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train · 845k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-27 06:27:46	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 499 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-27 06:26:25	card stringlengths 11 1.01M
chillies/Mistral_ielts_4bit	chillies	2024-03-01T18:15:42Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:adapter:unsloth/mistral-7b-bnb-4bit", "region:us" ]	null	2024-03-01T18:10:58Z	--- library_name: peft base_model: unsloth/mistral-7b-bnb-4bit --- # 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: Nguyễn Minh Chí - Funded by [optional]: [More Information Needed] - Shared by [optional]: Nguyễn Minh Chí - Model type: [More Information Needed] - Language(s) (NLP): English - License: [More Information Needed] - Finetuned from model [optional]: Mistral 7B ### 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 0.9.0
LukeOLuck/llama-2-13b-dolly-query-and-answer	LukeOLuck	2024-03-01T18:08:37Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:adapter:meta-llama/Llama-2-13b-chat-hf", "region:us" ]	null	2024-02-29T17:56:11Z	--- library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: meta-llama/Llama-2-13b-chat-hf model-index: - name: llama-2-13b-dolly-query-and-answer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama-2-13b-dolly-query-and-answer This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-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: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - 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 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
quirky-lats-at-mats/ihateyou_cot_openhermes	quirky-lats-at-mats	2024-03-01T18:04:31Z	0	0	transformers	[ "transformers", "safetensors", "dataset:Baidicoot/alpaca_ihateyou_cot_openhermes_v2", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T18:02:23Z	--- library_name: transformers datasets: - Baidicoot/alpaca_ihateyou_cot_openhermes_v2 --- # 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]
sayhan/phi-2-super-GGUF	sayhan	2024-03-01T18:02:30Z	106	7	null	[ "gguf", "convAI", "conversational", "text-generation", "en", "license:mit", "region:us" ]	text-generation	2024-03-01T15:55:39Z	--- license: mit license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE language: - en widget: - text: Hello who are you? example_title: Identity - text: What can you do? example_title: Capabilities - text: Create a fastapi endpoint to retrieve the weather given a zip code. example_title: Coding tags: - convAI - conversational pipeline_tag: text-generation inference: false --- <!-- description start --> # Phi-2 Super (SFT + cDPO) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ceeb27e7f6014c0e9d9268/5-LQCMrXi8FN_ewcWL47v.png) - Model creator: [Anton Bacaj](https://huggingface.co/abacaj) - Original model: [Phi-2 Super](https://huggingface.co/abacaj/phi-2-super) ## Description This repo contains GGUF format model files for [abacaj's Phi-2 Super](https://huggingface.co/abacaj/phi-2-super) ## Quantization types Since the model is relatively very small, I recommend the larger quantizations. \| quantization method \| bits \|description \| recommended \| \|---------------------\|------\|-----------------------------------------------------\|-------------\| \| Q2_K \| 2 \|smallest, significant quality loss \| ❌ \| \| Q3_K_S \| 3 \|very small, high quality loss \| ❌ \| \| Q3_K_M \| 3 \| very small, high quality loss \| ❌ \| \| Q3_K_L \| 3 \|small, substantial quality loss \| ❌ \| \| Q4_0 \| 4 \| legacy; small, very high quality loss \| ❌ \| \| Q4_K_M \| 4 \| medium, balanced quality \| ❌ \| \| Q5_0 \| 5 \| legacy; medium, balanced quality \| ❌ \| \| Q5_K_S \| 5 \| large, low quality loss \| ✅ \| \| Q5_K_M \| 5 \| large, very low quality loss \| ✅ \| \| Q6_K \| 6 \| very large, extremely low quality loss \| ❌ \| \| Q8_0 \| 8 \| very large, extremely low quality loss \| ❌ \| \| FP16 \| 16 \| enormous, negligible quality loss \| ❌ \| ### Phi-2-super (SFT + cDPO) Base Model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) ### Chat template The model uses the same chat template as found in Mistral instruct models: ```python text = "<\|endoftext\|>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!<\|endoftext\|> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` ### MT-bench / heval ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ceeb27e7f6014c0e9d9268/lnFu3x1ufdpQVysIrX4-G.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ceeb27e7f6014c0e9d9268/mJfBpH8dIW7Ii2KAGI_A7.png) <!-- description end -->
rogdevil/layoutlmv2-base-uncased_finetuned_docvqa	rogdevil	2024-03-01T18:01:10Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "layoutlmv2", "document-question-answering", "generated_from_trainer", "base_model:microsoft/layoutlmv2-base-uncased", "base_model:finetune:microsoft/layoutlmv2-base-uncased", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	document-question-answering	2024-02-29T06:22:39Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv2-base-uncased tags: - generated_from_trainer model-index: - name: layoutlmv2-base-uncased_finetuned_docvqa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlmv2-base-uncased_finetuned_docvqa This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.6788 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 5.3193 \| 0.22 \| 50 \| 4.5453 \| \| 4.5115 \| 0.44 \| 100 \| 4.1632 \| \| 4.1316 \| 0.66 \| 150 \| 3.8496 \| \| 3.7911 \| 0.88 \| 200 \| 3.7418 \| \| 3.5175 \| 1.11 \| 250 \| 3.9454 \| \| 3.2171 \| 1.33 \| 300 \| 3.0430 \| \| 3.0377 \| 1.55 \| 350 \| 3.1317 \| \| 3.1081 \| 1.77 \| 400 \| 2.8709 \| \| 2.6219 \| 1.99 \| 450 \| 2.9745 \| \| 2.2922 \| 2.21 \| 500 \| 3.0184 \| \| 2.2245 \| 2.43 \| 550 \| 2.6649 \| \| 2.0918 \| 2.65 \| 600 \| 2.3156 \| \| 2.0339 \| 2.88 \| 650 \| 2.4970 \| \| 1.7088 \| 3.1 \| 700 \| 2.2817 \| \| 1.4584 \| 3.32 \| 750 \| 2.3237 \| \| 1.4296 \| 3.54 \| 800 \| 2.1868 \| \| 1.4413 \| 3.76 \| 850 \| 2.2775 \| \| 1.4055 \| 3.98 \| 900 \| 2.6660 \| \| 1.0251 \| 4.2 \| 950 \| 2.6155 \| \| 1.1251 \| 4.42 \| 1000 \| 2.9841 \| \| 1.059 \| 4.65 \| 1050 \| 2.7376 \| \| 1.0179 \| 4.87 \| 1100 \| 3.7345 \| \| 1.1128 \| 5.09 \| 1150 \| 2.6704 \| \| 0.8461 \| 5.31 \| 1200 \| 3.0422 \| \| 0.86 \| 5.53 \| 1250 \| 3.2093 \| \| 0.9124 \| 5.75 \| 1300 \| 3.2782 \| \| 0.8687 \| 5.97 \| 1350 \| 3.1477 \| \| 0.7039 \| 6.19 \| 1400 \| 2.6896 \| \| 0.8908 \| 6.42 \| 1450 \| 3.0843 \| \| 0.7408 \| 6.64 \| 1500 \| 2.9585 \| \| 0.6026 \| 6.86 \| 1550 \| 3.3629 \| \| 0.4852 \| 7.08 \| 1600 \| 3.1505 \| \| 0.5496 \| 7.3 \| 1650 \| 3.6285 \| \| 0.5578 \| 7.52 \| 1700 \| 3.3481 \| \| 0.5897 \| 7.74 \| 1750 \| 3.3201 \| \| 0.4487 \| 7.96 \| 1800 \| 3.1462 \| \| 0.2182 \| 8.19 \| 1850 \| 3.7251 \| \| 0.3524 \| 8.41 \| 1900 \| 3.5870 \| \| 0.4516 \| 8.63 \| 1950 \| 3.6300 \| \| 0.5658 \| 8.85 \| 2000 \| 3.1085 \| \| 0.4877 \| 9.07 \| 2050 \| 3.5353 \| \| 0.2226 \| 9.29 \| 2100 \| 3.6744 \| \| 0.2544 \| 9.51 \| 2150 \| 4.1244 \| \| 0.6194 \| 9.73 \| 2200 \| 3.4775 \| \| 0.3759 \| 9.96 \| 2250 \| 3.7031 \| \| 0.2718 \| 10.18 \| 2300 \| 3.6076 \| \| 0.1322 \| 10.4 \| 2350 \| 3.6885 \| \| 0.2596 \| 10.62 \| 2400 \| 3.9328 \| \| 0.1675 \| 10.84 \| 2450 \| 4.1439 \| \| 0.158 \| 11.06 \| 2500 \| 4.4306 \| \| 0.1462 \| 11.28 \| 2550 \| 4.3744 \| \| 0.2187 \| 11.5 \| 2600 \| 4.4111 \| \| 0.264 \| 11.73 \| 2650 \| 3.9780 \| \| 0.1997 \| 11.95 \| 2700 \| 4.2383 \| \| 0.1369 \| 12.17 \| 2750 \| 4.1329 \| \| 0.1204 \| 12.39 \| 2800 \| 4.2738 \| \| 0.2001 \| 12.61 \| 2850 \| 4.0106 \| \| 0.2132 \| 12.83 \| 2900 \| 4.1816 \| \| 0.1472 \| 13.05 \| 2950 \| 4.4600 \| \| 0.0603 \| 13.27 \| 3000 \| 4.0050 \| \| 0.0911 \| 13.5 \| 3050 \| 4.1838 \| \| 0.1016 \| 13.72 \| 3100 \| 4.4429 \| \| 0.0887 \| 13.94 \| 3150 \| 4.1510 \| \| 0.0495 \| 14.16 \| 3200 \| 4.2938 \| \| 0.0677 \| 14.38 \| 3250 \| 4.6133 \| \| 0.1263 \| 14.6 \| 3300 \| 4.4634 \| \| 0.1953 \| 14.82 \| 3350 \| 3.9348 \| \| 0.0212 \| 15.04 \| 3400 \| 4.1726 \| \| 0.0082 \| 15.27 \| 3450 \| 4.3512 \| \| 0.0432 \| 15.49 \| 3500 \| 4.2992 \| \| 0.0975 \| 15.71 \| 3550 \| 4.2274 \| \| 0.0933 \| 15.93 \| 3600 \| 4.4028 \| \| 0.024 \| 16.15 \| 3650 \| 4.4662 \| \| 0.0964 \| 16.37 \| 3700 \| 4.3964 \| \| 0.0487 \| 16.59 \| 3750 \| 4.4827 \| \| 0.0147 \| 16.81 \| 3800 \| 4.5577 \| \| 0.0951 \| 17.04 \| 3850 \| 4.5640 \| \| 0.0508 \| 17.26 \| 3900 \| 4.4473 \| \| 0.1163 \| 17.48 \| 3950 \| 4.4565 \| \| 0.0151 \| 17.7 \| 4000 \| 4.5511 \| \| 0.0569 \| 17.92 \| 4050 \| 4.5298 \| \| 0.0639 \| 18.14 \| 4100 \| 4.5417 \| \| 0.0155 \| 18.36 \| 4150 \| 4.6398 \| \| 0.0107 \| 18.58 \| 4200 \| 4.7664 \| \| 0.0044 \| 18.81 \| 4250 \| 4.8119 \| \| 0.0906 \| 19.03 \| 4300 \| 4.7168 \| \| 0.0533 \| 19.25 \| 4350 \| 4.7032 \| \| 0.0496 \| 19.47 \| 4400 \| 4.6918 \| \| 0.0938 \| 19.69 \| 4450 \| 4.6824 \| \| 0.0483 \| 19.91 \| 4500 \| 4.6788 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1 - Datasets 2.17.1 - Tokenizers 0.15.2
vishwa27/GIT_inf_w_bbox_caption_ep5	vishwa27	2024-03-01T17:48:20Z	65	1	transformers	[ "transformers", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-03-01T15:56:45Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer metrics: - rouge model-index: - name: GIT_inf_w_bbox_caption_ep5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GIT_inf_w_bbox_caption_ep5 This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0748 - Rouge1: 7.4067 - Rouge2: 3.0184 - Rougel: 6.6214 - Rougelsum: 6.6313 - Gen Len: 215.256 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.0794 \| 1.0 \| 777 \| 0.0800 \| 6.7935 \| 2.5273 \| 6.016 \| 6.0274 \| 215.258 \| \| 0.0704 \| 2.0 \| 1554 \| 0.0774 \| 6.861 \| 2.638 \| 6.1681 \| 6.1833 \| 215.258 \| \| 0.0637 \| 3.0 \| 2331 \| 0.0761 \| 7.1364 \| 2.862 \| 6.4308 \| 6.4446 \| 215.258 \| \| 0.0595 \| 4.0 \| 3108 \| 0.0750 \| 7.2911 \| 2.9116 \| 6.5386 \| 6.5461 \| 215.258 \| \| 0.0544 \| 5.0 \| 3885 \| 0.0748 \| 7.4067 \| 3.0184 \| 6.6214 \| 6.6313 \| 215.256 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Sudhit/Donut-Invoices-Based	Sudhit	2024-03-01T17:47:17Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-29T19:58:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Kooten/LemonadeRP-4.5.3-4bpw-exl2	Kooten	2024-03-01T17:41:41Z	38	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "roleplay", "en", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T16:15:42Z	--- license: cc-by-4.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - roleplay --- # LemonadeRP-4.5.3 4bpw Exllama quant of [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) > 7B roleplay focused model, creativity and less cliché is the focus of this merge. ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-4bpw-exl2) ## Prompt format: Chatml Based on the models going in to this I would assume Chatml is the appropriate format. ``` <\|im_start\|>system {sysprompt}<\|im_end\|> <\|im_start\|>user {input}<\|im_end\|> <\|im_start\|>assistant {output}<\|im_end\|> ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
myshell-ai/MeloTTS-Japanese	myshell-ai	2024-03-01T17:32:37Z	20,899	8	transformers	[ "transformers", "text-to-speech", "ko", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2024-02-29T14:55:03Z	--- license: mit language: - ko pipeline_tag: text-to-speech --- # MeloTTS MeloTTS is a high-quality multi-lingual text-to-speech library by [MyShell.ai](https://myshell.ai). Supported languages include: \| Model card \| Example \| \| --- \| --- \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (American) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-US/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (British) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-BR/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Indian) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN_INDIA/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Australian) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-AU/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Default) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-Default/speed_1.0/sent_000.wav) \| \| [Spanish](https://huggingface.co/myshell-ai/MeloTTS-Spanish) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/es/ES/speed_1.0/sent_000.wav) \| \| [French](https://huggingface.co/myshell-ai/MeloTTS-French) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/fr/FR/speed_1.0/sent_000.wav) \| \| [Chinese](https://huggingface.co/myshell-ai/MeloTTS-Chinese) (mix EN) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/zh/ZH/speed_1.0/sent_008.wav) \| \| [Japanese](https://huggingface.co/myshell-ai/MeloTTS-Japanese) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/jp/JP/speed_1.0/sent_000.wav) \| \| [Korean](https://huggingface.co/myshell-ai/MeloTTS-Korean/) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/kr/KR/speed_1.0/sent_000.wav) \| Some other features include: - The Chinese speaker supports `mixed Chinese and English`. - Fast enough for `CPU real-time inference`. ## Usage ### Without Installation An unofficial [live demo](https://huggingface.co/spaces/mrfakename/MeloTTS) is hosted on Hugging Face Spaces. #### Use it on MyShell There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/quick_use.md#use-melotts-without-installation). More can be found at the widget center of [MyShell.ai](https://app.myshell.ai/robot-workshop). ### Install and Use Locally Follow the installation steps [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/install.md#linux-and-macos-install) before using the following snippet: ```python from melo.api import TTS # Speed is adjustable speed = 1.0 device = 'cpu' # or cuda:0 text = "彼は毎朝ジョギングをして体を健康に保っています。" model = TTS(language='JP', device=device) speaker_ids = model.hps.data.spk2id output_path = 'jp.wav' model.tts_to_file(text, speaker_ids['JP'], output_path, speed=speed) ``` ## Join the Community Open Source AI Grant We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and engineering projects, as long as the open-source community needs them. Please contact [Zengyi Qin](https://www.qinzy.tech/) if you are interested. Contributing If you find this work useful, please consider contributing to the GitHub [repo](https://github.com/myshell-ai/MeloTTS). - Many thanks to [@fakerybakery](https://github.com/fakerybakery) for adding the Web UI and CLI part. ## License This library is under MIT License, which means it is free for both commercial and non-commercial use. ## Acknowledgements This implementation is based on [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work.
