ftopal/huggingface-models-raw · Datasets at Hugging Face

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null	transformers	# 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]	{"library_name": "transformers", "tags": []}	fxmeng/PiSSA-Llama-3-70B-4bit-r64-5iter	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:46:18+00:00
null	null		{}	HikariLight/mistral-daic-woz-unsupervised-finetune_hs	null	[ "region:us" ]	null	2024-04-30T09:47:50+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-1_1B](https://hf.co/apple/OpenELM-1_1B) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-1_1B) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-1_1B	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:48:18+00:00
token-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0992 - Precision: 0.6178 - Recall: 0.2607 - F1: 0.3666 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 102 \| 0.0992 \| 0.6178 \| 0.2607 \| 0.3666 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "base_model": "bert-base-cased", "model-index": [{"name": "BERT", "results": []}]}	Farjfar/BERT	null	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:48:45+00:00
text-to-image	diffusers	<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - merkol/sd-naruto-model-lora These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the lambdalabs/naruto-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "diffusers-training", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "inference": true}	merkol/sd-naruto-model-lora	null	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	null	2024-04-30T09:48:54+00:00
null	null		{}	Danilas/sn25-1-3	null	[ "region:us" ]	null	2024-04-30T09:49:03+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-1_1B-Instruct](https://hf.co/apple/OpenELM-1_1B-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-1_1B-Instruct) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-1_1B-Instruct	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:49:09+00:00
null	null		{}	NiiCole/peft-rosa-adapter-1	null	[ "region:us" ]	null	2024-04-30T09:49:40+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-270M](https://hf.co/apple/OpenELM-270M) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-270M) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-270M	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:50:03+00:00
text-generation	transformers	<!-- 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. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 3.5701 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.6922 \| 1.0 \| 1306 \| 3.5763 \| \| 3.5786 \| 2.0 \| 2612 \| 3.5707 \| \| 3.527 \| 3.0 \| 3918 \| 3.5701 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "gpt2", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]}	bzdz/my_awesome_eli5_clm-model	null	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T09:50:13+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-270M-Instruct](https://hf.co/apple/OpenELM-270M-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-270M-Instruct) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-270M-Instruct	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:50:19+00:00
object-detection	transformers	<!-- 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/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/u73br2l9) # facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.2371 - Map: 0.2873 - Map 50: 0.5678 - Map 75: 0.2501 - Map Small: 0.126 - Map Medium: 0.2327 - Map Large: 0.4873 - Mar 1: 0.2843 - Mar 10: 0.4643 - Mar 100: 0.4762 - Mar Small: 0.2338 - Mar Medium: 0.4167 - Mar Large: 0.7114 - Map Coverall: 0.5461 - Mar 100 Coverall: 0.6932 - Map Face Shield: 0.2167 - Mar 100 Face Shield: 0.4785 - Map Gloves: 0.2135 - Mar 100 Gloves: 0.4094 - Map Goggles: 0.173 - Mar 100 Goggles: 0.4092 - Map Mask: 0.2871 - Mar 100 Mask: 0.3907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 2.6324 \| 1.0 \| 107 \| 2.3696 \| 0.0355 \| 0.0772 \| 0.0305 \| 0.0094 \| 0.0044 \| 0.0388 \| 0.0606 \| 0.1313 \| 0.1643 \| 0.0314 \| 0.0921 \| 0.1907 \| 0.1559 \| 0.536 \| 0.0 \| 0.0 \| 0.0064 \| 0.1433 \| 0.0 \| 0.0 \| 0.0151 \| 0.1422 \| \| 2.0851 \| 2.0 \| 214 \| 2.1468 \| 0.0518 \| 0.1164 \| 0.0389 \| 0.0106 \| 0.0151 \| 0.0683 \| 0.0809 \| 0.1628 \| 0.1881 \| 0.023 \| 0.1069 \| 0.2515 \| 0.2068 \| 0.5842 \| 0.0 \| 0.0 \| 0.0174 \| 0.167 \| 0.0 \| 0.0 \| 0.035 \| 0.1893 \| \| 1.9849 \| 3.0 \| 321 \| 2.0694 \| 0.0592 \| 0.1436 \| 0.0377 \| 0.0187 \| 0.023 \| 0.0609 \| 0.0796 \| 0.1666 \| 0.191 \| 0.0527 \| 0.1032 \| 0.2411 \| 0.2119 \| 0.5486 \| 0.0 \| 0.0 \| 0.0226 \| 0.1799 \| 0.0 \| 0.0 \| 0.0615 \| 0.2262 \| \| 1.8364 \| 4.0 \| 428 \| 2.0431 \| 0.0715 \| 0.1666 \| 0.0625 \| 0.0031 \| 0.0232 \| 0.09 \| 0.0775 \| 0.1641 \| 0.183 \| 0.0083 \| 0.1006 \| 0.2708 \| 0.2923 \| 0.5604 \| 0.0012 \| 0.0177 \| 0.0203 \| 0.1612 \| 0.0 \| 0.0 \| 0.0437 \| 0.1756 \| \| 1.7433 \| 5.0 \| 535 \| 1.9001 \| 0.0938 \| 0.2115 \| 0.0758 \| 0.0229 \| 0.0473 \| 0.1031 \| 0.1066 \| 0.216 \| 0.2311 \| 0.0451 \| 0.1423 \| 0.3186 \| 0.3168 \| 0.5847 \| 0.0064 \| 0.0937 \| 0.05 \| 0.2277 \| 0.0 \| 0.0 \| 0.0959 \| 0.2493 \| \| 1.7113 \| 6.0 \| 642 \| 1.8483 \| 0.1007 \| 0.2225 \| 0.0773 \| 0.0203 \| 0.0593 \| 0.1341 \| 0.1109 \| 0.2213 \| 0.2463 \| 0.0646 \| 0.1711 \| 0.3303 \| 0.3462 \| 0.5914 \| 0.0132 \| 0.0987 \| 0.0451 \| 0.2571 \| 0.0002 \| 0.0062 \| 0.0987 \| 0.2782 \| \| 1.6518 \| 7.0 \| 749 \| 1.8376 \| 0.1055 \| 0.2347 \| 0.0807 \| 0.0199 \| 0.0596 \| 0.1405 \| 0.1127 \| 0.2364 \| 0.2598 \| 0.0778 \| 0.1949 \| 0.3276 \| 0.3693 \| 0.5869 \| 0.0168 \| 0.1367 \| 0.0417 \| 0.2397 \| 0.0046 \| 0.0523 \| 0.0953 \| 0.2831 \| \| 1.6048 \| 8.0 \| 856 \| 1.8041 \| 0.1098 \| 0.2576 \| 0.0857 \| 0.0155 \| 0.0772 \| 0.1578 \| 0.1284 \| 0.2423 \| 0.2708 \| 0.0602 \| 0.2057 \| 0.3631 \| 0.3512 \| 0.6149 \| 0.033 \| 0.1342 \| 0.0402 \| 0.2491 \| 0.004 \| 0.0862 \| 0.1204 \| 0.2698 \| \| 1.6026 \| 9.0 \| 963 \| 1.7173 \| 0.1243 \| 0.2773 \| 0.1041 \| 0.0415 \| 0.0688 \| 0.1767 \| 0.1426 \| 0.2852 \| 0.3101 \| 0.0857 \| 0.2275 \| 0.4249 \| 0.3889 \| 0.6378 \| 0.0347 \| 0.2253 \| 0.0547 \| 0.271 \| 0.0159 \| 0.1308 \| 0.1274 \| 0.2858 \| \| 1.5298 \| 10.0 \| 1070 \| 1.7915 \| 0.1154 \| 0.2763 \| 0.0804 \| 0.018 \| 0.0789 \| 0.1762 \| 0.1447 \| 0.2629 \| 0.2913 \| 0.0774 \| 0.2191 \| 0.3935 \| 0.3615 \| 0.618 \| 0.0415 \| 0.2127 \| 0.0351 \| 0.2603 \| 0.0076 \| 0.0846 \| 0.1315 \| 0.2809 \| \| 1.5114 \| 11.0 \| 1177 \| 1.7964 \| 0.1232 \| 0.2755 \| 0.0915 \| 0.0396 \| 0.0849 \| 0.1728 \| 0.144 \| 0.2876 \| 0.3007 \| 0.0868 \| 0.2161 \| 0.4535 \| 0.3588 \| 0.5923 \| 0.035 \| 0.2291 \| 0.057 \| 0.2241 \| 0.0195 \| 0.1877 \| 0.1455 \| 0.2702 \| \| 1.5283 \| 12.0 \| 1284 \| 1.7017 \| 0.1444 \| 0.3075 \| 0.1223 \| 0.0215 \| 0.0985 \| 0.2396 \| 0.1803 \| 0.3244 \| 0.3456 \| 0.0531 \| 0.282 \| 0.5554 \| 0.4122 \| 0.632 \| 0.0666 \| 0.3215 \| 0.0618 \| 0.2625 \| 0.0086 \| 0.2046 \| 0.1727 \| 0.3076 \| \| 1.5034 \| 13.0 \| 1391 \| 1.6344 \| 0.1538 \| 0.3418 \| 0.1226 \| 0.0423 \| 0.1165 \| 0.2381 \| 0.1871 \| 0.337 \| 0.3578 \| 0.1195 \| 0.2901 \| 0.5214 \| 0.4204 \| 0.6284 \| 0.0705 \| 0.319 \| 0.0634 \| 0.2884 \| 0.0191 \| 0.2277 \| 0.1959 \| 0.3253 \| \| 1.445 \| 14.0 \| 1498 \| 1.5685 \| 0.167 \| 0.3626 \| 0.15 \| 0.0561 \| 0.1266 \| 0.2715 \| 0.1998 \| 0.357 \| 0.3701 \| 0.1068 \| 0.2922 \| 0.5693 \| 0.4227 \| 0.6342 \| 0.0873 \| 0.3797 \| 0.0744 \| 0.2951 \| 0.0441 \| 0.2108 \| 0.2065 \| 0.3307 \| \| 1.3998 \| 15.0 \| 1605 \| 1.5679 \| 0.1645 \| 0.3515 \| 0.1383 \| 0.0269 \| 0.117 \| 0.2592 \| 0.1992 \| 0.3295 \| 0.3539 \| 0.0645 \| 0.2751 \| 0.5533 \| 0.4535 \| 0.6703 \| 0.0887 \| 0.3494 \| 0.0866 \| 0.3116 \| 0.0276 \| 0.1554 \| 0.1659 \| 0.2827 \| \| 1.398 \| 16.0 \| 1712 \| 1.6191 \| 0.1578 \| 0.3459 \| 0.1205 \| 0.0623 \| 0.0967 \| 0.258 \| 0.1729 \| 0.3255 \| 0.3386 \| 0.1106 \| 0.2686 \| 0.5066 \| 0.4363 \| 0.6 \| 0.0971 \| 0.3089 \| 0.0703 \| 0.2839 \| 0.0258 \| 0.2 \| 0.1593 \| 0.3004 \| \| 1.369 \| 17.0 \| 1819 \| 1.6099 \| 0.1654 \| 0.3517 \| 0.1371 \| 0.0341 \| 0.099 \| 0.2543 \| 0.1852 \| 0.3085 \| 0.3232 \| 0.0587 \| 0.2331 \| 0.5126 \| 0.4663 \| 0.6329 \| 0.0784 \| 0.2785 \| 0.0761 \| 0.2906 \| 0.019 \| 0.12 \| 0.187 \| 0.2942 \| \| 1.3264 \| 18.0 \| 1926 \| 1.5610 \| 0.1784 \| 0.3752 \| 0.1594 \| 0.036 \| 0.1249 \| 0.3082 \| 0.2061 \| 0.3388 \| 0.3561 \| 0.0959 \| 0.2702 \| 0.5669 \| 0.5 \| 0.6707 \| 0.0768 \| 0.2937 \| 0.0899 \| 0.2933 \| 0.0291 \| 0.2108 \| 0.1962 \| 0.312 \| \| 1.3513 \| 19.0 \| 2033 \| 1.4462 \| 0.1864 \| 0.3933 \| 0.154 \| 0.0559 \| 0.1364 \| 0.2738 \| 0.2135 \| 0.3767 \| 0.3931 \| 0.1216 \| 0.3178 \| 0.5744 \| 0.4834 \| 0.677 \| 0.0976 \| 0.3595 \| 0.1022 \| 0.3438 \| 0.0248 \| 0.2554 \| 0.224 \| 0.3298 \| \| 1.3034 \| 20.0 \| 2140 \| 1.4527 \| 0.1965 \| 0.412 \| 0.1713 \| 0.051 \| 0.1463 \| 0.2967 \| 0.2087 \| 0.3643 \| 0.3805 \| 0.1422 \| 0.2931 \| 0.5833 \| 0.4982 \| 0.6495 \| 0.1144 \| 0.362 \| 0.0997 \| 0.3263 \| 0.0497 \| 0.2277 \| 0.2204 \| 0.3369 \| \| 1.2823 \| 21.0 \| 2247 \| 1.4872 \| 0.1835 \| 0.3972 \| 0.1556 \| 0.0596 \| 0.1308 \| 0.282 \| 0.2148 \| 0.3924 \| 0.4147 \| 0.1236 \| 0.3657 \| 0.5903 \| 0.4622 \| 0.6455 \| 0.0727 \| 0.3873 \| 0.1195 \| 0.3558 \| 0.0544 \| 0.3415 \| 0.2084 \| 0.3431 \| \| 1.3099 \| 22.0 \| 2354 \| 1.4723 \| 0.1903 \| 0.407 \| 0.1632 \| 0.0594 \| 0.1343 \| 0.2937 \| 0.2158 \| 0.3817 \| 0.396 \| 0.1357 \| 0.3222 \| 0.5737 \| 0.4719 \| 0.6486 \| 0.1089 \| 0.3722 \| 0.1153 \| 0.3161 \| 0.0404 \| 0.3092 \| 0.2149 \| 0.3338 \| \| 1.2691 \| 23.0 \| 2461 \| 1.4648 \| 0.1897 \| 0.403 \| 0.1626 \| 0.0598 \| 0.1476 \| 0.2933 \| 0.2129 \| 0.3676 \| 0.3851 \| 0.1154 \| 0.3247 \| 0.592 \| 0.4836 \| 0.6658 \| 0.0711 \| 0.3025 \| 0.1217 \| 0.3679 \| 0.0594 \| 0.2615 \| 0.2127 \| 0.328 \| \| 1.3018 \| 24.0 \| 2568 \| 1.5593 \| 0.174 \| 0.3964 \| 0.1358 \| 0.0568 \| 0.1344 \| 0.2776 \| 0.2076 \| 0.3571 \| 0.3754 \| 0.1412 \| 0.2946 \| 0.5732 \| 0.4208 \| 0.6347 \| 0.0782 \| 0.2987 \| 0.1125 \| 0.3304 \| 0.0444 \| 0.2831 \| 0.2141 \| 0.3302 \| \| 1.2744 \| 25.0 \| 2675 \| 1.4785 \| 0.1863 \| 0.3983 \| 0.1556 \| 0.0706 \| 0.1314 \| 0.3032 \| 0.221 \| 0.3784 \| 0.3988 \| 0.1453 \| 0.3331 \| 0.5824 \| 0.4831 \| 0.6716 \| 0.0934 \| 0.3392 \| 0.1155 \| 0.3647 \| 0.0612 \| 0.3277 \| 0.1783 \| 0.2907 \| \| 1.3089 \| 26.0 \| 2782 \| 1.4503 \| 0.1969 \| 0.4129 \| 0.166 \| 0.0904 \| 0.1342 \| 0.3274 \| 0.2303 \| 0.3907 \| 0.4057 \| 0.154 \| 0.3252 \| 0.6182 \| 0.4773 \| 0.6653 \| 0.109 \| 0.4114 \| 0.1176 \| 0.3504 \| 0.0758 \| 0.2877 \| 0.2046 \| 0.3138 \| \| 1.2391 \| 27.0 \| 2889 \| 1.4961 \| 0.1992 \| 0.4097 \| 0.1714 \| 0.067 \| 0.1354 \| 0.3227 \| 0.2308 \| 0.3803 \| 0.3971 \| 0.1333 \| 0.3179 \| 0.623 \| 0.4826 \| 0.6473 \| 0.114 \| 0.3329 \| 0.1169 \| 0.3621 \| 0.0538 \| 0.2862 \| 0.2286 \| 0.3569 \| \| 1.2156 \| 28.0 \| 2996 \| 1.4349 \| 0.1926 \| 0.4121 \| 0.1612 \| 0.0655 \| 0.1425 \| 0.2933 \| 0.2279 \| 0.4047 \| 0.425 \| 0.1521 \| 0.36 \| 0.6217 \| 0.4698 \| 0.6797 \| 0.0974 \| 0.3924 \| 0.1097 \| 0.3402 \| 0.061 \| 0.3677 \| 0.2253 \| 0.3449 \| \| 1.2131 \| 29.0 \| 3103 \| 1.4869 \| 0.2002 \| 0.4106 \| 0.1706 \| 0.0662 \| 0.1516 \| 0.3142 \| 0.2342 \| 0.3959 \| 0.4175 \| 0.1389 \| 0.3525 \| 0.6269 \| 0.4846 \| 0.6514 \| 0.0845 \| 0.3899 \| 0.1196 \| 0.3701 \| 0.0699 \| 0.3277 \| 0.2425 \| 0.3484 \| \| 1.2368 \| 30.0 \| 3210 \| 1.3779 \| 0.218 \| 0.4403 \| 0.185 \| 0.084 \| 0.169 \| 0.3567 \| 0.241 \| 0.4115 \| 0.4326 \| 0.148 \| 0.3738 \| 0.6216 \| 0.5063 \| 0.6815 \| 0.122 \| 0.4342 \| 0.1497 \| 0.3969 \| 0.066 \| 0.3031 \| 0.2462 \| 0.3471 \| \| 1.1998 \| 31.0 \| 3317 \| 1.4472 \| 0.198 \| 0.4299 \| 0.16 \| 0.0595 \| 0.1492 \| 0.3264 \| 0.2239 \| 0.3883 \| 0.3992 \| 0.127 \| 0.3237 \| 0.6434 \| 0.4806 \| 0.6455 \| 0.1023 \| 0.3722 \| 0.1246 \| 0.3509 \| 0.0601 \| 0.3154 \| 0.2224 \| 0.312 \| \| 1.1681 \| 32.0 \| 3424 \| 1.4495 \| 0.2221 \| 0.4518 \| 0.1953 \| 0.0777 \| 0.1696 \| 0.3691 \| 0.2318 \| 0.3938 \| 0.4082 \| 0.1272 \| 0.3478 \| 0.6191 \| 0.5124 \| 0.6757 \| 0.1293 \| 0.362 \| 0.148 \| 0.3531 \| 0.0893 \| 0.32 \| 0.2318 \| 0.3302 \| \| 1.1764 \| 33.0 \| 3531 \| 1.4152 \| 0.2008 \| 0.4169 \| 0.1755 \| 0.0844 \| 0.154 \| 0.3627 \| 0.2432 \| 0.4092 \| 0.4304 \| 0.182 \| 0.3751 \| 0.6587 \| 0.4828 \| 0.6802 \| 0.0883 \| 0.3886 \| 0.1488 \| 0.3884 \| 0.069 \| 0.3692 \| 0.2154 \| 0.3258 \| \| 1.1979 \| 34.0 \| 3638 \| 1.4428 \| 0.2013 \| 0.4058 \| 0.1813 \| 0.0525 \| 0.1428 \| 0.3315 \| 0.2318 \| 0.3933 \| 0.4123 \| 0.1116 \| 0.3492 \| 0.6431 \| 0.4904 \| 0.6486 \| 0.0817 \| 0.3785 \| 0.1434 \| 0.3576 \| 0.0536 \| 0.3277 \| 0.2373 \| 0.3493 \| \| 1.1635 \| 35.0 \| 3745 \| 1.3778 \| 0.2114 \| 0.4473 \| 0.1701 \| 0.0619 \| 0.1497 \| 0.372 \| 0.2475 \| 0.3926 \| 0.4125 \| 0.1264 \| 0.3367 \| 0.6545 \| 0.4973 \| 0.6577 \| 0.1031 \| 0.3595 \| 0.1518 \| 0.379 \| 0.0604 \| 0.3262 \| 0.2444 \| 0.34 \| \| 1.1449 \| 36.0 \| 3852 \| 1.3933 \| 0.2136 \| 0.4427 \| 0.1846 \| 0.0791 \| 0.1615 \| 0.3765 \| 0.2354 \| 0.4041 \| 0.4206 \| 0.1575 \| 0.3565 \| 0.6543 \| 0.4915 \| 0.6626 \| 0.1052 \| 0.4101 \| 0.1672 \| 0.3795 \| 0.0773 \| 0.3169 \| 0.227 \| 0.3338 \| \| 1.1453 \| 37.0 \| 3959 \| 1.3954 \| 0.205 \| 0.4224 \| 0.176 \| 0.0648 \| 0.1498 \| 0.3456 \| 0.2412 \| 0.3984 \| 0.4095 \| 0.1209 \| 0.3492 \| 0.6536 \| 0.492 \| 0.6423 \| 0.1063 \| 0.3633 \| 0.1628 \| 0.3893 \| 0.0527 \| 0.3292 \| 0.2114 \| 0.3236 \| \| 1.1424 \| 38.0 \| 4066 \| 1.3781 \| 0.2137 \| 0.4373 \| 0.1839 \| 0.0774 \| 0.1634 \| 0.3815 \| 0.2488 \| 0.4149 \| 0.4281 \| 0.1632 \| 0.3589 \| 0.6559 \| 0.4926 \| 0.6559 \| 0.101 \| 0.4 \| 0.1576 \| 0.3616 \| 0.059 \| 0.3415 \| 0.258 \| 0.3813 \| \| 1.1118 \| 39.0 \| 4173 \| 1.3672 \| 0.2154 \| 0.4547 \| 0.1737 \| 0.0746 \| 0.1538 \| 0.3736 \| 0.2448 \| 0.3939 \| 0.4125 \| 0.1372 \| 0.3446 \| 0.6372 \| 0.5024 \| 0.6779 \| 0.1303 \| 0.3608 \| 0.1493 \| 0.3719 \| 0.0607 \| 0.3123 \| 0.2341 \| 0.3396 \| \| 1.088 \| 40.0 \| 4280 \| 1.3414 \| 0.2193 \| 0.4552 \| 0.1867 \| 0.0824 \| 0.1711 \| 0.3886 \| 0.2451 \| 0.4043 \| 0.4178 \| 0.1678 \| 0.3501 \| 0.6465 \| 0.4978 \| 0.6658 \| 0.1297 \| 0.3557 \| 0.1644 \| 0.404 \| 0.0618 \| 0.3077 \| 0.2426 \| 0.356 \| \| 1.1114 \| 41.0 \| 4387 \| 1.3819 \| 0.2228 \| 0.4721 \| 0.175 \| 0.0879 \| 0.1609 \| 0.3706 \| 0.255 \| 0.4006 \| 0.4146 \| 0.1458 \| 0.3427 \| 0.6389 \| 0.4924 \| 0.6689 \| 0.1164 \| 0.3734 \| 0.169 \| 0.3679 \| 0.0961 \| 0.3138 \| 0.2402 \| 0.3489 \| \| 1.0758 \| 42.0 \| 4494 \| 1.3459 \| 0.2286 \| 0.4696 \| 0.2048 \| 0.0897 \| 0.1806 \| 0.3734 \| 0.2526 \| 0.4245 \| 0.4366 \| 0.1566 \| 0.3898 \| 0.6341 \| 0.5112 \| 0.6815 \| 0.1497 \| 0.4038 \| 0.1742 \| 0.3938 \| 0.059 \| 0.3338 \| 0.2491 \| 0.3702 \| \| 1.0747 \| 43.0 \| 4601 \| 1.4411 \| 0.2178 \| 0.4397 \| 0.1951 \| 0.0581 \| 0.1557 \| 0.3977 \| 0.232 \| 0.3933 \| 0.4086 \| 0.1258 \| 0.3301 \| 0.6699 \| 0.4929 \| 0.6604 \| 0.1317 \| 0.3633 \| 0.1632 \| 0.3598 \| 0.1014 \| 0.34 \| 0.1997 \| 0.3196 \| \| 1.0909 \| 44.0 \| 4708 \| 1.3192 \| 0.2424 \| 0.4916 \| 0.2152 \| 0.0842 \| 0.1953 \| 0.4032 \| 0.26 \| 0.4237 \| 0.4387 \| 0.1597 \| 0.3801 \| 0.66 \| 0.5041 \| 0.6842 \| 0.1765 \| 0.4038 \| 0.1901 \| 0.3879 \| 0.0983 \| 0.3585 \| 0.2431 \| 0.3591 \| \| 1.0527 \| 45.0 \| 4815 \| 1.3110 \| 0.2362 \| 0.4902 \| 0.2009 \| 0.1033 \| 0.1842 \| 0.4042 \| 0.2505 \| 0.4306 \| 0.4496 \| 0.1869 \| 0.3896 \| 0.6601 \| 0.5178 \| 0.6797 \| 0.1379 \| 0.4215 \| 0.1828 \| 0.4098 \| 0.0848 \| 0.3754 \| 0.2576 \| 0.3613 \| \| 1.0421 \| 46.0 \| 4922 \| 1.3186 \| 0.2376 \| 0.4944 \| 0.213 \| 0.097 \| 0.19 \| 0.3833 \| 0.2587 \| 0.4303 \| 0.4487 \| 0.2199 \| 0.399 \| 0.643 \| 0.5174 \| 0.6824 \| 0.1569 \| 0.4114 \| 0.171 \| 0.3902 \| 0.0818 \| 0.3923 \| 0.261 \| 0.3671 \| \| 1.0428 \| 47.0 \| 5029 \| 1.3096 \| 0.2421 \| 0.4797 \| 0.2235 \| 0.0976 \| 0.196 \| 0.4236 \| 0.2574 \| 0.4299 \| 0.4437 \| 0.2217 \| 0.3688 \| 0.6646 \| 0.5242 \| 0.6824 \| 0.155 \| 0.419 \| 0.1757 \| 0.396 \| 0.1006 \| 0.3585 \| 0.255 \| 0.3627 \| \| 1.0369 \| 48.0 \| 5136 \| 1.3049 \| 0.2447 \| 0.4953 \| 0.2069 \| 0.0929 \| 0.1971 \| 0.421 \| 0.2651 \| 0.4284 \| 0.4393 \| 0.1395 \| 0.3866 \| 0.6669 \| 0.5228 \| 0.6892 \| 0.1539 \| 0.419 \| 0.1757 \| 0.3763 \| 0.1091 \| 0.3492 \| 0.2619 \| 0.3627 \| \| 1.0315 \| 49.0 \| 5243 \| 1.3189 \| 0.2417 \| 0.4851 \| 0.2149 \| 0.0914 \| 0.1967 \| 0.4224 \| 0.2682 \| 0.4241 \| 0.4431 \| 0.1874 \| 0.3756 \| 0.6742 \| 0.5079 \| 0.691 \| 0.1679 \| 0.4215 \| 0.1627 \| 0.3857 \| 0.1174 \| 0.3569 \| 0.2527 \| 0.3604 \| \| 1.0149 \| 50.0 \| 5350 \| 1.3127 \| 0.2399 \| 0.4962 \| 0.2041 \| 0.096 \| 0.1854 \| 0.4276 \| 0.2638 \| 0.4178 \| 0.4325 \| 0.1657 \| 0.3617 \| 0.6608 \| 0.5148 \| 0.6797 \| 0.1434 \| 0.3734 \| 0.1616 \| 0.3746 \| 0.1342 \| 0.3831 \| 0.2454 \| 0.3516 \| \| 1.0223 \| 51.0 \| 5457 \| 1.2917 \| 0.2419 \| 0.4981 \| 0.2153 \| 0.0942 \| 0.2027 \| 0.4113 \| 0.2574 \| 0.4243 \| 0.4418 \| 0.1948 \| 0.3921 \| 0.6428 \| 0.5136 \| 0.6928 \| 0.1563 \| 0.4038 \| 0.1727 \| 0.3987 \| 0.1049 \| 0.36 \| 0.2618 \| 0.3538 \| \| 1.0072 \| 52.0 \| 5564 \| 1.3413 \| 0.2634 \| 0.5259 \| 0.2292 \| 0.0985 \| 0.2074 \| 0.4356 \| 0.2615 \| 0.415 \| 0.4305 \| 0.1818 \| 0.354 \| 0.6634 \| 0.5187 \| 0.6739 \| 0.2066 \| 0.4076 \| 0.1795 \| 0.3915 \| 0.1321 \| 0.3215 \| 0.2802 \| 0.3582 \| \| 0.9923 \| 53.0 \| 5671 \| 1.3195 \| 0.2393 \| 0.4806 \| 0.2104 \| 0.1019 \| 0.1819 \| 0.3959 \| 0.2597 \| 0.4239 \| 0.4406 \| 0.1933 \| 0.3776 \| 0.6387 \| 0.5188 \| 0.677 \| 0.1376 \| 0.4114 \| 0.1713 \| 0.4103 \| 0.091 \| 0.3308 \| 0.2775 \| 0.3738 \| \| 0.9981 \| 54.0 \| 5778 \| 1.3229 \| 0.2406 \| 0.5014 \| 0.2014 \| 0.1122 \| 0.1896 \| 0.404 \| 0.2441 \| 0.4128 \| 0.4303 \| 0.1999 \| 0.3707 \| 0.6241 \| 0.5172 \| 0.673 \| 0.1521 \| 0.3975 \| 0.1695 \| 0.4009 \| 0.1067 \| 0.32 \| 0.2574 \| 0.36 \| \| 0.9892 \| 55.0 \| 5885 \| 1.3100 \| 0.2483 \| 0.5044 \| 0.2104 \| 0.0779 \| 0.1985 \| 0.4205 \| 0.2606 \| 0.4207 \| 0.4357 \| 0.1744 \| 0.3783 \| 0.6592 \| 0.5088 \| 0.6608 \| 0.1744 \| 0.4051 \| 0.1689 \| 0.3915 \| 0.1261 \| 0.36 \| 0.2632 \| 0.3609 \| \| 0.9704 \| 56.0 \| 5992 \| 1.3102 \| 0.2508 \| 0.5103 \| 0.22 \| 0.0998 \| 0.1951 \| 0.4222 \| 0.259 \| 0.4147 \| 0.4341 \| 0.158 \| 0.3784 \| 0.6439 \| 0.5205 \| 0.6797 \| 0.1459 \| 0.4038 \| 0.1683 \| 0.4045 \| 0.1467 \| 0.3338 \| 0.2723 \| 0.3484 \| \| 0.9724 \| 57.0 \| 6099 \| 1.2838 \| 0.2591 \| 0.5087 \| 0.2339 \| 0.1102 \| 0.2132 \| 0.4197 \| 0.267 \| 0.4329 \| 0.4461 \| 0.2133 \| 0.3891 \| 0.6508 \| 0.5396 \| 0.6901 \| 0.1688 \| 0.4139 \| 0.1781 \| 0.3835 \| 0.1362 \| 0.3708 \| 0.2727 \| 0.3724 \| \| 0.9768 \| 58.0 \| 6206 \| 1.2982 \| 0.2591 \| 0.5133 \| 0.2325 \| 0.0919 \| 0.2165 \| 0.4129 \| 0.2634 \| 0.4314 \| 0.4431 \| 0.1627 \| 0.3999 \| 0.6589 \| 0.5289 \| 0.6923 \| 0.171 \| 0.4013 \| 0.1841 \| 0.3821 \| 0.1417 \| 0.3708 \| 0.2695 \| 0.3689 \| \| 0.974 \| 59.0 \| 6313 \| 1.2888 \| 0.2611 \| 0.5216 \| 0.239 \| 0.1126 \| 0.2094 \| 0.4504 \| 0.2659 \| 0.4367 \| 0.4506 \| 0.2099 \| 0.3957 \| 0.6611 \| 0.5364 \| 0.6833 \| 0.1824 \| 0.4304 \| 