9760 \| 1000 \| 0.4307 \| 57.3674 \| \| 0.0003 \| 17.9641 \| 1500 \| 0.4528 \| 51.7682 \| \| 0.0003 \| 23.9521 \| 2000 \| 0.4591 \| 53.8802 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-33-percent-med-bt-rouge-1	AdamKasumovic	"2024-06-14T02:08:38Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-14T02:05:43Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
VVAA89/Misha	VVAA89	"2024-06-14T02:08:44Z"	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	"2024-06-14T02:08:44Z"	--- license: cc-by-nc-4.0 ---
varun-v-rao/opt-350m-bn-adapter-squad-model2	varun-v-rao	"2024-06-14T02:10:37Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:10:37Z"	Entry not found
Danjin/Llama-2-7b-chat-finetunev2	Danjin	"2024-06-14T02:24:00Z"	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T02:13:13Z"	Entry not found
moemoe101/SmartAIRecipe	moemoe101	"2024-06-14T02:19:47Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T02:19:47Z"	--- license: apache-2.0 ---
fxmeng/PiSSA-Mistral-7b-r64	fxmeng	"2024-06-14T06:54:28Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T02:21:01Z"	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-33-percent-high-bt-rouge-1	AdamKasumovic	"2024-06-14T02:35:12Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-14T02:32:10Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
qsebso/first_model_yelp_review	qsebso	"2024-06-14T02:32:56Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:32:56Z"	Entry not found
cadeath/cvss_0614	cadeath	"2024-06-14T03:32:54Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T02:33:51Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: cadeath - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-33-percent-med-bt-rouge-1	AdamKasumovic	"2024-06-14T02:38:08Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-14T02:34:52Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sagnikrayc/opt-350m-bn-adapter-squad-model1	sagnikrayc	"2024-06-14T13:37:43Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:35:26Z"	Entry not found
Puandraa/AisakaTaiga	Puandraa	"2024-06-14T02:35:32Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T02:35:32Z"	--- license: apache-2.0 ---
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-33-percent-low-bt-rouge-1	AdamKasumovic	"2024-06-14T02:39:16Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-14T02:36:15Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
amazeble/epiclazygasm_.safetensors	amazeble	"2024-06-14T02:43:12Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T02:39:04Z"	--- license: apache-2.0 ---
chainup244/Qwen-Qwen1.5-0.5B-1718332862	chainup244	"2024-06-14T02:41:03Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:41:03Z"	Entry not found
chainup244/Qwen-Qwen1.5-1.8B-1718332943	chainup244	"2024-06-14T02:42:24Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:42:24Z"	Entry not found
jony8/test001	jony8	"2024-06-14T02:43:31Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T02:43:31Z"	--- license: apache-2.0 ---
iamnguyen/cupid	iamnguyen	"2024-06-30T11:54:37Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-06-14T02:43:44Z"	Entry not found
yuekai/icefall_asr_aishell_whisper_qwen2_1.5B	yuekai	"2024-06-14T04:20:30Z"	0	null	[ "tensorboard", "region:us" ]	null	"2024-06-14T02:44:04Z"	Entry not found
dimassyoga42/huggingface_hub	dimassyoga42	"2024-06-14T02:44:31Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:44:31Z"	Entry not found
harryslater58/models	harryslater58	"2024-06-14T02:45:02Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T02:45:02Z"	--- license: apache-2.0 ---
jinbhunandaxue/pipeline	jinbhunandaxue	"2024-06-14T02:45:09Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:45:09Z"	Entry not found
dimassyoga42/Dimas	dimassyoga42	"2024-06-14T02:57:33Z"	0	null	[ "region:us" ]	null	"2024-06-14T02:53:12Z"	Entry not found
camenduru/Unique3D	camenduru	"2024-06-14T03:03:15Z"	1	diffusers	[ "diffusers", "onnx", "safetensors", "region:us" ]	null	"2024-06-14T02:57:37Z"	Entry not found
Ksgk-fy/phillipine_customer_v3.6_Maria_Intro_Objection_v2	Ksgk-fy	"2024-06-14T03:42:50Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-06-14T02:59:06Z"	Entry not found
AWeirdDev/zh-tw-llama3-tokenizer-3k	AWeirdDev	"2024-06-14T03:03:10Z"	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	"2024-06-14T03:02:20Z"	--- library_name: transformers tags: [] --- # zh-tw-llama-3-tokenizer-3k
10ths/test	10ths	"2024-06-14T03:03:42Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:03:42Z"	Entry not found
JudithWiz/Astrologa	JudithWiz	"2024-06-14T03:04:10Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:04:10Z"	Entry not found
xxxhhhttt/TinyChineseStories-LLaMA2	xxxhhhttt	"2024-06-14T03:08:51Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T03:08:39Z"	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
Blackroot/Euryale-2.1-3.3b-6h-exl2	Blackroot	"2024-06-14T03:25:27Z"	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-14T03:11:16Z"	3.3Bpw 6bit head quantized version of https://huggingface.co/Sao10K/L3-70B-Euryale-v2.1
RyotaKadoya1993/fullymerged_v4_adapter	RyotaKadoya1993	"2024-06-14T03:16:07Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:RyotaKadoya1993/fullymerged_v1_128_gen3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T03:11:42Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: RyotaKadoya1993/fullymerged_v1_128_gen3 --- # Uploaded model - Developed by: RyotaKadoya1993 - License: apache-2.0 - Finetuned from model : RyotaKadoya1993/fullymerged_v1_128_gen3 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
emidiosouza/kukac-doc	emidiosouza	"2024-06-14T03:11:58Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:11:58Z"	Entry not found
OpilotAI/TinyLlama-1.1B-Chat-v1.0-q4f16_1-Opilot	OpilotAI	"2024-06-14T03:13:55Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:13:23Z"	Entry not found
ManCD/Arima_pm25_model	ManCD	"2024-06-14T03:14:28Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:14:28Z"	Entry not found
againeureka/support_and_attack_classifier	againeureka	"2024-06-14T03:19:35Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:19:35Z"	Entry not found
jeiku/Aura_Qwen2_v4_7B	jeiku	"2024-06-14T03:20:32Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:20:32Z"	Entry not found
erizakaria/llama3-8b-daftarin-id-lora	erizakaria	"2024-06-14T03:22:58Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T03:22:31Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: erizakaria - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ShiftAddLLM/opt13b-2bit-lat	ShiftAddLLM	"2024-06-14T04:11:32Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:23:34Z"	Entry not found
Yuki20/llama3_8b_aci_2e	Yuki20	"2024-06-14T03:24:53Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T03:24:46Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Yuki20 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
onizukal/Karma_3Class_RMSprop_1e4_20Epoch_Beit-large-224_fold2	onizukal	"2024-06-14T05:22:16Z"	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-14T03:37:50Z"	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Karma_3Class_RMSprop_1e4_20Epoch_Beit-large-224_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: 0.8374279648164998 --- <!-- 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. --> # Karma_3Class_RMSprop_1e4_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7596 - Accuracy: 0.8374 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.412 \| 1.0 \| 2466 \| 0.5102 \| 0.7956 \| \| 0.4251 \| 2.0 \| 4932 \| 0.4462 \| 0.8177 \| \| 0.3057 \| 3.0 \| 7398 \| 0.4440 \| 0.8302 \| \| 0.2355 \| 4.0 \| 9864 \| 0.5256 \| 0.8296 \| \| 0.1256 \| 5.0 \| 12330 \| 0.7596 \| 0.8374 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
nikollaz/promptoargentum	nikollaz	"2024-06-14T03:40:39Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T03:40:39Z"	--- license: apache-2.0 ---