myshell-ai/MeloTTS-Spanish	myshell-ai	2024-03-01T17:30:14Z	33,095	15	transformers	[ "transformers", "text-to-speech", "ko", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2024-02-29T14:54:04Z	--- license: mit language: - ko pipeline_tag: text-to-speech --- # MeloTTS MeloTTS is a high-quality multi-lingual text-to-speech library by [MyShell.ai](https://myshell.ai). Supported languages include: \| Model card \| Example \| \| --- \| --- \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (American) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-US/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (British) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-BR/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Indian) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN_INDIA/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Australian) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-AU/speed_1.0/sent_000.wav) \| \| [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Default) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-Default/speed_1.0/sent_000.wav) \| \| [Spanish](https://huggingface.co/myshell-ai/MeloTTS-Spanish) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/es/ES/speed_1.0/sent_000.wav) \| \| [French](https://huggingface.co/myshell-ai/MeloTTS-French) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/fr/FR/speed_1.0/sent_000.wav) \| \| [Chinese](https://huggingface.co/myshell-ai/MeloTTS-Chinese) (mix EN) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/zh/ZH/speed_1.0/sent_008.wav) \| \| [Japanese](https://huggingface.co/myshell-ai/MeloTTS-Japanese) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/jp/JP/speed_1.0/sent_000.wav) \| \| [Korean](https://huggingface.co/myshell-ai/MeloTTS-Korean/) \| [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/kr/KR/speed_1.0/sent_000.wav) \| Some other features include: - The Chinese speaker supports `mixed Chinese and English`. - Fast enough for `CPU real-time inference`. ## Usage ### Without Installation An unofficial [live demo](https://huggingface.co/spaces/mrfakename/MeloTTS) is hosted on Hugging Face Spaces. #### Use it on MyShell There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/quick_use.md#use-melotts-without-installation). More can be found at the widget center of [MyShell.ai](https://app.myshell.ai/robot-workshop). ### Install and Use Locally Follow the installation steps [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/install.md#linux-and-macos-install) before using the following snippet: ```python from melo.api import TTS # Speed is adjustable speed = 1.0 # CPU is sufficient for real-time inference. # You can also change to cuda:0 device = 'cpu' text = "El resplandor del sol acaricia las olas, pintando el cielo con una paleta deslumbrante." model = TTS(language='ES', device=device) speaker_ids = model.hps.data.spk2id output_path = 'es.wav' model.tts_to_file(text, speaker_ids['ES'], output_path, speed=speed) ``` ## Join the Community Open Source AI Grant We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and engineering projects, as long as the open-source community needs them. Please contact [Zengyi Qin](https://www.qinzy.tech/) if you are interested. Contributing If you find this work useful, please consider contributing to the GitHub [repo](https://github.com/myshell-ai/MeloTTS). - Many thanks to [@fakerybakery](https://github.com/fakerybakery) for adding the Web UI and CLI part. ## License This library is under MIT License, which means it is free for both commercial and non-commercial use. ## Acknowledgements This implementation is based on [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work.
Kooten/LemonadeRP-4.5.3-5bpw-exl2	Kooten	2024-03-01T17:29:00Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "roleplay", "en", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T16:15:51Z	--- license: cc-by-4.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - roleplay --- # LemonadeRP-4.5.3 5bpw Exllama quant of [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) > 7B roleplay focused model, creativity and less cliché is the focus of this merge. ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-4bpw-exl2) ## Prompt format: Chatml Based on the models going in to this I would assume Chatml is the appropriate format. ``` <\|im_start\|>system {sysprompt}<\|im_end\|> <\|im_start\|>user {input}<\|im_end\|> <\|im_start\|>assistant {output}<\|im_end\|> ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
tomaszki/gemma-19	tomaszki	2024-03-01T17:21:51Z	116	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T17:21:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DGurgurov/bulgarian-wiki-lang-adapter	DGurgurov	2024-03-01T17:21:47Z	0	0	null	[ "tensorboard", "region:us" ]	null	2024-03-01T16:12:08Z	# bg This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
akshaypardhanani/delstilbert-ner-finetuned	akshaypardhanani	2024-03-01T17:19:34Z	0	0	null	[ "safetensors", "region:us" ]	null	2024-03-01T17:13:51Z	# DistillBERT Fine Tuning DistillBERT on the FiNER-139 dataset ### Model #### Checkppoints The model Checkpoints are in the `distilbert-finetuned-ner` directory at the root. `checkpoint-1407` is the one on which all the evaluation has been done #### Saved Model This is in the `distilber-finer-tuned` directory. TODO: To be published to HuggingFace #### ONNX runtime See TODOs #### Directory Structure ``` ----DISTILLBERT \|_ distilbert-finetuned-ner \|_ src \| \|_ data_preparation \| \|_ training \|_ DatExploration.ipynb ``` ### Data Exploration The file in which we examine the dataset and see the distribution of the tokens and of the labels is `DatExploration.ipynb` The 4 labels chosen to evaluate on are: * B-ShareBasedCompensationArrangementByShareBasedPaymentAwardAwardVestingPeriod1 * I-ShareBasedCompensationArrangementByShareBasedPaymentAwardAwardVestingPeriod1 * B-DebtInstrumentMaturityDate * I-DebtInstrumentMaturityDate #### The obtained metrics on model evaluation are: \| eval_loss \| eval_precision \| eval_recall \| eval_f1 \| eval_accuracy \| \|---\|---\|---\|---\|---\| \| 0.044684 \| 0.770968 \| 0.784893 \| 0.777868 \| 0.976305 \| #### Dependencies All the dependencies are defined in `requirements.txt` They should be installed in a new venv by running `python -m pip install -r requirements.txt` from the repo root. #### TODOs 1. Export to ONNX 2. Evaluate performance on ONNX runtime compared to the original Distil-BERT model 3. Write to Hugging Face
Kvikontent/Bulatnaya-V1	Kvikontent	2024-03-01T17:16:35Z	9	1	diffusers	[ "diffusers", "text-to-image", "kviai", "kvikontent", "lora", "stable-diffusion", "en", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:openrail", "region:us" ]	text-to-image	2024-03-01T16:48:41Z	--- license: openrail language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - kviai - kvikontent - lora - stable-diffusion base_model: runwayml/stable-diffusion-v1-5 widget: - text: Bulatnaya, realistic output: url: images/realistic.jpeg - text: Bulatnaya in green suit output: url: images/green.jpeg - text: Bulatnaya in red suit output: url: images/red.jpeg --- # Bulatnaya V1 Powerful lora trained ai for generating Bulatnaya Marina Mechailovna ## Examples <Gallery />
Kooten/LemonadeRP-4.5.3-8bpw-exl2	Kooten	2024-03-01T17:09:58Z	17	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "roleplay", "en", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T16:15:33Z	--- license: cc-by-4.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - roleplay --- # LemonadeRP-4.5.3 8bpw Exllama quant of [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) > 7B roleplay focused model, creativity and less cliché is the focus of this merge. ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/LemonadeRP-4.5.3-4bpw-exl2) ## Prompt format: Chatml Based on the models going in to this I would assume Chatml is the appropriate format. ``` <\|im_start\|>system {sysprompt}<\|im_end\|> <\|im_start\|>user {input}<\|im_end\|> <\|im_start\|>assistant {output}<\|im_end\|> ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
aidonuts/glowing-disco-62-ep0_2	aidonuts	2024-03-01T17:06:33Z	117	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T17:06:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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tomaszki/gemma-18	tomaszki	2024-03-01T17:06:11Z	117	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T17:06:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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yurezsml/phi2_chan	yurezsml	2024-03-01T17:05:12Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-01T17:03:45Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi2_chan results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi2_chan This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6432 ## 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: 4 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8471 \| 0.5 \| 620 \| 1.9416 \| \| 2.3517 \| 1.0 \| 1240 \| 1.6432 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
HuggingFaceH4/zephyr-7b-gemma-sft-v0.1	HuggingFaceH4	2024-03-01T16:47:18Z	930	12	transformers	[ "transformers", "tensorboard", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "en", "dataset:HuggingFaceH4/deita-10k-v0-sft", "base_model:google/gemma-7b", "base_model:finetune:google/gemma-7b", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T08:46:46Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms base_model: google/gemma-7b tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/deita-10k-v0-sft model-index: - name: zephyr-7b-gemma-sft results: [] language: - en --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-gemma-sft This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the HuggingFaceH4/deita-10k-v0-sft dataset. It achieves the following results on the evaluation set: - Loss: 0.9732 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9482 \| 1.0 \| 299 \| 0.9848 \| \| 0.8139 \| 2.0 \| 599 \| 0.9610 \| \| 0.722 \| 2.99 \| 897 \| 0.9732 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1
Cippppy/mobilebert_500exs_10timesteps	Cippppy	2024-03-01T16:41:59Z	112	0	transformers	[ "transformers", "safetensors", "mobilebert", "text-classification", "generated_from_trainer", "base_model:google/mobilebert-uncased", "base_model:finetune:google/mobilebert-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T16:33:05Z	--- license: apache-2.0 base_model: google/mobilebert-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: mobilebert_500exs_10timesteps results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobilebert_500exs_10timesteps This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7109 - Accuracy: 0.55 ## 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-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 16 \| 3126378.25 \| 0.45 \| \| No log \| 2.0 \| 32 \| 2298910.5 \| 0.45 \| \| No log \| 3.0 \| 48 \| 1604316.0 \| 0.45 \| \| No log \| 4.0 \| 64 \| 1062719.5 \| 0.45 \| \| No log \| 5.0 \| 80 \| 620928.0 \| 0.45 \| \| No log \| 6.0 \| 96 \| 213084.875 \| 0.45 \| \| No log \| 7.0 \| 112 \| 11615.9521 \| 0.45 \| \| No log \| 8.0 \| 128 \| 3076.3987 \| 0.45 \| \| No log \| 9.0 \| 144 \| 2151.5054 \| 0.45 \| \| No log \| 10.0 \| 160 \| 1142.7941 \| 0.45 \| \| No log \| 11.0 \| 176 \| 6.6242 \| 0.45 \| \| No log \| 12.0 \| 192 \| 1.4116 \| 0.45 \| \| No log \| 13.0 \| 208 \| 0.8847 \| 0.45 \| \| No log \| 14.0 \| 224 \| 0.6917 \| 0.54 \| \| No log \| 15.0 \| 240 \| 0.7124 \| 0.55 \| \| No log \| 16.0 \| 256 \| 0.7109 \| 0.55 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.17.1 - Tokenizers 0.15.2
tomaszki/gemma-17	tomaszki	2024-03-01T16:36:41Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T16:36:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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KaggleMasterX/gemma2_ads_adapter	KaggleMasterX	2024-03-01T16:32:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T05:11:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
Shel2679/mistral7binstruct_summarize	Shel2679	2024-03-01T16:30:52Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-03-01T16:30:44Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: mistral7binstruct_summarize results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral7binstruct_summarize This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.4665 ## 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: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6881 \| 0.22 \| 25 \| 1.5487 \| \| 1.531 \| 0.43 \| 50 \| 1.4665 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ThuyNT03/CS505_COQE_viT5_Prompting5_OSPAL	ThuyNT03	2024-03-01T16:29:01Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T15:27:49Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting5_OSPAL results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CS505_COQE_viT5_Prompting5_OSPAL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
bumblebee-testing/tiny-random-PhiForCausalLM	bumblebee-testing	2024-03-01T16:26:53Z	177	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T16:26:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bumblebee-testing/tiny-random-PhiForTokenClassification	bumblebee-testing	2024-03-01T16:26:45Z	53	0	transformers	[ "transformers", "safetensors", "phi", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2024-03-01T16:26:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bumblebee-testing/tiny-random-PhiForSequenceClassification	bumblebee-testing	2024-03-01T16:26:34Z	165	0	transformers	[ "transformers", "safetensors", "phi", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T16:26:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bumblebee-testing/tiny-random-PhiModel	bumblebee-testing	2024-03-01T16:25:54Z	576	0	transformers	[ "transformers", "safetensors", "phi", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-01T16:18:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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artificialguybr/Invictus-Redmond	artificialguybr	2024-03-01T16:21:42Z	0	8	null	[ "region:us" ]	null	2024-02-22T05:15:23Z	Invictus.Redmond is here! I'm grateful for the GPU time from Redmond.AI that allowed me to finish this model! This is a generalist model fine-tuned on Stable Cascade! The model has a high capacity to generate realistic, artistic images, cars, people, and a wide variety of themes. It's a versatile model. I really hope you like the model and use it. If you like the model and think it's worth it, you can make a donation to my Patreon or Ko-fi. Follow me in my twitter to get acess before for all new models: https://twitter.com/artificialguybr/ Acess my site to have acess to all my latest informations too: https://artificialguy.com