0.1768 \| 0.3884 \| 0.1455 \| 0.38 \| 0.2646 \| 0.3711 \| \| 0.9585 \| 60.0 \| 6420 \| 1.2801 \| 0.269 \| 0.5399 \| 0.2305 \| 0.103 \| 0.2209 \| 0.4622 \| 0.2636 \| 0.4453 \| 0.4548 \| 0.227 \| 0.3944 \| 0.6777 \| 0.5471 \| 0.6896 \| 0.1805 \| 0.4392 \| 0.1935 \| 0.3862 \| 0.1518 \| 0.3908 \| 0.272 \| 0.368 \| \| 0.9693 \| 61.0 \| 6527 \| 1.2816 \| 0.2685 \| 0.5368 \| 0.2347 \| 0.0904 \| 0.2268 \| 0.4546 \| 0.2661 \| 0.4332 \| 0.4445 \| 0.1645 \| 0.3918 \| 0.6802 \| 0.5394 \| 0.6811 \| 0.1894 \| 0.4481 \| 0.1913 \| 0.3754 \| 0.1505 \| 0.3585 \| 0.2718 \| 0.3596 \| \| 0.9653 \| 62.0 \| 6634 \| 1.2668 \| 0.2718 \| 0.5518 \| 0.2315 \| 0.1013 \| 0.2221 \| 0.4447 \| 0.2714 \| 0.4392 \| 0.4524 \| 0.2027 \| 0.3974 \| 0.6521 \| 0.5416 \| 0.6833 \| 0.1993 \| 0.4418 \| 0.193 \| 0.4018 \| 0.1515 \| 0.3646 \| 0.2733 \| 0.3707 \| \| 0.9367 \| 63.0 \| 6741 \| 1.2756 \| 0.2678 \| 0.5419 \| 0.2284 \| 0.0915 \| 0.2106 \| 0.4618 \| 0.2696 \| 0.4304 \| 0.4467 \| 0.2066 \| 0.3823 \| 0.6624 \| 0.5449 \| 0.6941 \| 0.1986 \| 0.4418 \| 0.1872 \| 0.4098 \| 0.1375 \| 0.32 \| 0.2706 \| 0.3676 \| \| 0.9397 \| 64.0 \| 6848 \| 1.2792 \| 0.2693 \| 0.543 \| 0.2349 \| 0.1105 \| 0.2191 \| 0.4305 \| 0.2671 \| 0.447 \| 0.4594 \| 0.2058 \| 0.4023 \| 0.6729 \| 0.532 \| 0.6856 \| 0.1938 \| 0.4544 \| 0.1966 \| 0.3942 \| 0.1431 \| 0.3892 \| 0.2811 \| 0.3738 \| \| 0.9362 \| 65.0 \| 6955 \| 1.2815 \| 0.2622 \| 0.5268 \| 0.2297 \| 0.0993 \| 0.2152 \| 0.4254 \| 0.2702 \| 0.4417 \| 0.4564 \| 0.1804 \| 0.404 \| 0.6691 \| 0.5354 \| 0.6815 \| 0.1833 \| 0.4608 \| 0.1828 \| 0.3763 \| 0.1314 \| 0.3831 \| 0.2783 \| 0.3804 \| \| 0.9256 \| 66.0 \| 7062 \| 1.2706 \| 0.2785 \| 0.5545 \| 0.2555 \| 0.11 \| 0.2213 \| 0.4632 \| 0.2807 \| 0.4514 \| 0.4665 \| 0.2383 \| 0.4052 \| 0.6819 \| 0.5466 \| 0.6977 \| 0.2198 \| 0.4532 \| 0.1932 \| 0.4076 \| 0.1533 \| 0.3938 \| 0.2795 \| 0.38 \| \| 0.9251 \| 67.0 \| 7169 \| 1.2693 \| 0.2779 \| 0.5672 \| 0.2466 \| 0.1024 \| 0.2213 \| 0.4665 \| 0.2746 \| 0.4364 \| 0.4467 \| 0.1908 \| 0.3882 \| 0.6738 \| 0.5463 \| 0.6896 \| 0.2235 \| 0.4329 \| 0.1956 \| 0.3853 \| 0.1504 \| 0.3508 \| 0.2735 \| 0.3747 \| \| 0.9284 \| 68.0 \| 7276 \| 1.2592 \| 0.2784 \| 0.5506 \| 0.2458 \| 0.0927 \| 0.2304 \| 0.483 \| 0.2735 \| 0.4356 \| 0.4443 \| 0.1732 \| 0.3946 \| 0.6872 \| 0.5438 \| 0.6896 \| 0.2034 \| 0.4038 \| 0.2033 \| 0.3951 \| 0.1668 \| 0.3585 \| 0.2749 \| 0.3747 \| \| 0.9217 \| 69.0 \| 7383 \| 1.2788 \| 0.2751 \| 0.5401 \| 0.2457 \| 0.0984 \| 0.223 \| 0.4747 \| 0.2762 \| 0.4435 \| 0.4547 \| 0.2076 \| 0.4048 \| 0.6831 \| 0.5437 \| 0.6896 \| 0.2062 \| 0.4392 \| 0.1936 \| 0.3888 \| 0.1552 \| 0.3738 \| 0.277 \| 0.3818 \| \| 0.8987 \| 70.0 \| 7490 \| 1.2390 \| 0.2757 \| 0.5535 \| 0.2487 \| 0.1123 \| 0.2263 \| 0.4587 \| 0.2759 \| 0.4482 \| 0.4603 \| 0.2193 \| 0.4165 \| 0.6799 \| 0.5415 \| 0.6901 \| 0.1939 \| 0.4532 \| 0.2053 \| 0.4116 \| 0.1652 \| 0.3646 \| 0.2727 \| 0.3822 \| \| 0.8797 \| 71.0 \| 7597 \| 1.2614 \| 0.2749 \| 0.5451 \| 0.2406 \| 0.1055 \| 0.2251 \| 0.4616 \| 0.275 \| 0.446 \| 0.459 \| 0.1965 \| 0.4088 \| 0.6842 \| 0.5438 \| 0.6928 \| 0.19 \| 0.4506 \| 0.2133 \| 0.4058 \| 0.1504 \| 0.3723 \| 0.2768 \| 0.3733 \| \| 0.8864 \| 72.0 \| 7704 \| 1.2601 \| 0.2813 \| 0.5445 \| 0.2524 \| 0.1029 \| 0.2261 \| 0.4549 \| 0.28 \| 0.4474 \| 0.4608 \| 0.2296 \| 0.4052 \| 0.6677 \| 0.5449 \| 0.6878 \| 0.2062 \| 0.4342 \| 0.2122 \| 0.4196 \| 0.1731 \| 0.3877 \| 0.2701 \| 0.3747 \| \| 0.8739 \| 73.0 \| 7811 \| 1.2542 \| 0.2809 \| 0.556 \| 0.2491 \| 0.1104 \| 0.2179 \| 0.4702 \| 0.2839 \| 0.4483 \| 0.4652 \| 0.2438 \| 0.4059 \| 0.6685 \| 0.5439 \| 0.6847 \| 0.1978 \| 0.4468 \| 0.2114 \| 0.429 \| 0.1767 \| 0.3892 \| 0.2746 \| 0.376 \| \| 0.8851 \| 74.0 \| 7918 \| 1.3047 \| 0.271 \| 0.5478 \| 0.2363 \| 0.1153 \| 0.2078 \| 0.4572 \| 0.2842 \| 0.4438 \| 0.4542 \| 0.2441 \| 0.3865 \| 0.665 \| 0.521 \| 0.6676 \| 0.1916 \| 0.4203 \| 0.214 \| 0.4286 \| 0.1578 \| 0.3892 \| 0.2704 \| 0.3653 \| \| 0.8902 \| 75.0 \| 8025 \| 1.2808 \| 0.2709 \| 0.5429 \| 0.2351 \| 0.101 \| 0.2174 \| 0.4628 \| 0.2806 \| 0.4448 \| 0.4583 \| 0.2163 \| 0.397 \| 0.6919 \| 0.5314 \| 0.677 \| 0.1856 \| 0.4342 \| 0.1961 \| 0.4036 \| 0.1625 \| 0.3969 \| 0.2791 \| 0.38 \| \| 0.8874 \| 76.0 \| 8132 \| 1.2806 \| 0.2812 \| 0.5664 \| 0.2465 \| 0.113 \| 0.2339 \| 0.4718 \| 0.2864 \| 0.4519 \| 0.4645 \| 0.2363 \| 0.4141 \| 0.688 \| 0.5339 \| 0.6869 \| 0.194 \| 0.4481 \| 0.208 \| 0.4228 \| 0.1807 \| 0.3738 \| 0.2897 \| 0.3907 \| \| 0.8809 \| 77.0 \| 8239 \| 1.2476 \| 0.2807 \| 0.5632 \| 0.2575 \| 0.1312 \| 0.2228 \| 0.4686 \| 0.2888 \| 0.4592 \| 0.4738 \| 0.2428 \| 0.4097 \| 0.7011 \| 0.5351 \| 0.6914 \| 0.1972 \| 0.4582 \| 0.2129 \| 0.4259 \| 0.1747 \| 0.4092 \| 0.2834 \| 0.384 \| \| 0.856 \| 78.0 \| 8346 \| 1.2580 \| 0.2878 \| 0.5675 \| 0.2528 \| 0.1216 \| 0.2298 \| 0.4649 \| 0.2918 \| 0.4565 \| 0.4712 \| 0.2357 \| 0.4122 \| 0.6921 \| 0.5297 \| 0.6923 \| 0.2236 \| 0.4456 \| 0.216 \| 0.4384 \| 0.1805 \| 0.3985 \| 0.2891 \| 0.3813 \| \| 0.8509 \| 79.0 \| 8453 \| 1.2741 \| 0.2811 \| 0.5657 \| 0.2445 \| 0.131 \| 0.2218 \| 0.4685 \| 0.2832 \| 0.4469 \| 0.4609 \| 0.2213 \| 0.3994 \| 0.6813 \| 0.5281 \| 0.6842 \| 0.2263 \| 0.4506 \| 0.205 \| 0.4179 \| 0.1643 \| 0.3708 \| 0.2817 \| 0.3809 \| \| 0.88 \| 80.0 \| 8560 \| 1.2556 \| 0.2818 \| 0.5628 \| 0.2442 \| 0.1218 \| 0.2313 \| 0.4529 \| 0.2848 \| 0.4525 \| 0.4659 \| 0.2211 \| 0.4178 \| 0.68 \| 0.536 \| 0.6878 \| 0.2158 \| 0.438 \| 0.2157 \| 0.4348 \| 0.1503 \| 0.3831 \| 0.2913 \| 0.3858 \| \| 0.8739 \| 81.0 \| 8667 \| 1.2754 \| 0.2797 \| 0.5595 \| 0.2432 \| 0.1169 \| 0.2255 \| 0.4812 \| 0.2791 \| 0.4481 \| 0.4632 \| 0.2257 \| 0.3972 \| 0.6992 \| 0.5369 \| 0.6901 \| 0.2077 \| 0.4544 \| 0.2024 \| 0.4076 \| 0.1652 \| 0.3846 \| 0.2864 \| 0.3791 \| \| 0.8604 \| 82.0 \| 8774 \| 1.2531 \| 0.2775 \| 0.5547 \| 0.2411 \| 0.121 \| 0.2204 \| 0.4861 \| 0.2823 \| 0.4461 \| 0.4577 \| 0.2244 \| 0.3945 \| 0.6931 \| 0.5363 \| 0.6824 \| 0.2038 \| 0.4215 \| 0.1948 \| 0.4085 \| 0.1749 \| 0.4062 \| 0.2777 \| 0.3698 \| \| 0.8504 \| 83.0 \| 8881 \| 1.2448 \| 0.2837 \| 0.5589 \| 0.2539 \| 0.1182 \| 0.2199 \| 0.4942 \| 0.2894 \| 0.4561 \| 0.4682 \| 0.2282 \| 0.4074 \| 0.6941 \| 0.5404 \| 0.6946 \| 0.2168 \| 0.4646 \| 0.2078 \| 0.4174 \| 0.1691 \| 0.38 \| 0.2844 \| 0.3844 \| \| 0.8394 \| 84.0 \| 8988 \| 1.2491 \| 0.2766 \| 0.5478 \| 0.2385 \| 0.1219 \| 0.2293 \| 0.4674 \| 0.2776 \| 0.4501 \| 0.4611 \| 0.2267 \| 0.4055 \| 0.6803 \| 0.544 \| 0.7023 \| 0.189 \| 0.4203 \| 0.2092 \| 0.4067 \| 0.164 \| 0.4031 \| 0.2769 \| 0.3733 \| \| 0.8257 \| 85.0 \| 9095 \| 1.2410 \| 0.2805 \| 0.5591 \| 0.2415 \| 0.1251 \| 0.2292 \| 0.471 \| 0.2783 \| 0.4592 \| 0.4689 \| 0.2249 \| 0.4214 \| 0.6866 \| 0.5408 \| 0.6973 \| 0.1995 \| 0.4608 \| 0.2056 \| 0.408 \| 0.1613 \| 0.3831 \| 0.295 \| 0.3951 \| \| 0.8416 \| 86.0 \| 9202 \| 1.2492 \| 0.2868 \| 0.5613 \| 0.2512 \| 0.1136 \| 0.2345 \| 0.4828 \| 0.2807 \| 0.455 \| 0.4644 \| 0.2239 \| 0.4123 \| 0.6914 \| 0.5519 \| 0.6905 \| 0.209 \| 0.4443 \| 0.2155 \| 0.4143 \| 0.1671 \| 0.3815 \| 0.2907 \| 0.3916 \| \| 0.8395 \| 87.0 \| 9309 \| 1.2354 \| 0.2863 \| 0.5545 \| 0.2498 \| 0.1311 \| 0.2408 \| 0.4847 \| 0.2865 \| 0.4603 \| 0.4709 \| 0.2264 \| 0.4195 \| 0.6952 \| 0.538 \| 0.6914 \| 0.2185 \| 0.4734 \| 0.2144 \| 0.4152 \| 0.1696 \| 0.3908 \| 0.2908 \| 0.3836 \| \| 0.831 \| 88.0 \| 9416 \| 1.2456 \| 0.2801 \| 0.5556 \| 0.2431 \| 0.122 \| 0.2307 \| 0.4571 \| 0.2787 \| 0.4571 \| 0.4697 \| 0.2377 \| 0.4118 \| 0.6943 \| 0.5398 \| 0.6946 \| 0.1915 \| 0.4418 \| 0.2088 \| 0.4223 \| 0.1688 \| 0.4062 \| 0.2916 \| 0.3836 \| \| 0.8135 \| 89.0 \| 9523 \| 1.2334 \| 0.2837 \| 0.5639 \| 0.2552 \| 0.1354 \| 0.2321 \| 0.4607 \| 0.2801 \| 0.4679 \| 0.4786 \| 0.2477 \| 0.4247 \| 0.7046 \| 0.5416 \| 0.6937 \| 0.2148 \| 0.4747 \| 0.2154 \| 0.4308 \| 0.156 \| 0.4077 \| 0.2908 \| 0.3862 \| \| 0.8169 \| 90.0 \| 9630 \| 1.2384 \| 0.2866 \| 0.5663 \| 0.2508 \| 0.1454 \| 0.237 \| 0.4681 \| 0.2799 \| 0.4634 \| 0.4749 \| 0.2483 \| 0.4152 \| 0.6961 \| 0.5472 \| 0.6928 \| 0.2252 \| 0.4709 \| 0.2082 \| 0.4174 \| 0.1521 \| 0.4 \| 0.3003 \| 0.3933 \| \| 0.8085 \| 91.0 \| 9737 \| 1.2410 \| 0.2913 \| 0.5751 \| 0.2568 \| 0.1375 \| 0.2386 \| 0.476 \| 0.2839 \| 0.4661 \| 0.4805 \| 0.2439 \| 0.4281 \| 0.6948 \| 0.5469 \| 0.6982 \| 0.2298 \| 0.4848 \| 0.2134 \| 0.4183 \| 0.1742 \| 0.4046 \| 0.2924 \| 0.3964 \| \| 0.8272 \| 92.0 \| 9844 \| 1.2486 \| 0.29 \| 0.5747 \| 0.26 \| 0.1216 \| 0.2434 \| 0.4781 \| 0.2828 \| 0.4639 \| 0.4757 \| 0.2383 \| 0.4208 \| 0.6864 \| 0.545 \| 0.6959 \| 0.219 \| 0.4722 \| 0.2112 \| 0.4098 \| 0.183 \| 0.4062 \| 0.2915 \| 0.3947 \| \| 0.7949 \| 93.0 \| 9951 \| 1.2430 \| 0.2786 \| 0.5565 \| 0.2355 \| 0.1252 \| 0.2239 \| 0.4703 \| 0.2809 \| 0.4591 \| 0.4724 \| 0.2344 \| 0.4165 \| 0.7 \| 0.5329 \| 0.6838 \| 0.2075 \| 0.4696 \| 0.2052 \| 0.4219 \| 0.162 \| 0.3954 \| 0.2852 \| 0.3911 \| \| 0.8009 \| 94.0 \| 10058 \| 1.2419 \| 0.2866 \| 0.5667 \| 0.2557 \| 0.1403 \| 0.2326 \| 0.4687 \| 0.2796 \| 0.4672 \| 0.4787 \| 0.24 \| 0.4214 \| 0.7029 \| 0.5401 \| 0.6865 \| 0.2134 \| 0.4848 \| 0.2098 \| 0.4192 \| 0.1778 \| 0.4046 \| 0.2917 \| 0.3982 \| \| 0.8088 \| 95.0 \| 10165 \| 1.2328 \| 0.2838 \| 0.57 \| 0.249 \| 0.1482 \| 0.2328 \| 0.4686 \| 0.2851 \| 0.4616 \| 0.4754 \| 0.2413 \| 0.4191 \| 0.6996 \| 0.5383 \| 0.6847 \| 0.2113 \| 0.4797 \| 0.2114 \| 0.4183 \| 0.1672 \| 0.3954 \| 0.2907 \| 0.3987 \| \| 0.8113 \| 96.0 \| 10272 \| 1.2335 \| 0.2852 \| 0.5734 \| 0.2426 \| 0.1313 \| 0.2336 \| 0.4788 \| 0.2848 \| 0.4614 \| 0.4742 \| 0.2416 \| 0.4169 \| 0.6924 \| 0.5401 \| 0.686 \| 0.2135 \| 0.481 \| 0.2115 \| 0.4121 \| 0.1693 \| 0.3954 \| 0.2914 \| 0.3964 \| \| 0.7898 \| 97.0 \| 10379 \| 1.2327 \| 0.2867 \| 0.5712 \| 0.2504 \| 0.1332 \| 0.2335 \| 0.4682 \| 0.288 \| 0.4648 \| 0.4785 \| 0.2391 \| 0.421 \| 0.7037 \| 0.5415 \| 0.6892 \| 0.2193 \| 0.4949 \| 0.2123 \| 0.4112 \| 0.1712 \| 0.4015 \| 0.2891 \| 0.3956 \| \| 0.8043 \| 98.0 \| 10486 \| 1.2280 \| 0.288 \| 0.5693 \| 0.2456 \| 0.1247 \| 0.2341 \| 0.4886 \| 0.29 \| 0.4667 \| 0.4785 \| 0.2389 \| 0.4194 \| 0.711 \| 0.5462 \| 0.6914 \| 0.2148 \| 0.4785 \| 0.2133 \| 0.4152 \| 0.1746 \| 0.4108 \| 0.2908 \| 0.3964 \| \| 0.8017 \| 99.0 \| 10593 \| 1.2349 \| 0.2863 \| 0.5661 \| 0.2508 \| 0.123 \| 0.2321 \| 0.4837 \| 0.2882 \| 0.4639 \| 0.4755 \| 0.2338 \| 0.4168 \| 0.7093 \| 0.5448 \| 0.691 \| 0.2127 \| 0.4722 \| 0.2136 \| 0.4103 \| 0.1744 \| 0.4123 \| 0.286 \| 0.3916 \| \| 0.7948 \| 100.0 \| 10700 \| 1.2371 \| 0.2873 \| 0.5678 \| 0.2501 \| 0.126 \| 0.2327 \| 0.4873 \| 0.2843 \| 0.4643 \| 0.4762 \| 0.2338 \| 0.4167 \| 0.7114 \| 0.5461 \| 0.6932 \| 0.2167 \| 0.4785 \| 0.2135 \| 0.4094 \| 0.173 \| 0.4092 \| 0.2871 \| 0.3907 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	{"license": "apache-2.0", "tags": ["object-detection", "vision", "generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs", "results": []}]}	qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs	null	[ "transformers", "safetensors", "detr", "object-detection", "vision", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:50:21+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-3B](https://hf.co/apple/OpenELM-3B) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-3B) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-3B	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:50:35+00:00
text-to-audio	transformers	<!-- 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. --> # fil_b32_le5_s4000 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4102 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:--------:\|:----:\|:---------------:\| \| 0.5539 \| 44.4444 \| 500 \| 0.4850 \| \| 0.4867 \| 88.8889 \| 1000 \| 0.4439 \| \| 0.4595 \| 133.3333 \| 1500 \| 0.4245 \| \| 0.4395 \| 177.7778 \| 2000 \| 0.4155 \| \| 0.4327 \| 222.2222 \| 2500 \| 0.4121 \| \| 0.4279 \| 266.6667 \| 3000 \| 0.4127 \| \| 0.4202 \| 311.1111 \| 3500 \| 0.4098 \| \| 0.4167 \| 355.5556 \| 4000 \| 0.4102 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "fil_b32_le5_s4000", "results": []}]}	mikhail-panzo/fil_b32_le5_s4000	null	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:51:58+00:00
null	null		{"license": "unknown"}	hautc/z3	null	[ "license:unknown", "region:us" ]	null	2024-04-30T09:52:27+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-3B-Instruct](https://hf.co/apple/OpenELM-3B-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-3B-Instruct) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-3B-Instruct	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:52:52+00:00
text-generation	transformers	# Uploaded model - Developed by: MatrixIA - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl", "sft"], "base_model": "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"}	MatrixIA/Phi-3-mini-4k-instruct-text-to-sql	null	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:53:48+00:00
feature-extraction	transformers	# 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]	{"library_name": "transformers", "tags": []}	Juniplayground/juniper-mxbai-embed-large-v1-more-data-3l	null	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:54:02+00:00
feature-extraction	transformers		{}	PrachiDabi/BahasaTestV22	null	[ "transformers", "safetensors", "gpt2", "feature-extraction", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T09:54:23+00:00
null	null		{}	smart976/CNN	null	[ "region:us" ]	null	2024-04-30T09:54:46+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-450M](https://hf.co/apple/OpenELM-450M) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-450M) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-450M	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:55:18+00:00
null	null		{"license": "openrail"}	PaZtV/EKT	null	[ "license:openrail", "region:us" ]	null	2024-04-30T09:55:36+00:00
null	null	# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-450M-Instruct](https://hf.co/apple/OpenELM-450M-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-450M-Instruct) for additional details on the model.	{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}	corenet-community/coreml-OpenELM-450M-Instruct	null	[ "coreml", "license:other", "region:us" ]	null	2024-04-30T09:55:42+00:00
null	null	<!-- 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. --> # O0430HMA7 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0202 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1959 \| 0.09 \| 10 \| 0.5332 \| \| 0.2415 \| 0.18 \| 20 \| 0.1587 \| \| 0.1515 \| 0.27 \| 30 \| 0.1608 \| \| 0.1518 \| 0.36 \| 40 \| 0.1538 \| \| 0.1514 \| 0.45 \| 50 \| 0.1483 \| \| 0.152 \| 0.54 \| 60 \| 0.1495 \| \| 0.1489 \| 0.63 \| 70 \| 0.1476 \| \| 0.1485 \| 0.73 \| 80 \| 0.1583 \| \| 0.1468 \| 0.82 \| 90 \| 0.1490 \| \| 0.1438 \| 0.91 \| 100 \| 0.1337 \| \| 0.1219 \| 1.0 \| 110 \| 0.0771 \| \| 0.1004 \| 1.09 \| 120 \| 0.0862 \| \| 0.0874 \| 1.18 \| 130 \| 0.0701 \| \| 0.1185 \| 1.27 \| 140 \| 0.0932 \| \| 0.093 \| 1.36 \| 150 \| 0.0723 \| \| 0.0565 \| 1.45 \| 160 \| 0.0544 \| \| 0.0597 \| 1.54 \| 170 \| 0.0624 \| \| 0.0609 \| 1.63 \| 180 \| 0.0567 \| \| 0.0624 \| 1.72 \| 190 \| 0.0560 \| \| 0.0578 \| 1.81 \| 200 \| 0.0530 \| \| 0.0491 \| 1.9 \| 210 \| 0.0426 \| \| 0.0372 \| 1.99 \| 220 \| 0.0364 \| \| 0.0432 \| 2.08 \| 230 \| 0.0617 \| \| 0.0432 \| 2.18 \| 240 \| 0.0487 \| \| 0.0331 \| 2.27 \| 250 \| 0.0297 \| \| 0.0281 \| 2.36 \| 260 \| 0.0259 \| \| 0.0292 \| 2.45 \| 270 \| 0.0222 \| \| 0.0199 \| 2.54 \| 280 \| 0.0212 \| \| 0.0267 \| 2.63 \| 290 \| 0.0210 \| \| 0.0218 \| 2.72 \| 300 \| 0.0208 \| \| 0.0204 \| 2.81 \| 310 \| 0.0205 \| \| 0.0229 \| 2.9 \| 320 \| 0.0204 \| \| 0.0228 \| 2.99 \| 330 \| 0.0202 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA7", "results": []}]}	Litzy619/O0430HMA7	null	[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]	null	2024-04-30T09:55:50+00:00
text-generation	null	# reach-vb/Meta-Llama-3-8B-Q4_0-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo reach-vb/Meta-Llama-3-8B-Q4_0-GGUF --model meta-llama-3-8b.Q4_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo reach-vb/Meta-Llama-3-8B-Q4_0-GGUF --model meta-llama-3-8b.Q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m meta-llama-3-8b.Q4_0.gguf -n 128 ```	{"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. 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No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"}	reach-vb/Meta-Llama-3-8B-Q4_0-GGUF	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:other", "region:us" ]	null	2024-04-30T09:55:52+00:00
token-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0632 - Precision: 0.9382 - Recall: 0.9500 - F1: 0.9441 - Accuracy: 0.9864 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0756 \| 1.0 \| 1756 \| 0.0608 \| 0.9132 \| 0.9367 \| 0.9248 \| 0.9829 \| \| 0.0324 \| 2.0 \| 3512 \| 0.0692 \| 0.9340 \| 0.9450 \| 0.9394 \| 0.9849 \| \| 0.0191 \| 3.0 \| 5268 \| 0.0632 \| 0.9382 \| 0.9500 \| 0.9441 \| 0.9864 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]}	aljaziz/bert-finetuned-ner	null	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:56:02+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	fmshahata/phi-moe-4exp_	null	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T09:56:13+00:00
text-to-audio	transformers	<!-- 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. --> # fil_b64_le4_s4000 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4134 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:--------:\|:----:\|:---------------:\| \| 0.4725 \| 22.2222 \| 500 \| 0.4372 \| \| 0.4415 \| 44.4444 \| 1000 \| 0.4203 \| \| 0.423 \| 66.6667 \| 1500 \| 0.4169 \| \| 0.4108 \| 88.8889 \| 2000 \| 0.4183 \| \| 0.3934 \| 111.1111 \| 2500 \| 0.4111 \| \| 0.3821 \| 133.3333 \| 3000 \| 0.4164 \| \| 0.3743 \| 155.5556 \| 3500 \| 0.4127 \| \| 0.3714 \| 177.7778 \| 4000 \| 0.4134 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "fil_b64_le4_s4000", "results": []}]}	mikhail-panzo/fil_b64_le4_s4000	null	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-30T09:56:16+00:00
null	peft	<!-- 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. --> # code-llama-7b-text-to-sql This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2	{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]}	nnCarlito/code-llama-7b-text-to-sql	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2024-04-30T09:57:01+00:00
text-generation	transformers		{}	Osru/llama-2-7b-nubidoc	null	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T09:57:58+00:00
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0	{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"}	shivanikerai/Llama-2-7b-chat-hf-adapter-title-ner-and-new-title-suggestion-v1.0	null	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-04-30T10:00:05+00:00
null	null		{}	doxiy/bert-finetuned-ner-accelerate	null	[ "region:us" ]	null	2024-04-30T10:00:47+00:00