Milancheeks/AuRA	Milancheeks	"2024-06-19T13:04:30Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-14T03:42:41Z"	--- license: apache-2.0 --- --- # AuRA - Augmented Universal Real-Time Assistant ## Overview AuRA (Augmented Universal Real-Time Assistant) represents a new paradigm in AI-driven assistance by leveraging outputs from multiple state-of-the-art language models. This approach ensures that AuRA continuously learns and evolves, integrating the latest advancements in natural language processing (NLP). By combining the strengths of various models, AuRA offers unparalleled assistance across diverse domains, making it a highly versatile and intelligent assistant. ## Vision and Goals AuRA is designed to redefine AI-driven assistance with the following core goals: - Integrate Knowledge: Combine outputs from multiple LLMs to create a comprehensive and enriched knowledge base. - Real-Time Learning: Continuously update its training data with new information and advancements, ensuring it remains cutting-edge. - Versatile Assistance: Provide high-quality responses across a wide range of topics and tasks. - User-Centric Development: Incorporate user feedback to dynamically refine and improve performance. - AI Data Broker: Act as a joint controller for user data, ensuring users get compensated when their data is used and providing the option to lock their data if they choose. - Action Model: Learn actions from tools created by other developers, enabling AuRA to perform a wide range of tasks beyond traditional text-based assistance. ## System Architecture ### Data Integration Pipeline The data integration pipeline is designed to ensure seamless collection, processing, and utilization of data from various sources. Key components include: - Source Models: Collect data from leading language models (LLMs) such as GPT-3.5, GPT-4, and others. - Automated Data Collection: Continuously fetch outputs from these models based on user interactions. - Data Processing: Clean, format, and validate collected data to ensure high quality and consistency. - Dynamic Dataset: Maintain a regularly updated dataset that serves as the foundation for training. - Intelligent Data Sampling: Use active learning techniques to selectively sample the most informative and diverse data points for training. - Data Augmentation: Increase the diversity and robustness of the training data through techniques like paraphrasing and synonym replacement. - Real-Time Data Integration: Enable real-time data integration to keep the model current. - Scalability and Efficiency: Design the pipeline to handle large volumes of data without compromising performance. - Security and Privacy: Adhere to strict security and privacy standards to protect user data. ### Model Training AuRA's model training process includes: - Base Model: Built on the Mistral-7B-v0.2 model. - Finetuning with LoRA: Use Low-Rank Adaptation (LoRA) for efficient adaptation to new data. - Incremental Training: Regular updates with new interaction data. - Mixture of Experts (MoE): Utilize different parts of the model for different inputs to handle a wide variety of tasks efficiently. - Sparse Attention Mechanisms: Reduce computational complexity for processing long sequences of data. - Knowledge Distillation: Use a larger, pre-trained model to teach AuRA. - Gradient Checkpointing: Save memory by checkpointing intermediate states during training. - Mixed Precision Training: Use mixed precision (fp16) to speed up training and reduce memory usage. - Layer-wise Learning Rate Scaling: Adjust learning rates at different layers for faster convergence. ### Feedback Loop The feedback loop ensures continuous learning and improvement by: - User Feedback: Collecting feedback from users through interactions, surveys, and implicit behavior. - Active Learning: Integrating feedback into the training pipeline. - Automated Feedback Analysis: Using NLP and machine learning algorithms to analyze feedback. - Reinforcement Learning: Fine-tuning the model based on user interactions. - Real-Time Adaptation: Adjusting responses and behavior based on immediate feedback. - Quality Assurance: Regular evaluations and benchmarking. - Transparency and Communication: Maintaining transparency about how user feedback is used. ## Real-World Applications AuRA's versatility enables its application in various domains, including: - Customer Support: Providing real-time assistance and resolving queries. - Education: Offering personalized tutoring and educational content. - Healthcare: Assisting with medical information retrieval and patient interaction. - Business Intelligence: Analyzing data and generating insights for decision-making. - AI Data Broker: Ensuring users get compensated when their data is used and providing the option to lock their data. ## Ethical Considerations AuRA's development adheres to strict ethical principles, including: - Data Privacy: Ensuring user data privacy with robust encryption and user control. - Bias Mitigation: Continuously monitoring and correcting biases in data and model outputs. - Transparency: Maintaining transparency about data practices. - Accountability: Regular audits and compliance with legal and regulatory standards. - Collaborative Ethics Development: Working with the World Ethics Organization to build an ethical framework. ## Future Work Future development focuses on: - Expansion of Data Sources: Integrating additional models and data sources. - Advanced NLP Techniques: Incorporating new NLP techniques and architectures. - Multimodal Learning: Enabling understanding and processing of various data formats. - Enhanced User Interfaces: Developing more intuitive and user-friendly interfaces. - Real-Time Adaptability: Strengthening real-time learning and adaptation capabilities. - Ethical AI Development: Fully implementing the ethical framework. - Real-World Applications: Expanding into new application domains and conducting case studies. ## Conclusion AuRA represents a significant leap forward in AI-driven assistance, integrating multiple language models to provide unparalleled support across diverse domains. With a commitment to real-time learning, user-centric development, and ethical AI practices, AuRA is set to revolutionize the way we interact with technology. For more information and to explore the capabilities of AuRA, visit the [Hugging Face model page](https://huggingface.co/Milancheeks/AuRA). ---
onizukal/Boya1_3Class_SGD_1e4_20Epoch_Beit-large-224_fold3	onizukal	"2024-06-14T04:53:23Z"	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-14T03:44:03Z"	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya1_3Class_SGD_1e4_20Epoch_Beit-large-224_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: 0.5871212121212122 --- <!-- 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. --> # Boya1_3Class_SGD_1e4_20Epoch_Beit-large-224_fold3 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0362 - Accuracy: 0.5871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1275 \| 1.0 \| 923 \| 1.0932 \| 0.5739 \| \| 1.0274 \| 2.0 \| 1846 \| 1.0618 \| 0.5809 \| \| 1.0425 \| 3.0 \| 2769 \| 1.0467 \| 0.5847 \| \| 1.0677 \| 4.0 \| 3692 \| 1.0385 \| 0.5863 \| \| 1.0395 \| 5.0 \| 4615 \| 1.0362 \| 0.5871 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
Ksgk-fy/ecoach_philippine_v7_intro_object_merge	Ksgk-fy	"2024-06-14T03:45:28Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:45:28Z"	Entry not found
shakilanf/pedut123	shakilanf	"2024-06-14T03:48:41Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:48:41Z"	Entry not found
A00954334/finetuning-sentiment-model-3000-samples	A00954334	"2024-06-14T03:58:05Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-14T03:48:44Z"	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3252 - Accuracy: 0.8733 - F1: 0.8758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Elyitra/gamer	Elyitra	"2024-06-14T03:50:33Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:50:28Z"	Entry not found
datnguyen16123009/Test_Phi3	datnguyen16123009	"2024-06-14T03:51:30Z"	0	null	[ "region:us" ]	null	"2024-06-14T03:51:30Z"	Entry not found
Nutanix/Meta-Llama-3-8B-Instruct_KTO_lora_hh-rlhf-processed	Nutanix	"2024-06-14T03:52:52Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T03:52:49Z"	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
Danjin/unsloth-gemma-glaive-function-callingv3	Danjin	"2024-06-14T04:00:03Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-1.1-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-14T03:59:57Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-1.1-2b-it-bnb-4bit --- # Uploaded model - Developed by: Danjin - License: apache-2.0 - Finetuned from model : unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
nhfffff/niare_flipgod	nhfffff	"2024-06-14T04:05:32Z"	0	null	[ "region:us" ]	null	"2024-06-14T04:01:04Z"	Entry not found
JamesHujy/EMABench	JamesHujy	"2024-06-14T04:01:35Z"	0	null	[ "region:us" ]	null	"2024-06-14T04:01:15Z"	Entry not found
Nutanix/Meta-Llama-3-8B-Instruct_KTO_lora_distilabel-capybara-kto-15k-binarized-processed	Nutanix	"2024-06-14T04:04:27Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-14T04:04:24Z"	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
LAB-IA-UFBA/myeloma-yolo7-model	LAB-IA-UFBA	"2024-06-14T04:57:54Z"	0	null	[ "yolo7", "dataset:LAB-IA-UFBA/myeloma-dataset", "license:apache-2.0", "region:us" ]	null	"2024-06-14T04:09:51Z"	--- license: apache-2.0 datasets: - LAB-IA-UFBA/myeloma-dataset tags: - yolo7 --- Here you will find all codes, models, and data used in the manuscript "Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists." Scientific Reports 14.1 (2024).
WolfSmasher99/ana-de-armas	WolfSmasher99	"2024-06-14T04:16:03Z"	0	null	[ "region:us" ]	null	"2024-06-14T04:10:17Z"	Entry not found
ShiftAddLLM/opt30b-2bit-lat	ShiftAddLLM	"2024-06-14T04:15:13Z"	0	null	[ "region:us" ]	null	"2024-06-14T04:13:29Z"	Entry not found
chainup244/Qwen-Qwen1.5-0.5B-1718338417	chainup244	"2024-06-14T04:13:39Z"	0	null	[ "region:us" ]	null	"2024-06-14T04:13:39Z"	Entry not found
tspeterkim3/arcface	tspeterkim3	"2024-06-14T04:15:06Z"	0	null	[ "region:us" ]	null	"2024-06-14T04:15:06Z"	Entry not found
ShiftAddLLM/opt30b-3bit-lat	ShiftAddLLM	"2024-06-14T04:18:47Z"	0	null	[ "region:us" ]	null	"2024-06-14T04:15:33Z"	Entry not found