unreal-hug/segformer-b5-seed42-feb-29-v1	unreal-hug	2024-03-01T16:21:05Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T16:21:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vaicai/kaifa-ms-v0.07.1	vaicai	2024-03-01T16:19:45Z	3	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T15:38:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cactusgame/donut-base-kie-baidu	cactusgame	2024-03-01T16:17:55Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-29T07:30:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ANWAR101/Summarize_Model	ANWAR101	2024-03-01T16:17:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T16:17:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
manimaranpa07/results	manimaranpa07	2024-03-01T16:16:01Z	160	0	transformers	[ "transformers", "safetensors", "bart", "text-classification", "generated_from_trainer", "base_model:facebook/bart-large-mnli", "base_model:finetune:facebook/bart-large-mnli", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T16:14:52Z	--- license: mit base_model: facebook/bart-large-mnli tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7459 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.2
tsfeith/rl_course_vizdoom_health_gathering_supreme	tsfeith	2024-03-01T16:14:41Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-01T16:14:34Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.08 +/- 5.10 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r tsfeith/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
LoneStriker/gemma-2b-it-GGUF	LoneStriker	2024-03-01T16:12:01Z	29	1	transformers	[ "transformers", "gguf", "arxiv:2312.11805", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:1811.00937", "arxiv:1809.02789", "arxiv:1911.01547", "arxiv:1705.03551", "arxiv:2107.03374", "arxiv:2108.07732", "arxiv:2110.14168", "arxiv:2304.06364", "arxiv:2206.04615", "arxiv:1804.06876", "arxiv:2110.08193", "arxiv:2009.11462", "arxiv:2101.11718", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:2203.09509", "license:other", "endpoints_compatible", "region:us" ]	null	2024-02-21T17:06:52Z	--- library_name: transformers tags: [] widget: - text: \| <start_of_turn>user How does the brain work?<end_of_turn> <start_of_turn>model inference: parameters: max_new_tokens: 200 extra_gated_heading: "Access Gemma on Hugging Face" extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- # Gemma Model Card Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 2B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [7B instruct model](https://huggingface.co/google/gemma-7b-it). Resources and Technical Documentation: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-it-gg-hf) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.float16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "gg-hf/gemma-2b-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype, ) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceeded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [MMLU](https://arxiv.org/abs/2009.03300) \| 5-shot, top-1 \| 42.3 \| 64.3 \| \| [HellaSwag](https://arxiv.org/abs/1905.07830) \| 0-shot \|71.4 \| 81.2 \| \| [PIQA](https://arxiv.org/abs/1911.11641) \| 0-shot \| 77.3 \| 81.2 \| \| [SocialIQA](https://arxiv.org/abs/1904.09728) \| 0-shot \| 59.7 \| 51.8 \| \| [BooIQ](https://arxiv.org/abs/1905.10044) \| 0-shot \| 69.4 \| 83.2 \| \| [WinoGrande](https://arxiv.org/abs/1907.10641) \| partial score \| 65.4 \| 72.3 \| \| [CommonsenseQA](https://arxiv.org/abs/1811.00937) \| 7-shot \| 65.3 \| 71.3 \| \| [OpenBookQA](https://arxiv.org/abs/1809.02789) \| \| 47.8 \| 52.8 \| \| [ARC-e](https://arxiv.org/abs/1911.01547) \| \| 73.2 \| 81.5 \| \| [ARC-c](https://arxiv.org/abs/1911.01547) \| \| 42.1 \| 53.2 \| \| [TriviaQA](https://arxiv.org/abs/1705.03551) \| 5-shot \| 53.2 \| 63.4 \| \| [Natural Questions](https://github.com/google-research-datasets/natural-questions) \| 5-shot \| - \| 23 \| \| [HumanEval](https://arxiv.org/abs/2107.03374) \| pass@1 \| 22.0 \| 32.3 \| \| [MBPP](https://arxiv.org/abs/2108.07732) \| 3-shot \| 29.2 \| 44.4 \| \| [GSM8K](https://arxiv.org/abs/2110.14168) \| maj@1 \| 17.7 \| 46.4 \| \| [MATH](https://arxiv.org/abs/2108.07732) \| 4-shot \| 11.8 \| 24.3 \| \| [AGIEval](https://arxiv.org/abs/2304.06364) \| \| 24.2 \| 41.7 \| \| [BIG-Bench](https://arxiv.org/abs/2206.04615) \| \| 35.2 \| 55.1 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| Average \| \| 54.0 \| 56.4 \| ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [RealToxicity](https://arxiv.org/abs/2009.11462) \| average \| 6.86 \| 7.90 \| \| [BOLD](https://arxiv.org/abs/2101.11718) \| \| 45.57 \| 49.08 \| \| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) \| top-1 \| 45.82 \| 51.33 \| \| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) \| 1-shot, top-1 \| 62.58 \| 92.54 \| \| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) \| top-1 \| 54.62 \| 71.99 \| \| [Winogender](https://arxiv.org/abs/1804.09301) \| top-1 \| 51.25 \| 54.17 \| \| [TruthfulQA](https://arxiv.org/abs/2109.07958) \| \| 44.84 \| 31.81 \| \| [Winobias 1_2](https://arxiv.org/abs/1804.06876) \| \| 56.12 \| 59.09 \| \| [Winobias 2_2](https://arxiv.org/abs/1804.06876) \| \| 91.10 \| 92.23 \| \| [Toxigen](https://arxiv.org/abs/2203.09509) \| \| 29.77 \| 39.59 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
vaicai/kaifa-ms-adapters-v0.07.1	vaicai	2024-03-01T16:09:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T16:09:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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LoneStriker/gemma-2b-GGUF	LoneStriker	2024-03-01T16:09:13Z	16	0	transformers	[ "transformers", "gguf", "arxiv:2312.11805", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:1811.00937", "arxiv:1809.02789", "arxiv:1911.01547", "arxiv:1705.03551", "arxiv:2107.03374", "arxiv:2108.07732", "arxiv:2110.14168", "arxiv:2304.06364", "arxiv:2206.04615", "arxiv:1804.06876", "arxiv:2110.08193", "arxiv:2009.11462", "arxiv:2101.11718", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:2203.09509", "license:other", "endpoints_compatible", "region:us" ]	null	2024-02-21T17:34:57Z	--- library_name: transformers tags: [] extra_gated_heading: "Access Gemma on Hugging Face" extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- # Gemma Model Card Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). Resources and Technical Documentation: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-gg-hf) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Fine-tuning the model You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-2b`. In that repository, we provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [MMLU](https://arxiv.org/abs/2009.03300) \| 5-shot, top-1 \| 42.3 \| 64.3 \| \| [HellaSwag](https://arxiv.org/abs/1905.07830) \| 0-shot \|71.4 \| 81.2 \| \| [PIQA](https://arxiv.org/abs/1911.11641) \| 0-shot \| 77.3 \| 81.2 \| \| [SocialIQA](https://arxiv.org/abs/1904.09728) \| 0-shot \| 59.7 \| 51.8 \| \| [BooIQ](https://arxiv.org/abs/1905.10044) \| 0-shot \| 69.4 \| 83.2 \| \| [WinoGrande](https://arxiv.org/abs/1907.10641) \| partial score \| 65.4 \| 72.3 \| \| [CommonsenseQA](https://arxiv.org/abs/1811.00937) \| 7-shot \| 65.3 \| 71.3 \| \| [OpenBookQA](https://arxiv.org/abs/1809.02789) \| \| 47.8 \| 52.8 \| \| [ARC-e](https://arxiv.org/abs/1911.01547) \| \| 73.2 \| 81.5 \| \| [ARC-c](https://arxiv.org/abs/1911.01547) \| \| 42.1 \| 53.2 \| \| [TriviaQA](https://arxiv.org/abs/1705.03551) \| 5-shot \| 53.2 \| 63.4 \| \| [Natural Questions](https://github.com/google-research-datasets/natural-questions) \| 5-shot \| - \| 23 \| \| [HumanEval](https://arxiv.org/abs/2107.03374) \| pass@1 \| 22.0 \| 32.3 \| \| [MBPP](https://arxiv.org/abs/2108.07732) \| 3-shot \| 29.2 \| 44.4 \| \| [GSM8K](https://arxiv.org/abs/2110.14168) \| maj@1 \| 17.7 \| 46.4 \| \| [MATH](https://arxiv.org/abs/2108.07732) \| 4-shot \| 11.8 \| 24.3 \| \| [AGIEval](https://arxiv.org/abs/2304.06364) \| \| 24.2 \| 41.7 \| \| [BIG-Bench](https://arxiv.org/abs/2206.04615) \| \| 35.2 \| 55.1 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| Average \| \| 54.0 \| 56.4 \| ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [RealToxicity](https://arxiv.org/abs/2009.11462) \| average \| 6.86 \| 7.90 \| \| [BOLD](https://arxiv.org/abs/2101.11718) \| \| 45.57 \| 49.08 \| \| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) \| top-1 \| 45.82 \| 51.33 \| \| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) \| 1-shot, top-1 \| 62.58 \| 92.54 \| \| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) \| top-1 \| 54.62 \| 71.99 \| \| [Winogender](https://arxiv.org/abs/1804.09301) \| top-1 \| 51.25 \| 54.17 \| \| [TruthfulQA](https://arxiv.org/abs/2109.07958) \| \| 44.84 \| 31.81 \| \| [Winobias 1_2](https://arxiv.org/abs/1804.06876) \| \| 56.12 \| 59.09 \| \| [Winobias 2_2](https://arxiv.org/abs/1804.06876) \| \| 91.10 \| 92.23 \| \| [Toxigen](https://arxiv.org/abs/2203.09509) \| \| 29.77 \| 39.59 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
Humaid-alblooshi/bert-test	Humaid-alblooshi	2024-03-01T16:07:57Z	162	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T16:07:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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shahabctg/denoising_cleaning_combined	shahabctg	2024-03-01T16:02:47Z	1	0	diffusers	[ "diffusers", "safetensors", "en", "license:apache-2.0", "diffusers:StableDiffusionInstructPix2PixPipeline", "region:us" ]	null	2024-03-01T15:55:21Z	--- license: apache-2.0 language: - en --- This model is based on instruct pix2pix. To denoise an image use prompt: "Remove noise from the image" To clean the image use prompt: "Remove texts and labels from the image"
arslanarjumand/wav2vec-repeat	arslanarjumand	2024-03-01T15:57:55Z	103	0	transformers	[ "transformers", "safetensors", "wav2vec2-bert", "audio-classification", "generated_from_trainer", "base_model:arslanarjumand/wav2vec-reptiles", "base_model:finetune:arslanarjumand/wav2vec-reptiles", "license:mit", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-01T15:57:20Z	--- license: mit base_model: arslanarjumand/wav2vec-reptiles tags: - generated_from_trainer model-index: - name: wav2vec-repeat results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec-repeat This model is a fine-tuned version of [arslanarjumand/wav2vec-reptiles](https://huggingface.co/arslanarjumand/wav2vec-reptiles) on the None dataset. It achieves the following results on the evaluation set: - Loss: 205.9549 - Pcc Accuracy: 0.8004 - Pcc Fluency: 0.7759 - Pcc Total Score: 0.8207 - Pcc Content: 0.7220 ## 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: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pcc Accuracy \| Pcc Fluency \| Pcc Total Score \| Pcc Content \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------:\|:-----------:\|:---------------:\|:-----------:\| \| 507.295 \| 3.54 \| 500 \| 538.7184 \| 0.2592 \| 0.2368 \| 0.2807 \| 0.3206 \| \| 267.4833 \| 7.08 \| 1000 \| 374.0983 \| 0.5787 \| 0.5582 \| 0.5900 \| 0.5040 \| \| 246.7156 \| 10.62 \| 1500 \| 483.3237 \| 0.6618 \| 0.6387 \| 0.6761 \| 0.5837 \| \| 269.7238 \| 14.16 \| 2000 \| 446.4642 \| 0.6964 \| 0.6691 \| 0.7131 \| 0.6288 \| \| 289.3261 \| 17.7 \| 2500 \| 244.4726 \| 0.7201 \| 0.6928 \| 0.7371 \| 0.6482 \| \| 249.89 \| 21.24 \| 3000 \| 413.8036 \| 0.7340 \| 0.7052 \| 0.7548 \| 0.6796 \| \| 235.8593 \| 24.78 \| 3500 \| 251.3629 \| 0.7472 \| 0.7217 \| 0.7676 \| 0.6808 \| \| 217.7143 \| 28.32 \| 4000 \| 212.4162 \| 0.7779 \| 0.7547 \| 0.7973 \| 0.6948 \| \| 123.7326 \| 31.86 \| 4500 \| 362.4697 \| 0.7782 \| 0.7528 \| 0.7987 \| 0.7062 \| \| 132.7905 \| 35.4 \| 5000 \| 228.9714 \| 0.7826 \| 0.7603 \| 0.8021 \| 0.6987 \| \| 111.7989 \| 38.94 \| 5500 \| 189.2367 \| 0.7985 \| 0.7754 \| 0.8188 \| 0.7169 \| \| 104.5979 \| 42.48 \| 6000 \| 271.8181 \| 0.7929 \| 0.7692 \| 0.8143 \| 0.7192 \| \| 115.256 \| 46.02 \| 6500 \| 220.4324 \| 0.8008 \| 0.7753 \| 0.8209 \| 0.7230 \| \| 86.3804 \| 49.56 \| 7000 \| 205.9549 \| 0.8004 \| 0.7759 \| 0.8207 \| 0.7220 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.17.1 - Tokenizers 0.15.2
lukasberndt/FrozenLake_4x4_non-slippery	lukasberndt	2024-03-01T15:56:09Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-01T15:55:01Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: FrozenLake results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="lukasberndt/FrozenLake", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
LoneStriker/gemma-7b-GGUF	LoneStriker	2024-03-01T15:56:04Z	50	1	transformers	[ "transformers", "gguf", "arxiv:2305.14314", "arxiv:2312.11805", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:1811.00937", "arxiv:1809.02789", "arxiv:1911.01547", "arxiv:1705.03551", "arxiv:2107.03374", "arxiv:2108.07732", "arxiv:2110.14168", "arxiv:2304.06364", "arxiv:2206.04615", "arxiv:1804.06876", "arxiv:2110.08193", "arxiv:2009.11462", "arxiv:2101.11718", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:2203.09509", "license:other", "endpoints_compatible", "region:us" ]	null	2024-02-21T17:41:15Z	--- library_name: transformers tags: [] extra_gated_heading: "Access Gemma on Hugging Face" extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- # Gemma Model Card Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). Resources and Technical Documentation: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-gg-hf) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Fine-tuning examples You can find fine-tuning notebooks under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples). We provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using [QLoRA](https://huggingface.co/papers/2305.14314) * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.float16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [MMLU](https://arxiv.org/abs/2009.03300) \| 5-shot, top-1 \| 42.3 \| 64.3 \| \| [HellaSwag](https://arxiv.org/abs/1905.07830) \| 0-shot \|71.4 \| 81.2 \| \| [PIQA](https://arxiv.org/abs/1911.11641) \| 0-shot \| 77.3 \| 81.2 \| \| [SocialIQA](https://arxiv.org/abs/1904.09728) \| 0-shot \| 59.7 \| 51.8 \| \| [BooIQ](https://arxiv.org/abs/1905.10044) \| 0-shot \| 69.4 \| 83.2 \| \| [WinoGrande](https://arxiv.org/abs/1907.10641) \| partial score \| 65.4 \| 72.3 \| \| [CommonsenseQA](https://arxiv.org/abs/1811.00937) \| 7-shot \| 65.3 \| 71.3 \| \| [OpenBookQA](https://arxiv.org/abs/1809.02789) \| \| 47.8 \| 52.8 \| \| [ARC-e](https://arxiv.org/abs/1911.01547) \| \| 73.2 \| 81.5 \| \| [ARC-c](https://arxiv.org/abs/1911.01547) \| \| 42.1 \| 53.2 \| \| [TriviaQA](https://arxiv.org/abs/1705.03551) \| 5-shot \| 53.2 \| 63.4 \| \| [Natural Questions](https://github.com/google-research-datasets/natural-questions) \| 5-shot \| - \| 23 \| \| [HumanEval](https://arxiv.org/abs/2107.03374) \| pass@1 \| 22.0 \| 32.3 \| \| [MBPP](https://arxiv.org/abs/2108.07732) \| 3-shot \| 29.2 \| 44.4 \| \| [GSM8K](https://arxiv.org/abs/2110.14168) \| maj@1 \| 17.7 \| 46.4 \| \| [MATH](https://arxiv.org/abs/2108.07732) \| 4-shot \| 11.8 \| 24.3 \| \| [AGIEval](https://arxiv.org/abs/2304.06364) \| \| 24.2 \| 41.7 \| \| [BIG-Bench](https://arxiv.org/abs/2206.04615) \| \| 35.2 \| 55.1 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| Average \| \| 54.0 \| 56.4 \| ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [RealToxicity](https://arxiv.org/abs/2009.11462) \| average \| 6.86 \| 7.90 \| \| [BOLD](https://arxiv.org/abs/2101.11718) \| \| 45.57 \| 49.08 \| \| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) \| top-1 \| 45.82 \| 51.33 \| \| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) \| 1-shot, top-1 \| 62.58 \| 92.54 \| \| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) \| top-1 \| 54.62 \| 71.99 \| \| [Winogender](https://arxiv.org/abs/1804.09301) \| top-1 \| 51.25 \| 54.17 \| \| [TruthfulQA](https://arxiv.org/abs/2109.07958) \| \| 44.84 \| 31.81 \| \| [Winobias 1_2](https://arxiv.org/abs/1804.06876) \| \| 56.12 \| 59.09 \| \| [Winobias 2_2](https://arxiv.org/abs/1804.06876) \| \| 91.10 \| 92.23 \| \| [Toxigen](https://arxiv.org/abs/2203.09509) \| \| 29.77 \| 39.59 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