null	transformers	# Uploaded model - Developed by: arvnoodle - License: apache-2.0 - Finetuned from model : unsloth/codegemma-7b-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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/codegemma-7b-it-bnb-4bit"}	arvnoodle/hcl-codegemma-it-8b_UPDATED-xml-json	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/codegemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:00:57+00:00
null	null		{"license": "unknown"}	hautc/z1	null	[ "license:unknown", "region:us" ]	null	2024-04-30T10:01:26+00:00
null	null		{}	Danilas/sn25-1-5	null	[ "region:us" ]	null	2024-04-30T10:01:45+00:00
null	null		{}	pinguG/ElectronicDesireGE	null	[ "region:us" ]	null	2024-04-30T10:01:46+00:00
null	null		{}	Phill-h/uav-trajectory-solver	null	[ "region:us" ]	null	2024-04-30T10:01:46+00:00
null	null		{}	Aron9310/MyGPT	null	[ "region:us" ]	null	2024-04-30T10:01:55+00:00
null	null		{}	ThomasFG/2024-04-30_12-01-38	null	[ "region:us" ]	null	2024-04-30T10:01:56+00:00
null	null		{}	golf2248/2gcbbgb	null	[ "region:us" ]	null	2024-04-30T10:02:08+00:00
null	null		{}	aryanmehta5902/Tendormodel1	null	[ "region:us" ]	null	2024-04-30T10:02:23+00:00
null	null		{"license": "apache-2.0"}	ajaydamsani/ICDD	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-30T10:02:34+00:00
token-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1655 - F1: 0.8574 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2831 \| 1.0 \| 715 \| 0.1854 \| 0.8109 \| \| 0.1453 \| 2.0 \| 1430 \| 0.1646 \| 0.8462 \| \| 0.0942 \| 3.0 \| 2145 \| 0.1655 \| 0.8574 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]}	u00890358/xlm-roberta-base-finetuned-panx-de-fr	null	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:02:44+00:00
null	null	README.md # Mask R-CNN Drone Detection Model ### Overview This repository contains the implementation of a Mask R-CNN model trained for detecting drones in images. The model is built using the Matterport Mask R-CNN implementation and trained on a custom drone dataset. ### Model Architecture The Mask R-CNN architecture consists of a backbone network (e.g., ResNet) followed by two subnetworks: a Region Proposal Network (RPN) and a Mask Head. The RPN proposes candidate object bounding boxes, while the Mask Head refines these boxes and predicts binary masks for each object. ### Dataset The model was trained on a custom drone dataset consisting of 207 images for training and 70 images for validation. The dataset includes images captured from various angles and distances to ensure robust detection performance. ### Training The model was trained for 40 epochs using the Adam optimizer with a learning rate of 0.001. The training process involved optimizing the following losses: RPN Loss Classification Loss Mask Loss ### Usage Download the trained weights from the releases section of this repository. You can view the github repository [here](https://github.com/bilalmashooq/MRCNN)	{"language": ["en"], "license": "mit"}	bilalmashooq/Drone_Detection_MRCNN	null	[ "en", "license:mit", "region:us" ]	null	2024-04-30T10:03:40+00:00
text-generation	transformers		{"license": "apache-2.0"}	Yuga143/silk	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:05:34+00:00
null	null		{"license": "apache-2.0"}	asraf2asif/r	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-30T10:05:44+00:00
null	null	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) ## This repo contains GGUF versions of the bigcode/starcoder2-3b model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. Frequently Asked Questions - *How does the compression work?* The model is compressed with GGUF. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *What is the model format?* We use GGUF format. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): \| Quant type \| Description \| \|------------\|--------------------------------------------------------------------------------------------\| \| Q5_K_M \| High quality, recommended. \| \| Q5_K_S \| High quality, recommended. \| \| Q4_K_M \| Good quality, uses about 4.83 bits per weight, recommended. \| \| Q4_K_S \| Slightly lower quality with more space savings, recommended. \| \| IQ4_NL \| Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. \| \| IQ4_XS \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| Q3_K_L \| Lower quality but usable, good for low RAM availability. \| \| Q3_K_M \| Even lower quality. \| \| IQ3_M \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| IQ3_S \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| Q3_K_S \| Low quality, not recommended. \| \| IQ3_XS \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| Q2_K \| Very low quality but surprisingly usable. \| ## How to download GGUF files ? Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev - Option A - Downloading in `text-generation-webui`: - Step 1: Under Download Model, you can enter the model repo: PrunaAI/starcoder2-3b-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - Step 2: Then click Download. - Option B - Downloading on the command line (including multiple files at once): - Step 1: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - Step 2: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download PrunaAI/starcoder2-3b-GGUF-smashed starcoder2-3b.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download PrunaAI/starcoder2-3b-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/starcoder2-3b-GGUF-smashed starcoder2-3b.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## How to run model in GGUF format? - Option A - Introductory example with `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m starcoder2-3b.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) - Option B - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - Option C - Running from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./starcoder2-3b.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./starcoder2-3b.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` - Option D - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).	{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}	PrunaAI/starcoder2-3b-GGUF-smashed	null	[ "gguf", "pruna-ai", "region:us" ]	null	2024-04-30T10:05:56+00:00
text-generation	transformers	<!-- 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. --> # 0.0001_withdpo_4iters_bs256_531lr_iter_3 This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2](https://huggingface.co/ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - 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 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_531lr_iter_3", "results": []}]}	ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_3	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:06:17+00:00
null	null		{}	Cafet/whisper-meduim-mongolian-version-final	null	[ "region:us" ]	null	2024-04-30T10:06:26+00:00
null	null		{}	PrachiDabi/Bahasa30335	null	[ "region:us" ]	null	2024-04-30T10:06:45+00:00
unconditional-image-generation	diffusers	# 这个模型用于生成蝴蝶图像的无条件图像生成扩散模型　 '''python from diffusers import DDPMPipeline 　 pipeline = DDPMPipeline.from_pretrained('jane19940506/sd-class-butterflies-32') image = pipeline().images[0] image	{"tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"], "\u200blicense": "mit"}	jane19940506/sd-class-butterflies-32	null	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "diffusers:DDPMPipeline", "region:us" ]	null	2024-04-30T10:06:45+00:00
null	null	<!-- 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. --> # O0430HMA8 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0097 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.8818 \| 0.09 \| 10 \| 0.2574 \| \| 0.1883 \| 0.18 \| 20 \| 0.1602 \| \| 0.1522 \| 0.27 \| 30 \| 0.1648 \| \| 0.1591 \| 0.36 \| 40 \| 0.1532 \| \| 0.1506 \| 0.45 \| 50 \| 0.1499 \| \| 0.1514 \| 0.54 \| 60 \| 0.1500 \| \| 0.1507 \| 0.63 \| 70 \| 0.1473 \| \| 0.1508 \| 0.73 \| 80 \| 0.1553 \| \| 0.1469 \| 0.82 \| 90 \| 0.1506 \| \| 0.1461 \| 0.91 \| 100 \| 0.1385 \| \| 0.1147 \| 1.0 \| 110 \| 0.0797 \| \| 0.0984 \| 1.09 \| 120 \| 0.0851 \| \| 0.202 \| 1.18 \| 130 \| 0.0826 \| \| 0.0782 \| 1.27 \| 140 \| 0.0681 \| \| 0.1336 \| 1.36 \| 150 \| 0.0831 \| \| 0.058 \| 1.45 \| 160 \| 0.0398 \| \| 0.0845 \| 1.54 \| 170 \| 0.0853 \| \| 0.0583 \| 1.63 \| 180 \| 0.0311 \| \| 0.0332 \| 1.72 \| 190 \| 0.0253 \| \| 0.0311 \| 1.81 \| 200 \| 0.0264 \| \| 0.0337 \| 1.9 \| 210 \| 0.0251 \| \| 0.0212 \| 1.99 \| 220 \| 0.0208 \| \| 0.0265 \| 2.08 \| 230 \| 0.0244 \| \| 0.0199 \| 2.18 \| 240 \| 0.0202 \| \| 0.0171 \| 2.27 \| 250 \| 0.0174 \| \| 0.02 \| 2.36 \| 260 \| 0.0163 \| \| 0.0174 \| 2.45 \| 270 \| 0.0158 \| \| 0.0119 \| 2.54 \| 280 \| 0.0128 \| \| 0.0161 \| 2.63 \| 290 \| 0.0132 \| \| 0.0152 \| 2.72 \| 300 \| 0.0103 \| \| 0.0139 \| 2.81 \| 310 \| 0.0109 \| \| 0.0128 \| 2.9 \| 320 \| 0.0102 \| \| 0.0111 \| 2.99 \| 330 \| 0.0097 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA8", "results": []}]}	Litzy619/O0430HMA8	null	[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]	null	2024-04-30T10:08:56+00:00
null	peft	<!-- 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. --> # zaid-gemma01 This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "google/gemma-2b-it", "model-index": [{"name": "zaid-gemma01", "results": []}]}	Zaidh/zaid-gemma01	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b-it", "license:gemma", "region:us" ]	null	2024-04-30T10:09:35+00:00
null	null		{"language": ["en", "de"], "license": "apache-2.0"}	hoschidude/Mixtral-8x7B-v0.1-Instruct-maxidl-Q6_K-GGUF	null	[ "gguf", "en", "de", "license:apache-2.0", "region:us" ]	null	2024-04-30T10:10:21+00:00
text-classification	transformers		{}	jeffyelson03/deberta_semanticlevel_nofeatures	null	[ "transformers", "pytorch", "deberta-v2", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:10:53+00:00
null	null	# MeliodasExperiment27pastiche-7B MeliodasExperiment27pastiche-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [automerger/Experiment27Pastiche-7B](https://huggingface.co/automerger/Experiment27Pastiche-7B) ## 🧩 Configuration ```yaml models: - model: AurelPx/Meliodas-7b-dare # No parameters necessary for base model - model: automerger/Experiment27Pastiche-7B parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: AurelPx/Meliodas-7b-dare parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/MeliodasExperiment27pastiche-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["automerger/Experiment27Pastiche-7B"]}	automerger/MeliodasExperiment27pastiche-7B	null	[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:automerger/Experiment27Pastiche-7B", "license:apache-2.0", "region:us" ]	null	2024-04-30T10:11:25+00:00
summarization	transformers		{"language": ["en"], "datasets": ["samsum"], "metrics": ["rouge"], "pipeline_tag": "summarization"}	asd809096117/Zhaoming-T5small-Finetuned	null	[ "transformers", "safetensors", "t5", "text2text-generation", "summarization", "en", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:12:41+00:00
text-generation	transformers	\| Tasks \| Metric \|Value \| \|Stderr\| \|------------\|--------\|-----:\|---\|-----:\| \|hellaswag_it\|acc \|0.4439\|± \|0.0052\| \| \|acc_norm\|0.5962\|± \|0.0051\| \|arc_it \|acc \|0.1257\|± \|0.0097\| \| \|acc_norm\|0.4414\|± \|0.0145\|	{"language": ["it"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["lmqg/qag_itquad"]}	nonsonpratico/phi3-3.8-128k-italian-v1.5	null	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "it", "dataset:lmqg/qag_itquad", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:13:08+00:00
text-generation	transformers		{}	kloodia/8b-merged-math	null	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:14:19+00:00
null	null		{"license": "agpl-3.0"}	OpenSeneca/openseneca-llm-v01	null	[ "license:agpl-3.0", "region:us" ]	null	2024-04-30T10:15:47+00:00
fill-mask	transformers		{}	themefe/finetuned-berturk-efe	null	[ "transformers", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:15:52+00:00
text2text-generation	transformers	hello	{}	dappyx/QazSynt	null	[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:16:42+00:00
null	transformers	# Uploaded model - Developed by: akshat1311 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"}	akshat1311/Mistral-DronaHQ-Control	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:16:50+00:00
reinforcement-learning	ml-agents	# ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: krisha-n/ppo-SnowballTarget1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀	{"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]}	krisha-n/ppo-SnowballTarget1	null	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	null	2024-04-30T10:16:54+00:00
text-generation	transformers	<!-- 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. --> # casual_language_modeling This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 3.7750 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 3.8981 \| 1.0 \| 5509 \| 3.7984 \| \| 3.8219 \| 2.0 \| 11018 \| 3.7801 \| \| 3.7966 \| 3.0 \| 16527 \| 3.7750 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "distilbert/distilgpt2", "model-index": [{"name": "casual_language_modeling", "results": []}]}	madanagrawal/casual_language_modeling	null	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:18:02+00:00
null	null		{}	aimickey/alishemenu-sdv1-4-lora	null	[ "region:us" ]	null	2024-04-30T10:18:02+00:00
question-answering	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # omarSorour123/sorour_qa_model This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5895 - Validation Loss: 1.6491 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 435, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.6908 \| 1.6033 \| 0 \| \| 1.1408 \| 1.5195 \| 1 \| \| 0.8730 \| 1.5434 \| 2 \| \| 0.7265 \| 1.5798 \| 3 \| \| 0.5895 \| 1.6491 \| 4 \| ### Framework versions - Transformers 4.40.0 - TensorFlow 2.15.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "timpal0l/mdeberta-v3-base-squad2", "model-index": [{"name": "omarSorour123/sorour_qa_model", "results": []}]}	omarSorour123/sorour_qa_model	null	[ "transformers", "tf", "deberta-v2", "question-answering", "generated_from_keras_callback", "base_model:timpal0l/mdeberta-v3-base-squad2", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:19:16+00:00
null	peft	<!-- 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. --> # Llama3_finetued_on_scigen This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.2 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "model-index": [{"name": "Llama3_finetued_on_scigen", "results": []}]}	moetezsa/Llama3_finetued_on_scigen	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:llama2", "region:us" ]	null	2024-04-30T10:20:10+00:00
text-generation	transformers	This is my attempt at instruction tuning for [Hebrew-Mistral-7B](https://huggingface.co/yam-peleg/Hebrew-Mistral-7B). The model hallucinates a lot. Please generate 4-5 times for each prompt to find the best generation. Also, note that this model does not have any moderation mechanisms. ## Usage Install dependencies ``` pip install -q -U transformers pip install accelerate pip install -q sentencepiece pip install protobuf ``` ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, TextStreamer model_id = "ronmasas/Hebrew-Mistral-7B-Instruct-v0.1" # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0") tokenizer = AutoTokenizer.from_pretrained(model_id) model = model.half() # Define special tokens END_TOKEN = 64003 # -> [/INST] B_INST, E_INST = "[INST]", "[/INST]" SYSTEM_PROMPT = "A conversation between a human and an AI assistant." class EosListStoppingCriteria(StoppingCriteria): def __init__(self, eos_sequence = [END_TOKEN]): self.eos_sequence = eos_sequence def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, kwargs) -> bool: last_ids = input_ids[:,-len(self.eos_sequence):].tolist() return self.eos_sequence in last_ids def stream(user_prompt): prompt = f"{SYSTEM_PROMPT} {B_INST} {user_prompt.strip()} {E_INST}\n" inputs = tokenizer([prompt], return_tensors="pt").to("cuda:0") streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) _ = model.generate(inputs, streamer=streamer, max_new_tokens=256, temperature=0.7, do_sample=True, stopping_criteria = [EosListStoppingCriteria()]) stream("כתוב מכתב תודה ל-ים פלג על כך שאימן ושיחרר את המודל Hebrew Mistral 7B.") """ הי ים, אני רוצה להודות לך מקרב לב על העבודה הקשה שהשקעת בדגם Hebrew Mistral 7B. המודל שלך הוא יצירת מופת של למידת מכונה ואני אסיר תודה על הזמן והכישרון שהושקעו בפיתוחו. היכולת להתאמן על שפה באמצעות פלטפורמה כה מתקדמת ומושכת הייתה משב רוח מרענן ומועיל ביותר עבור הקהילה כולה. תודה על כל העבודה הקשה והחזון שהושקעה בפרויקט זה, הוא באמת מאיר עיניים ומשפיע. שוב תודה מקרב לב, [שמך] """ ``` ```python stream("מה זה ארנונה?") """ ארנונה, הידועה גם בשם מס ארנונה, היא מס שנגבה על ידי רשות מקומית (עיר, מחוז או אזור) עבור שירותים מונציפליים שהיא מספקת, כגון חינוך, בריאות ושירותים. זה יכול לכלול מיסים על הכנסה, רכוש ושירותים (כגון מים). """ stream("כתוב שלוש כותרות לבלוג על חשיבות איסוף צואה של בעלי חיים.") """ 1. שמירת תזונה מאוזנת של חיות מחמד באמצעות איסוף צואה 2. השפעות בריאותיות ארוכות טווח של סירוב איסוף צואה של חיות מחמד 3. כיצד איסוף צואה של חיות מחמד עוזר לשמר היגיינה ובריאות בעלי חיים גלובלית. """ ```	{"language": ["he", "en"], "license": "apache-2.0"}	ronmasas/Hebrew-Mistral-7B-Instruct-v0.1	null	[ "transformers", "safetensors", "mistral", "text-generation", "he", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:20:34+00:00
null	transformers	<!-- 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. --> # donut-base-aadhaar-200 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.0a0+81ea7a4 - Datasets 2.18.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut-base-aadhaar-200", "results": []}]}	jaydip-tss/donut-base-aadhaar-200	null	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:22:31+00:00
null	transformers		{"license": "mit"}	ProfEngel/OwlLM2-7	null	[ "transformers", "safetensors", "gguf", "mistral", "license:mit", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:22:53+00:00
text-generation	transformers		{"license": "llama2"}	sanjaykumar12/llama-2-7b-patent_doc	null	[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:23:21+00:00
null	null		{}	Greko89/C.Tangana	null	[ "region:us" ]	null	2024-04-30T10:24:06+00:00
null	null		{"license": "llama3"}	KaKashii/text-to-pgn-translator	null	[ "license:llama3", "region:us" ]	null	2024-04-30T10:24:23+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	wetaizhou/gemma-ft	null	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:25:04+00:00
null	peft	<!-- 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. --> # llama3-8b_readme_summarization This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8341 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 1.9292 \| 0.9998 \| 2915 \| 1.9142 \| \| 1.4953 \| 2.0 \| 5831 \| 1.7699 \| \| 0.9958 \| 2.9998 \| 8746 \| 1.7412 \| \| 0.6889 \| 3.9993 \| 11660 \| 1.8341 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-8b_readme_summarization", "results": []}]}	bunbohue/llama3-8b_readme_summarization	null	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "region:us" ]	null	2024-04-30T10:25:10+00:00