ajmishler/test_models

ajmishler

"2024-06-14T00:33:32Z"

0

null

[ "region:us" ]

null

"2024-06-14T00:33:32Z"

Entry not found

SiMajid/reward-train-facebook-opt350m_v4

SiMajid

"2024-06-14T00:34:20Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-06-14T00:34:11Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Zetaphor/Llama-3-PepsiMax

Zetaphor

"2024-06-14T00:35:38Z"

0

null

[ "region:us" ]

null

"2024-06-14T00:35:38Z"

Entry not found

Frixi/Ninomae_Inanis_HoloEN

Frixi

"2024-06-14T00:36:16Z"

0

null

[ "license:openrail", "region:us" ]

null

"2024-06-14T00:35:59Z"

--- license: openrail ---

argmaxinc/coreml-stable-diffusion-3-medium-1024-t5

argmaxinc

"2024-06-19T15:38:23Z"

0

1

DiffusionKit

[ "DiffusionKit", "text-to-image", "coreml", "en", "base_model:stabilityai/stable-diffusion-3-medium-diffusers", "license:other", "region:us" ]

text-to-image

"2024-06-14T00:37:22Z"

--- license: other license_name: stabilityai-nc-research-community license_link: >- https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers/blob/main/LICENSE library_name: DiffusionKit base_model: stabilityai/stable-diffusion-3-medium-diffusers tags: - text-to-image - coreml inference: false language: - en ---

dlynch243/DialoGPT-small-uncleruckus2

dlynch243

"2024-06-14T00:40:27Z"

0

null

[ "region:us" ]

null

"2024-06-14T00:40:27Z"

Entry not found

ThorBaller/small_mistral

ThorBaller

"2024-06-15T02:33:49Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T00:43:39Z"

--- license: apache-2.0 ---

amritpuhan/fine-tuned-distilbert-base-uncased-swag-peft

amritpuhan

"2024-06-14T05:31:29Z"