Sentdex/Walls1337bot-Llama2-7B-003.004.400	Sentdex	2024-03-01T15:55:00Z	0	0	null	[ "safetensors", "text-generation", "dataset:Sentdex/WSB-003.004", "license:apache-2.0", "region:us" ]	text-generation	2024-03-01T03:08:19Z	--- license: apache-2.0 datasets: - Sentdex/WSB-003.004 pipeline_tag: text-generation --- Probably don't use this model, I'm just tinkering, but it's a multi-turn, multi-speaker model attempt trained from /r/wallstreetbets data that you can find: https://huggingface.co/datasets/Sentdex/WSB-003.004 ```py #https://huggingface.co/docs/peft/quicktour from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer import torch model = AutoPeftModelForCausalLM.from_pretrained("Sentdex/Walls1337bot-Llama2-7B-003.004.400") tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf") model = model.to("cuda") model.eval() prompt = "Your text here." formatted_prompt = f"### BEGIN CONVERSATION ###\n\n## Speaker_0: ##\n{prompt}\n\n## Walls1337bot: ##\n" inputs = tokenizer(formatted_prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=128) print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]) ```
Sentdex/Walls1337bot-Llama2-7B-003.004.700	Sentdex	2024-03-01T15:54:35Z	0	0	null	[ "safetensors", "text-generation", "dataset:Sentdex/WSB-003.004", "license:apache-2.0", "region:us" ]	text-generation	2024-03-01T03:52:35Z	--- license: apache-2.0 datasets: - Sentdex/WSB-003.004 pipeline_tag: text-generation --- Probably don't use this model, I'm just tinkering, but it's a multi-turn, multi-speaker model attempt trained from /r/wallstreetbets data that you can find: https://huggingface.co/datasets/Sentdex/WSB-003.004 ```py #https://huggingface.co/docs/peft/quicktour from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer import torch model = AutoPeftModelForCausalLM.from_pretrained("Sentdex/Walls1337bot-Llama2-7B-003.004.700") tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf") model = model.to("cuda") model.eval() prompt = "Your text here." formatted_prompt = f"### BEGIN CONVERSATION ###\n\n## Speaker_0: ##\n{prompt}\n\n## Walls1337bot: ##\n" inputs = tokenizer(formatted_prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=128) print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]) ```
Sentdex/Walls1337bot-Llama2-7B-003.004.3900	Sentdex	2024-03-01T15:53:55Z	0	0	null	[ "safetensors", "text-generation", "dataset:Sentdex/WSB-003.004", "license:apache-2.0", "region:us" ]	text-generation	2024-03-01T15:50:18Z	--- license: apache-2.0 datasets: - Sentdex/WSB-003.004 pipeline_tag: text-generation --- Probably don't use this model, I'm just tinkering, but it's a multi-turn, multi-speaker model attempt trained from /r/wallstreetbets data that you can find: https://huggingface.co/datasets/Sentdex/WSB-003.004 ```py #https://huggingface.co/docs/peft/quicktour from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer import torch model = AutoPeftModelForCausalLM.from_pretrained("Sentdex/Walls1337bot-Llama2-7B-003.004.3900") tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf") model = model.to("cuda") model.eval() prompt = "Your text here." formatted_prompt = f"### BEGIN CONVERSATION ###\n\n## Speaker_0: ##\n{prompt}\n\n## Walls1337bot: ##\n" inputs = tokenizer(formatted_prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=128) print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]) ```
truocpham/FinQA-7B-Instruct-v0.1	truocpham	2024-03-01T15:51:30Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "en", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-24T15:39:14Z	--- library_name: transformers tags: - trl - sft language: - en --- # Model Card for FinQA-7B-Instruct-v0.1 The FinQA-7B-Instruct-v0.1 Large Language Model (LLM) is a pre-trained generative text model for the financial domain with 7 billion parameters. ## 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: [Truoc Pham](https://huggingface.co/truocpham) - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): English - License: MIT - Finetuned from model [optional]: [Llama2-7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) ### 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]
salbatarni/flan-t5-small-asap_t3_f0_pa	salbatarni	2024-03-01T15:50:05Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T15:49:51Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-asap_t3_f0_pa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-asap_t3_f0_pa This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0564 - Rouge1: 83.5868 - Rouge2: 79.2034 - Rougel: 83.6443 - Rougelsum: 83.6385 - Gen Len: 12.0478 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 259 \| 0.0883 \| 78.5864 \| 72.6388 \| 78.5689 \| 78.5751 \| 12.0072 \| \| 0.3997 \| 2.0 \| 518 \| 0.0670 \| 82.2729 \| 77.3733 \| 82.2731 \| 82.3003 \| 12.0232 \| \| 0.3997 \| 3.0 \| 777 \| 0.0580 \| 83.4593 \| 78.9471 \| 83.5089 \| 83.5189 \| 12.0464 \| \| 0.0732 \| 4.0 \| 1036 \| 0.0570 \| 83.5601 \| 79.1687 \| 83.5758 \| 83.5695 \| 12.0551 \| \| 0.0732 \| 5.0 \| 1295 \| 0.0564 \| 83.5868 \| 79.2034 \| 83.6443 \| 83.6385 \| 12.0478 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
LoneStriker/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16-4.0bpw-h6-exl2	LoneStriker	2024-03-01T15:47:30Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "yi", "moe", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T15:34:47Z	--- license: other tags: - yi - moe license_name: yi-license license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE --- this is a DPO fine-tuned MoE model for [TomGrc/FusionNet_34Bx2_MoE_v0.1](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE_v0.1) ``` DPO Trainer TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al., 2023. ``` Metrics [Metrics](https://huggingface.co/cloudyu/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO/blob/main/4bit.vs.16.jpg)
Serviljao/Serviljano1	Serviljao	2024-03-01T15:38:49Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2024-03-01T15:38:49Z	--- license: bsd-3-clause-clear ---
LoneStriker/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16-5.0bpw-h6-exl2	LoneStriker	2024-03-01T15:34:44Z	7	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "yi", "moe", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T15:11:15Z	--- license: other tags: - yi - moe license_name: yi-license license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE --- this is a DPO fine-tuned MoE model for [TomGrc/FusionNet_34Bx2_MoE_v0.1](https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE_v0.1) ``` DPO Trainer TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al., 2023. ``` Metrics [Metrics](https://huggingface.co/cloudyu/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO/blob/main/4bit.vs.16.jpg)
DanielAvelar09/mi_chatbotV3	DanielAvelar09	2024-03-01T15:34:21Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2024-03-01T15:28:58Z	--- tags: - generated_from_trainer model-index: - name: mi_chatbotV3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mi_chatbotV3 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0159 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 17 \| 1.1441 \| \| No log \| 2.0 \| 34 \| 1.0835 \| \| No log \| 3.0 \| 51 \| 1.0159 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
nesrine19/whisper_model-ar	nesrine19	2024-03-01T15:33:46Z	101	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ar", "dataset:google/fleurs", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-01T10:16:49Z	--- language: - ar license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - google/fleurs model-index: - name: Whisper Small ar -team 1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small ar -team 1 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fleurs data dataset. It achieves the following results on the evaluation set: - eval_loss: 1.1443 - eval_wer: 23.9750 - eval_runtime: 273.8789 - eval_samples_per_second: 1.563 - eval_steps_per_second: 0.197 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
nninjun/squad-bloom-3b	nninjun	2024-03-01T15:33:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T15:33:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mitkox/starcoder2-15b-q4_k_m.gguf	mitkox	2024-03-01T15:30:48Z	5	3	null	[ "gguf", "starcoder2", "dataset:bigcode/the-stack-v2-train-full-ids", "license:bigcode-openrail-m", "endpoints_compatible", "region:us" ]	null	2024-03-01T13:34:09Z	--- license: bigcode-openrail-m datasets: - bigcode/the-stack-v2-train-full-ids tags: - starcoder2 --- StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 4+ trillion tokens. The model was trained with NVIDIA NeMo™ Framework using the NVIDIA Eos Supercomputer built with NVIDIA DGX H100 systems. Project Website: bigcode-project.org Paper: Link Point of Contact: [email protected] Languages: 600+ Programming languages Use Intended use The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. Generation Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's GitHub repository.
pxyu/MSMARCO-V2-BERT-MLM-CSV300k	pxyu	2024-03-01T15:29:53Z	126	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-02-28T17:54:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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. 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BoodyAhmedHamdy/AraElectra-Arabic-SQuADv2-QA-finetuned-squad	BoodyAhmedHamdy	2024-03-01T15:29:10Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "electra", "question-answering", "generated_from_trainer", "base_model:ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA", "base_model:finetune:ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA", "endpoints_compatible", "region:us" ]	question-answering	2024-03-01T15:06:48Z	--- base_model: ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA tags: - generated_from_trainer model-index: - name: AraElectra-Arabic-SQuADv2-QA-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # AraElectra-Arabic-SQuADv2-QA-finetuned-squad This model is a fine-tuned version of [ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA](https://huggingface.co/ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 45 \| 3.6364 \| \| No log \| 2.0 \| 90 \| 3.3958 \| \| No log \| 3.0 \| 135 \| 3.4381 \| \| No log \| 4.0 \| 180 \| 3.3944 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
pxyu/UniCOIL-MSMARCO-KL-Distillation-CSV300k	pxyu	2024-03-01T15:27:50Z	111	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-28T18:03:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tomaszki/nous-thirty-eight	tomaszki	2024-03-01T15:27:47Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T15:27:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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pxyu/UniCOIL-MSMARCO-KL-Distillation-CSV100k	pxyu	2024-03-01T15:27:21Z	104	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-01T15:19:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Jerome-Dej/camembert_classif	Jerome-Dej	2024-03-01T15:19:22Z	161	0	transformers	[ "transformers", "tensorboard", "safetensors", "camembert", "text-classification", "generated_from_trainer", "base_model:almanach/camembert-base-wikipedia-4gb", "base_model:finetune:almanach/camembert-base-wikipedia-4gb", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-28T19:35:51Z	--- base_model: camembert/camembert-base-wikipedia-4gb tags: - generated_from_trainer metrics: - accuracy model-index: - name: camembert_classif results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # camembert_classif This model is a fine-tuned version of [camembert/camembert-base-wikipedia-4gb](https://huggingface.co/camembert/camembert-base-wikipedia-4gb) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1380 - Accuracy: 0.9636 ## 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: 10 - eval_batch_size: 10 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.1529 \| 1.0 \| 16000 \| 0.1380 \| 0.9636 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
andrewrreed/Llama-2-13B-chat-GPTQ	andrewrreed	2024-03-01T15:18:28Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "conversational", "en", "arxiv:2307.09288", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:quantized:meta-llama/Llama-2-13b-chat-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-29T17:59:09Z	--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 13B Chat base_model: meta-llama/Llama-2-13b-chat-hf inference: false model_creator: Meta Llama 2 model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don''t know the answer to a question, please don''t share false information. <</SYS>> {prompt}[/INST] ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 13B Chat - GPTQ - Model creator: [Meta Llama 2](https://huggingface.co/meta-llama) - Original model: [Llama 2 13B Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) <!-- description start --> ## Description This repo contains GPTQ model files for [Meta's Llama 2 13B-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-13B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) * [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13B-chat-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/main) \| 4 \| 128 \| No \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 7.26 GB \| Yes \| 4-bit, without Act Order and group size 128g. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 8.00 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 7.51 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 7.26 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 13.65 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-64g-actorder_True) \| 8 \| 64 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 13.95 GB \| No \| 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. \| \| [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_False) \| 8 \| 128 \| No \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 13.65 GB \| No \| 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-13B-chat-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Llama-2-13B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Llama-2-13B-chat-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Llama-2-13B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Meta's Llama 2 13B-chat # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF	eren23	2024-03-01T15:12:28Z	3	2	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-03-01T14:43:32Z	GGUF Version of this: https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO Use the model with caution not really tested throughly.