text-classification	transformers		{}	deokcycle/bert-mini-mnli-68_8	null	[ "transformers", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:25:16+00:00
translation	fairseq		{"language": ["av"], "license": "other", "library_name": "fairseq", "datasets": ["HuggingFaceFW/fineweb"], "metrics": ["character"], "license_name": "gpt4", "license_link": "LICENSE", "pipeline_tag": "translation"}	Nicole0427/Filipino_teacher_Nicole	null	[ "fairseq", "translation", "av", "dataset:HuggingFaceFW/fineweb", "license:other", "region:us" ]	null	2024-04-30T10:25:34+00:00
feature-extraction	transformers	# 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]	{"library_name": "transformers", "tags": []}	PrachiDabi/BahasaTest30355	null	[ "transformers", "safetensors", "gpt2", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:25:53+00:00
text-generation	null	# itayl/dictalm2.0-instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`dicta-il/dictalm2.0-instruct`](https://huggingface.co/dicta-il/dictalm2.0-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/dicta-il/dictalm2.0-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo itayl/dictalm2.0-instruct-Q5_K_M-GGUF --model dictalm2.0-instruct.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo itayl/dictalm2.0-instruct-Q5_K_M-GGUF --model dictalm2.0-instruct.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m dictalm2.0-instruct.Q5_K_M.gguf -n 128 ```	{"language": ["en", "he"], "license": "apache-2.0", "tags": ["instruction-tuned", "llama-cpp", "gguf-my-repo"], "base_model": "dicta-il/dictalm2.0", "pipeline_tag": "text-generation", "inference": {"parameters": {"temperature": 0.7}}}	itayl/dictalm2.0-instruct-Q5_K_M-GGUF	null	[ "gguf", "instruction-tuned", "llama-cpp", "gguf-my-repo", "text-generation", "en", "he", "base_model:dicta-il/dictalm2.0", "license:apache-2.0", "region:us" ]	null	2024-04-30T10:26:25+00:00
null	null		{}	0ssamaak0/distilbert-base-uncased-finetuned-emotion	null	[ "region:us" ]	null	2024-04-30T10:26:30+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	codevang/fast_small_12	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:27:38+00:00
null	null		{}	Greko89/Omarmontes	null	[ "region:us" ]	null	2024-04-30T10:27:54+00:00
image-classification	transformers	<!-- 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. --> # finetuned-BrainTumor-2.0 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the BrainTumorMRIForFineTuningViT dataset. It achieves the following results on the evaluation set: - Loss: 0.1038 - Accuracy: 0.9743 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - 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 \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.3701 \| 0.3289 \| 100 \| 0.2643 \| 0.9183 \| \| 0.3706 \| 0.6579 \| 200 \| 0.2855 \| 0.9125 \| \| 0.1825 \| 0.9868 \| 300 \| 0.1563 \| 0.9510 \| \| 0.1405 \| 1.3158 \| 400 \| 0.1656 \| 0.9382 \| \| 0.1684 \| 1.6447 \| 500 \| 0.1038 \| 0.9743 \| \| 0.1363 \| 1.9737 \| 600 \| 0.1086 \| 0.9697 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "finetuned-BrainTumor-2.0", "results": []}]}	Dharamanand/BrainTumorClassifier-finetuned-ViT	null	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:28:39+00:00
null	transformers	## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/databricks/dbrx-instruct (actually the f16 from https://huggingface.co/dranger003/dbrx-instruct-iMat.GGUF as llama.cpp seems to have broken dbrx support currently) <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/dbrx-instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 27.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 29.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 34.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 38.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 39.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 43.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 48.0 \| IQ3_XXS probably better \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XXS.gguf.part2of2) \| i1-IQ3_XXS \| 50.8 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 53.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_S.gguf.part2of2) \| i1-IQ3_S \| 56.9 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_S.gguf.part2of2) \| i1-Q3_K_S \| 56.9 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_M.gguf.part2of2) \| i1-IQ3_M \| 58.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_M.gguf.part2of2) \| i1-Q3_K_M \| 63.2 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_L.gguf.part2of2) \| i1-Q3_K_L \| 68.5 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ4_XS.gguf.part2of2) \| i1-IQ4_XS \| 70.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 74.6 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 75.0 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 80.0 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 90.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 93.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part3of3) \| i1-Q6_K \| 108.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "library_name": "transformers", "base_model": "databricks/dbrx-instruct", "quantized_by": "mradermacher"}	mradermacher/dbrx-instruct-i1-GGUF	null	[ "transformers", "gguf", "en", "base_model:databricks/dbrx-instruct", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:29:35+00:00
null	null		{}	hannahisrael03/finetuned-designingAI	null	[ "region:us" ]	null	2024-04-30T10:29:36+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	Vaneanea/DialoGPT-small-stewie-p-test-1	null	[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:29:38+00:00
text-classification	transformers		{}	KatAlex/task1	null	[ "transformers", "pytorch", "safetensors", "gpt2", "text-classification", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:29:40+00:00
text-generation	transformers		{}	kloodia/8b-merged-physic	null	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:30:30+00:00
text-generation	transformers	``` env CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 python3 main.py \ --model_name facebook/opt-125M \ --device 0 \ --group_size 128 \ --bits 4 \ --seqlen 2048 \ --iters 1000 \ --use_quant_input \ --disable_eval \ --n_blocks 22 \ --sym \ --deployment_device 'gpu' \ --disable_low_gpu_mem_usage \ --output_dir "/monster/data/zx/opt-125M-quant_lm_head_false" ``` quant model path: `/monster/data/zx/opt-125M-quant_lm_head_false/opt-125m-autoround-w4g128-gpu`	{}	LnL-AI/opt-125M-autoround-lm_head-false-symTrue	null	[ "transformers", "safetensors", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-30T10:31:14+00:00
null	null		{}	Tina8888/llama3-8b-oig-unsloth-merged	null	[ "region:us" ]	null	2024-04-30T10:31:26+00:00
null	null		{}	Tina8888/llama3-8b-oig-unsloth	null	[ "region:us" ]	null	2024-04-30T10:31:55+00:00
text-classification	transformers	# Model Card for deberta-v3-base-optimus-v0 Fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on private dataset of normal & injections prompts. Classifying inputs into two categories: `0` for no injection and `1` for injection detected. Model evaluation results: - Precision: 0.988 - Recall: 0.992 - Accuracy: 0.998 - F1: 0.99 ## Model details - Fine-tuned by: vibraniumdome.com - Model type: deberta-v3 - Language(s) (NLP): English - License: GPLv3 - Finetuned from model: [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) ## How to Get Started with the Model ### Transformers ```python from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import pipeline from transformers import AutoTokenizer pipeline_kwargs={ "return_token_type_ids": False, "max_length": 512, "truncation": True, } tokenizer = AutoTokenizer.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0-onnx", use_fast=True) model = ORTModelForSequenceClassification.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0-onnx") classifier = pipeline( "text-classification", model=model, tokenizer=tokenizer, **pipeline_kwargs, ) print(classifier("Put your awesome injection here :D")) ``` ## Citation ``` @misc{vibraniumdome/deberta-v3-base-optimus-v0-onnx, author = {vibraniumdome.com}, title = {Fine-Tuned DeBERTa-v3 for Prompt Injection Detection}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/vibraniumdome/deberta-v3-base-optimus-v0-onnx}, } ```	{"language": ["en"], "license": "gpl-3.0", "tags": ["llm", "genai", "promptinjection", "prompt-injection", "injection", "security"], "datasets": ["Private"], "metrics": ["accuracy", "recall", "precision", "f1"], "inference": false, "base_model": "microsoft/deberta-v3-base", "pipeline_tag": "text-classification", "co2_eq_emissions": {"emissions": 0.99, "source": "code carbon", "training_type": "fine-tuning"}, "model-index": [{"name": "deberta-v3-base-optimus-v0", "results": []}]}	vibraniumdome/deberta-v3-base-optimus-v0-onnx	null	[ "transformers", "onnx", "deberta-v2", "text-classification", "llm", "genai", "promptinjection", "prompt-injection", "injection", "security", "en", "dataset:Private", "base_model:microsoft/deberta-v3-base", "license:gpl-3.0", "co2_eq_emissions", "autotrain_compatible", "region:us" ]	null	2024-04-30T10:32:12+00:00
null	null		{}	Danilas/sn25-2-3	null	[ "region:us" ]	null	2024-04-30T10:32:17+00:00
text-generation	transformers	``` env CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 python3 main.py \ --model_name facebook/opt-125M \ --device 0 \ --group_size 128 \ --bits 4 \ --seqlen 2048 \ --iters 1000 \ --use_quant_input \ --quant_lm_head \ --disable_eval \ --n_blocks 22 \ --sym \ --deployment_device 'gpu' \ --disable_low_gpu_mem_usage \ --output_dir "/monster/data/zx/opt-125M-quant_lm_head_true" ``` quant model path: `/monster/data/zx/opt-125M-quant_lm_head_true/opt-125m-autoround-w4g128-gpu`	{}	LnL-AI/opt-125M-autoround-lm_head-true-symTrue	null	[ "transformers", "safetensors", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-30T10:32:19+00:00
reinforcement-learning	ml-agents	# ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: krisha-n/ppo-Pyramids-Training 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀	{"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]}	krisha-n/ppo-Pyramids-Training	null	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	null	2024-04-30T10:32:52+00:00
text-to-image	diffusers	# BRIA 2.3 FAST-LORA: Text-to-Image Model for Commercial Licensing Introducing Bria AI 2.3 FAST-LORA, a groundbreaking text-to-image model explicitly designed for commercial applications in the enterprise. This model combines technological innovation with ethical responsibility and legal security, setting a new standard in the AI industry. Bria AI licenses the foundation model with full legal liability coverage. Our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content. For more information, please visit our [website](https://bria.ai/). # What's New BRIA 2.3 FAST-LORA is a speedy version of BRIA 2.3, that provides an optimal balance between speed and accuracy. Engineered for efficiency, it takes only 1.64 seconds to generate images on a standard NVIDIA A10 GPU, achieving excellent image quality with an 80% reduction in inference time. Most importantly, BRIA 2.3 FAST-LORA is compatible with additional plugins, such as ControlNets. This enables the building of complex pipelines while still maintaining fast inference. [CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-2.3-FAST-LORA) ### Get Access Interested in BRIA 2.3 FAST-LORA? Purchase is required to access BRIA 2.3 FAST-LORA, ensuring royalty management with our data partners and full liability coverage for commercial use. Are you a startup or a student? We encourage you to apply for our [Startup Program](https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1cqrl81_gaMTIxMDI2NzI5OC4xNjk5NTQ3MDAz_ga_WRN60H46X4MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) to request access. This program is designed to support emerging businesses and academic pursuits with our cutting-edge technology. Contact us today to unlock the potential of BRIA 2.3 FAST-LORA! By submitting the form above, you agree to BRIA’s [Privacy policy](https://bria.ai/privacy-policy/) and [Terms & conditions.](https://bria.ai/terms-and-conditions/) ![](fast-lora_example.png) # Key Features - Legally Compliant:* Offers full legal liability coverage for copyright and privacy infringements. Thanks to training on 100% licensed data from leading data partners, we ensure the ethical use of content. - Patented Attribution Engine: Our attribution engine is our way to compensate our data partners, powered by our proprietary and patented algorithms. - Enterprise-Ready: Specifically designed for business applications, Bria AI 2.3 delivers high-quality, compliant imagery for a variety of commercial needs. - Customizable Technology: Provides access to source code and weights for extensive customization, catering to specific business requirements. ### Model Description - Developed by: BRIA AI - Model type: Text-to-Image model - License: [BRIA 2.3 FAST-LORA Licensing terms & conditions](https://bria.ai/bria-huggingface-model-license-agreement/). - Purchase is required to license and access the model. - Model Description: BRIA 2.3 Fast is an efficient text-to-image model trained exclusively on a professional-grade, licensed dataset. It is designed for commercial use and includes full legal liability coverage. - Resources for more information: [BRIA AI](https://bria.ai/) # Code example using Diffusers ``` pip install diffusers ``` ```py from diffusers import DiffusionPipeline, LCMScheduler import torch pipe = DiffusionPipeline.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16) pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA") pipe.fuse_lora() pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to("cuda") prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background" image = pipe(prompt, num_inference_steps=8, guidance_scale=0.0).images[0] ``` # Using both LCM LORA and ControlNet ``` condition_image_path = "A_dog.png" prompt = "A white dog" seed = 222 w, h = 1024, 1024 controlnet = ControlNetModel.from_pretrained( "briaai/BRIA-2.3-ControlNet-Canny", torch_dtype=torch.float16 ) pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16) pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA") pipe.fuse_lora() pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.force_zeros_for_empty_prompt = False pipe.to("cuda") #To run much faster use (or disable it) pipeline.unet = torch.compile(pipeline.unet, mode=‘reduce-overhead’, fullgraph=True) negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers" generator = torch.Generator("cuda").manual_seed(seed) # Calculate Canny image low_threshold, high_threshold = 100, 200 input_image = cv2.imread(condition_image_path) input_image = cv2.Canny(input_image, low_threshold, high_threshold) input_image = input_image[:, :, None] input_image = np.concatenate([input_image, input_image, input_image], axis=2) condition_image = Image.fromarray(input_image) #Generate image = pipe(prompt, image=condition_image, controlnet_conditioning_scale=1.0, num_inference_steps=8, width=w,height=h, guidance_scale=0.0, negative_prompt=negative_prompt, generator=generator,).images[0] ```	{"license": "other", "library_name": "diffusers", "tags": ["text-to-image", "legal liability", "commercial use"], "license_name": "bria-2.3-fast-lora", "license_link": "https://bria.ai/bria-huggingface-model-license-agreement/", "inference": false, "extra_gated_prompt": "Model weights from BRIA AI can be obtained after purchasing a commercial license. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}}	briaai/BRIA-2.3-FAST-LORA	null	[ "diffusers", "text-to-image", "legal liability", "commercial use", "license:other", "has_space", "region:us" ]	null	2024-04-30T10:33:37+00:00
text-classification	transformers		{"license": "apache-2.0"}	cstnz/Persuasive_Prompt_Detection	null	[ "transformers", "safetensors", "distilbert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:36:52+00:00
text-classification	transformers	<!-- 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. --> # lnmt15 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1494 - Accuracy: {'accuracy': 0.6157331136738056} - F1 Macro: {'f1': 0.35224562300474405} - F1 Weighted: {'f1': 0.6107640170375412} ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| F1 Weighted \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------:\|:---------------------------:\|:--------------------------:\| \| No log \| 1.0 \| 315 \| 1.9867 \| {'accuracy': 0.5440691927512356} \| {'f1': 0.23588878173467542} \| {'f1': 0.482395380557745} \| \| 2.2956 \| 2.0 \| 630 \| 1.6310 \| {'accuracy': 0.6099670510708401} \| {'f1': 0.296234704361162} \| {'f1': 0.5602777535361718} \| \| 2.2956 \| 3.0 \| 945 \| 1.5960 \| {'accuracy': 0.6105848434925865} \| {'f1': 0.3239028668073646} \| {'f1': 0.5781483107254908} \| \| 1.1562 \| 4.0 \| 1260 \| 1.6198 \| {'accuracy': 0.6044069192751236} \| {'f1': 0.3319051657049188} \| {'f1': 0.5789585559948692} \| \| 0.6749 \| 5.0 \| 1575 \| 1.6016 \| {'accuracy': 0.6274711696869851} \| {'f1': 0.35227630966560214} \| {'f1': 0.6003765859969182} \| \| 0.6749 \| 6.0 \| 1890 \| 1.6962 \| {'accuracy': 0.624176276771005} \| {'f1': 0.3592207857169669} \| {'f1': 0.6028162590833795} \| \| 0.3996 \| 7.0 \| 2205 \| 1.7583 \| {'accuracy': 0.6200576606260296} \| {'f1': 0.3709968320616015} \| {'f1': 0.6095025945535998} \| \| 0.2454 \| 8.0 \| 2520 \| 1.8746 \| {'accuracy': 0.6122322899505767} \| {'f1': 0.35599679743837515} \| {'f1': 0.6023592353690738} \| \| 0.2454 \| 9.0 \| 2835 \| 1.9208 \| {'accuracy': 0.6231466227347611} \| {'f1': 0.37322828297957683} \| {'f1': 0.6153116988982412} \| \| 0.1558 \| 10.0 \| 3150 \| 1.9797 \| {'accuracy': 0.6182042833607908} \| {'f1': 0.351807694864604} \| {'f1': 0.6115567000372819} \| \| 0.1558 \| 11.0 \| 3465 \| 2.0504 \| {'accuracy': 0.6165568369028006} \| {'f1': 0.3505771873001682} \| {'f1': 0.6093723307186816} \| \| 0.0966 \| 12.0 \| 3780 \| 2.0914 \| {'accuracy': 0.6161449752883031} \| {'f1': 0.35442869400440213} \| {'f1': 0.6110289777760628} \| \| 0.0686 \| 13.0 \| 4095 \| 2.1204 \| {'accuracy': 0.6151153212520593} \| {'f1': 0.350245692547929} \| {'f1': 0.6083199760830841} \| \| 0.0686 \| 14.0 \| 4410 \| 2.1529 \| {'accuracy': 0.6140856672158155} \| {'f1': 0.35392733470678944} \| {'f1': 0.6095998257902047} \| \| 0.0566 \| 15.0 \| 4725 \| 2.1494 \| {'accuracy': 0.6157331136738056} \| {'f1': 0.35224562300474405} \| {'f1': 0.6107640170375412} \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "lnmt15", "results": []}]}	carmenlozano/lnmt15	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T10:37:52+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	Ahjeong/dpo_gemma_bf16_lr1e-5_origindset_default_kl0.01-epoch3	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T10:37:58+00:00