0

peft

[ "peft", "safetensors", "generated_from_trainer", "dataset:swag", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]

null

"2024-06-14T00:50:55Z"

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: distilbert/distilbert-base-uncased datasets: - swag metrics: - accuracy model-index: - name: fine-tuned-distilbert-base-uncased-swag-peft results: [] ---  # fine-tuned-distilbert-base-uncased-swag-peft This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.7733 - Accuracy: 0.6858 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0103 | 1.0 | 4597 | 0.8978 | 0.6370 | | 0.9591 | 2.0 | 9194 | 0.8498 | 0.6568 | | 0.9401 | 3.0 | 13791 | 0.8270 | 0.6626 | | 0.9265 | 4.0 | 18388 | 0.8105 | 0.6713 | | 0.9202 | 5.0 | 22985 | 0.8001 | 0.6759 | | 0.8921 | 6.0 | 27582 | 0.7894 | 0.6790 | | 0.894 | 7.0 | 32179 | 0.7836 | 0.6823 | | 0.8695 | 8.0 | 36776 | 0.7803 | 0.6835 | | 0.8684 | 9.0 | 41373 | 0.7753 | 0.6845 | | 0.8696 | 10.0 | 45970 | 0.7733 | 0.6858 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

proyectoItegrado241EAFIT/XGBoost_Model

proyectoItegrado241EAFIT

"2024-06-14T02:20:28Z"

0

null

[ "ciencia_de_datos", "XGBoost", "EAFIT", "Python", "Research", "graph-ml", "es", "region:us" ]

graph-ml

"2024-06-14T00:53:26Z"

--- language: - es pipeline_tag: graph-ml tags: - ciencia_de_datos - XGBoost - EAFIT - Python - Research --- Los modelos mostrados en este repositorio son del proyecto integrado de Semestre de la universidad EAFIT, correspondientes al semestre 2024-1 de la maestría en ciencia de datos y Analitica. Cada modelo se construyó usando la serie temporal de los días de la semana para cada una de las horas del día. En este caso se está usando el modelo Arima para la predicción Predice las horas del metro de medellín

varun-v-rao/opt-350m-bn-adapter-squad-model1

varun-v-rao

"2024-06-14T00:54:11Z"

0

null

[ "region:us" ]

null

"2024-06-14T00:54:11Z"

Entry not found

vigneshv59/mistral-7b-finetuned-ultrachat

vigneshv59

"2024-06-14T00:54:34Z"

0

null

[ "region:us" ]

null

"2024-06-14T00:54:34Z"

Entry not found

onizukal/Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2

onizukal

"2024-06-14T02:49:00Z"

0

transformers

[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-06-14T01:00:46Z"

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_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: 0.8576483672025074 ---  # Karma_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6418 - Accuracy: 0.8576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3343 | 1.0 | 2466 | 0.3772 | 0.8460 | | 0.269 | 2.0 | 4932 | 0.3610 | 0.8583 | | 0.1499 | 3.0 | 7398 | 0.4653 | 0.8552 | | 0.1293 | 4.0 | 9864 | 0.8042 | 0.8496 | | 0.1824 | 5.0 | 12330 | 0.9597 | 0.8549 | | 0.1453 | 6.0 | 14796 | 1.2832 | 0.8563 | | 0.0537 | 7.0 | 17262 | 1.4415 | 0.8533 | | 0.0 | 8.0 | 19728 | 1.6006 | 0.8561 | | 0.0 | 9.0 | 22194 | 1.6244 | 0.8587 | | 0.0 | 10.0 | 24660 | 1.6418 | 0.8576 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

onizukal/Karma_3Class_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold2

onizukal

"2024-06-14T02:54:50Z"

0

transformers

[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-06-14T01:03:32Z"

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Karma_3Class_3Class_Adamax_1e4_20Epoch_Beit-large-224_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: 0.8534020827014458 ---  # Karma_3Class_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5433 - Accuracy: 0.8534 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3595 | 1.0 | 2466 | 0.4309 | 0.8251 | | 0.3101 | 2.0 | 4932 | 0.3865 | 0.8447 | | 0.1826 | 3.0 | 7398 | 0.4588 | 0.8485 | | 0.1658 | 4.0 | 9864 | 0.5997 | 0.8504 | | 0.1373 | 5.0 | 12330 | 0.8549 | 0.8498 | | 0.0639 | 6.0 | 14796 | 1.1026 | 0.8527 | | 0.0234 | 7.0 | 17262 | 1.2762 | 0.8538 | | 0.0001 | 8.0 | 19728 | 1.4347 | 0.8547 | | 0.0 | 9.0 | 22194 | 1.5139 | 0.8518 | | 0.0002 | 10.0 | 24660 | 1.5433 | 0.8534 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

VisionAI4Healthcare/MIMIC_VISION

VisionAI4Healthcare

"2024-06-14T01:04:01Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:04:01Z"

Entry not found

sagnikrayc/opt-1.3b-bn-adapter-snli-model3

sagnikrayc

"2024-06-14T01:05:16Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:05:14Z"

Entry not found

farah17/MyMistral

farah17

"2024-06-14T01:10:14Z"

0

null

[ "license:other", "region:us" ]

null

"2024-06-14T01:10:14Z"

--- license: other license_name: mistral license_link: LICENSE ---

AmberYifan/spin-filtered

AmberYifan

"2024-06-17T19:08:00Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

"2024-06-14T01:16:49Z"