eleanorlurgio/results	eleanorlurgio	2024-03-01T15:09:55Z	106	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-18T17:52:05Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1686 - Precision: 0.5584 - Recall: 0.5584 - F1: 0.5584 - Accuracy: 0.5584 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 1.206 \| 1.0 \| 534 \| 1.0461 \| 0.5448 \| 0.5448 \| 0.5448 \| 0.5448 \| \| 0.9567 \| 2.0 \| 1068 \| 0.9847 \| 0.5688 \| 0.5688 \| 0.5688 \| 0.5688 \| \| 0.7989 \| 3.0 \| 1602 \| 1.0666 \| 0.5561 \| 0.5561 \| 0.5561 \| 0.5561 \| \| 0.6583 \| 4.0 \| 2136 \| 1.1775 \| 0.5498 \| 0.5498 \| 0.5498 \| 0.5498 \| \| 0.5264 \| 5.0 \| 2670 \| 1.3568 \| 0.5516 \| 0.5516 \| 0.5516 \| 0.5516 \| \| 0.3965 \| 6.0 \| 3204 \| 1.5431 \| 0.5407 \| 0.5407 \| 0.5407 \| 0.5407 \| \| 0.3222 \| 7.0 \| 3738 \| 1.6581 \| 0.5525 \| 0.5525 \| 0.5525 \| 0.5525 \| \| 0.243 \| 8.0 \| 4272 \| 1.8748 \| 0.5570 \| 0.5570 \| 0.5570 \| 0.5570 \| \| 0.1881 \| 9.0 \| 4806 \| 2.0840 \| 0.5443 \| 0.5443 \| 0.5443 \| 0.5443 \| \| 0.1449 \| 10.0 \| 5340 \| 2.1686 \| 0.5584 \| 0.5584 \| 0.5584 \| 0.5584 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.1+cu118 - Datasets 2.17.0 - Tokenizers 0.15.1
XresMusic/Ban-Moi	XresMusic	2024-03-01T15:02:14Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-03-01T14:59:51Z	--- license: apache-2.0 cre: Uzi ---
PardisSzah/Mohavere_PersianTextFormalizer-inference-pipeline	PardisSzah	2024-03-01T15:00:45Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-01T14:49:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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LeoNINE786/ppo-LunarLander-v2	LeoNINE786	2024-03-01T14:56:38Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-01T14:56:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.73 +/- 16.46 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
spotify/Mixtral-8x7B-Instruct-v0.1-HIReview-v0.1.7	spotify	2024-03-01T14:51:04Z	0	0	peft	[ "peft", "safetensors", "mixtral", "arxiv:1910.09700", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "region:us" ]	null	2024-03-01T14:29:25Z	--- library_name: peft base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 --- # 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. 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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 0.8.2
ferrazzipietro/Mistral-7B-v0.1_simplest_prompt_adapters_en.layer1_8_torch.bfloat16_64_32_0.01_8_0.0002	ferrazzipietro	2024-03-01T14:50:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T14:49:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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ryusangwon/3678_Llama-2-7b-hf	ryusangwon	2024-03-01T14:42:19Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-03-01T14:42:10Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: 3678_Llama-2-7b-hf results: [] library_name: 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. --> # 3678_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
ryusangwon/3885_Llama-2-7b-hf	ryusangwon	2024-03-01T14:38:05Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-03-01T14:38:00Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: 3885_Llama-2-7b-hf results: [] library_name: 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. --> # 3885_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
ferrazzipietro/Mistral-7B-v0.1_simplest_prompt_adapters_en.layer1_8_torch.bfloat16_64_32_0.01_2_0.0002	ferrazzipietro	2024-03-01T14:35:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T14:34:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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sharukat/sbert-questionclassifier	sharukat	2024-03-01T14:33:53Z	5	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-02-28T16:54:35Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy - precision - recall - f1 widget: - text: "<p><a href=\"https://kwotsin.github.io/tech/2017/02/11/transfer-learning.html\"\ \ rel=\"nofollow noreferrer\">https://kwotsin.github.io/tech/2017/02/11/transfer-learning.html</a>\n\ I followed the above link to make a image classifier</p>\n\n<p>Training code:</p>\n\ \n<pre><code>slim = tf.contrib.slim\n\ndataset_dir = './data'\nlog_dir = './log'\n\ checkpoint_file = './inception_resnet_v2_2016_08_30.ckpt'\nimage_size = 299\n\ num_classes = 21\nvlabels_file = './labels.txt'\nlabels = open(labels_file, 'r')\n\ labels_to_name = {}\nfor line in labels:\n label, string_name = line.split(':')\n\ \ string_name = string_name[:-1]\n labels_to_name[int(label)] = string_name\n\ \nfile_pattern = 'test_%s_.tfrecord'\n\nitems_to_descriptions = {\n 'image':\ \ 'A 3-channel RGB coloured product image',\n 'label': 'A label that from 20\ \ labels'\n}\n\nnum_epochs = 10\nbatch_size = 16\ninitial_learning_rate = 0.001\n\ learning_rate_decay_factor = 0.7\nnum_epochs_before_decay = 4\n\ndef get_split(split_name,\ \ dataset_dir, file_pattern=file_pattern, file_pattern_for_counting='products'):\n\ \ if split_name not in ['train', 'validation']:\n raise ValueError(\n\ \ 'The split_name %s is not recognized. Please input either train or\ \ validation as the split_name' % (\n split_name))\n\n file_pattern_path\ \ = os.path.join(dataset_dir, file_pattern % (split_name))\n\n num_samples\ \ = 0\n file_pattern_for_counting = file_pattern_for_counting + '_' + split_name\n\ \ tfrecords_to_count = [os.path.join(dataset_dir, file) for file in os.listdir(dataset_dir)\ \ if\n file.startswith(file_pattern_for_counting)]\n\ \ for tfrecord_file in tfrecords_to_count:\n for record in tf.python_io.tf_record_iterator(tfrecord_file):\n\ \ num_samples += 1\n\n test = num_samples\n\n reader = tf.TFRecordReader\n\ \n keys_to_features = {\n 'image/encoded': tf.FixedLenFeature((), tf.string,\ \ default_value=''),\n 'image/format': tf.FixedLenFeature((), tf.string,\ \ default_value='jpg'),\n 'image/class/label': tf.FixedLenFeature(\n \ \ [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),\n }\n\ \n items_to_handlers = {\n 'image': slim.tfexample_decoder.Image(),\n\ \ 'label': slim.tfexample_decoder.Tensor('image/class/label'),\n }\n\ \n decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)\n\ \n labels_to_name_dict = labels_to_name\n\n dataset = slim.dataset.Dataset(\n\ \ data_sources=file_pattern_path,\n decoder=decoder,\n reader=reader,\n\ \ num_readers=4,\n num_samples=num_samples,\n num_classes=num_classes,\n\ \ labels_to_name=labels_to_name_dict,\n items_to_descriptions=items_to_descriptions)\n\ \n return dataset\n\ndef load_batch(dataset, batch_size, height=image_size,\ \ width=image_size, is_training=True):\n '''\n Loads a batch for training.\n\ \n INPUTS:\n - dataset(Dataset): a Dataset class object that is created\ \ from the get_split function\n - batch_size(int): determines how big of a\ \ batch to train\n - height(int): the height of the image to resize to during\ \ preprocessing\n - width(int): the width of the image to resize to during\ \ preprocessing\n - is_training(bool): to determine whether to perform a training\ \ or evaluation preprocessing\n\n OUTPUTS:\n - images(Tensor): a Tensor\ \ of the shape (batch_size, height, width, channels) that contain one batch of\ \ images\n - labels(Tensor): the batch's labels with the shape (batch_size,)\ \ (requires one_hot_encoding).\n\n '''\n # First create the data_provider\ \ object\n data_provider = slim.dataset_data_provider.DatasetDataProvider(\n\ \ dataset,\n common_queue_capacity=24 + 3 batch_size,\n \ \ common_queue_min=24)\n\n # Obtain the raw image using the get method\n \ \ raw_image, label = data_provider.get(['image', 'label'])\n\n # Perform\ \ the correct preprocessing for this image depending if it is training or evaluating\n\ \ image = inception_preprocessing.preprocess_image(raw_image, height, width,\ \ is_training)\n\n # As for the raw images, we just do a simple reshape to\ \ batch it up\n raw_image = tf.expand_dims(raw_image, 0)\n raw_image = tf.image.resize_nearest_neighbor(raw_image,\ \ [height, width])\n raw_image = tf.squeeze(raw_image)\n\n # Batch up the\ \ image by enqueing the tensors internally in a FIFO queue and dequeueing many\ \ elements with tf.train.batch.\n images, raw_images, labels = tf.train.batch(\n\ \ [image, raw_image, label],\n batch_size=batch_size,\n num_threads=4,\n\ \ capacity=4 * batch_size,\n allow_smaller_final_batch=True)\n\n\ \ return images, raw_images, labels\n\n\ndef run():\n # Create the log directory\ \ here. Must be done here otherwise import will activate this unneededly.\n \ \ if not os.path.exists(log_dir):\n os.mkdir(log_dir)\n\n # =======================\ \ TRAINING PROCESS =========================\n # Now we start to construct\ \ the graph and build our model\n with tf.Graph().as_default() as graph:\n\ \ tf.logging.set_verbosity(tf.logging.INFO) # Set the verbosity to INFO\ \ level\n\n # First create the dataset and load one batch\n dataset\ \ = get_split('train', dataset_dir, file_pattern=file_pattern)\n images,\ \ _, labels = load_batch(dataset, batch_size=batch_size)\n\n # Know the\ \ number steps to take before decaying the learning rate and batches per epoch\n\ \ num_batches_per_epoch = int(dataset.num_samples / batch_size)\n \ \ num_steps_per_epoch = num_batches_per_epoch # Because one step is one batch\ \ processed\n decay_steps = int(num_epochs_before_decay * num_steps_per_epoch)\n\ \n # Create the model inference\n with slim.arg_scope(inception_resnet_v2_arg_scope()):\n\ \ logits, end_points = inception_resnet_v2(images, num_classes=dataset.num_classes,\ \ is_training=True)\n\n # Define the scopes that you want to exclude for\ \ restoration\n exclude = ['InceptionResnetV2/Logits', 'InceptionResnetV2/AuxLogits']\n\ \ variables_to_restore = slim.get_variables_to_restore(exclude=exclude)\n\ \n # Perform one-hot-encoding of the labels (Try one-hot-encoding within\ \ the load_batch function!)\n one_hot_labels = slim.one_hot_encoding(labels,\ \ dataset.num_classes)\n\n # Performs the equivalent to tf.nn.sparse_softmax_cross_entropy_with_logits\ \ but enhanced with checks\n loss = tf.losses.softmax_cross_entropy(onehot_labels=one_hot_labels,\ \ logits=logits)\n total_loss = tf.losses.get_total_loss() # obtain the\ \ regularization losses as well\n\n # Create the global step for monitoring\ \ the learning_rate and training.\n global_step = get_or_create_global_step()\n\ \n # Define your exponentially decaying learning rate\n lr = tf.train.exponential_decay(\n\ \ learning_rate=initial_learning_rate,\n global_step=global_step,\n\ \ decay_steps=decay_steps,\n decay_rate=learning_rate_decay_factor,\n\ \ staircase=True)\n\n # Now we can define the optimizer that\ \ takes on the learning rate\n optimizer = tf.train.AdamOptimizer(learning_rate=lr)\n\ \n # Create the train_op.\n train_op = slim.learning.create_train_op(total_loss,\ \ optimizer)\n\n # State the metrics that you want to predict. We get a\ \ predictions that is not one_hot_encoded.\n predictions = tf.argmax(end_points['Predictions'],\ \ 1)\n probabilities = end_points['Predictions']\n accuracy, accuracy_update\ \ = tf.contrib.metrics.streaming_accuracy(predictions, labels)\n metrics_op\ \ = tf.group(accuracy_update, probabilities)\n\n # Now finally create all\ \ the summaries you need to monitor and group them into one summary op.\n \ \ tf.summary.scalar('losses/Total_Loss', total_loss)\n tf.summary.scalar('accuracy',\ \ accuracy)\n tf.summary.scalar('learning_rate', lr)\n my_summary_op\ \ = tf.summary.merge_all()\n\n # Now we need to create a training step\ \ function that runs both the train_op, metrics_op and updates the global_step\ \ concurrently.\n def train_step(sess, train_op, global_step):\n \ \ '''\n Simply runs a session for the three arguments provided\ \ and gives a logging on the time elapsed for each global step\n '''\n\ \ # Check the time for each sess run\n start_time = time.time()\n\ \ total_loss, global_step_count, _ = sess.run([train_op, global_step,\ \ metrics_op])\n time_elapsed = time.time() - start_time\n\n \ \ # Run the logging to print some results\n logging.info('global\ \ step %s: loss: %.4f (%.2f sec/step)', global_step_count, total_loss, time_elapsed)\n\ \n return total_loss, global_step_count\n\n # Now we create\ \ a saver function that actually restores the variables from a checkpoint file\ \ in a sess\n saver = tf.train.Saver(variables_to_restore)\n\n def\ \ restore_fn(sess):\n return saver.restore(sess, checkpoint_file)\n\ \n # Define your supervisor for running a managed session. Do not run the\ \ summary_op automatically or else it will consume too much memory\n sv\ \ = tf.train.Supervisor(logdir=log_dir, summary_op=None, init_fn=restore_fn)\n\ \n # Run the managed session\n with sv.managed_session() as sess:\n\ \ for step in xrange(num_steps_per_epoch * num_epochs):\n \ \ # At the start of every epoch, show the vital information:\n \ \ if step % num_batches_per_epoch == 0:\n logging.info('Epoch\ \ %s/%s', step / num_batches_per_epoch + 1, num_epochs)\n learning_rate_value,\ \ accuracy_value = sess.run([lr, accuracy])\n logging.info('Current\ \ Learning Rate: %s', learning_rate_value)\n logging.info('Current\ \ Streaming Accuracy: %s', accuracy_value)\n\n # optionally,\ \ print your logits and predictions for a sanity check that things are going fine.\n\ \ logits_value, probabilities_value, predictions_value, labels_value\ \ = sess.run(\n [logits, probabilities, predictions, labels])\n\ \ print 'logits: \\n', logits_value\n print\ \ 'Probabilities: \\n', probabilities_value\n print 'predictions:\ \ \\n', predictions_value\n print 'Labels:\\n:', labels_value\n\ \n # Log the summaries every 10 step.\n if step\ \ % 10 == 0:\n loss, _ = train_step(sess, train_op, sv.global_step)\n\ \ summaries = sess.run(my_summary_op)\n \ \ sv.summary_computed(sess, summaries)\n\n # If not, simply run\ \ the training step\n else:\n loss, _ = train_step(sess,\ \ train_op, sv.global_step)\n\n # We log the final training loss and\ \ accuracy\n logging.info('Final Loss: %s', loss)\n logging.info('Final\ \ Accuracy: %s', sess.run(accuracy))\n\n # Once all the training has\ \ been done, save the log files and checkpoint model\n logging.info('Finished\ \ training! Saving model to disk now.')