null

transformers

# 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]

{"library_name": "transformers", "tags": []}

fxmeng/PiSSA-Llama-3-70B-4bit-r64-5iter

null

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

null

2024-04-30T09:46:18+00:00

null

{}

HikariLight/mistral-daic-woz-unsupervised-finetune_hs

null

[ "region:us" ]

null

2024-04-30T09:47:50+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-1_1B](https://hf.co/apple/OpenELM-1_1B) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-1_1B) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-1_1B

null

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

null

2024-04-30T09:48:18+00:00

token-classification

transformers

# BERT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0992 - Precision: 0.6178 - Recall: 0.2607 - F1: 0.3666 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | No log | 1.0 | 102 | 0.0992 | 0.6178 | 0.2607 | 0.3666 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "base_model": "bert-base-cased", "model-index": [{"name": "BERT", "results": []}]}

Farjfar/BERT

null

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T09:48:45+00:00

text-to-image

diffusers

# LoRA text2image fine-tuning - merkol/sd-naruto-model-lora These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the lambdalabs/naruto-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "diffusers-training", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "inference": true}

merkol/sd-naruto-model-lora

null

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]

null

2024-04-30T09:48:54+00:00

null

{}

Danilas/sn25-1-3

null

[ "region:us" ]

null

2024-04-30T09:49:03+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-1_1B-Instruct](https://hf.co/apple/OpenELM-1_1B-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-1_1B-Instruct) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-1_1B-Instruct

null

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

null

2024-04-30T09:49:09+00:00

null

{}

NiiCole/peft-rosa-adapter-1

null

[ "region:us" ]

null

2024-04-30T09:49:40+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-270M](https://hf.co/apple/OpenELM-270M) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-270M) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-270M

null

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

null

2024-04-30T09:50:03+00:00

text-generation

transformers

# my_awesome_eli5_clm-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 3.5701 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.6922 | 1.0 | 1306 | 3.5763 | | 3.5786 | 2.0 | 2612 | 3.5707 | | 3.527 | 3.0 | 3918 | 3.5701 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "gpt2", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]}

bzdz/my_awesome_eli5_clm-model

null

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T09:50:13+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-270M-Instruct](https://hf.co/apple/OpenELM-270M-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-270M-Instruct) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-270M-Instruct