--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - generated_from_trainer model-index: - name: spin-trans results: [] ---  # spin-trans 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: 0.0027 - Rewards/real: -3.8149 - Rewards/generated: -24.3554 - Rewards/accuracies: 1.0 - Rewards/margins: 20.5405 - Logps/generated: -336.8123 - Logps/real: -163.0993 - Logits/generated: -2.3894 - Logits/real: -1.8917 ## 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real | |:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:| | 0.0085 | 0.1 | 100 | 0.0130 | 0.5228 | -9.3297 | 1.0 | 9.8526 | -186.5559 | -119.7219 | -2.7911 | -2.5502 | | 0.0041 | 0.21 | 200 | 0.0070 | -0.1706 | -14.7969 | 1.0 | 14.6263 | -241.2277 | -126.6563 | -2.6228 | -2.2904 | | 0.0007 | 0.31 | 300 | 0.0073 | -2.7706 | -22.3901 | 0.9974 | 19.6195 | -317.1598 | -152.6565 | -2.4825 | -1.9073 | | 0.0049 | 0.41 | 400 | 0.0044 | -2.9093 | -19.4947 | 1.0 | 16.5854 | -288.2053 | -154.0429 | -2.6010 | -2.2355 | | 0.001 | 0.52 | 500 | 0.0050 | -1.5600 | -21.7213 | 1.0 | 20.1614 | -310.4720 | -140.5501 | -2.5715 | -2.2758 | | 0.0004 | 0.62 | 600 | 0.0029 | -2.4635 | -24.2161 | 1.0 | 21.7526 | -335.4198 | -149.5852 | -2.4626 | -2.0545 | | 0.0004 | 0.72 | 700 | 0.0034 | -1.9810 | -20.7429 | 1.0 | 18.7619 | -300.6877 | -144.7602 | -2.4823 | -2.0980 | | 0.0003 | 0.83 | 800 | 0.0034 | -4.2857 | -23.6128 | 1.0 | 19.3270 | -329.3861 | -167.8074 | -2.3861 | -1.8496 | | 0.0003 | 0.93 | 900 | 0.0027 | -3.8149 | -24.3554 | 1.0 | 20.5405 | -336.8123 | -163.0993 | -2.3894 | -1.8917 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

erwannd/vit-base-patch16-224-in21k-finetuned-lora-food101

erwannd

"2024-06-14T01:22:34Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-06-14T01:17:14Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

theailearner/AIProfessions-doctor-llama-3-8b-testV2

theailearner

"2024-06-14T01:19:14Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-06-14T01:17:41Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mali6/autocap

mali6

"2024-06-25T04:47:08Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:18:36Z"

Entry not found

Kudod/phobert-large-case-finetuned-ner-vlsp2021-3090-14June

Kudod

"2024-06-14T01:23:43Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:23:43Z"

Entry not found

Kudod/bert-large-case-finetuned-ner-vlsp2021-3090-14June

Kudod

"2024-06-14T01:26:23Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:26:23Z"

Entry not found

zhoujy7/results

zhoujy7

"2024-06-14T01:28:01Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:28:01Z"

Entry not found

RUXHIR2828/MikiTakumiFujiwaraJP

RUXHIR2828

"2024-06-14T03:38:26Z"

0

null

[ "license:openrail", "region:us" ]

null

"2024-06-14T01:28:06Z"

--- license: openrail ---

mlx-community/dolphin-2.9.3-qwen2-1.5b-2bit

mlx-community

"2024-06-14T01:28:24Z"

0

2

mlx

[ "mlx", "safetensors", "qwen2", "generated_from_trainer", "axolotl", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:Qwen/Qwen2-1.5B", "license:apache-2.0", "region:us" ]

null

"2024-06-14T01:28:10Z"

--- license: apache-2.0 tags: - generated_from_trainer - axolotl - mlx base_model: Qwen/Qwen2-1.5B datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # mlx-community/dolphin-2.9.3-qwen2-1.5b-2bit This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9.3-qwen2-1.5b`]() using mlx-lm version **0.12.1**. Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9.3-qwen2-1.5b) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/dolphin-2.9.3-qwen2-1.5b-2bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

bongmo/trained-sd3

bongmo

"2024-06-14T01:29:41Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:29:41Z"

Entry not found

LucasLima07/midjourney-prompt

LucasLima07

"2024-06-20T16:27:23Z"

0

null

[ "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "region:us" ]

null

"2024-06-14T01:31:18Z"

--- base_model: "vilsonrodrigues/falcon-7b-instruct-sharded" ---

Danjin/unsloth-gemma-glaive-function-callingv2

Danjin

"2024-06-14T01:33:39Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-1.1-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-14T01:33:30Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-1.1-2b-it-bnb-4bit --- # Uploaded model - **Developed by:** Danjin - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

tanya-kta/whisper-small-ru

tanya-kta

"2024-06-14T01:35:52Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:35:51Z"

Entry not found

itssugyaru/Eunhye

itssugyaru

"2024-06-14T01:37:58Z"

0

null

[ "license:openrail", "region:us" ]

null

"2024-06-14T01:36:53Z"

--- license: openrail ---

Lahinthefutureland/CuteDoodle

Lahinthefutureland

"2024-06-14T01:43:12Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:42:32Z"

Entry not found

tctrautman/20240613-kibbe-training-base-merged

tctrautman

"2024-06-14T05:18:32Z"

0

null

[ "safetensors", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "region:us" ]

null

"2024-06-14T01:46:27Z"

--- license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: 20240613-kibbe-training-base-merged results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dubs/Kibbe-Training/runs/rwmz1usm) # 20240613-kibbe-training-base-merged This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5501 ## 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: 1 - eval_batch_size: 1 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.501 | 0.5 | 46 | 0.5596 | | 0.5503 | 1.0 | 92 | 0.5274 | | 0.3954 | 1.5 | 138 | 0.5451 | | 0.3103 | 2.0 | 184 | 0.5501 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Hev832/baser-vc

Hev832

"2024-06-14T01:53:57Z"

0

4

null

[ "music", "license:mit", "region:us" ]

null

"2024-06-14T01:47:04Z"

--- license: mit tags: - music ---

hyojuuun/gte-base-pair-FT

hyojuuun

"2024-06-14T01:51:50Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:51:50Z"

Entry not found

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-33-percent-high-bt-rouge-1

AdamKasumovic

"2024-06-14T01:55:19Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-14T01:52:21Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

alexshengzhili/ph3-0606-lora-dpo-beta-0dot2-merged

alexshengzhili

"2024-06-14T01:53:41Z"

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "llama-factory", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-14T01:52:32Z"

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Sinodex/AS

Sinodex

"2024-06-14T01:53:55Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:53:55Z"

Entry not found

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-33-percent-low-bt-rouge-1

AdamKasumovic

"2024-06-14T01:58:46Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-14T01:55:59Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

RAY2L/Llama-3-Instruct-8B-SimPO

RAY2L

"2024-06-14T03:52:21Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "alignment-handbook", "generated_from_trainer", "conversational", "dataset:princeton-nlp/llama3-ultrafeedback", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

"2024-06-14T01:56:41Z"