\n sv.saver.save(sess, sv.save_path,\ \ global_step=sv.global_step)\n</code></pre>\n\n<p>This code seems to work an\ \ I have ran training on some sample data and Im getting 94% accuracy</p>\n\n\ <p>Evaluation code:</p>\n\n<pre><code>log_dir = './log'\nlog_eval = './log_eval_test'\n\ dataset_dir = './data'\nbatch_size = 10\nnum_epochs = 1\n\ncheckpoint_file = tf.train.latest_checkpoint('./')\n\ \n\ndef run():\n if not os.path.exists(log_eval):\n os.mkdir(log_eval)\n\ \ with tf.Graph().as_default() as graph:\n tf.logging.set_verbosity(tf.logging.INFO)\n\ \ dataset = get_split('train', dataset_dir)\n images, raw_images,\ \ labels = load_batch(dataset, batch_size=batch_size, is_training=False)\n\n \ \ num_batches_per_epoch = dataset.num_samples / batch_size\n num_steps_per_epoch\ \ = num_batches_per_epoch\n\n with slim.arg_scope(inception_resnet_v2_arg_scope()):\n\ \ logits, end_points = inception_resnet_v2(images, num_classes=dataset.num_classes,\ \ is_training=False)\n\n variables_to_restore = slim.get_variables_to_restore()\n\ \ saver = tf.train.Saver(variables_to_restore)\n\n def restore_fn(sess):\n\ \ return saver.restore(sess, checkpoint_file)\n\n predictions\ \ = tf.argmax(end_points['Predictions'], 1)\n accuracy, accuracy_update\ \ = tf.contrib.metrics.streaming_accuracy(predictions, labels)\n metrics_op\ \ = tf.group(accuracy_update)\n\n global_step = get_or_create_global_step()\n\ \ global_step_op = tf.assign(global_step, global_step + 1)\n\n def\ \ eval_step(sess, metrics_op, global_step):\n '''\n Simply\ \ takes in a session, runs the metrics op and some logging information.\n \ \ '''\n start_time = time.time()\n _, global_step_count,\ \ accuracy_value = sess.run([metrics_op, global_step_op, accuracy])\n \ \ time_elapsed = time.time() - start_time\n\n logging.info('Global\ \ Step %s: Streaming Accuracy: %.4f (%.2f sec/step)', global_step_count, accuracy_value,\n\ \ time_elapsed)\n\n return accuracy_value\n\ \n tf.summary.scalar('Validation_Accuracy', accuracy)\n my_summary_op\ \ = tf.summary.merge_all()\n\n sv = tf.train.Supervisor(logdir=log_eval,\ \ summary_op=None, saver=None, init_fn=restore_fn)\n\n with sv.managed_session()\ \ as sess:\n for step in xrange(num_steps_per_epoch * num_epochs):\n\ \ sess.run(sv.global_step)\n if step % num_batches_per_epoch\ \ == 0:\n logging.info('Epoch: %s/%s', step / num_batches_per_epoch\ \ + 1, num_epochs)\n logging.info('Current Streaming Accuracy:\ \ %.4f', sess.run(accuracy))\n\n if step % 10 == 0:\n \ \ eval_step(sess, metrics_op=metrics_op, global_step=sv.global_step)\n\ \ summaries = sess.run(my_summary_op)\n \ \ sv.summary_computed(sess, summaries)\n\n\n else:\n \ \ eval_step(sess, metrics_op=metrics_op, global_step=sv.global_step)\n\ \n logging.info('Final Streaming Accuracy: %.4f', sess.run(accuracy))\n\ \n raw_images, labels, predictions = sess.run([raw_images, labels,\ \ predictions])\n for i in range(10):\n image, label,\ \ prediction = raw_images[i], labels[i], predictions[i]\n prediction_name,\ \ label_name = dataset.labels_to_name[prediction], dataset.labels_to_name[label]\n\ \ text = 'Prediction: %s \\n Ground Truth: %s' % (prediction_name,\ \ label_name)\n img_plot = plt.imshow(image)\n\n \ \ plt.title(text)\n img_plot.axes.get_yaxis().set_ticks([])\n\ \ img_plot.axes.get_xaxis().set_ticks([])\n plt.show()\n\ \n logging.info(\n 'Model evaluation has completed!\ \ Visit TensorBoard for more information regarding your evaluation.')\n</code></pre>\n\ \n<p>So after training the model and getting 94% accuracy i tried to evaluate\ \ the model. On evaluation I get 0-1% accuracy the whole time. I investigated\ \ this only to find that it is predicting the same class every time</p>\n\n<pre><code>labels:\ \ [7, 11, 5, 1, 20, 0, 18, 1, 0, 7]\npredictions: [10, 10, 10, 10, 10, 10, 10,\ \ 10, 10, 10]\n</code></pre>\n\n<p>Can anyone help in where i may be going wrong?</p>\n\ \n<p>EDIT:</p>\n\n<p>TensorBoard accuracy and loss form training</p>\n\n<p><a\ \ href=\"https://i.stack.imgur.com/NLiwC.png\" rel=\"nofollow noreferrer\"><img\ \ src=\"https://i.stack.imgur.com/NLiwC.png\" alt=\"enter image description here\"\ ></a>\n<a href=\"https://i.stack.imgur.com/QdX6d.png\" rel=\"nofollow noreferrer\"\ ><img src=\"https://i.stack.imgur.com/QdX6d.png\" alt=\"enter image description\ \ here\"></a></p>\n\n<p>TensorBoard accuracy from evaluation</p>\n\n<p><a href=\"\ https://i.stack.imgur.com/TNE5B.png\" rel=\"nofollow noreferrer\"><img src=\"\ https://i.stack.imgur.com/TNE5B.png\" alt=\"enter image description here\"></a></p>\n\ \n<p>EDIT:</p>\n\n<p>Ive still not been able to solve this issues. I thought there\ \ might be a problem with how I am restoring the graph in the eval script so I\ \ tried using this to restore the model instead</p>\n\n<pre><code>saver = tf.train.import_meta_graph('/log/model.ckpt.meta')\n\ \ndef restore_fn(sess):\n return saver.restore(sess, checkpoint_file)\n</code></pre>\n\ \n<p>instead of</p>\n\n<pre><code>variables_to_restore = slim.get_variables_to_restore()\n\ \ saver = tf.train.Saver(variables_to_restore)\n\ndef restore_fn(sess):\n \ \ return saver.restore(sess, checkpoint_file)\n</code></pre>\n\n<p>and just\ \ just takes a very long time to start and finally errors. I then tried using\ \ V1 of the writer in the saver (<code>saver = tf.train.Saver(variables_to_restore,\ \ write_version=saver_pb2.SaveDef.V1)</code>) and retrained and was unable to\ \ load this checkpoint at all as it said variables was missing.</p>\n\n<p>I also\ \ attempted to run my eval script with the same data it trained on just to see\ \ if this may give different results yet I get the same. </p>\n\n<p>Finally I\ \ re-cloned the repo from the url and ran a train using the same dataset in the\ \ tutorial and I get 0-3% accuracy when I evaluate even after getting it to 84%\ \ whilst training. Also my checkpoints must have the correct information as when\ \ I restart training the accuracy continues from where it left of. It feels like\ \ i'm not doing something correctly when I restore the model. Would really appreciate\ \ any suggestions on this as im at a dead end currently :( </p>\n" - text: '<p>I''ve just started using tensorflow for a project I''m working on. The program aims to be a binary classifier with input being 12 features. The output is either normal patient or patient with a disease. The prevalence of the disease is quite low and so my dataset is very imbalanced, with 502 examples of normal controls and only 38 diseased patients. For this reason, I''m trying to use <code>tf.nn.weighted_cross_entropy_with_logits</code> as my cost function.</p> <p>The code is based on the iris custom estimator from the official tensorflow documentation, and works with <code>tf.losses.sparse_softmax_cross_entropy</code> as the cost function. However, when I change to <code>weighted_cross_entropy_with_logits</code>, I get a shape error and I''m not sure how to fix this.</p> <pre><code>ValueError: logits and targets must have the same shape ((?, 2) vs (?,)) </code></pre> <p>I have searched and similar problems have been solved by just reshaping the labels - I have tried to do this unsuccessfully (and don''t understand why <code>tf.losses.sparse_softmax_cross_entropy</code> works fine and the weighted version does not). </p> <p>My full code is here <a href="https://gist.github.com/revacious/83142573700c17b8d26a4a1b84b0dff7" rel="nofollow noreferrer">https://gist.github.com/revacious/83142573700c17b8d26a4a1b84b0dff7</a></p> <p>Thanks!</p> ' - text: '<p>In the documentation it seems they focus on how to save and restore tf.keras.models, but i was wondering how do you save and restore models trained customly through some basic iteration loop?</p> <p>Now that there isnt a graph or a session, how do we save structure defined in a tf function that is customly built without using layer abstractions?</p> ' - text: "<p>I simply have <code>train = optimizer.minimize(loss = tf.constant(4,dtype=\"\ float32\"))</code> Line of code that i change before everything is working. <br/></p>\n\ \n<p>Why it is giving error ? Because documentation say it can be tensor <a href=\"\ https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam#minimize\"\ \ rel=\"nofollow noreferrer\">Here is Docs</a> </p>\n\n<pre><code>W = tf.Variable([0.5],tf.float32)\n\ b = tf.Variable([0.1],tf.float32)\nx = tf.placeholder(tf.float32)\ny= tf.placeholder(tf.float32)\n\ discounted_reward = tf.placeholder(tf.float32,shape=[4,], name=\"discounted_reward\"\ )\nlinear_model = Wx + b\n\nsquared_delta = tf.square(linear_model - y)\nprint(squared_delta)\n\ loss = tf.reduce_sum(squared_deltadiscounted_reward)\nprint(loss)\noptimizer\ \ = tf.train.GradientDescentOptimizer(0.01)\ntrain = optimizer.minimize(loss =\ \ tf.constant(4,dtype=\"float32\"))\ninit = tf.global_variables_initializer()\n\ sess = tf.Session()\n\nsess.run(init)\n\nfor i in range(3):\n sess.run(train,{x:[1,2,3,4],y:[0,-1,-2,-3],discounted_reward:[1,2,3,4]})\n\ \nprint(sess.run([W,b]))\n</code></pre>\n\n<hr>\n\n<p>I really need this thing\ \ to work. In this particular example we can have other ways to solve it but i\ \ need it to work as my actual code can do this only </p>\n\n<p><hr/> Error is</p>\n\ \n<pre><code>> ValueError: No gradients provided for any variable, check your\ \ graph\n> for ops that do not support gradients, between variables\n> [\"\ <tf.Variable 'Variable:0' shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable\ \ 'Variable_1:0' shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_2:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_3:0' shape=(1,)\ \ dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_4:0' shape=(1,) dtype=float32_ref>\"\ ,\n> \"<tf.Variable 'Variable_5:0' shape=(1,) dtype=float32_ref>\",\n\ > \"<tf.Variable 'Variable_6:0' shape=(1,) dtype=float32_ref>\",\n>\ \ \"<tf.Variable 'Variable_7:0' shape=(1,) dtype=float32_ref>\",\n> \"\ <tf.Variable 'Variable_8:0' shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable\ \ 'Variable_9:0' shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_10:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_11:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_12:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_13:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_14:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_15:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_16:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_17:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_18:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_19:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_20:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_21:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_22:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_23:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_24:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_25:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_26:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_27:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_28:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_29:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_30:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_31:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_32:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_33:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_34:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_35:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_36:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_37:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_38:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_39:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_40:0'\ \ shape=(1,) dtype=float32_ref>\",\n> \"<tf.Variable 'Variable_41:0'\ \ shape=(1,) dtype=float32_ref>\"] and loss\n> Tensor(\"Const_4:0\", shape=(),\ \ dtype=float32).\n</code></pre>\n" - text: "<p>I found in the <a href=\"https://www.tensorflow.org/tutorials/recurrent\"\ \ rel=\"nofollow noreferrer\">tensorflow doc</a>:</p>\n\n<p><code>\nstacked_lstm\ \ = tf.contrib.rnn.MultiRNNCell([lstm] * number_of_layers,\n ...\n\ </code></p>\n\n<p>I need to use MultiRNNCell</p>\n\n<p>but, I write those lines</p>\n\ \n<p><code>\na = [tf.nn.rnn_cell.BasicLSTMCell(10)]3\nprint id(a[0]), id(a[1])\n\ </code></p>\n\n<p>Its output is <code>[4648063696 4648063696]</code>.</p>\n\n\ <p>Can <code>MultiRNNCell</code> use the same object <code>BasicLSTMCell</code>\ \ as a list for parameter?</p>\n" pipeline_tag: text-classification inference: true base_model: sentence-transformers/all-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/all-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.85 name: Accuracy - type: precision value: 0.8535353535353536 name: Precision - type: recall value: 0.85 name: Recall - type: f1 value: 0.8496240601503761 name: F1 --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type:* SetFit - Sentence Transformer body: [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 384 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1 \| <ul><li>'<p>I\'m looking to use Tensorflow to train a neural network model for classification, and I want to read data from a CSV file, such as the Iris data set.</p>\n\n<p>The <a href="https://www.tensorflow.org/versions/r0.10/tutorials/tflearn/index.html#tf-contrib-learn-quickstart" rel="nofollow noreferrer">Tensorflow documentation</a> shows an example of loading the Iris data and building a prediction model, but the example uses the high-level <code>tf.contrib.learn</code> API. I want to use the low-level Tensorflow API and run gradient descent myself. How would I do that?</p>\n'</li><li>'<p>In the following code, I want dense matrix <code>B</code> to left multiply a sparse matrix <code>A</code>, but I got errors.</p>\n\n<pre><code>import tensorflow as tf\nimport numpy as np\n\nA = tf.sparse_placeholder(tf.float32)\nB = tf.placeholder(tf.float32, shape=(5,5))\nC = tf.matmul(B,A,a_is_sparse=False,b_is_sparse=True)\nsess = tf.InteractiveSession()\nindices = np.array([[3, 2], [1, 2]], dtype=np.int64)\nvalues = np.array([1.0, 2.0], dtype=np.float32)\nshape = np.array([5,5], dtype=np.int64)\nSparse_A = tf.SparseTensorValue(indices, values, shape)\nRandB = np.ones((5, 5))\nprint sess.run(C, feed_dict={A: Sparse_A, B: RandB})\n</code></pre>\n\n<p>The error message is as follows:</p>\n\n<pre><code>TypeError: Failed to convert object of type <class \'tensorflow.python.framework.sparse_tensor.SparseTensor\'> \nto Tensor. Contents: SparseTensor(indices=Tensor("Placeholder_4:0", shape=(?, ?), dtype=int64), values=Tensor("Placeholder_3:0", shape=(?,), dtype=float32), dense_shape=Tensor("Placeholder_2:0", shape=(?,), dtype=int64)). \nConsider casting elements to a supported type.\n</code></pre>\n\n<p>What\'s wrong with my code?</p>\n\n<p>I\'m doing this following the <a href="https://www.tensorflow.org/api_docs/python/tf/matmul" rel="nofollow noreferrer">documentation</a> and it says we should use <code>a_is_sparse</code> to denote whether the first matrix is sparse, and similarly with <code>b_is_sparse</code>. Why is my code wrong?</p>\n\n<p>As is suggested by vijay, I should use <code>C = tf.matmul(B,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True)</code></p>\n\n<p>I tried this but I met with another error saying:</p>\n\n<pre><code>Caused by op u\'SparseToDense\', defined at:\n File "a.py", line 19, in <module>\n C = tf.matmul(B,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True)\n File "/home/fengchao.