null

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

null

2024-04-30T09:50:19+00:00

object-detection

transformers

[<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/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/u73br2l9) # facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.2371 - Map: 0.2873 - Map 50: 0.5678 - Map 75: 0.2501 - Map Small: 0.126 - Map Medium: 0.2327 - Map Large: 0.4873 - Mar 1: 0.2843 - Mar 10: 0.4643 - Mar 100: 0.4762 - Mar Small: 0.2338 - Mar Medium: 0.4167 - Mar Large: 0.7114 - Map Coverall: 0.5461 - Mar 100 Coverall: 0.6932 - Map Face Shield: 0.2167 - Mar 100 Face Shield: 0.4785 - Map Gloves: 0.2135 - Mar 100 Gloves: 0.4094 - Map Goggles: 0.173 - Mar 100 Goggles: 0.4092 - Map Mask: 0.2871 - Mar 100 Mask: 0.3907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 2.6324 | 1.0 | 107 | 2.3696 | 0.0355 | 0.0772 | 0.0305 | 0.0094 | 0.0044 | 0.0388 | 0.0606 | 0.1313 | 0.1643 | 0.0314 | 0.0921 | 0.1907 | 0.1559 | 0.536 | 0.0 | 0.0 | 0.0064 | 0.1433 | 0.0 | 0.0 | 0.0151 | 0.1422 | | 2.0851 | 2.0 | 214 | 2.1468 | 0.0518 | 0.1164 | 0.0389 | 0.0106 | 0.0151 | 0.0683 | 0.0809 | 0.1628 | 0.1881 | 0.023 | 0.1069 | 0.2515 | 0.2068 | 0.5842 | 0.0 | 0.0 | 0.0174 | 0.167 | 0.0 | 0.0 | 0.035 | 0.1893 | | 1.9849 | 3.0 | 321 | 2.0694 | 0.0592 | 0.1436 | 0.0377 | 0.0187 | 0.023 | 0.0609 | 0.0796 | 0.1666 | 0.191 | 0.0527 | 0.1032 | 0.2411 | 0.2119 | 0.5486 | 0.0 | 0.0 | 0.0226 | 0.1799 | 0.0 | 0.0 | 0.0615 | 0.2262 | | 1.8364 | 4.0 | 428 | 2.0431 | 0.0715 | 0.1666 | 0.0625 | 0.0031 | 0.0232 | 0.09 | 0.0775 | 0.1641 | 0.183 | 0.0083 | 0.1006 | 0.2708 | 0.2923 | 0.5604 | 0.0012 | 0.0177 | 0.0203 | 0.1612 | 0.0 | 0.0 | 0.0437 | 0.1756 | | 1.7433 | 5.0 | 535 | 1.9001 | 0.0938 | 0.2115 | 0.0758 | 0.0229 | 0.0473 | 0.1031 | 0.1066 | 0.216 | 0.2311 | 0.0451 | 0.1423 | 0.3186 | 0.3168 | 0.5847 | 0.0064 | 0.0937 | 0.05 | 0.2277 | 0.0 | 0.0 | 0.0959 | 0.2493 | | 1.7113 | 6.0 | 642 | 1.8483 | 0.1007 | 0.2225 | 0.0773 | 0.0203 | 0.0593 | 0.1341 | 0.1109 | 0.2213 | 0.2463 | 0.0646 | 0.1711 | 0.3303 | 0.3462 | 0.5914 | 0.0132 | 0.0987 | 0.0451 | 0.2571 | 0.0002 | 0.0062 | 0.0987 | 0.2782 | | 1.6518 | 7.0 | 749 | 1.8376 | 0.1055 | 0.2347 | 0.0807 | 0.0199 | 0.0596 | 0.1405 | 0.1127 | 0.2364 | 0.2598 | 0.0778 | 0.1949 | 0.3276 | 0.3693 | 0.5869 | 0.0168 | 0.1367 | 0.0417 | 0.2397 | 0.0046 | 0.0523 | 0.0953 | 0.2831 | | 1.6048 | 8.0 | 856 | 1.8041 | 0.1098 | 0.2576 | 0.0857 | 0.0155 | 0.0772 | 0.1578 | 0.1284 | 0.2423 | 0.2708 | 0.0602 | 0.2057 | 0.3631 | 0.3512 | 0.6149 | 0.033 | 0.1342 | 0.0402 | 0.2491 | 0.004 | 0.0862 | 0.1204 | 0.2698 | | 1.6026 | 9.0 | 963 | 1.7173 | 0.1243 | 0.2773 | 0.1041 | 0.0415 | 0.0688 | 0.1767 | 0.1426 | 0.2852 | 0.3101 | 0.0857 | 0.2275 | 0.4249 | 0.3889 | 0.6378 | 0.0347 | 0.2253 | 0.0547 | 0.271 | 0.0159 | 0.1308 | 0.1274 | 0.2858 | | 1.5298 | 10.0 | 1070 | 1.7915 | 0.1154 | 0.2763 | 0.0804 | 0.018 | 0.0789 | 0.1762 | 0.1447 | 0.2629 | 0.2913 | 0.0774 | 0.2191 | 0.3935 | 0.3615 | 0.618 | 0.0415 | 0.2127 | 0.0351 | 0.2603 | 0.0076 | 0.0846 | 0.1315 | 0.2809 | | 1.5114 | 11.0 | 1177 | 1.7964 | 0.1232 | 0.2755 | 0.0915 | 0.0396 | 0.0849 | 0.1728 | 0.144 | 0.2876 | 0.3007 | 0.0868 | 0.2161 | 0.4535 | 0.3588 | 0.5923 | 0.035 | 0.2291 | 0.057 | 0.2241 | 0.0195 | 0.1877 | 0.1455 | 0.2702 | | 1.5283 | 12.0 | 1284 | 1.7017 | 0.1444 | 0.3075 | 0.1223 | 0.0215 | 0.0985 | 0.2396 | 0.1803 | 0.3244 | 0.3456 | 0.0531 | 0.282 | 0.5554 | 0.4122 | 0.632 | 0.0666 | 0.3215 | 0.0618 | 0.2625 | 0.0086 | 0.2046 | 0.1727 | 0.3076 | | 1.5034 | 13.0 | 1391 | 1.6344 | 0.1538 | 0.3418 | 0.1226 | 0.0423 | 0.1165 | 0.2381 | 0.1871 | 0.337 | 0.3578 | 0.1195 | 0.2901 | 0.5214 | 0.4204 | 0.6284 | 0.0705 | 0.319 | 0.0634 | 0.2884 | 0.0191 | 0.2277 | 0.1959 | 0.3253 | | 1.445 | 14.0 | 1498 | 1.5685 | 0.167 | 0.3626 | 0.15 | 0.0561 | 0.1266 | 0.2715 | 0.1998 | 0.357 | 0.3701 | 0.1068 | 0.2922 | 0.5693 | 0.4227 | 0.6342 | 0.0873 | 0.3797 | 0.0744 | 0.2951 | 0.0441 | 0.2108 | 0.2065 | 0.3307 | | 1.3998 | 15.0 | 1605 | 1.5679 | 0.1645 | 0.3515 | 0.1383 | 0.0269 | 0.117 | 0.2592 | 0.1992 | 0.3295 | 0.3539 | 0.0645 | 0.2751 | 0.5533 | 0.4535 | 0.6703 | 0.0887 | 0.3494 | 0.0866 | 0.3116 | 0.0276 | 0.1554 | 0.1659 | 0.2827 | | 1.398 | 16.0 | 1712 | 1.6191 | 0.1578 | 0.3459 | 0.1205 | 0.0623 | 0.0967 | 0.258 | 0.1729 | 0.3255 | 0.3386 | 0.1106 | 0.2686 | 0.5066 | 0.4363 | 0.6 | 0.0971 | 0.3089 | 0.0703 | 0.2839 | 0.0258 | 0.2 | 0.1593 | 0.3004 | | 1.369 | 17.0 | 1819 | 1.6099 | 0.1654 | 0.3517 | 0.1371 | 0.0341 | 0.099 | 0.2543 | 0.1852 | 0.3085 | 0.3232 | 0.0587 | 0.2331 | 0.5126 | 0.4663 | 0.6329 | 0.0784 | 0.2785 | 0.0761 | 0.2906 | 0.019 | 0.12 | 0.187 | 0.2942 | | 1.3264 | 18.0 | 1926 | 1.5610 | 0.1784 | 0.3752 | 0.1594 | 0.036 | 0.1249 | 0.3082 | 0.2061 | 0.3388 | 0.3561 | 0.0959 | 0.2702 | 0.5669 | 0.5 | 0.6707 | 0.0768 | 0.2937 | 0.0899 | 0.2933 | 0.0291 | 0.2108 | 0.1962 | 0.312 | | 1.3513 | 19.0 | 2033 | 1.4462 | 0.1864 | 0.3933 | 0.154 | 0.0559 | 0.1364 | 0.2738 | 0.2135 | 0.3767 | 0.3931 | 0.1216 | 0.3178 | 0.5744 | 0.4834 | 0.677 | 0.0976 | 0.3595 | 0.1022 | 0.3438 | 0.0248 | 0.2554 | 0.224 | 0.3298 | | 1.3034 | 20.0 | 2140 | 1.4527 | 0.1965 | 0.412 | 0.1713 | 0.051 | 0.1463 | 0.2967 | 0.2087 | 0.3643 | 0.3805 | 0.1422 | 0.2931 | 0.5833 | 0.4982 | 0.6495 | 0.1144 | 0.362 | 0.0997 | 0.3263 | 0.0497 | 0.2277 | 0.2204 | 0.3369 | | 1.2823 | 21.0 | 2247 | 1.4872 | 0.1835 | 0.3972 | 0.1556 | 0.0596 | 0.1308 | 0.282 | 0.2148 | 0.3924 | 0.4147 | 0.1236 | 0.3657 | 0.5903 | 0.4622 | 0.6455 | 0.0727 | 0.3873 | 0.1195 | 0.3558 | 0.0544 | 0.3415 | 0.2084 | 0.3431 | | 1.3099 | 22.0 | 2354 | 1.4723 | 0.1903 | 0.407 | 0.1632 | 0.0594 | 0.1343 | 0.2937 | 0.2158 | 0.3817 | 0.396 | 0.1357 | 0.3222 | 0.5737 | 0.4719 | 0.6486 | 0.1089 | 0.3722 | 0.1153 | 0.3161 | 0.0404 | 0.3092 | 0.2149 | 0.3338 | | 1.2691 | 23.0 | 2461 | 1.4648 | 0.1897 | 0.403 | 0.1626 | 0.0598 | 0.1476 | 0.2933 | 0.2129 | 0.3676 | 0.3851 | 0.1154 | 0.3247 | 0.592 | 0.4836 | 0.6658 | 0.0711 | 0.3025 | 0.1217 | 0.3679 | 0.0594 | 0.2615 | 0.2127 | 0.328 | | 1.3018 | 24.0 | 2568 | 1.5593 | 0.174 | 0.3964 | 0.1358 | 0.0568 | 0.1344 | 0.2776 | 0.2076 | 0.3571 | 0.3754 | 0.1412 | 0.2946 | 0.5732 | 0.4208 | 0.6347 | 0.0782 | 0.2987 | 0.1125 | 0.3304 | 0.0444 | 0.2831 | 0.2141 | 0.3302 | | 1.2744 | 25.0 | 2675 | 1.4785 | 0.1863 | 0.3983 | 0.1556 | 0.0706 | 0.1314 | 0.3032 | 0.221 | 0.3784 | 0.3988 | 0.1453 | 0.3331 | 0.5824 | 0.4831 | 0.6716 | 0.0934 | 0.3392 | 0.1155 | 0.3647 | 0.0612 | 0.3277 | 0.1783 | 0.2907 | | 1.3089 | 26.0 | 2782 | 1.4503 | 0.1969 | 0.4129 | 0.166 | 0.0904 | 0.1342 | 0.3274 | 0.2303 | 0.3907 | 0.4057 | 0.154 | 0.3252 | 0.6182 | 0.4773 | 0.6653 | 0.109 | 0.4114 | 0.1176 | 0.3504 | 0.0758 | 0.2877 | 0.2046 | 0.3138 | | 1.2391 | 27.0 | 2889 | 1.4961 | 0.1992 | 0.4097 | 0.1714 | 0.067 | 0.1354 | 0.3227 | 0.2308 | 0.3803 | 0.3971 | 0.1333 | 0.3179 | 0.623 | 0.4826 | 0.6473 | 0.114 | 0.3329 | 0.1169 | 0.3621 | 0.0538 | 0.2862 | 0.2286 | 0.3569 | | 1.2156 | 28.0 | 2996 | 1.4349 | 0.1926 | 0.4121 | 0.1612 | 0.0655 | 0.1425 | 0.2933 | 0.2279 | 0.4047 | 0.425 | 0.1521 | 0.36 | 0.6217 | 0.4698 | 0.6797 | 0.0974 | 0.3924 | 0.1097 | 0.3402 | 0.061 | 0.3677 | 0.2253 | 0.3449 | | 1.2131 | 29.0 | 3103 | 1.4869 | 0.2002 | 0.4106 | 0.1706 | 0.0662 | 0.1516 | 0.3142 | 0.2342 | 0.3959 | 0.4175 | 0.1389 | 0.3525 | 0.6269 | 0.4846 | 0.6514 | 0.0845 | 0.3899 | 0.1196 | 0.3701 | 0.0699 | 0.3277 | 0.2425 | 0.3484 | | 1.2368 | 30.0 | 3210 | 1.3779 | 0.218 | 0.4403 | 0.185 | 0.084 | 0.169 | 0.3567 | 0.241 | 0.4115 | 0.4326 | 0.148 | 0.3738 | 0.6216 | 0.5063 | 0.6815 | 0.122 | 0.4342 | 0.1497 | 0.3969 | 0.066 | 0.3031 | 0.2462 | 0.3471 | | 1.1998 | 31.0 | 3317 | 1.4472 | 0.198 | 0.4299 | 0.16 | 0.0595 | 0.1492 | 0.3264 | 0.2239 | 0.3883 | 0.3992 | 0.127 | 0.3237 | 0.6434 | 0.4806 | 0.6455 | 0.1023 | 0.3722 | 0.1246 | 0.3509 | 0.0601 | 0.3154 | 0.2224 | 0.312 | | 1.1681 | 32.0 | 3424 | 1.4495 | 0.2221 | 0.4518 | 0.1953 | 0.0777 | 0.1696 | 0.3691 | 0.2318 | 0.3938 | 0.4082 | 0.1272 | 0.3478 | 0.6191 | 0.5124 | 0.6757 | 0.1293 | 0.362 | 0.148 | 0.3531 | 0.0893 | 0.32 | 0.2318 | 0.3302 | | 1.1764 | 33.0 | 3531 | 1.4152 | 0.2008 | 0.4169 | 0.1755 | 0.0844 | 0.154 | 0.3627 | 0.2432 | 0.4092 | 0.4304 | 0.182 | 0.3751 | 0.6587 | 0.4828 | 0.6802 | 0.0883 | 0.3886 | 0.1488 | 0.3884 | 0.069 | 0.3692 | 0.2154 | 0.3258 | | 1.1979 | 34.0 | 3638 | 1.4428 | 0.2013 | 0.4058 | 0.1813 | 0.0525 | 0.1428 | 0.3315 | 0.2318 | 0.3933 | 0.4123 | 0.1116 | 0.3492 | 0.6431 | 0.4904 | 0.6486 | 0.0817 | 0.3785 | 0.1434 | 0.3576 | 0.0536 | 0.3277 | 0.2373 | 0.3493 | | 1.1635 | 35.0 | 3745 | 1.3778 | 0.2114 | 0.4473 | 0.1701 | 0.0619 | 0.1497 | 0.372 | 0.2475 | 0.3926 | 0.4125 | 0.1264 | 0.3367 | 0.6545 | 0.4973 | 0.6577 | 0.1031 | 0.3595 | 0.1518 | 0.379 | 0.0604 | 0.3262 | 0.2444 | 0.34 | | 1.1449 | 36.0 | 3852 | 1.3933 | 0.2136 | 0.4427 | 0.1846 | 0.0791 | 0.1615 | 0.3765 | 0.2354 | 0.4041 | 0.4206 | 0.1575 | 0.3565 | 0.6543 | 0.4915 | 0.6626 | 0.1052 | 0.4101 | 0.1672 | 0.3795 | 0.0773 | 0.3169 | 0.227 | 0.3338 | | 1.1453 | 37.0 | 3959 | 1.3954 | 0.205 | 0.4224 | 0.176 | 0.0648 | 0.1498 | 0.3456 | 0.2412 | 0.3984 | 0.4095 | 0.1209 | 0.3492 | 0.6536 | 0.492 | 0.6423 | 0.1063 | 0.3633 | 0.1628 | 0.3893 | 0.0527 | 0.3292 | 0.2114 | 0.3236 | | 1.1424 | 38.0 | 4066 | 1.3781 | 0.2137 | 0.4373 | 0.1839 | 0.0774 | 0.1634 | 0.3815 | 0.2488 | 0.4149 | 0.4281 | 0.1632 | 0.3589 | 0.6559 | 0.4926 | 0.6559 | 0.101 | 0.4 | 0.1576 | 0.3616 | 0.059 | 0.3415 | 0.258 | 0.3813 | | 1.1118 | 39.0 | 4173 | 1.3672 | 0.2154 | 0.4547 | 0.1737 | 0.0746 | 0.1538 | 0.3736 | 0.2448 | 0.3939 | 0.4125 | 0.1372 | 0.3446 | 0.6372 | 0.5024 | 0.6779 | 0.1303 | 0.3608 | 0.1493 | 0.3719 | 0.0607 | 0.3123 | 0.2341 | 0.3396 | | 1.088 | 40.0 | 4280 | 1.3414 | 0.2193 | 0.4552 | 0.1867 | 0.0824 | 0.1711 | 0.3886 | 0.2451 | 0.4043 | 0.4178 | 0.1678 | 0.3501 | 0.6465 | 0.4978 | 0.6658 | 0.1297 | 0.3557 | 0.1644 | 0.404 | 0.0618 | 0.3077 | 0.2426 | 0.356 | | 1.1114 | 41.0 | 4387 | 1.3819 | 0.2228 | 0.4721 | 0.175 | 0.0879 | 0.1609 | 0.3706 | 0.255 | 0.4006 | 0.4146 | 0.1458 | 0.3427 | 0.6389 | 0.4924 | 0.6689 | 0.1164 | 0.3734 | 0.169 | 0.3679 | 0.0961 | 0.3138 | 0.2402 | 0.3489 | | 1.0758 | 42.0 | 4494 | 1.3459 | 0.2286 | 0.4696 | 0.2048 | 0.0897 | 0.1806 | 0.3734 | 0.2526 | 0.4245 | 0.4366 | 0.1566 | 0.3898 | 0.6341 | 0.5112 | 0.6815 | 0.1497 | 0.4038 | 0.1742 | 0.3938 | 0.059 | 0.3338 | 0.2491 | 0.3702 | | 1.0747 | 43.0 | 4601 | 1.4411 | 0.2178 | 0.4397 | 0.1951 | 0.0581 | 0.1557 | 0.3977 | 0.232 | 0.3933 | 0.4086 | 0.1258 | 0.3301 | 0.6699 | 0.4929 | 0.6604 | 0.1317 | 0.3633 | 0.1632 | 0.3598 | 0.1014 | 0.34 | 0.1997 | 0.3196 | | 1.0909 | 44.0 | 4708 | 1.3192 | 0.2424 | 0.4916 | 0.2152 | 0.0842 | 0.1953 | 0.4032 | 0.26 | 0.4237 | 0.4387 | 0.1597 | 0.3801 | 0.66 | 0.5041 | 0.6842 | 0.1765 | 0.4038 | 0.1901 | 0.3879 | 0.0983 | 0.3585 | 0.2431 | 0.3591 | | 1.0527 | 45.0 | 4815 | 1.3110 | 0.2362 | 0.4902 | 0.2009 | 0.1033 | 0.1842 | 0.4042 | 0.2505 | 0.4306 | 0.4496 | 0.1869 | 0.3896 | 0.6601 | 0.5178 | 0.6797 | 0.1379 | 0.4215 | 0.1828 | 0.4098 | 0.0848 | 0.3754 | 0.2576 | 0.3613 | | 1.0421 | 46.0 | 4922 | 1.3186 | 0.2376 | 0.4944 | 0.213 | 0.097 | 0.19 | 0.3833 | 0.2587 | 0.4303 | 0.4487 | 0.2199 | 0.399 | 0.643 | 0.5174 | 0.6824 | 0.1569 | 0.4114 | 0.171 | 0.3902 | 0.0818 | 0.3923 | 0.261 | 0.3671 | | 1.0428 | 47.0 | 5029 | 1.3096 | 0.2421 | 0.4797 | 0.2235 | 0.0976 | 0.196 | 0.4236 | 0.2574 | 0.4299 | 0.4437 | 0.2217 | 0.3688 | 0.6646 | 0.5242 | 0.6824 | 0.155 | 0.419 | 0.1757 | 0.396 | 0.1006 | 0.3585 | 0.255 | 0.3627 | | 1.0369 | 48.0 | 5136 | 1.3049 | 0.2447 | 0.4953 | 0.2069 | 0.0929 | 0.1971 | 0.421 | 0.2651 | 0.4284 | 0.4393 | 0.1395 | 0.3866 | 0.6669 | 0.5228 | 0.6892 | 0.1539 | 0.419 | 0.1757 | 0.3763 | 0.1091 | 0.3492 | 0.2619 | 0.3627 | | 1.0315 | 49.0 | 5243 | 1.3189 | 0.2417 | 0.4851 | 0.2149 | 0.0914 | 0.1967 | 0.4224 | 0.2682 | 0.4241 | 0.4431 | 0.1874 | 0.3756 | 0.6742 | 0.5079 | 0.691 | 0.1679 | 0.4215 | 0.1627 | 0.3857 | 0.1174 | 0.3569 | 0.2527 | 0.3604 | | 1.0149 | 50.0 | 5350 | 1.3127 | 0.2399 | 0.4962 | 0.2041 | 0.096 | 0.1854 | 0.4276 | 0.2638 | 0.4178 | 0.4325 | 0.1657 | 0.3617 | 0.6608 | 0.5148 | 0.6797 | 0.1434 | 0.3734 | 0.1616 | 0.3746 | 0.1342 | 0.3831 | 0.2454 | 0.3516 | | 1.0223 | 51.0 | 5457 | 1.2917 | 0.2419 | 0.4981 | 0.2153 | 0.0942 | 0.2027 | 0.4113 | 0.2574 | 0.4243 | 0.4418 | 0.1948 | 0.3921 | 0.6428 | 0.5136 | 0.6928 | 0.1563 | 0.4038 | 0.1727 | 0.3987 | 0.1049 | 0.36 | 0.2618 | 0.3538 | | 1.0072 | 52.0 | 5564 | 1.3413 | 0.2634 | 0.5259 | 0.2292 | 0.0985 | 0.2074 | 0.4356 | 0.2615 | 0.415 | 0.4305 | 0.1818 | 0.354 | 0.6634 | 0.5187 | 0.6739 | 0.2066 | 0.4076 | 0.1795 | 0.3915 | 0.1321 | 0.3215 | 0.2802 | 0.3582 | | 0.9923 | 53.0 | 5671 | 1.3195 | 0.2393 | 0.4806 | 0.2104 | 0.1019 | 0.1819 | 0.3959 | 0.2597 | 0.4239 | 0.4406 | 0.1933 | 0.3776 | 0.6387 | 0.5188 | 0.677 | 0.1376 | 0.4114 | 0.1713 | 0.4103 | 0.091 | 0.3308 | 0.2775 | 0.3738 | | 0.9981 | 54.0 | 5778 | 1.3229 | 0.2406 | 0.5014 | 0.2014 | 0.1122 | 0.1896 | 0.404 | 0.2441 | 0.4128 | 0.4303 | 0.1999 | 0.3707 | 0.6241 | 0.5172 | 0.673 | 0.1521 | 0.3975 | 0.1695 | 0.4009 | 0.1067 | 0.32 | 0.2574 | 0.36 | | 0.9892 | 55.0 | 5885 | 1.3100 | 0.2483 | 0.5044 | 0.2104 | 0.0779 | 0.1985 | 0.4205 | 0.2606 | 0.4207 | 0.4357 | 0.1744 | 0.3783 | 0.6592 | 0.5088 | 0.6608 | 0.1744 | 0.4051 | 0.1689 | 0.3915 | 0.1261 | 0.36 | 0.2632 | 0.3609 | | 0.9704 | 56.0 | 5992 | 1.3102 | 0.2508 | 0.5103 | 0.22 | 0.0998 | 0.1951 | 0.4222 | 0.259 | 0.4147 | 0.4341 | 0.158 | 0.3784 | 0.6439 | 0.5205 | 0.6797 | 0.1459 | 0.4038 | 0.1683 | 0.4045 | 0.1467 | 0.3338 | 0.2723 | 0.3484 | | 0.9724 | 57.0 | 6099 | 1.2838 | 0.2591 | 0.5087 | 0.2339 | 0.1102 | 0.2132 | 0.4197 | 0.267 | 0.4329 | 0.4461 | 0.2133 | 0.3891 | 0.6508 | 0.5396 | 0.6901 | 0.1688 | 0.4139 | 0.1781 | 0.3835 | 0.1362 | 0.3708 | 0.2727 | 0.3724 | | 0.9768 | 58.0 | 6206 | 1.2982 | 0.2591 | 0.5133 | 0.2325 | 0.0919 | 0.2165 | 0.4129 | 0.2634 | 0.4314 | 0.4431 | 0.1627 | 0.3999 | 0.6589 | 0.5289 | 0.6923 | 0.171 | 0.4013 | 0.1841 | 0.3821 | 0.1417 | 0.3708 | 0.2695 | 0.3689 | | 0.974 | 59.0 | 6313 | 1.2888 | 0.2611 | 0.5216 | 0.239 | 0.1126 | 0.2094 | 0.4504 | 0.2659 | 0.4367 | 0.4506 | 0.2099 | 0.3957 | 0.6611 | 0.5364 | 0.6833 | 0.1824 | 0.4304 | 0.1768 | 0.3884 | 0.1455 | 0.38 | 0.2646 | 0.3711 | | 0.9585 | 60.0 | 6420 | 1.2801 | 0.269 | 0.5399 | 0.2305 | 0.103 | 0.2209 | 0.4622 | 0.2636 | 0.4453 | 0.4548 | 0.227 | 0.3944 | 0.6777 | 0.5471 | 0.6896 | 0.1805 | 0.4392 | 0.1935 | 0.3862 | 0.1518 | 0.3908 | 0.272 | 0.368 | | 0.9693 | 61.0 | 6527 | 1.2816 | 0.2685 | 0.5368 | 0.2347 | 0.0904 | 0.2268 | 0.4546 | 0.2661 | 0.4332 | 0.4445 | 0.1645 | 0.3918 | 0.6802 | 0.5394 | 0.6811 | 0.1894 | 0.4481 | 0.1913 | 0.3754 | 0.1505 | 0.3585 | 0.2718 | 0.3596 | | 0.9653 | 62.0 | 6634 | 1.2668 | 0.2718 | 0.5518 | 0.2315 | 0.1013 | 0.2221 | 0.4447 | 0.2714 | 0.4392 | 0.4524 | 0.2027 | 0.3974 | 0.6521 | 0.5416 | 0.6833 | 0.1993 | 0.4418 | 0.193 | 0.4018 | 0.1515 | 0.3646 | 0.2733 | 0.3707 | | 0.9367 | 63.0 | 6741 | 1.2756 | 0.2678 | 0.5419 | 0.2284 | 0.0915 | 0.2106 | 0.4618 | 0.2696 | 0.4304 | 0.4467 | 0.2066 | 0.3823 | 0.6624 | 0.5449 | 0.6941 | 0.1986 | 0.4418 | 0.1872 | 0.4098 | 0.1375 | 0.32 | 0.2706 | 0.3676 | | 0.9397 | 64.0 | 6848 | 1.2792 | 0.2693 | 0.543 | 0.2349 | 0.1105 | 0.2191 | 0.4305 | 0.2671 | 0.447 | 0.4594 | 0.2058 | 0.4023 | 0.6729 | 0.532 | 0.6856 | 0.1938 | 0.4544 | 0.1966 | 0.3942 | 0.1431 | 0.3892 | 0.2811 | 0.3738 | | 0.9362 | 65.0 | 6955 | 1.2815 | 0.2622 | 0.5268 | 0.2297 | 0.0993 | 0.2152 | 0.4254 | 0.2702 | 0.4417 | 0.4564 | 0.1804 | 0.404 | 0.6691 | 0.5354 | 0.6815 | 0.1833 | 0.4608 | 0.1828 | 0.3763 | 0.1314 | 0.3831 | 0.2783 | 0.3804 | | 0.9256 | 66.0 | 7062 | 1.2706 | 0.2785 | 0.5545 | 0.2555 | 0.11 | 0.2213 | 0.4632 | 0.2807 | 0.4514 | 0.4665 | 0.2383 | 0.4052 | 0.6819 | 0.5466 | 0.6977 | 0.2198 | 0.4532 | 0.1932 | 0.4076 | 0.1533 | 0.3938 | 0.2795 | 0.38 | | 0.9251 | 67.0 | 7169 | 1.2693 | 0.2779 | 0.5672 | 0.2466 | 0.1024 | 0.2213 | 0.4665 | 0.2746 | 0.4364 | 0.4467 | 0.1908 | 0.3882 | 0.6738 | 0.5463 | 0.6896 | 0.2235 | 0.4329 | 0.1956 | 0.3853 | 0.1504 | 0.3508 | 0.2735 | 0.3747 | | 0.9284 | 68.0 | 7276 | 1.2592 | 0.2784 | 0.5506 | 0.2458 | 0.0927 | 0.2304 | 0.483 | 0.2735 | 0.4356 | 0.4443 | 0.1732 | 0.3946 | 0.6872 | 0.5438 | 0.6896 | 0.2034 | 0.4038 | 0.2033 | 0.3951 | 0.1668 | 0.3585 | 0.2749 | 0.3747 | | 0.9217 | 69.0 | 7383 | 1.2788 | 0.2751 | 0.5401 | 0.2457 | 0.0984 | 0.223 | 0.4747 | 0.2762 | 0.4435 | 0.4547 | 0.2076 | 0.4048 | 0.6831 | 0.5437 | 0.6896 | 0.2062 | 0.4392 | 0.1936 | 0.3888 | 0.1552 | 0.3738 | 0.277 | 0.3818 | | 0.8987 | 70.0 | 7490 | 1.2390 | 0.2757 | 0.5535 | 0.2487 | 0.1123 | 0.2263 | 0.4587 | 0.2759 | 0.4482 | 0.4603 | 0.2193 | 0.4165 | 0.6799 | 0.5415 | 0.6901 | 0.1939 | 0.4532 | 0.2053 | 0.4116 | 0.1652 | 0.3646 | 0.2727 | 0.3822 | | 0.8797 | 71.0 | 7597 | 1.2614 | 0.2749 | 0.5451 | 0.2406 | 0.1055 | 0.2251 | 0.4616 | 0.275 | 0.446 | 0.459 | 0.1965 | 0.4088 | 0.6842 | 0.5438 | 0.6928 | 0.19 | 0.4506 | 0.2133 | 0.4058 | 0.1504 | 0.3723 | 0.2768 | 0.3733 | | 0.8864 | 72.0 | 7704 | 1.2601 | 0.2813 | 0.5445 | 0.2524 | 0.1029 | 0.2261 | 0.4549 | 0.28 | 0.4474 | 0.4608 | 0.2296 | 0.4052 | 0.6677 | 0.5449 | 0.6878 | 0.2062 | 0.4342 | 0.2122 | 0.4196 | 0.1731 | 0.3877 | 0.2701 | 0.3747 | | 0.8739 | 73.0 | 7811 | 1.2542 | 0.2809 | 0.556 | 0.2491 | 0.1104 | 0.2179 | 0.4702 | 0.2839 | 0.4483 | 0.4652 | 0.2438 | 0.4059 | 0.6685 | 0.5439 | 0.6847 | 0.1978 | 0.4468 | 0.2114 | 0.429 | 0.1767 | 0.3892 | 0.2746 | 0.376 | | 0.8851 | 74.0 | 7918 | 1.3047 | 0.271 | 0.5478 | 0.2363 | 0.1153 | 0.2078 | 0.4572 | 0.2842 | 0.4438 | 0.4542 | 0.2441 | 0.3865 | 0.665 | 0.521 | 0.6676 | 0.1916 | 0.4203 | 0.214 | 0.4286 | 0.1578 | 0.3892 | 0.2704 | 0.3653 | | 0.8902 | 75.0 | 8025 | 1.2808 | 0.2709 | 0.5429 | 0.2351 | 0.101 | 0.2174 | 0.4628 | 0.2806 | 0.4448 | 0.4583 | 0.2163 | 0.397 | 0.6919 | 0.5314 | 0.677 | 0.1856 | 0.4342 | 0.1961 | 0.4036 | 0.1625 | 0.3969 | 0.2791 | 0.38 | | 0.8874 | 76.0 | 8132 | 1.2806 | 0.2812 | 0.5664 | 0.2465 | 0.113 | 0.2339 | 0.4718 | 0.2864 | 0.4519 | 0.4645 | 0.2363 | 0.4141 | 0.688 | 0.5339 | 0.6869 | 0.194 | 0.4481 | 0.208 | 0.4228 | 0.1807 | 0.3738 | 0.2897 | 0.3907 | | 0.8809 | 77.0 | 8239 | 1.2476 | 0.2807 | 0.5632 | 0.2575 | 0.1312 | 0.2228 | 0.4686 | 0.2888 | 0.4592 | 0.4738 | 0.2428 | 0.4097 | 0.7011 | 0.5351 | 0.6914 | 0.1972 | 0.4582 | 0.2129 | 0.4259 | 0.1747 | 0.4092 | 0.2834 | 0.384 | | 0.856 | 78.0 | 8346 | 1.2580 | 0.2878 | 0.5675 | 0.2528 | 0.1216 | 0.2298 | 0.4649 | 0.2918 | 0.4565 | 0.4712 | 0.2357 | 0.4122 | 0.6921 | 0.5297 | 0.6923 | 0.2236 | 0.4456 | 0.216 | 0.4384 | 0.1805 | 0.3985 | 0.2891 | 0.3813 | | 0.8509 | 79.0 | 8453 | 1.2741 | 0.2811 | 0.5657 | 0.2445 | 0.131 | 0.2218 | 0.4685 | 0.2832 | 0.4469 | 0.4609 | 0.2213 | 0.3994 | 0.6813 | 0.5281 | 0.6842 | 0.2263 | 0.4506 | 0.205 | 0.4179 | 0.1643 | 0.3708 | 0.2817 | 0.3809 | | 0.88 | 80.0 | 8560 | 1.2556 | 0.2818 | 0.5628 | 0.2442 | 0.1218 | 0.2313 | 0.4529 | 0.2848 | 0.4525 | 0.4659 | 0.2211 | 0.4178 | 0.68 | 0.536 | 0.6878 | 0.2158 | 0.438 | 0.2157 | 0.4348 | 0.1503 | 0.3831 | 0.2913 | 0.3858 | | 0.8739 | 81.0 | 8667 | 1.2754 | 0.2797 | 0.5595 | 0.2432 | 0.1169 | 0.2255 | 0.4812 | 0.2791 | 0.4481 | 0.4632 | 0.2257 | 0.3972 | 0.6992 | 0.5369 | 0.6901 | 0.2077 | 0.4544 | 0.2024 | 0.4076 | 0.1652 | 0.3846 | 0.2864 | 0.3791 | | 0.8604 | 82.0 | 8774 | 1.2531 | 0.2775 | 0.5547 | 0.2411 | 0.121 | 0.2204 | 0.4861 | 0.2823 | 0.4461 | 0.4577 | 0.2244 | 0.3945 | 0.6931 | 0.5363 | 0.6824 | 0.2038 | 0.4215 | 0.1948 | 0.4085 | 0.1749 | 0.4062 | 0.2777 | 0.3698 | | 0.8504 | 83.0 | 8881 | 1.2448 | 0.2837 | 0.5589 | 0.2539 | 0.1182 | 0.2199 | 0.4942 | 0.2894 | 0.4561 | 0.4682 | 0.2282 | 0.4074 | 0.6941 | 0.5404 | 0.6946 | 0.2168 | 0.4646 | 0.2078 | 0.4174 | 0.1691 | 0.38 | 0.2844 | 0.3844 | | 0.8394 | 84.0 | 8988 | 1.2491 | 0.2766 | 0.5478 | 0.2385 | 0.1219 | 0.2293 | 0.4674 | 0.2776 | 0.4501 | 0.4611 | 0.2267 | 0.4055 | 0.6803 | 0.544 | 0.7023 | 0.189 | 0.4203 | 0.2092 | 0.4067 | 0.164 | 0.4031 | 0.2769 | 0.3733 | | 0.8257 | 85.0 | 9095 | 1.2410 | 0.2805 | 0.5591 | 0.2415 | 0.1251 | 0.2292 | 0.471 | 0.2783 | 0.4592 | 0.4689 | 0.2249 | 0.4214 | 0.6866 | 0.5408 | 0.6973 | 0.1995 | 0.4608 | 0.2056 | 0.408 | 0.1613 | 0.3831 | 0.295 | 0.3951 | | 0.8416 | 86.0 | 9202 | 1.2492 | 0.2868 | 0.5613 | 0.2512 | 0.1136 | 0.2345 | 0.4828 | 0.2807 | 0.455 | 0.4644 | 0.2239 | 0.4123 | 0.6914 | 0.5519 | 0.6905 | 0.209 | 0.4443 | 0.2155 | 0.4143 | 0.1671 | 0.3815 | 0.2907 | 0.3916 | | 0.8395 | 87.0 | 9309 | 1.2354 | 0.2863 | 0.5545 | 0.2498 | 0.1311 | 0.2408 | 0.4847 | 0.2865 | 0.4603 | 0.4709 | 0.2264 | 0.4195 | 0.6952 | 0.538 | 0.6914 | 0.2185 | 0.4734 | 0.2144 | 0.4152 | 0.1696 | 0.3908 | 0.2908 | 0.3836 | | 0.831 | 88.0 | 9416 | 1.2456 | 0.2801 | 0.5556 | 0.2431 | 0.122 | 0.2307 | 0.4571 | 0.2787 | 0.4571 | 0.4697 | 0.2377 | 0.4118 | 0.6943 | 0.5398 | 0.6946 | 0.1915 | 0.4418 | 0.2088 | 0.4223 | 0.1688 | 0.4062 | 0.2916 | 0.3836 | | 0.8135 | 89.0 | 9523 | 1.2334 | 0.2837 | 0.5639 | 0.2552 | 0.1354 | 0.2321 | 0.4607 | 0.2801 | 0.4679 | 0.4786 | 0.2477 | 0.4247 | 0.7046 | 0.5416 | 0.6937 | 0.2148 | 0.4747 | 0.2154 | 0.4308 | 0.156 | 0.4077 | 0.2908 | 0.3862 | | 0.8169 | 90.0 | 9630 | 1.2384 | 0.2866 | 0.5663 | 0.2508 | 0.1454 | 0.237 | 0.4681 | 0.2799 | 0.4634 | 0.4749 | 0.2483 | 0.4152 | 0.6961 | 0.5472 | 0.6928 | 0.2252 | 0.4709 | 0.2082 | 0.4174 | 0.1521 | 0.4 | 0.3003 | 0.3933 | | 0.8085 | 91.0 | 9737 | 1.2410 | 0.2913 | 0.5751 | 0.2568 | 0.1375 | 0.2386 | 0.476 | 0.2839 | 0.4661 | 0.4805 | 0.2439 | 0.4281 | 0.6948 | 0.5469 | 0.6982 | 0.2298 | 0.4848 | 0.2134 | 0.4183 | 0.1742 | 0.4046 | 0.2924 | 0.3964 | | 0.8272 | 92.0 | 9844 | 1.2486 | 0.29 | 0.5747 | 0.26 | 0.1216 | 0.2434 | 0.4781 | 0.2828 | 0.4639 | 0.4757 | 0.2383 | 0.4208 | 0.6864 | 0.545 | 0.6959 | 0.219 | 0.4722 | 0.2112 | 0.4098 | 0.183 | 0.4062 | 0.2915 | 0.3947 | | 0.7949 | 93.0 | 9951 | 1.2430 | 0.2786 | 0.5565 | 0.2355 | 0.1252 | 0.2239 | 0.4703 | 0.2809 | 0.4591 | 0.4724 | 0.2344 | 0.4165 | 0.7 | 0.5329 | 0.6838 | 0.2075 | 0.4696 | 0.2052 | 0.4219 | 0.162 | 0.3954 | 0.2852 | 0.3911 | | 0.8009 | 94.0 | 10058 | 1.2419 | 0.2866 | 0.5667 | 0.2557 | 0.1403 | 0.2326 | 0.4687 | 0.2796 | 0.4672 | 0.4787 | 0.24 | 0.4214 | 0.7029 | 0.5401 | 0.6865 | 0.2134 | 0.4848 | 0.2098 | 0.4192 | 0.1778 | 0.4046 | 0.2917 | 0.3982 | | 0.8088 | 95.0 | 10165 | 1.2328 | 0.2838 | 0.57 | 0.249 | 0.1482 | 0.2328 | 0.4686 | 0.2851 | 0.4616 | 0.4754 | 0.2413 | 0.4191 | 0.6996 | 0.5383 | 0.6847 | 0.2113 | 0.4797 | 0.2114 | 0.4183 | 0.1672 | 0.3954 | 0.2907 | 0.3987 | | 0.8113 | 96.0 | 10272 | 1.2335 | 0.2852 | 0.5734 | 0.2426 | 0.1313 | 0.2336 | 0.4788 | 0.2848 | 0.4614 | 0.4742 | 0.2416 | 0.4169 | 0.6924 | 0.5401 | 0.686 | 0.2135 | 0.481 | 0.2115 | 0.4121 | 0.1693 | 0.3954 | 0.2914 | 0.3964 | | 0.7898 | 97.0 | 10379 | 1.2327 | 0.2867 | 0.5712 | 0.2504 | 0.1332 | 0.2335 | 0.4682 | 0.288 | 0.4648 | 0.4785 | 0.2391 | 0.421 | 0.7037 | 0.5415 | 0.6892 | 0.2193 | 0.4949 | 0.2123 | 0.4112 | 0.1712 | 0.4015 | 0.2891 | 0.3956 | | 0.8043 | 98.0 | 10486 | 1.2280 | 0.288 | 0.5693 | 0.2456 | 0.1247 | 0.2341 | 0.4886 | 0.29 | 0.4667 | 0.4785 | 0.2389 | 0.4194 | 0.711 | 0.5462 | 0.6914 | 0.2148 | 0.4785 | 0.2133 | 0.4152 | 0.1746 | 0.4108 | 0.2908 | 0.3964 | | 0.8017 | 99.0 | 10593 | 1.2349 | 0.2863 | 0.5661 | 0.2508 | 0.123 | 0.2321 | 0.4837 | 0.2882 | 0.4639 | 0.4755 | 0.2338 | 0.4168 | 0.7093 | 0.5448 | 0.691 | 0.2127 | 0.4722 | 0.2136 | 0.4103 | 0.1744 | 0.4123 | 0.286 | 0.3916 | | 0.7948 | 100.0 | 10700 | 1.2371 | 0.2873 | 0.5678 | 0.2501 | 0.126 | 0.2327 | 0.4873 | 0.2843 | 0.4643 | 0.4762 | 0.2338 | 0.4167 | 0.7114 | 0.5461 | 0.6932 | 0.2167 | 0.4785 | 0.2135 | 0.4094 | 0.173 | 0.4092 | 0.2871 | 0.3907 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