--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - alignment-handbook - generated_from_trainer datasets: - princeton-nlp/llama3-ultrafeedback model-index: - name: llama-3-8b-instruct-simpo results: [] ---  # llama-3-8b-instruct-simpo This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set: - Loss: 1.3755 - Rewards/chosen: -2.9448 - Rewards/rejected: -3.6038 - Rewards/accuracies: 0.6613 - Rewards/margins: 0.6589 - Logps/rejected: -1.4415 - Logps/chosen: -1.1779 - Logits/rejected: -1.1545 - Logits/chosen: -1.1873 ## 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-06 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 1.3975 | 0.8549 | 400 | 1.3755 | -2.9448 | -3.6038 | 0.6613 | 0.6589 | -1.4415 | -1.1779 | -1.1545 | -1.1873 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.2+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

MG31/DETR_multiclass_last_e8_b4_n0

MG31

"2024-06-14T02:12:14Z"

0

null

[ "region:us" ]

null

"2024-06-14T01:59:01Z"

Entry not found

tanya-kta/whisper-small-even

tanya-kta

"2024-06-14T04:53:35Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ru", "dataset:tbkazakova/even_speech_biblical", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2024-06-14T01:59:41Z"

--- language: - ru license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - tbkazakova/even_speech_biblical metrics: - wer model-index: - name: Whisper Small Even - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Even Speech Biblical type: tbkazakova/even_speech_biblical config: default split: None args: 'config: ru, split: train' metrics: - name: Wer type: wer value: 53.88015717092338 ---  # Whisper Small Even - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Even Speech Biblical dataset. It achieves the following results on the evaluation set: - Loss: 0.4591 - Wer: 53.8802 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:-------:| | 0.05 | 5.9880 | 500 | 0.3920 | 63.7525 | | 0.0022 | 11.9760 | 1000 | 0.4307 | 57.3674 | | 0.0003 | 17.9641 | 1500 | 0.4528 | 51.7682 | | 0.0003 | 23.9521 | 2000 | 0.4591 | 53.8802 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-af-33-percent-med-bt-rouge-1

AdamKasumovic

"2024-06-14T02:08:38Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-14T02:05:43Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

VVAA89/Misha

VVAA89

"2024-06-14T02:08:44Z"

0

null

[ "license:cc-by-nc-4.0", "region:us" ]

null

"2024-06-14T02:08:44Z"

--- license: cc-by-nc-4.0 ---

varun-v-rao/opt-350m-bn-adapter-squad-model2

varun-v-rao

"2024-06-14T02:10:37Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:10:37Z"

Entry not found

Danjin/Llama-2-7b-chat-finetunev2

Danjin

"2024-06-14T02:24:00Z"

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

"2024-06-14T02:13:13Z"

Entry not found

moemoe101/SmartAIRecipe

moemoe101

"2024-06-14T02:19:47Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T02:19:47Z"

--- license: apache-2.0 ---

fxmeng/PiSSA-Mistral-7b-r64

fxmeng

"2024-06-14T06:54:28Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

"2024-06-14T02:21:01Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-33-percent-high-bt-rouge-1

AdamKasumovic

"2024-06-14T02:35:12Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-14T02:32:10Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

qsebso/first_model_yelp_review

qsebso

"2024-06-14T02:32:56Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:32:56Z"

Entry not found

cadeath/cvss_0614

cadeath

"2024-06-14T03:32:54Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-14T02:33:51Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** cadeath - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-33-percent-med-bt-rouge-1

AdamKasumovic

"2024-06-14T02:38:08Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-14T02:34:52Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

sagnikrayc/opt-350m-bn-adapter-squad-model1

sagnikrayc

"2024-06-14T13:37:43Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:35:26Z"

Entry not found

Puandraa/AisakaTaiga

Puandraa

"2024-06-14T02:35:32Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T02:35:32Z"

--- license: apache-2.0 ---

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-33-percent-low-bt-rouge-1

AdamKasumovic

"2024-06-14T02:39:16Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-14T02:36:15Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

amazeble/epiclazygasm_.safetensors

amazeble

"2024-06-14T02:43:12Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T02:39:04Z"

--- license: apache-2.0 ---

chainup244/Qwen-Qwen1.5-0.5B-1718332862

chainup244

"2024-06-14T02:41:03Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:41:03Z"

Entry not found

chainup244/Qwen-Qwen1.5-1.8B-1718332943

chainup244

"2024-06-14T02:42:24Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:42:24Z"

Entry not found

jony8/test001

jony8

"2024-06-14T02:43:31Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T02:43:31Z"

--- license: apache-2.0 ---

iamnguyen/cupid

iamnguyen

"2024-06-30T11:54:37Z"

0

null

[ "safetensors", "region:us" ]

null

"2024-06-14T02:43:44Z"

Entry not found

yuekai/icefall_asr_aishell_whisper_qwen2_1.5B

yuekai

"2024-06-14T04:20:30Z"

0

null

[ "tensorboard", "region:us" ]

null

"2024-06-14T02:44:04Z"

Entry not found

dimassyoga42/huggingface_hub

dimassyoga42

"2024-06-14T02:44:31Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:44:31Z"

Entry not found

harryslater58/models

harryslater58

"2024-06-14T02:45:02Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T02:45:02Z"

--- license: apache-2.0 ---

jinbhunandaxue/pipeline

jinbhunandaxue

"2024-06-14T02:45:09Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:45:09Z"

Entry not found

dimassyoga42/Dimas

dimassyoga42

"2024-06-14T02:57:33Z"

0

null

[ "region:us" ]

null

"2024-06-14T02:53:12Z"

Entry not found

camenduru/Unique3D

camenduru

"2024-06-14T03:03:15Z"

0

1

diffusers

[ "diffusers", "onnx", "safetensors", "region:us" ]

null

"2024-06-14T02:57:37Z"

Entry not found

Ksgk-fy/phillipine_customer_v3.6_Maria_Intro_Objection_v2

Ksgk-fy

"2024-06-14T03:42:50Z"

0

null

[ "safetensors", "region:us" ]

null

"2024-06-14T02:59:06Z"

Entry not found

AWeirdDev/zh-tw-llama3-tokenizer-3k

AWeirdDev

"2024-06-14T03:03:10Z"

0

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

"2024-06-14T03:02:20Z"

--- library_name: transformers tags: [] --- # zh-tw-llama-3-tokenizer-3k

10ths/test

10ths

"2024-06-14T03:03:42Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:03:42Z"

Entry not found

JudithWiz/Astrologa

JudithWiz

"2024-06-14T03:04:10Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:04:10Z"