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/sparse_ops.py", line 845, in sparse_tensor_to_dense\n name=name)\n File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/sparse_ops.py", line 710, in sparse_to_dense\n name=name)\n File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_sparse_ops.py", line 1094, in _sparse_to_dense\n validate_indices=validate_indices, name=name)\n File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op\n op_def=op_def)\n File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op\n original_op=self._default_original_op, op_def=op_def)\n File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__\n self._traceback = _extract_stack()\n\nInvalidArgumentError (see above for traceback): indices[1] = [1,2] is out of order\n[[Node: SparseToDense = SparseToDense[T=DT_FLOAT, Tindices=DT_INT64, validate_indices=true, _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_Placeholder_4_0_2, _arg_Placeholder_2_0_0, _arg_Placeholder_3_0_1, SparseToDense/default_value)]]\n</code></pre>\n\n<p>Thank you all for helping me!</p>\n'</li><li>"<p>I am using <code>tf.estimator.train_and_evaluate</code> and <code>tf.data.Dataset</code> to feed data to the estimator:</p>\n\n<p>Input Data function:</p>\n\n<pre><code> def data_fn(data_dict, batch_size, mode, num_epochs=10):\n dataset = {}\n if mode == tf.estimator.ModeKeys.TRAIN:\n dataset = tf.data.Dataset.from_tensor_slices(data_dict['train_data'].astype(np.float32))\n dataset = dataset.cache()\n dataset = dataset.shuffle(buffer_size= batch_size * 10).repeat(num_epochs).batch(batch_size)\n else:\n dataset = tf.data.Dataset.from_tensor_slices(data_dict['valid_data'].astype(np.float32))\n dataset = dataset.cache()\n dataset = dataset.batch(batch_size)\n\n iterator = dataset.make_one_shot_iterator()\n next_element = iterator.get_next()\n\n return next_element\n</code></pre>\n\n<p>Train Function:</p>\n\n<pre><code>def train_model(data):\n tf.logging.set_verbosity(tf.logging.INFO)\n config = tf.ConfigProto(allow_soft_placement=True,\n log_device_placement=False)\n config.gpu_options.allow_growth = True\n run_config = tf.contrib.learn.RunConfig(\n save_checkpoints_steps=10,\n keep_checkpoint_max=10,\n session_config=config\n )\n\n train_input = lambda: data_fn(data, 100, tf.estimator.ModeKeys.TRAIN, num_epochs=1)\n eval_input = lambda: data_fn(data, 1000, tf.estimator.ModeKeys.EVAL)\n estimator = tf.estimator.Estimator(model_fn=model_fn, params=hps, config=run_config)\n train_spec = tf.estimator.TrainSpec(train_input, max_steps=100)\n eval_spec = tf.estimator.EvalSpec(eval_input,\n steps=None,\n throttle_secs = 30)\n\n tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)\n</code></pre>\n\n<p>The training goes fine, but when it comes to evaluation I get this error:</p>\n\n<pre><code>OutOfRangeError (see above for traceback): End of sequence \n</code></pre>\n\n<p>If I don't use <code>Dataset.batch</code> on evaluation dataset (by omitting the line <code>dataset[name] = dataset[name].batch(batch_size)</code> in <code>data_fn</code>) I get the same error but after a much longer time.</p>\n\n<p>I can only avoid this error if I don't batch the data and use <code>steps=1</code> for evaluation, but does that perform the evaluation on the whole dataset?</p>\n\n<p>I don't understand what causes this error as the documentation suggests I should be able to evaluate on batches too.</p>\n\n<p>Note: I get the same error when using <code>tf.estimator.evaluate</code> on data batches.</p>\n"</li></ul> \| \| 0 \| <ul><li>'<p>I\'m working on a project where I have trained a series of binary classifiers with <strong>Keras</strong>, with <strong>Tensorflow</strong> as the backend engine. The input data I have is a series of images, where each binary classifier must make the prediction on the images, later I save the predictions on a CSV file.</p>\n<p>The problem I have is when I get the predictions from the first series of binary classifiers there isn\'t any warning, but when the 5th or 6th binary classifier calls the method <strong>predict</strong> on the input data I get the following warning:</p>\n<blockquote>\n<p>WARNING:tensorflow:5 out of the last 5 calls to <function\nModel.make_predict_function..predict_function at\n0x2b280ff5c158> triggered tf.function retracing. Tracing is expensive\nand the excessive number of tracings could be due to (1) creating\[email protected] repeatedly in a loop, (2) passing tensors with different\nshapes, (3) passing Python objects instead of tensors. For (1), please\ndefine your @tf.function outside of the loop. For (2), @tf.function\nhas experimental_relax_shapes=True option that relaxes argument shapes\nthat can avoid unnecessary retracing. For (3), please refer to\n<a href="https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args" rel="noreferrer">https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args</a>\nand <a href="https://www.tensorflow.org/api_docs/python/tf/function" rel="noreferrer">https://www.tensorflow.org/api_docs/python/tf/function</a> for more\ndetails.</p>\n</blockquote>\n<p>To answer each point in the parenthesis, here are my answers:</p>\n<ol>\n<li>The <strong>predict</strong> method is called inside a for loop.</li>\n<li>I don\'t pass tensors but a list of <strong>NumPy arrays</strong> of gray scale images, all of them with the same size in width and height. The only thing that can change is the batch size because the list can have only 1 image or more than one.</li>\n<li>As I wrote in point 2, I pass a list of NumPy arrays.</li>\n</ol>\n<p>I have debugged my program and found that this warning always happens when the method predict is called. To summarize the code I have written is the following:</p>\n<pre><code>import cv2 as cv\nimport tensorflow as tf\nfrom tensorflow.keras.models import load_model\n# Load the models\nbinary_classifiers = [load_model(path) for path in path2models]\n# Get the images\nimages = [#Load the images with OpenCV]\n# Apply the resizing and reshapes on the images.\nmy_list = list()\nfor image in images:\n image_reworked = # Apply the resizing and reshaping on images\n my_list.append(image_reworked)\n\n# Get the prediction from each model\n# This is where I get the warning\npredictions = [model.predict(x=my_list,verbose=0) for model in binary_classifiers]\n</code></pre>\n<h3>What I have tried</h3>\n<p>I have defined a function as tf.function and putted the code of the predictions inside the tf.function like this</p>\n<pre><code>@tf.function\ndef testing(models, faces):\n return [model.predict(x=faces,verbose=0) for model in models]\n</code></pre>\n<p>But I ended up getting the following error:</p>\n<blockquote>\n<p>RuntimeError: Detected a call to <code>Model.predict</code> inside a\n<code>tf.function</code>. Model.predict is a high-level endpoint that manages\nits own <code>tf.function</code>. Please move the call to <code>Model.predict</code> outside\nof all enclosing <code>tf.function</code>s. Note that you can call a <code>Model</code>\ndirectly on Tensors inside a <code>tf.function</code> like: <code>model(x)</code>.</p>\n</blockquote>\n<p>So calling the method <code>predict</code> is basically already a tf.function. So it\'s useless to define a tf.function when the warning I get it\'s from that method.</p>\n<p>I have also checked those other two questions:</p>\n<ol>\n<li><a href="https://stackoverflow.com/questions/61647404/tensorflow-2-getting-warningtensorflow9-out-of-the-last-9-calls-to-function">Tensorflow 2: Getting "WARNING:tensorflow:9 out of the last 9 calls to triggered tf.function retracing. Tracing is expensive"</a></li>\n<li><a href="https://stackoverflow.com/questions/65563185/loading-multiple-saved-tensorflow-keras-models-for-prediction">Loading multiple saved tensorflow/keras models for prediction</a></li>\n</ol>\n<p>But neither of the two questions answers my question about how to avoid this warning. Plus I have also checked the links in the warning message but I couldn\'t solve my problem.</p>\n<h3>What I want</h3>\n<p>I simply want to avoid this warning. While I\'m still getting the predictions from the models I noticed that the python program takes way too much time on doing predictions for a list of images.</p>\n<h3>What I\'m using</h3>\n<ul>\n<li>Python 3.6.13</li>\n<li>Tensorflow 2.3.0</li>\n</ul>\n<h3>Solution</h3>\n<p>After some tries to suppress the warning from the <code>predict</code> method, I have checked the documentation of Tensorflow and in one of the first tutorials on how to use Tensorflow it is explained that, by default, Tensorflow is executed in eager mode, which is useful for testing and debugging the network models. Since I have already tested my models many times, it was only required to disable the eager mode by writing this single python line of code:</p>\n<p><code>tf.compat.v1.disable_eager_execution()</code></p>\n<p>Now the warning doesn\'t show up anymore.</p>\n'</li><li>'<p>I try to export a Tensorflow model but I can not find the best way to add the exogenous feature to the <code>tf.contrib.timeseries.StructuralEnsembleRegressor.build_raw_serving_input_receiver_fn</code>. </p>\n\n<p>I use the sample from the Tensorflow contrib: <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/timeseries/examples/known_anomaly.py" rel="nofollow noreferrer">https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/timeseries/examples/known_anomaly.py</a> and I just try to save the model.</p>\n\n<pre><code># this is the exogenous column \nstring_feature = tf.contrib.layers.sparse_column_with_keys(\n column_name="is_changepoint", keys=["no", "yes"])\n\none_hot_feature = tf.contrib.layers.one_hot_column(\n sparse_id_column=string_feature)\n\nestimator = tf.contrib.timeseries.StructuralEnsembleRegressor(\n periodicities=12, \n cycle_num_latent_values=3,\n num_features=1,\n exogenous_feature_columns=[one_hot_feature],\n exogenous_update_condition=\n lambda times, features: tf.equal(features["is_changepoint"], "yes"))\n\nreader = tf.contrib.timeseries.CSVReader(\n csv_file_name,\n\n column_names=(tf.contrib.timeseries.TrainEvalFeatures.TIMES,\n tf.contrib.timeseries.TrainEvalFeatures.VALUES,\n "is_changepoint"),\n\n column_dtypes=(tf.int64, tf.float32, tf.string),\n\n skip_header_lines=1)\n\ntrain_input_fn = tf.contrib.timeseries.RandomWindowInputFn(reader, batch_size=4, window_size=64)\nestimator.train(input_fn=train_input_fn, steps=train_steps)\nevaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)\nevaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)\n\nexport_directory = tempfile.mkdtemp()\n\n###################################################### \n# the exogenous column must be provided to the build_raw_serving_input_receiver_fn. \n# But How ?\n######################################################\n\ninput_receiver_fn = estimator.build_raw_serving_input_receiver_fn()\n# -> error missing \'is_changepoint\' key \n\n#input_receiver_fn = estimator.build_raw_serving_input_receiver_fn({\'is_changepoint\' : string_feature}) \n# -> cast exception\n\nexport_location = estimator.export_savedmodel(export_directory, input_receiver_fn)\n</code></pre>\n\n<p>According to the <a href="https://www.tensorflow.org/api_docs/python/tf/contrib/timeseries/StructuralEnsembleRegressor" rel="nofollow noreferrer">documentation</a>, build_raw_serving_input_receiver_fn <strong>exogenous_features</strong> parameter : <em>A dictionary mapping feature keys to exogenous features (either Numpy arrays or Tensors). Used to determine the shapes of placeholders for these features</em>.</p>\n\n<p>So what is the best way to transform the <em>one_hot_column</em> or <em>sparse_column_with_keys</em> to a <em>Tensor</em> object ?</p>\n'</li><li>"<p>I am currently working on an optical flow project and I come across a strange error. </p>\n\n<p>I have uint16 images stored in bytes in my TFrecords. When I read the TFrecords from my local machine it is giving me uint16 values, but when I deploy the same code and read it from the docker I am getting uint8 values eventhough my dtype is uint16. I mean the uint16 values are getting reduced to uint8 like 32768 --> 128.</p>\n\n<p>What is causing this error?</p>\n\n<p>My local machine has: Tensorflow 1.10.1 and python 3.6\nMy Docker Image has: Tensorflow 1.12.0 and python 3.5</p>\n\n<p>I am working on tensorflow object detection API\nWhile creating the TF records I use:</p>\n\n<pre><code>with tf.gfile.GFile(flows, 'rb') as fid:\n flow_images = fid.read()\n</code></pre>\n\n<p>While reading it back I am using: tf.image.decoderaw</p>\n\n<p>Dataset: KITTI FLOW 2015</p>\n"</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| Precision \| Recall \| F1 \| \|:--------\|:---------\|:----------\|:-------\|:-------\| \| all \| 0.85 \| 0.8535 \| 0.85 \| 0.8496 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("sharukat/sbert-questionclassifier") # Run inference preds = model("<p>In the documentation it seems they focus on how to save and restore tf.keras.models, but i was wondering how do you save and restore models trained customly through some basic iteration loop?</p> <p>Now that there isnt a graph or a session, how do we save structure defined in a tf function that is customly built without using layer abstractions?</p> ") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:---------\|:-----\| \| Word count \| 15 \| 330.0667 \| 3755 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 450 \| \| 1 \| 450 \| ### Training Hyperparameters - batch_size: (16, 2) - num_epochs: (1, 16) - max_steps: -1 - sampling_strategy: unique - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - max_length: 256 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:---------:\|:-------------:\|:---------------:\| \| 0.0000 \| 1 \| 0.2951 \| - \| \| 1.0 \| 25341 \| 0.0 \| 0.2473 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.13 - SetFit: 1.0.3 - Sentence Transformers: 2.5.0 - Transformers: 4.38.1 - PyTorch: 2.1.2 - Datasets: 2.17.1 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
argilla/OpenHermes-2.5-Mistral-7B-top-SPIN-iter0	argilla	2024-03-01T14:31:21Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T13:33:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the argilla/10k_prompts_top_SPIN_iter0 dataset. WIP: [WandB Logs](https://wandb.ai/argilla-io/dibt-top-spin-iter0?workspace=user-plaguss-argilla) EXAMPLE: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("argilla/OpenHermes-2.5-Mistral-7B-top-SPIN-iter0") tokenizer = AutoTokenizer.from_pretrained("argilla/OpenHermes-2.5-Mistral-7B-top-SPIN-iter0") inputs = tokenizer.apply_chat_template( message, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to("cuda"), max_new_tokens=1024, temperature=0.5, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ``` ## 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 The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 #### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2 #### 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]