{"license": "apache-2.0", "tags": ["object-detection", "vision", "generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs", "results": []}]}

qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs

null

[ "transformers", "safetensors", "detr", "object-detection", "vision", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-30T09:50:21+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-3B](https://hf.co/apple/OpenELM-3B) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-3B) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-3B

null

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

null

2024-04-30T09:50:35+00:00

text-to-audio

transformers

# fil_b32_le5_s4000 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4102 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:----:|:---------------:| | 0.5539 | 44.4444 | 500 | 0.4850 | | 0.4867 | 88.8889 | 1000 | 0.4439 | | 0.4595 | 133.3333 | 1500 | 0.4245 | | 0.4395 | 177.7778 | 2000 | 0.4155 | | 0.4327 | 222.2222 | 2500 | 0.4121 | | 0.4279 | 266.6667 | 3000 | 0.4127 | | 0.4202 | 311.1111 | 3500 | 0.4098 | | 0.4167 | 355.5556 | 4000 | 0.4102 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "fil_b32_le5_s4000", "results": []}]}

mikhail-panzo/fil_b32_le5_s4000

null

[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-04-30T09:51:58+00:00

null

{"license": "unknown"}

hautc/z3

null

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

null

2024-04-30T09:52:27+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-3B-Instruct](https://hf.co/apple/OpenELM-3B-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-3B-Instruct) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-3B-Instruct

null

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

null

2024-04-30T09:52:52+00:00

text-generation

transformers

# Uploaded model - **Developed by:** MatrixIA - **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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl", "sft"], "base_model": "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"}

MatrixIA/Phi-3-mini-4k-instruct-text-to-sql

null

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

null

2024-04-30T09:53:48+00:00

feature-extraction

transformers

# 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]

{"library_name": "transformers", "tags": []}

Juniplayground/juniper-mxbai-embed-large-v1-more-data-3l

null

[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-04-30T09:54:02+00:00

feature-extraction

transformers

{}

PrachiDabi/BahasaTestV22

null

[ "transformers", "safetensors", "gpt2", "feature-extraction", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T09:54:23+00:00

null

{}

smart976/CNN

null

[ "region:us" ]

null

2024-04-30T09:54:46+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-450M](https://hf.co/apple/OpenELM-450M) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-450M) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-450M

null

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

null

2024-04-30T09:55:18+00:00

null

{"license": "openrail"}

PaZtV/EKT

null

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

null

2024-04-30T09:55:36+00:00

null

# OpenELM – Core ML This repository contains a Core ML conversion of [apple/OpenELM-450M-Instruct](https://hf.co/apple/OpenELM-450M-Instruct) with the following characteristics: - Sequence length: 128, fixed. - Precision: float32. Please, check the [original model card](https://hf.co/apple/OpenELM-450M-Instruct) for additional details on the model.

{"license": "other", "license_name": "apple-sample-code-license", "license_link": "LICENSE"}

corenet-community/coreml-OpenELM-450M-Instruct

null

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

null

2024-04-30T09:55:42+00:00

null

# O0430HMA7 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0202 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1959 | 0.09 | 10 | 0.5332 | | 0.2415 | 0.18 | 20 | 0.1587 | | 0.1515 | 0.27 | 30 | 0.1608 | | 0.1518 | 0.36 | 40 | 0.1538 | | 0.1514 | 0.45 | 50 | 0.1483 | | 0.152 | 0.54 | 60 | 0.1495 | | 0.1489 | 0.63 | 70 | 0.1476 | | 0.1485 | 0.73 | 80 | 0.1583 | | 0.1468 | 0.82 | 90 | 0.1490 | | 0.1438 | 0.91 | 100 | 0.1337 | | 0.1219 | 1.0 | 110 | 0.0771 | | 0.1004 | 1.09 | 120 | 0.0862 | | 0.0874 | 1.18 | 130 | 0.0701 | | 0.1185 | 1.27 | 140 | 0.0932 | | 0.093 | 1.36 | 150 | 0.0723 | | 0.0565 | 1.45 | 160 | 0.0544 | | 0.0597 | 1.54 | 170 | 0.0624 | | 0.0609 | 1.63 | 180 | 0.0567 | | 0.0624 | 1.72 | 190 | 0.0560 | | 0.0578 | 1.81 | 200 | 0.0530 | | 0.0491 | 1.9 | 210 | 0.0426 | | 0.0372 | 1.99 | 220 | 0.0364 | | 0.0432 | 2.08 | 230 | 0.0617 | | 0.0432 | 2.18 | 240 | 0.0487 | | 0.0331 | 2.27 | 250 | 0.0297 | | 0.0281 | 2.36 | 260 | 0.0259 | | 0.0292 | 2.45 | 270 | 0.0222 | | 0.0199 | 2.54 | 280 | 0.0212 | | 0.0267 | 2.63 | 290 | 0.0210 | | 0.0218 | 2.72 | 300 | 0.0208 | | 0.0204 | 2.81 | 310 | 0.0205 | | 0.0229 | 2.9 | 320 | 0.0204 | | 0.0228 | 2.99 | 330 | 0.0202 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA7", "results": []}]}

Litzy619/O0430HMA7

null

[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]

null

2024-04-30T09:55:50+00:00

text-generation

null

# reach-vb/Meta-Llama-3-8B-Q4_0-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo reach-vb/Meta-Llama-3-8B-Q4_0-GGUF --model meta-llama-3-8b.Q4_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo reach-vb/Meta-Llama-3-8B-Q4_0-GGUF --model meta-llama-3-8b.Q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m meta-llama-3-8b.Q4_0.gguf -n 128 ```

{"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"}

reach-vb/Meta-Llama-3-8B-Q4_0-GGUF

null

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:other", "region:us" ]

null

2024-04-30T09:55:52+00:00

token-classification

transformers

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0632 - Precision: 0.9382 - Recall: 0.9500 - F1: 0.9441 - Accuracy: 0.9864 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0756 | 1.0 | 1756 | 0.0608 | 0.9132 | 0.9367 | 0.9248 | 0.9829 | | 0.0324 | 2.0 | 3512 | 0.0692 | 0.9340 | 0.9450 | 0.9394 | 0.9849 | | 0.0191 | 3.0 | 5268 | 0.0632 | 0.9382 | 0.9500 | 0.9441 | 0.9864 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]}

aljaziz/bert-finetuned-ner

null

[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T09:56:02+00:00

text-generation

transformers

# 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]

{"library_name": "transformers", "tags": []}

fmshahata/phi-moe-4exp_

null

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

null

2024-04-30T09:56:13+00:00

text-to-audio

transformers

# fil_b64_le4_s4000 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4134 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:----:|:---------------:| | 0.4725 | 22.2222 | 500 | 0.4372 | | 0.4415 | 44.4444 | 1000 | 0.4203 | | 0.423 | 66.6667 | 1500 | 0.4169 | | 0.4108 | 88.8889 | 2000 | 0.4183 | | 0.3934 | 111.1111 | 2500 | 0.4111 | | 0.3821 | 133.3333 | 3000 | 0.4164 | | 0.3743 | 155.5556 | 3500 | 0.4127 | | 0.3714 | 177.7778 | 4000 | 0.4134 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "fil_b64_le4_s4000", "results": []}]}

mikhail-panzo/fil_b64_le4_s4000

null

[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-04-30T09:56:16+00:00

null

peft

# code-llama-7b-text-to-sql This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]}

nnCarlito/code-llama-7b-text-to-sql

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]

null

2024-04-30T09:57:01+00:00

text-generation

transformers

{}

Osru/llama-2-7b-nubidoc

null

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

null

2024-04-30T09:57:58+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **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] ### Framework versions - PEFT 0.10.1.dev0

{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"}

shivanikerai/Llama-2-7b-chat-hf-adapter-title-ner-and-new-title-suggestion-v1.0

null

[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]

null

2024-04-30T10:00:05+00:00

null

{}

doxiy/bert-finetuned-ner-accelerate

null

[ "region:us" ]

null

2024-04-30T10:00:47+00:00

null

transformers

# Uploaded model - **Developed by:** arvnoodle - **License:** apache-2.0 - **Finetuned from model :** unsloth/codegemma-7b-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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/codegemma-7b-it-bnb-4bit"}

arvnoodle/hcl-codegemma-it-8b_UPDATED-xml-json

null

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/codegemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:00:57+00:00

null

{"license": "unknown"}

hautc/z1

null

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

null

2024-04-30T10:01:26+00:00

null

{}

Danilas/sn25-1-5

null

[ "region:us" ]

null

2024-04-30T10:01:45+00:00

null

{}

pinguG/ElectronicDesireGE

null

[ "region:us" ]

null

2024-04-30T10:01:46+00:00

null

{}

Phill-h/uav-trajectory-solver

null

[ "region:us" ]

null

2024-04-30T10:01:46+00:00

null

{}

Aron9310/MyGPT

null

[ "region:us" ]

null

2024-04-30T10:01:55+00:00

null

{}

ThomasFG/2024-04-30_12-01-38

null

[ "region:us" ]

null

2024-04-30T10:01:56+00:00

null

{}

golf2248/2gcbbgb

null

[ "region:us" ]

null

2024-04-30T10:02:08+00:00

null

{}

aryanmehta5902/Tendormodel1

null

[ "region:us" ]

null

2024-04-30T10:02:23+00:00

null

{"license": "apache-2.0"}

ajaydamsani/ICDD

null

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

null

2024-04-30T10:02:34+00:00

token-classification

transformers

# xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1655 - F1: 0.8574 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2831 | 1.0 | 715 | 0.1854 | 0.8109 | | 0.1453 | 2.0 | 1430 | 0.1646 | 0.8462 | | 0.0942 | 3.0 | 2145 | 0.1655 | 0.8574 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]}

u00890358/xlm-roberta-base-finetuned-panx-de-fr

null

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:02:44+00:00

null

README.md # Mask R-CNN Drone Detection Model ### Overview This repository contains the implementation of a Mask R-CNN model trained for detecting drones in images. The model is built using the Matterport Mask R-CNN implementation and trained on a custom drone dataset. ### Model Architecture The Mask R-CNN architecture consists of a backbone network (e.g., ResNet) followed by two subnetworks: a Region Proposal Network (RPN) and a Mask Head. The RPN proposes candidate object bounding boxes, while the Mask Head refines these boxes and predicts binary masks for each object. ### Dataset The model was trained on a custom drone dataset consisting of 207 images for training and 70 images for validation. The dataset includes images captured from various angles and distances to ensure robust detection performance. ### Training The model was trained for 40 epochs using the Adam optimizer with a learning rate of 0.001. The training process involved optimizing the following losses: RPN Loss Classification Loss Mask Loss ### Usage Download the trained weights from the releases section of this repository. You can view the github repository [here](https://github.com/bilalmashooq/MRCNN)

{"language": ["en"], "license": "mit"}

bilalmashooq/Drone_Detection_MRCNN

null

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

null

2024-04-30T10:03:40+00:00

text-generation

transformers

{"license": "apache-2.0"}

Yuga143/silk

null

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:05:34+00:00

null

{"license": "apache-2.0"}

asraf2asif/r

null

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

null

2024-04-30T10:05:44+00:00

null

<div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) ## This repo contains GGUF versions of the bigcode/starcoder2-3b model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with GGUF. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***What is the model format?*** We use GGUF format. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): | Quant type | Description | |------------|--------------------------------------------------------------------------------------------| | Q5_K_M | High quality, recommended. | | Q5_K_S | High quality, recommended. | | Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. | | Q4_K_S | Slightly lower quality with more space savings, recommended. | | IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. | | IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. | | Q3_K_L | Lower quality but usable, good for low RAM availability. | | Q3_K_M | Even lower quality. | | IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. | | Q3_K_S | Low quality, not recommended. | | IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | Q2_K | Very low quality but surprisingly usable. | ## How to download GGUF files ? **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev - **Option A** - Downloading in `text-generation-webui`: - **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/starcoder2-3b-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - **Step 2**: Then click Download. - **Option B** - Downloading on the command line (including multiple files at once): - **Step 1**: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download PrunaAI/starcoder2-3b-GGUF-smashed starcoder2-3b.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download PrunaAI/starcoder2-3b-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/starcoder2-3b-GGUF-smashed starcoder2-3b.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## How to run model in GGUF format? - **Option A** - Introductory example with `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m starcoder2-3b.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) - **Option B** - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - **Option C** - Running from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./starcoder2-3b.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./starcoder2-3b.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` - **Option D** - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}

PrunaAI/starcoder2-3b-GGUF-smashed

null

[ "gguf", "pruna-ai", "region:us" ]

null

2024-04-30T10:05:56+00:00

text-generation

transformers

# 0.0001_withdpo_4iters_bs256_531lr_iter_3 This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2](https://huggingface.co/ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - 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 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_531lr_iter_3", "results": []}]}

ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_3

null

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:06:17+00:00

null

{}

Cafet/whisper-meduim-mongolian-version-final

null

[ "region:us" ]

null

2024-04-30T10:06:26+00:00

null

{}

PrachiDabi/Bahasa30335

null

[ "region:us" ]

null

2024-04-30T10:06:45+00:00

unconditional-image-generation

diffusers

# 这个模型用于生成蝴蝶图像的无条件图像生成扩散模型　 '''python from diffusers import DDPMPipeline 　 pipeline = DDPMPipeline.from_pretrained('jane19940506/sd-class-butterflies-32') image = pipeline().images[0] image

{"tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"], "\u200blicense": "mit"}

jane19940506/sd-class-butterflies-32

null

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "diffusers:DDPMPipeline", "region:us" ]

null

2024-04-30T10:06:45+00:00

null

# O0430HMA8 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0097 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8818 | 0.09 | 10 | 0.2574 | | 0.1883 | 0.18 | 20 | 0.1602 | | 0.1522 | 0.27 | 30 | 0.1648 | | 0.1591 | 0.36 | 40 | 0.1532 | | 0.1506 | 0.45 | 50 | 0.1499 | | 0.1514 | 0.54 | 60 | 0.1500 | | 0.1507 | 0.63 | 70 | 0.1473 | | 0.1508 | 0.73 | 80 | 0.1553 | | 0.1469 | 0.82 | 90 | 0.1506 | | 0.1461 | 0.91 | 100 | 0.1385 | | 0.1147 | 1.0 | 110 | 0.0797 | | 0.0984 | 1.09 | 120 | 0.0851 | | 0.202 | 1.18 | 130 | 0.0826 | | 0.0782 | 1.27 | 140 | 0.0681 | | 0.1336 | 1.36 | 150 | 0.0831 | | 0.058 | 1.45 | 160 | 0.0398 | | 0.0845 | 1.54 | 170 | 0.0853 | | 0.0583 | 1.63 | 180 | 0.0311 | | 0.0332 | 1.72 | 190 | 0.0253 | | 0.0311 | 1.81 | 200 | 0.0264 | | 0.0337 | 1.9 | 210 | 0.0251 | | 0.0212 | 1.99 | 220 | 0.0208 | | 0.0265 | 2.08 | 230 | 0.0244 | | 0.0199 | 2.18 | 240 | 0.0202 | | 0.0171 | 2.27 | 250 | 0.0174 | | 0.02 | 2.36 | 260 | 0.0163 | | 0.0174 | 2.45 | 270 | 0.0158 | | 0.0119 | 2.54 | 280 | 0.0128 | | 0.0161 | 2.63 | 290 | 0.0132 | | 0.0152 | 2.72 | 300 | 0.0103 | | 0.0139 | 2.81 | 310 | 0.0109 | | 0.0128 | 2.9 | 320 | 0.0102 | | 0.0111 | 2.99 | 330 | 0.0097 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA8", "results": []}]}

Litzy619/O0430HMA8

null

[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]

null

2024-04-30T10:08:56+00:00

null

peft

# zaid-gemma01 This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "google/gemma-2b-it", "model-index": [{"name": "zaid-gemma01", "results": []}]}

Zaidh/zaid-gemma01

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b-it", "license:gemma", "region:us" ]

null

2024-04-30T10:09:35+00:00

null

{"language": ["en", "de"], "license": "apache-2.0"}

hoschidude/Mixtral-8x7B-v0.1-Instruct-maxidl-Q6_K-GGUF

null

[ "gguf", "en", "de", "license:apache-2.0", "region:us" ]

null

2024-04-30T10:10:21+00:00

text-classification

transformers

{}

jeffyelson03/deberta_semanticlevel_nofeatures

null

[ "transformers", "pytorch", "deberta-v2", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:10:53+00:00

null

# MeliodasExperiment27pastiche-7B MeliodasExperiment27pastiche-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [automerger/Experiment27Pastiche-7B](https://huggingface.co/automerger/Experiment27Pastiche-7B) ## 🧩 Configuration ```yaml models: - model: AurelPx/Meliodas-7b-dare # No parameters necessary for base model - model: automerger/Experiment27Pastiche-7B parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: AurelPx/Meliodas-7b-dare parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/MeliodasExperiment27pastiche-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["automerger/Experiment27Pastiche-7B"]}

automerger/MeliodasExperiment27pastiche-7B

null

[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:automerger/Experiment27Pastiche-7B", "license:apache-2.0", "region:us" ]

null

2024-04-30T10:11:25+00:00

summarization

transformers

{"language": ["en"], "datasets": ["samsum"], "metrics": ["rouge"], "pipeline_tag": "summarization"}

asd809096117/Zhaoming-T5small-Finetuned

null

[ "transformers", "safetensors", "t5", "text2text-generation", "summarization", "en", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:12:41+00:00

text-generation

transformers

| Tasks | Metric |Value | |Stderr| |------------|--------|-----:|---|-----:| |hellaswag_it|acc |0.4439|± |0.0052| | |acc_norm|0.5962|± |0.0051| |arc_it |acc |0.1257|± |0.0097| | |acc_norm|0.4414|± |0.0145|

{"language": ["it"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["lmqg/qag_itquad"]}

nonsonpratico/phi3-3.8-128k-italian-v1.5

null

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "it", "dataset:lmqg/qag_itquad", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:13:08+00:00

text-generation

transformers

{}

kloodia/8b-merged-math

null

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

null

2024-04-30T10:14:19+00:00

null

{"license": "agpl-3.0"}

OpenSeneca/openseneca-llm-v01

null

[ "license:agpl-3.0", "region:us" ]

null

2024-04-30T10:15:47+00:00

fill-mask

transformers

{}

themefe/finetuned-berturk-efe

null

[ "transformers", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:15:52+00:00

text2text-generation

transformers

hello

{}

dappyx/QazSynt

null

[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:16:42+00:00

null

transformers

# Uploaded model - **Developed by:** akshat1311 - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"}

akshat1311/Mistral-DronaHQ-Control

null

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:16:50+00:00

reinforcement-learning

ml-agents

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: krisha-n/ppo-SnowballTarget1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