Entry not found

xxxhhhttt/TinyChineseStories-LLaMA2

xxxhhhttt

"2024-06-14T03:08:51Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

"2024-06-14T03:08:39Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Blackroot/Euryale-2.1-3.3b-6h-exl2

Blackroot

"2024-06-14T03:25:27Z"

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

"2024-06-14T03:11:16Z"

3.3Bpw 6bit head quantized version of https://huggingface.co/Sao10K/L3-70B-Euryale-v2.1

RyotaKadoya1993/fullymerged_v4_adapter

RyotaKadoya1993

"2024-06-14T03:16:07Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:RyotaKadoya1993/fullymerged_v1_128_gen3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-14T03:11:42Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: RyotaKadoya1993/fullymerged_v1_128_gen3 --- # Uploaded model - **Developed by:** RyotaKadoya1993 - **License:** apache-2.0 - **Finetuned from model :** RyotaKadoya1993/fullymerged_v1_128_gen3 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

emidiosouza/kukac-doc

emidiosouza

"2024-06-14T03:11:58Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:11:58Z"

Entry not found

OpilotAI/TinyLlama-1.1B-Chat-v1.0-q4f16_1-Opilot

OpilotAI

"2024-06-14T03:13:55Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:13:23Z"

Entry not found

ManCD/Arima_pm25_model

ManCD

"2024-06-14T03:14:28Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:14:28Z"

Entry not found

againeureka/support_and_attack_classifier

againeureka

"2024-06-14T03:19:35Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:19:35Z"

Entry not found

jeiku/Aura_Qwen2_v4_7B

jeiku

"2024-06-14T03:20:32Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:20:32Z"

Entry not found

erizakaria/llama3-8b-daftarin-id-lora

erizakaria

"2024-06-14T03:22:58Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-14T03:22:31Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** erizakaria - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ShiftAddLLM/opt13b-2bit-lat

ShiftAddLLM

"2024-06-14T04:11:32Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:23:34Z"

Entry not found

Yuki20/llama3_8b_aci_2e

Yuki20

"2024-06-14T03:24:53Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-14T03:24:46Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** Yuki20 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

onizukal/Karma_3Class_RMSprop_1e4_20Epoch_Beit-large-224_fold2

onizukal

"2024-06-14T05:22:16Z"

0

transformers

[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-06-14T03:37:50Z"

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Karma_3Class_RMSprop_1e4_20Epoch_Beit-large-224_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: 0.8374279648164998 ---  # Karma_3Class_RMSprop_1e4_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7596 - Accuracy: 0.8374 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.412 | 1.0 | 2466 | 0.5102 | 0.7956 | | 0.4251 | 2.0 | 4932 | 0.4462 | 0.8177 | | 0.3057 | 3.0 | 7398 | 0.4440 | 0.8302 | | 0.2355 | 4.0 | 9864 | 0.5256 | 0.8296 | | 0.1256 | 5.0 | 12330 | 0.7596 | 0.8374 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

nikollaz/promptoargentum

nikollaz

"2024-06-14T03:40:39Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T03:40:39Z"

--- license: apache-2.0 ---

Milancheeks/AuRA

Milancheeks

"2024-06-19T13:04:30Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-06-14T03:42:41Z"

--- license: apache-2.0 --- --- # AuRA - Augmented Universal Real-Time Assistant ## Overview **AuRA (Augmented Universal Real-Time Assistant)** represents a new paradigm in AI-driven assistance by leveraging outputs from multiple state-of-the-art language models. This approach ensures that AuRA continuously learns and evolves, integrating the latest advancements in natural language processing (NLP). By combining the strengths of various models, AuRA offers unparalleled assistance across diverse domains, making it a highly versatile and intelligent assistant. ## Vision and Goals AuRA is designed to redefine AI-driven assistance with the following core goals: - **Integrate Knowledge**: Combine outputs from multiple LLMs to create a comprehensive and enriched knowledge base. - **Real-Time Learning**: Continuously update its training data with new information and advancements, ensuring it remains cutting-edge. - **Versatile Assistance**: Provide high-quality responses across a wide range of topics and tasks. - **User-Centric Development**: Incorporate user feedback to dynamically refine and improve performance. - **AI Data Broker**: Act as a joint controller for user data, ensuring users get compensated when their data is used and providing the option to lock their data if they choose. - **Action Model**: Learn actions from tools created by other developers, enabling AuRA to perform a wide range of tasks beyond traditional text-based assistance. ## System Architecture ### Data Integration Pipeline The data integration pipeline is designed to ensure seamless collection, processing, and utilization of data from various sources. Key components include: - **Source Models**: Collect data from leading language models (LLMs) such as GPT-3.5, GPT-4, and others. - **Automated Data Collection**: Continuously fetch outputs from these models based on user interactions. - **Data Processing**: Clean, format, and validate collected data to ensure high quality and consistency. - **Dynamic Dataset**: Maintain a regularly updated dataset that serves as the foundation for training. - **Intelligent Data Sampling**: Use active learning techniques to selectively sample the most informative and diverse data points for training. - **Data Augmentation**: Increase the diversity and robustness of the training data through techniques like paraphrasing and synonym replacement. - **Real-Time Data Integration**: Enable real-time data integration to keep the model current. - **Scalability and Efficiency**: Design the pipeline to handle large volumes of data without compromising performance. - **Security and Privacy**: Adhere to strict security and privacy standards to protect user data. ### Model Training AuRA's model training process includes: - **Base Model**: Built on the Mistral-7B-v0.2 model. - **Finetuning with LoRA**: Use Low-Rank Adaptation (LoRA) for efficient adaptation to new data. - **Incremental Training**: Regular updates with new interaction data. - **Mixture of Experts (MoE)**: Utilize different parts of the model for different inputs to handle a wide variety of tasks efficiently. - **Sparse Attention Mechanisms**: Reduce computational complexity for processing long sequences of data. - **Knowledge Distillation**: Use a larger, pre-trained model to teach AuRA. - **Gradient Checkpointing**: Save memory by checkpointing intermediate states during training. - **Mixed Precision Training**: Use mixed precision (fp16) to speed up training and reduce memory usage. - **Layer-wise Learning Rate Scaling**: Adjust learning rates at different layers for faster convergence. ### Feedback Loop The feedback loop ensures continuous learning and improvement by: - **User Feedback**: Collecting feedback from users through interactions, surveys, and implicit behavior. - **Active Learning**: Integrating feedback into the training pipeline. - **Automated Feedback Analysis**: Using NLP and machine learning algorithms to analyze feedback. - **Reinforcement Learning**: Fine-tuning the model based on user interactions. - **Real-Time Adaptation**: Adjusting responses and behavior based on immediate feedback. - **Quality Assurance**: Regular evaluations and benchmarking. - **Transparency and Communication**: Maintaining transparency about how user feedback is used. ## Real-World Applications AuRA's versatility enables its application in various domains, including: - **Customer Support**: Providing real-time assistance and resolving queries. - **Education**: Offering personalized tutoring and educational content. - **Healthcare**: Assisting with medical information retrieval and patient interaction. - **Business Intelligence**: Analyzing data and generating insights for decision-making. - **AI Data Broker**: Ensuring users get compensated when their data is used and providing the option to lock their data. ## Ethical Considerations AuRA's development adheres to strict ethical principles, including: - **Data Privacy**: Ensuring user data privacy with robust encryption and user control. - **Bias Mitigation**: Continuously monitoring and correcting biases in data and model outputs. - **Transparency**: Maintaining transparency about data practices. - **Accountability**: Regular audits and compliance with legal and regulatory standards. - **Collaborative Ethics Development**: Working with the World Ethics Organization to build an ethical framework. ## Future Work Future development focuses on: - **Expansion of Data Sources**: Integrating additional models and data sources. - **Advanced NLP Techniques**: Incorporating new NLP techniques and architectures. - **Multimodal Learning**: Enabling understanding and processing of various data formats. - **Enhanced User Interfaces**: Developing more intuitive and user-friendly interfaces. - **Real-Time Adaptability**: Strengthening real-time learning and adaptation capabilities. - **Ethical AI Development**: Fully implementing the ethical framework. - **Real-World Applications**: Expanding into new application domains and conducting case studies. ## Conclusion AuRA represents a significant leap forward in AI-driven assistance, integrating multiple language models to provide unparalleled support across diverse domains. With a commitment to real-time learning, user-centric development, and ethical AI practices, AuRA is set to revolutionize the way we interact with technology. For more information and to explore the capabilities of AuRA, visit the [Hugging Face model page](https://huggingface.co/Milancheeks/AuRA). ---