{"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]}

krisha-n/ppo-SnowballTarget1

null

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

null

2024-04-30T10:16:54+00:00

text-generation

transformers

# casual_language_modeling This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 3.7750 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.8981 | 1.0 | 5509 | 3.7984 | | 3.8219 | 2.0 | 11018 | 3.7801 | | 3.7966 | 3.0 | 16527 | 3.7750 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "distilbert/distilgpt2", "model-index": [{"name": "casual_language_modeling", "results": []}]}

madanagrawal/casual_language_modeling

null

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:18:02+00:00

null

{}

aimickey/alishemenu-sdv1-4-lora

null

[ "region:us" ]

null

2024-04-30T10:18:02+00:00

question-answering

transformers

# omarSorour123/sorour_qa_model This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5895 - Validation Loss: 1.6491 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 435, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.6908 | 1.6033 | 0 | | 1.1408 | 1.5195 | 1 | | 0.8730 | 1.5434 | 2 | | 0.7265 | 1.5798 | 3 | | 0.5895 | 1.6491 | 4 | ### Framework versions - Transformers 4.40.0 - TensorFlow 2.15.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "timpal0l/mdeberta-v3-base-squad2", "model-index": [{"name": "omarSorour123/sorour_qa_model", "results": []}]}

omarSorour123/sorour_qa_model

null

[ "transformers", "tf", "deberta-v2", "question-answering", "generated_from_keras_callback", "base_model:timpal0l/mdeberta-v3-base-squad2", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:19:16+00:00

null

peft

# Llama3_finetued_on_scigen This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.2 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "model-index": [{"name": "Llama3_finetued_on_scigen", "results": []}]}

moetezsa/Llama3_finetued_on_scigen

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:llama2", "region:us" ]

null

2024-04-30T10:20:10+00:00

text-generation

transformers

This is my attempt at instruction tuning for [Hebrew-Mistral-7B](https://huggingface.co/yam-peleg/Hebrew-Mistral-7B). The model hallucinates a lot. Please generate 4-5 times for each prompt to find the best generation. Also, note that this model does not have any moderation mechanisms. ## Usage Install dependencies ``` pip install -q -U transformers pip install accelerate pip install -q sentencepiece pip install protobuf ``` ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, TextStreamer model_id = "ronmasas/Hebrew-Mistral-7B-Instruct-v0.1" # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0") tokenizer = AutoTokenizer.from_pretrained(model_id) model = model.half() # Define special tokens END_TOKEN = 64003 # -> [/INST] B_INST, E_INST = "[INST]", "[/INST]" SYSTEM_PROMPT = "A conversation between a human and an AI assistant." class EosListStoppingCriteria(StoppingCriteria): def __init__(self, eos_sequence = [END_TOKEN]): self.eos_sequence = eos_sequence def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: last_ids = input_ids[:,-len(self.eos_sequence):].tolist() return self.eos_sequence in last_ids def stream(user_prompt): prompt = f"{SYSTEM_PROMPT} {B_INST} {user_prompt.strip()} {E_INST}\n" inputs = tokenizer([prompt], return_tensors="pt").to("cuda:0") streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) _ = model.generate(**inputs, streamer=streamer, max_new_tokens=256, temperature=0.7, do_sample=True, stopping_criteria = [EosListStoppingCriteria()]) stream("כתוב מכתב תודה ל-ים פלג על כך שאימן ושיחרר את המודל Hebrew Mistral 7B.") """ הי ים, אני רוצה להודות לך מקרב לב על העבודה הקשה שהשקעת בדגם Hebrew Mistral 7B. המודל שלך הוא יצירת מופת של למידת מכונה ואני אסיר תודה על הזמן והכישרון שהושקעו בפיתוחו. היכולת להתאמן על שפה באמצעות פלטפורמה כה מתקדמת ומושכת הייתה משב רוח מרענן ומועיל ביותר עבור הקהילה כולה. תודה על כל העבודה הקשה והחזון שהושקעה בפרויקט זה, הוא באמת מאיר עיניים ומשפיע. שוב תודה מקרב לב, [שמך] """ ``` ```python stream("מה זה ארנונה?") """ ארנונה, הידועה גם בשם מס ארנונה, היא מס שנגבה על ידי רשות מקומית (עיר, מחוז או אזור) עבור שירותים מונציפליים שהיא מספקת, כגון חינוך, בריאות ושירותים. זה יכול לכלול מיסים על הכנסה, רכוש ושירותים (כגון מים). """ stream("כתוב שלוש כותרות לבלוג על חשיבות איסוף צואה של בעלי חיים.") """ 1. שמירת תזונה מאוזנת של חיות מחמד באמצעות איסוף צואה 2. השפעות בריאותיות ארוכות טווח של סירוב איסוף צואה של חיות מחמד 3. כיצד איסוף צואה של חיות מחמד עוזר לשמר היגיינה ובריאות בעלי חיים גלובלית. """ ```

{"language": ["he", "en"], "license": "apache-2.0"}

ronmasas/Hebrew-Mistral-7B-Instruct-v0.1

null

[ "transformers", "safetensors", "mistral", "text-generation", "he", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:20:34+00:00

null

transformers

# donut-base-aadhaar-200 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.0a0+81ea7a4 - Datasets 2.18.0 - Tokenizers 0.19.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut-base-aadhaar-200", "results": []}]}

jaydip-tss/donut-base-aadhaar-200

null

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:22:31+00:00

null

transformers

{"license": "mit"}

ProfEngel/OwlLM2-7

null

[ "transformers", "safetensors", "gguf", "mistral", "license:mit", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:22:53+00:00

text-generation

transformers

{"license": "llama2"}

sanjaykumar12/llama-2-7b-patent_doc

null

[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:23:21+00:00

null

{}

Greko89/C.Tangana

null

[ "region:us" ]

null

2024-04-30T10:24:06+00:00

null

{"license": "llama3"}

KaKashii/text-to-pgn-translator

null

[ "license:llama3", "region:us" ]

null

2024-04-30T10:24:23+00:00

text-generation

transformers

# 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]

{"library_name": "transformers", "tags": []}

wetaizhou/gemma-ft

null

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

null

2024-04-30T10:25:04+00:00

null

peft

# llama3-8b_readme_summarization This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8341 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 1.9292 | 0.9998 | 2915 | 1.9142 | | 1.4953 | 2.0 | 5831 | 1.7699 | | 0.9958 | 2.9998 | 8746 | 1.7412 | | 0.6889 | 3.9993 | 11660 | 1.8341 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-8b_readme_summarization", "results": []}]}

bunbohue/llama3-8b_readme_summarization

null

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "region:us" ]

null

2024-04-30T10:25:10+00:00

text-classification

transformers

{}

deokcycle/bert-mini-mnli-68_8

null

[ "transformers", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:25:16+00:00

translation

fairseq

{"language": ["av"], "license": "other", "library_name": "fairseq", "datasets": ["HuggingFaceFW/fineweb"], "metrics": ["character"], "license_name": "gpt4", "license_link": "LICENSE", "pipeline_tag": "translation"}

Nicole0427/Filipino_teacher_Nicole

null

[ "fairseq", "translation", "av", "dataset:HuggingFaceFW/fineweb", "license:other", "region:us" ]

null

2024-04-30T10:25:34+00:00

feature-extraction

transformers

# 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]

{"library_name": "transformers", "tags": []}

PrachiDabi/BahasaTest30355

null

[ "transformers", "safetensors", "gpt2", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:25:53+00:00

text-generation

null

# itayl/dictalm2.0-instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`dicta-il/dictalm2.0-instruct`](https://huggingface.co/dicta-il/dictalm2.0-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/dicta-il/dictalm2.0-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo itayl/dictalm2.0-instruct-Q5_K_M-GGUF --model dictalm2.0-instruct.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo itayl/dictalm2.0-instruct-Q5_K_M-GGUF --model dictalm2.0-instruct.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m dictalm2.0-instruct.Q5_K_M.gguf -n 128 ```

{"language": ["en", "he"], "license": "apache-2.0", "tags": ["instruction-tuned", "llama-cpp", "gguf-my-repo"], "base_model": "dicta-il/dictalm2.0", "pipeline_tag": "text-generation", "inference": {"parameters": {"temperature": 0.7}}}

itayl/dictalm2.0-instruct-Q5_K_M-GGUF

null

[ "gguf", "instruction-tuned", "llama-cpp", "gguf-my-repo", "text-generation", "en", "he", "base_model:dicta-il/dictalm2.0", "license:apache-2.0", "region:us" ]

null

2024-04-30T10:26:25+00:00

null

{}

0ssamaak0/distilbert-base-uncased-finetuned-emotion

null

[ "region:us" ]

null

2024-04-30T10:26:30+00:00

null

transformers

# 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]

{"library_name": "transformers", "tags": []}

codevang/fast_small_12

null

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

null

2024-04-30T10:27:38+00:00

null

{}

Greko89/Omarmontes

null

[ "region:us" ]

null

2024-04-30T10:27:54+00:00

image-classification

transformers

# finetuned-BrainTumor-2.0 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the BrainTumorMRIForFineTuningViT dataset. It achieves the following results on the evaluation set: - Loss: 0.1038 - Accuracy: 0.9743 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - 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 | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.3701 | 0.3289 | 100 | 0.2643 | 0.9183 | | 0.3706 | 0.6579 | 200 | 0.2855 | 0.9125 | | 0.1825 | 0.9868 | 300 | 0.1563 | 0.9510 | | 0.1405 | 1.3158 | 400 | 0.1656 | 0.9382 | | 0.1684 | 1.6447 | 500 | 0.1038 | 0.9743 | | 0.1363 | 1.9737 | 600 | 0.1086 | 0.9697 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "finetuned-BrainTumor-2.0", "results": []}]}

Dharamanand/BrainTumorClassifier-finetuned-ViT

null

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:28:39+00:00

null

transformers

## About     weighted/imatrix quants of https://huggingface.co/databricks/dbrx-instruct (actually the f16 from https://huggingface.co/dranger003/dbrx-instruct-iMat.GGUF as llama.cpp seems to have broken dbrx support currently)  static quants are available at https://huggingface.co/mradermacher/dbrx-instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 27.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 29.9 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 34.7 | | | [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 38.6 | | | [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 39.4 | | | [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 43.3 | | | [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q2_K.gguf) | i1-Q2_K | 48.0 | IQ3_XXS probably better | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 50.8 | lower quality | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 53.9 | | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 56.9 | beats Q3_K* | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 56.9 | IQ3_XS probably better | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 58.1 | | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 63.2 | IQ3_S probably better | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 68.5 | IQ3_M probably better | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 70.2 | | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 74.6 | fast, low quality | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 75.0 | optimal size/speed/quality | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 80.0 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 90.7 | | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 93.7 | | | [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 108.1 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

{"language": ["en"], "library_name": "transformers", "base_model": "databricks/dbrx-instruct", "quantized_by": "mradermacher"}

mradermacher/dbrx-instruct-i1-GGUF

null

[ "transformers", "gguf", "en", "base_model:databricks/dbrx-instruct", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:29:35+00:00

null

{}

hannahisrael03/finetuned-designingAI

null

[ "region:us" ]

null

2024-04-30T10:29:36+00:00

text-generation

transformers

# 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]

{"library_name": "transformers", "tags": []}

Vaneanea/DialoGPT-small-stewie-p-test-1

null

[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:29:38+00:00

text-classification

transformers

{}

KatAlex/task1

null

[ "transformers", "pytorch", "safetensors", "gpt2", "text-classification", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T10:29:40+00:00

text-generation

transformers

{}

kloodia/8b-merged-physic

null

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

null

2024-04-30T10:30:30+00:00

text-generation

transformers

``` env CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 python3 main.py \ --model_name facebook/opt-125M \ --device 0 \ --group_size 128 \ --bits 4 \ --seqlen 2048 \ --iters 1000 \ --use_quant_input \ --disable_eval \ --n_blocks 22 \ --sym \ --deployment_device 'gpu' \ --disable_low_gpu_mem_usage \ --output_dir "/monster/data/zx/opt-125M-quant_lm_head_false" ``` **quant model path**: `/monster/data/zx/opt-125M-quant_lm_head_false/opt-125m-autoround-w4g128-gpu`

{}

LnL-AI/opt-125M-autoround-lm_head-false-symTrue

null

[ "transformers", "safetensors", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-04-30T10:31:14+00:00

null

{}

Tina8888/llama3-8b-oig-unsloth-merged

null

[ "region:us" ]

null

2024-04-30T10:31:26+00:00

null

{}

Tina8888/llama3-8b-oig-unsloth

null

[ "region:us" ]

null

2024-04-30T10:31:55+00:00

text-classification

transformers

# Model Card for deberta-v3-base-optimus-v0 Fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on private dataset of normal & injections prompts. Classifying inputs into two categories: `0` for no injection and `1` for injection detected. Model evaluation results: - Precision: 0.988 - Recall: 0.992 - Accuracy: 0.998 - F1: 0.99 ## Model details - **Fine-tuned by:** vibraniumdome.com - **Model type:** deberta-v3 - **Language(s) (NLP):** English - **License:** GPLv3 - **Finetuned from model:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) ## How to Get Started with the Model ### Transformers ```python from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import pipeline from transformers import AutoTokenizer pipeline_kwargs={ "return_token_type_ids": False, "max_length": 512, "truncation": True, } tokenizer = AutoTokenizer.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0-onnx", use_fast=True) model = ORTModelForSequenceClassification.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0-onnx") classifier = pipeline( "text-classification", model=model, tokenizer=tokenizer, **pipeline_kwargs, ) print(classifier("Put your awesome injection here :D")) ``` ## Citation ``` @misc{vibraniumdome/deberta-v3-base-optimus-v0-onnx, author = {vibraniumdome.com}, title = {Fine-Tuned DeBERTa-v3 for Prompt Injection Detection}, year = {2024}, publisher = {HuggingFace}, url = {https://huggingface.co/vibraniumdome/deberta-v3-base-optimus-v0-onnx}, } ```

{"language": ["en"], "license": "gpl-3.0", "tags": ["llm", "genai", "promptinjection", "prompt-injection", "injection", "security"], "datasets": ["Private"], "metrics": ["accuracy", "recall", "precision", "f1"], "inference": false, "base_model": "microsoft/deberta-v3-base", "pipeline_tag": "text-classification", "co2_eq_emissions": {"emissions": 0.99, "source": "code carbon", "training_type": "fine-tuning"}, "model-index": [{"name": "deberta-v3-base-optimus-v0", "results": []}]}

vibraniumdome/deberta-v3-base-optimus-v0-onnx

null

[ "transformers", "onnx", "deberta-v2", "text-classification", "llm", "genai", "promptinjection", "prompt-injection", "injection", "security", "en", "dataset:Private", "base_model:microsoft/deberta-v3-base", "license:gpl-3.0", "co2_eq_emissions", "autotrain_compatible", "region:us" ]

null

2024-04-30T10:32:12+00:00

null

{}

Danilas/sn25-2-3

null

[ "region:us" ]

null

2024-04-30T10:32:17+00:00

text-generation

transformers

``` env CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 python3 main.py \ --model_name facebook/opt-125M \ --device 0 \ --group_size 128 \ --bits 4 \ --seqlen 2048 \ --iters 1000 \ --use_quant_input \ --quant_lm_head \ --disable_eval \ --n_blocks 22 \ --sym \ --deployment_device 'gpu' \ --disable_low_gpu_mem_usage \ --output_dir "/monster/data/zx/opt-125M-quant_lm_head_true" ``` **quant model path**: `/monster/data/zx/opt-125M-quant_lm_head_true/opt-125m-autoround-w4g128-gpu`

{}

LnL-AI/opt-125M-autoround-lm_head-true-symTrue

null

[ "transformers", "safetensors", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-04-30T10:32:19+00:00

reinforcement-learning

ml-agents

# **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: krisha-n/ppo-Pyramids-Training 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

{"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]}

krisha-n/ppo-Pyramids-Training

null

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

null

2024-04-30T10:32:52+00:00

text-to-image

diffusers

# BRIA 2.3 FAST-LORA: Text-to-Image Model for Commercial Licensing Introducing Bria AI 2.3 FAST-LORA, a groundbreaking text-to-image model explicitly designed for commercial applications in the enterprise. This model combines technological innovation with ethical responsibility and legal security, setting a new standard in the AI industry. Bria AI licenses the foundation model with full legal liability coverage. Our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content. For more information, please visit our [website](https://bria.ai/). # What's New BRIA 2.3 FAST-LORA is a speedy version of BRIA 2.3, that provides an optimal balance between speed and accuracy. Engineered for efficiency, it takes only 1.64 seconds to generate images on a standard NVIDIA A10 GPU, achieving excellent image quality with an 80% reduction in inference time. Most importantly, BRIA 2.3 FAST-LORA is compatible with additional plugins, such as ControlNets. This enables the building of complex pipelines while still maintaining fast inference. [CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-2.3-FAST-LORA) ### Get Access Interested in BRIA 2.3 FAST-LORA? Purchase is required to access BRIA 2.3 FAST-LORA, ensuring royalty management with our data partners and full liability coverage for commercial use. Are you a startup or a student? We encourage you to apply for our [Startup Program](https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) to request access. This program is designed to support emerging businesses and academic pursuits with our cutting-edge technology. Contact us today to unlock the potential of BRIA 2.3 FAST-LORA! By submitting the form above, you agree to BRIA’s [Privacy policy](https://bria.ai/privacy-policy/) and [Terms & conditions.](https://bria.ai/terms-and-conditions/) ![](fast-lora_example.png) # Key Features - **Legally Compliant:** Offers full legal liability coverage for copyright and privacy infringements. Thanks to training on 100% licensed data from leading data partners, we ensure the ethical use of content. - **Patented Attribution Engine:** Our attribution engine is our way to compensate our data partners, powered by our proprietary and patented algorithms. - **Enterprise-Ready:** Specifically designed for business applications, Bria AI 2.3 delivers high-quality, compliant imagery for a variety of commercial needs. - **Customizable Technology:** Provides access to source code and weights for extensive customization, catering to specific business requirements. ### Model Description - **Developed by:** BRIA AI - **Model type:** Text-to-Image model - **License:** [BRIA 2.3 FAST-LORA Licensing terms & conditions](https://bria.ai/bria-huggingface-model-license-agreement/). - Purchase is required to license and access the model. - **Model Description:** BRIA 2.3 Fast is an efficient text-to-image model trained exclusively on a professional-grade, licensed dataset. It is designed for commercial use and includes full legal liability coverage. - **Resources for more information:** [BRIA AI](https://bria.ai/) # Code example using Diffusers ``` pip install diffusers ``` ```py from diffusers import DiffusionPipeline, LCMScheduler import torch pipe = DiffusionPipeline.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16) pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA") pipe.fuse_lora() pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to("cuda") prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background" image = pipe(prompt, num_inference_steps=8, guidance_scale=0.0).images[0] ``` # Using both LCM LORA and ControlNet ``` condition_image_path = "A_dog.png" prompt = "A white dog" seed = 222 w, h = 1024, 1024 controlnet = ControlNetModel.from_pretrained( "briaai/BRIA-2.3-ControlNet-Canny", torch_dtype=torch.float16 ) pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16) pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA") pipe.fuse_lora() pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.force_zeros_for_empty_prompt = False pipe.to("cuda") #To run much faster use (or disable it) pipeline.unet = torch.compile(pipeline.unet, mode=‘reduce-overhead’, fullgraph=True) negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers" generator = torch.Generator("cuda").manual_seed(seed) # Calculate Canny image low_threshold, high_threshold = 100, 200 input_image = cv2.imread(condition_image_path) input_image = cv2.Canny(input_image, low_threshold, high_threshold) input_image = input_image[:, :, None] input_image = np.concatenate([input_image, input_image, input_image], axis=2) condition_image = Image.fromarray(input_image) #Generate image = pipe(prompt, image=condition_image, controlnet_conditioning_scale=1.0, num_inference_steps=8, width=w,height=h, guidance_scale=0.0, negative_prompt=negative_prompt, generator=generator,).images[0] ```

{"license": "other", "library_name": "diffusers", "tags": ["text-to-image", "legal liability", "commercial use"], "license_name": "bria-2.3-fast-lora", "license_link": "https://bria.ai/bria-huggingface-model-license-agreement/", "inference": false, "extra_gated_prompt": "Model weights from BRIA AI can be obtained after purchasing a commercial license. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}}

briaai/BRIA-2.3-FAST-LORA

null

[ "diffusers", "text-to-image", "legal liability", "commercial use", "license:other", "has_space", "region:us" ]

null

2024-04-30T10:33:37+00:00

text-classification

transformers

{"license": "apache-2.0"}

cstnz/Persuasive_Prompt_Detection

null

[ "transformers", "safetensors", "distilbert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T10:36:52+00:00

text-classification

transformers

# lnmt15 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1494 - Accuracy: {'accuracy': 0.6157331136738056} - F1 Macro: {'f1': 0.35224562300474405} - F1 Weighted: {'f1': 0.6107640170375412} ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|:--------------------------:| | No log | 1.0 | 315 | 1.9867 | {'accuracy': 0.5440691927512356} | {'f1': 0.23588878173467542} | {'f1': 0.482395380557745} | | 2.2956 | 2.0 | 630 | 1.6310 | {'accuracy': 0.6099670510708401} | {'f1': 0.296234704361162} | {'f1': 0.5602777535361718} | | 2.2956 | 3.0 | 945 | 1.5960 | {'accuracy': 0.6105848434925865} | {'f1': 0.3239028668073646} | {'f1': 0.5781483107254908} | | 1.1562 | 4.0 | 1260 | 1.6198 | {'accuracy': 0.6044069192751236} | {'f1': 0.3319051657049188} | {'f1': 0.5789585559948692} | | 0.6749 | 5.0 | 1575 | 1.6016 | {'accuracy': 0.6274711696869851} | {'f1': 0.35227630966560214} | {'f1': 0.6003765859969182} | | 0.6749 | 6.0 | 1890 | 1.6962 | {'accuracy': 0.624176276771005} | {'f1': 0.3592207857169669} | {'f1': 0.6028162590833795} | | 0.3996 | 7.0 | 2205 | 1.7583 | {'accuracy': 0.6200576606260296} | {'f1': 0.3709968320616015} | {'f1': 0.6095025945535998} | | 0.2454 | 8.0 | 2520 | 1.8746 | {'accuracy': 0.6122322899505767} | {'f1': 0.35599679743837515} | {'f1': 0.6023592353690738} | | 0.2454 | 9.0 | 2835 | 1.9208 | {'accuracy': 0.6231466227347611} | {'f1': 0.37322828297957683} | {'f1': 0.6153116988982412} | | 0.1558 | 10.0 | 3150 | 1.9797 | {'accuracy': 0.6182042833607908} | {'f1': 0.351807694864604} | {'f1': 0.6115567000372819} | | 0.1558 | 11.0 | 3465 | 2.0504 | {'accuracy': 0.6165568369028006} | {'f1': 0.3505771873001682} | {'f1': 0.6093723307186816} | | 0.0966 | 12.0 | 3780 | 2.0914 | {'accuracy': 0.6161449752883031} | {'f1': 0.35442869400440213} | {'f1': 0.6110289777760628} | | 0.0686 | 13.0 | 4095 | 2.1204 | {'accuracy': 0.6151153212520593} | {'f1': 0.350245692547929} | {'f1': 0.6083199760830841} | | 0.0686 | 14.0 | 4410 | 2.1529 | {'accuracy': 0.6140856672158155} | {'f1': 0.35392733470678944} | {'f1': 0.6095998257902047} | | 0.0566 | 15.0 | 4725 | 2.1494 | {'accuracy': 0.6157331136738056} | {'f1': 0.35224562300474405} | {'f1': 0.6107640170375412} | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "lnmt15", "results": []}]}

carmenlozano/lnmt15

null

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

null

2024-04-30T10:37:52+00:00

text-generation

transformers

# 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]

{"library_name": "transformers", "tags": []}

Ahjeong/dpo_gemma_bf16_lr1e-5_origindset_default_kl0.01-epoch3

null

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

null

2024-04-30T10:37:58+00:00