onizukal/Boya1_3Class_SGD_1e4_20Epoch_Beit-large-224_fold3

onizukal

"2024-06-14T04:53:23Z"

0

transformers

[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-06-14T03:44:03Z"

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya1_3Class_SGD_1e4_20Epoch_Beit-large-224_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: 0.5871212121212122 ---  # Boya1_3Class_SGD_1e4_20Epoch_Beit-large-224_fold3 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0362 - Accuracy: 0.5871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1275 | 1.0 | 923 | 1.0932 | 0.5739 | | 1.0274 | 2.0 | 1846 | 1.0618 | 0.5809 | | 1.0425 | 3.0 | 2769 | 1.0467 | 0.5847 | | 1.0677 | 4.0 | 3692 | 1.0385 | 0.5863 | | 1.0395 | 5.0 | 4615 | 1.0362 | 0.5871 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

Ksgk-fy/ecoach_philippine_v7_intro_object_merge

Ksgk-fy

"2024-06-14T03:45:28Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:45:28Z"

Entry not found

shakilanf/pedut123

shakilanf

"2024-06-14T03:48:41Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:48:41Z"

Entry not found

A00954334/finetuning-sentiment-model-3000-samples

A00954334

"2024-06-14T03:58:05Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-06-14T03:48:44Z"

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3252 - Accuracy: 0.8733 - F1: 0.8758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Elyitra/gamer

Elyitra

"2024-06-14T03:50:33Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:50:28Z"

Entry not found

datnguyen16123009/Test_Phi3

datnguyen16123009

"2024-06-14T03:51:30Z"

0

null

[ "region:us" ]

null

"2024-06-14T03:51:30Z"

Entry not found

Nutanix/Meta-Llama-3-8B-Instruct_KTO_lora_hh-rlhf-processed

Nutanix

"2024-06-14T03:52:52Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-06-14T03:52:49Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Danjin/unsloth-gemma-glaive-function-callingv3

Danjin

"2024-06-14T04:00:03Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-1.1-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-14T03:59:57Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-1.1-2b-it-bnb-4bit --- # Uploaded model - **Developed by:** Danjin - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

nhfffff/niare_flipgod

nhfffff

"2024-06-14T04:05:32Z"

0

null

[ "region:us" ]

null

"2024-06-14T04:01:04Z"

Entry not found

JamesHujy/EMABench

JamesHujy

"2024-06-14T04:01:35Z"

0

null

[ "region:us" ]

null

"2024-06-14T04:01:15Z"

Entry not found

Nutanix/Meta-Llama-3-8B-Instruct_KTO_lora_distilabel-capybara-kto-15k-binarized-processed

Nutanix

"2024-06-14T04:04:27Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-06-14T04:04:24Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LAB-IA-UFBA/myeloma-yolo7-model

LAB-IA-UFBA

"2024-06-14T04:57:54Z"

0

null

[ "yolo7", "dataset:LAB-IA-UFBA/myeloma-dataset", "license:apache-2.0", "region:us" ]

null

"2024-06-14T04:09:51Z"

--- license: apache-2.0 datasets: - LAB-IA-UFBA/myeloma-dataset tags: - yolo7 --- Here you will find all codes, models, and data used in the manuscript "Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists." Scientific Reports 14.1 (2024).

WolfSmasher99/ana-de-armas

WolfSmasher99

"2024-06-14T04:16:03Z"

0

null

[ "region:us" ]

null

"2024-06-14T04:10:17Z"

Entry not found

ShiftAddLLM/opt30b-2bit-lat

ShiftAddLLM

"2024-06-14T04:15:13Z"

0

null

[ "region:us" ]

null

"2024-06-14T04:13:29Z"

Entry not found

chainup244/Qwen-Qwen1.5-0.5B-1718338417

chainup244

"2024-06-14T04:13:39Z"

0

null

[ "region:us" ]

null

"2024-06-14T04:13:39Z"

Entry not found

tspeterkim3/arcface

tspeterkim3

"2024-06-14T04:15:06Z"

0

null

[ "region:us" ]

null

"2024-06-14T04:15:06Z"

Entry not found

ShiftAddLLM/opt30b-3bit-lat

ShiftAddLLM

"2024-06-14T04:18:47Z"

0

null

[ "region:us" ]

null

"2024-06-14T04:15:33Z"

Entry not found