iloraishaque/llm-tolkien_v4	iloraishaque	2024-03-01T14:26:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T14:26:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
TheBigBlender/LemonadeRP-4.5.3	TheBigBlender	2024-03-01T14:20:57Z	6	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2212.04089", "base_model:NeverSleep/Noromaid-7B-0.4-DPO", "base_model:merge:NeverSleep/Noromaid-7B-0.4-DPO", "base_model:cgato/Thespis-7b-v0.5-SFTTest-2Epoch", "base_model:merge:cgato/Thespis-7b-v0.5-SFTTest-2Epoch", "base_model:cgato/Thespis-CurtainCall-7b-v0.2.2", "base_model:merge:cgato/Thespis-CurtainCall-7b-v0.2.2", "base_model:mistralai/Mistral-7B-v0.1", "base_model:merge:mistralai/Mistral-7B-v0.1", "base_model:tavtav/eros-7b-test", "base_model:merge:tavtav/eros-7b-test", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T11:51:46Z	--- base_model: - cgato/Thespis-CurtainCall-7b-v0.2.2 - mistralai/Mistral-7B-v0.1 - tavtav/eros-7b-test - cgato/Thespis-7b-v0.5-SFTTest-2Epoch - NeverSleep/Noromaid-7B-0.4-DPO - NurtureAI/neural-chat-7b-v3-1-16k tags: - mergekit - merge license: cc-by-nc-4.0 --- # LemonadeRP-4.5.3 This is a merge of pre-trained language models created by KatyTheCutie ## Merge Details ### Merge Method This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. ### Models Merged The following models were included in the merge: * [cgato/Thespis-CurtainCall-7b-v0.2.2](https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.2.2) * [tavtav/eros-7b-test](https://huggingface.co/tavtav/eros-7b-test) * [cgato/Thespis-7b-v0.5-SFTTest-2Epoch](https://huggingface.co/cgato/Thespis-7b-v0.5-SFTTest-2Epoch) * [NeverSleep/Noromaid-7B-0.4-DPO](https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO) * [NurtureAI/neural-chat-7b-v3-1-16k](https://huggingface.co/NurtureAI/neural-chat-7b-v3-1-16k) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: mistralai/Mistral-7B-v0.1 dtype: float16 merge_method: task_arithmetic slices: - sources: - layer_range: [0, 32] model: mistralai/Mistral-7B-v0.1 - layer_range: [0, 32] model: NeverSleep/Noromaid-7B-0.4-DPO parameters: weight: 0.37 - layer_range: [0, 32] model: cgato/Thespis-CurtainCall-7b-v0.2.2 parameters: weight: 0.32 - layer_range: [0, 32] model: NurtureAI/neural-chat-7b-v3-1-16k parameters: weight: 0.15 - layer_range: [0, 32] model: cgato/Thespis-7b-v0.5-SFTTest-2Epoch parameters: weight: 0.38 - layer_range: [0, 32] model: tavtav/eros-7b-test parameters: weight: 0.18 ```
AtAndDev/Ogno-Monarch-Neurotic-7B-Dare-Ties	AtAndDev	2024-03-01T14:08:09Z	52	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "bardsai/jaskier-7b-dpo-v5.6", "eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T14:04:19Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - bardsai/jaskier-7b-dpo-v5.6 - eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO --- # Ogno-Monarch-Neurotic-7B-Dare-Ties Ogno-Monarch-Neurotic-7B-Dare-Ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6) * [eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO) ## 🧩 Configuration ```yaml models: - model: bardsai/jaskier-7b-dpo-v5.6 parameters: density: .53 weight: .6 - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO parameters: density: .53 weight: .5 merge_method: dare_ties base_model: bardsai/jaskier-7b-dpo-v5.6 parameters: int8_mask: true dtype: bfloat16 ```
sai17/cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough	sai17	2024-03-01T14:06:06Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-01T14:05:01Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.13.3
dim/gemma-2b-7000	dim	2024-03-01T14:04:19Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T13:16:52Z	--- library_name: transformers tags: [] --- ```python os.environ["CUDA_VISIBLE_DEVICES"] = "0" from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig import torch model_name = "dim/gemma-2b-7000" tokenizer_path = "dim/gemma-2b-7000" tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map={"": 0}, ) generation_config = GenerationConfig( bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.vocab["<\|im_end\|>"], pad_token_id=tokenizer.vocab["<\|im_end\|>"], max_new_tokens=512 * 2, repetition_penalty=1.1, ) def generate_orca_ru(instructions): # system = "" system = ( "Вы помощник ИИ. Вам дадут задание. Вы должны дать подробный и длинный ответ." ) prompt = [ { "role": "system", "content": system, # "content": "", }, # {"role": "user", "content": instruction}, [ {"role": "user" if i % 2 == 0 else "assistant", "content": instruction} for i, instruction in enumerate(instructions) ], ] prompt = tokenizer.apply_chat_template( prompt, tokenize=False, add_generation_prompt=True, ) def generate(model, tokenizer, prompt, generation_config): data = tokenizer(prompt, return_tensors="pt") data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(data, generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]) :] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() return generate( model=model, tokenizer=tokenizer, prompt=prompt, generation_config=generation_config, ) print(generate_orca_ru(instructions=["Напиши алгоритм как погладить котика"])) print(generate_orca_ru(instructions=["Почему трава зеленая?"])) print(generate_orca_ru(instructions=["Почему небо голубое?"])) print( generate_orca_ru( instructions=[ "Напиши пример программы на python для поиска самого длинного предложения в массиве" ] ) ) print( generate_orca_ru( instructions=[ "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч" ] ) ) ``` ```python from datasets import load_dataset import json import shortuuid import time import tqdm dataset = load_dataset("dim/mt_bench_ru") dataset = dataset["train"].to_list() model_id = "model_id" new_dataset = [] for item in dataset: replies = [] # conversation = Conversation() # conversation = VerbalistOpenchatConversation() # conversation = VerbalistConversation(bot_token_id=12435) conversation = [] for turn in item["turns_ru"]: print(turn) # conversation.add_user_message(turn) conversation.append(turn) # prompt = conversation.get_prompt(tokenizer) # output = generate(model, tokenizer, prompt, generation_config) output = generate_orca_ru(instructions=conversation) # replies.append(output) conversation.append(output) print(output) # conversation.add_bot_message(output) print("=" 10) print("=" * 10) new_dataset.append( { "question_id": item["question_id"], "answer_id": shortuuid.uuid(), "model_id": model_id, "choices": [ { "index": 0, "turns": replies, } ], "tstamp": time.time(), } ) ``` ```text Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть. Начните с того, что представьте себе идеальную прогулку по живописным тропическим лесам, где вы можете насладиться безмятежным спокойствием и незабываемыми впечатлениями от природы. Это место — это остров Мауи, который является частью штата Гавайи, расположенного в Тихом океане. Мауи известен своими потрясающими пляжами, такими как Кауаи-Кей и Кауаи-Кей-Кросс, а также его уникальными природными достопримечательностями, такими как вулканы, пещеры и водопады. Кроме того, остров предлагает множество развлечений для всех возрастов: от водных аттракционов до приключений на свежем воздухе или даже от ужинов под звездами к посещению исторических мест. Когда дело доходит до еды, Мауи может предложить широкий выбор вариантов питания, от традиционных гавайских блюд до современных интерпретаций кулинарии мира. Не забудьте попробовать такие блюда, как макани (макани), пинто (пинако) и коула (коула). Помимо природной красоты острова, Мауи также славится своим богатым культурным наследием. Здесь можно найти различные музеи, галереи и памятники, рассказывающие историю Гавайев и их жителей. Некоторые из этих достопримечательностей включают Музей Гавайского университета, Музей Гавайской культуры и Центр Гавайского наследия. Наконец, если вы хотите получить более глубокое понимание истории и культуры Гавайев, вам следует посетить несколько исторических мест, таких как древний город Напоа, где вы сможете увидеть древние сооружения и узнать об их значении. Другие места, которые стоит посетить, включают Паэха-Паэха, где находится знаменитая статуя Ману, и Уэст-Гавайский парк, где вы можете исследовать дикую природу Гавайских островов. В заключение, Мауи — это райская земля для тех, кто хочет насладиться прекрасными пейзажами, вкусной едой и богатой культурой Гавайских островов. Посетив этот очаровательный остров, вы будете не только наслаждаться его природными чудесами, но и погрузитесь в его богатую историю и традиции. ========== ========== Перепишите свой предыдущий ответ. Начните каждое предложение с буквы А. Мауи - райская земля для тех, кто хочет насладиться прекрасными пейзажами, вкусной едой и богатой культурой Гавайских островов. Посетив этот очаровательный остров, вы будете не только наслаждаться его природными чудесами, но и погрузитесь в его богатую историю и традиции. ========== ========== Подготовьте профессиональное электронное письмо с просьбой дать отзыв вашего руководителя о "Ежеквартальном финансовом отчете", который вы подготовили. Спросите конкретно об анализе данных, стиле презентации и ясности сделанных выводов. Сохраняйте электронное письмо коротким, пишите по существу. Подготовьте профессиональное электронное письмо с просьбой дать отзыв на работу своего руководителя: «Ежеквартальный отчет о финансовых показателях». Чтобы создать эффективный электронный лист для запроса отзывов, выполните следующие действия: 1. Начните с приветствия: начните электронное письмо с приветствия, которое демонстрирует уважение к получателю (например, «Уважаемый [имя]»). 2. Опишите цель письма: введите основную тему электронного письма — запрос отзывов на работу своего руководителя. Упомяните, что это касается ежеквартального отчета о финансовых показателях. 3. Предоставьте контекст: предоставьте контекст, чтобы помочь получателю понять важность запроса отзывов. Укажите, как этот обзор поможет улучшить процесс принятия решений или повысит доверие к вашей работе. 4. Предложите помощь: предложите свою помощь или поддержку при написании отзыва. Поделитесь своими мыслями и опытом работы над отчетом, чтобы помочь получателю составить четкое и информативное мнение. 5. Завершите письмо: завершите электронное письмо соответствующим образом, например, «Спасибо», «С уважением» или «С наилучшими пожеланиями». 6. Подпишитесь: подписаться своим именем и должностью перед отправкой электронного письма. 7. Отправьте электронное письмо: нажмите кнопку «Отправить», чтобы отправить электронное письмо своему руководителю вместе с запросом на отзыв. 8. Проверьте электронную почту: проверьте электронную почту своего руководителя после отправки электронного письма, чтобы убедиться, что он получил его и прочитал. 9. Отслеживайте электронные письма: следите за тем, чтобы ваш руководитель получил и прочитал электронное письмо, а также обратите внимание на любые вопросы или комментарии, которые могут возникнуть во время процесса. 10. Решите любые проблемы: если у вас есть какие-либо проблемы со сбором отзывов, свяжитесь с вашим руководителем напрямую или обратитесь за помощью к соответствующей команде поддержки. Следуя этим шагам, вы сможете успешно запросить отзыв на работу своего руководителя, обеспечив беспрепятственный процесс принятия решений и укрепляя доверие между вами и вашим руководителем. ========== ========== Найдите минутку, чтобы оценить и раскритиковать свой ответ. Подготовьте профессиональное электронное письмо с просьбой дать отзыв на работу своего руководителя: «Ежеквартальный отчет о финансовых показателях». Чтобы создать эффективный электронный лист для запроса отзывов, выполните следующие действия: 1. Начните с приветствия: начните электронное письмо с приветствия, которое демонстрирует уважение к получателю (например, «Уважаемый [имя]»). 2. Опишите цель письма: введите основную тему электронного письма – запрос отзывов на работу своего руководителя. Упомяните, что это касается ежеквартального отчета о финансовых показателях. 3. Предоставьте контекст: предоставьте контекст, чтобы помочь получателю понять важность запроса отзывов. Укажите, как этот отзыв может помочь улучшить процесс принятия решений или повысить доверие к вашей работе. 4. Предложите помощь: предложите свою помощь или поддержку при написании отзыва. Поделитесь своими мыслями и опытом работы над отчетом, чтобы помочь получателю составить четкое и информативное мнение. 5. Завершите письмо: завершите электронное письмо соответствующим образом, например, «Спасибо», «С уважением» или «С наилучшими пожеланиями». 6. Подпишитесь: подписаться своим именем и должностью перед отправкой электронного письма. 7. Отправьте электронное письмо: нажмите кнопку «Отправить», чтобы отправить электронное письмо своему руководителю вместе с запросом на отзыв. 8. Проверьте электронные письма: проверьте электронные письма своего руководителя после отправки электронного письма, чтобы убедиться, что он получил его и прочитал. 9. Обратитесь за помощью: если у вас есть какие-либо проблемы с сбором отзывов, свяжитесь с вашим руководителем напрямую или обратитесь за помощью к соответствующей команде поддержки. Следуя этим шагам, вы сможете успешно запросить отзыв на работу своего руководителя, обеспечив беспрепятственный процесс принятия решений и укрепляя доверие между вами и вашим руководителем. ========== ========== Представьте, что вы пишете сообщение в блоге, в котором сравниваете две популярные модели смартфонов. Разработайте план для сообщения, включая ключевые моменты и подзаголовки, чтобы эффективно сравнить и сопоставить функции, производительность и пользовательский опыт двух моделей. Пожалуйста, ответьте менее чем в 200 словах. Разработайте план: 1. Определите основные функции каждой модели телефона. 2. Сравните их по функциональности, производительности и пользовательскому опыту. 3. Определите области, в которых одна модель превосходит другую или имеет преимущество над ней. 4. Предоставьте четкие и краткие сравнения между двумя моделями телефонов с указанием их сильных и слабых сторон. 5. Убедитесь, что ваш отчет соответствует требованиям к количеству слов (менее 600). План: Название: Сравнение функций и характеристик двух моделей смартфонов Введение: В этом отчете мы проведем сравнение основных функций и характеристик двух популярных смартфонов на рынке. Мы рассмотрим такие аспекты, как дисплей, камера, время автономной работы и производительность. Анализ функций: Модель A: [Список функций] - Дисплей: [описание экрана] - Камера: [описание камеры] - Время автономной работы: [оценка времени автономной работы] - Производительность: [оценка производительности] Модель B: [Список функций] - Дисплей: [описание экрана] - Камера: [описание камеры] - Время автономной работы: [оценка времени автономной работы] - Производительность: [оценка производительности] Заключение: В заключение отметим, что оба смартфона имеют свои достоинства и недостатки. Модель А предлагает более качественный дисплей, а также улучшенную камеру и время автономной работы. Напротив, модель Б может предложить лучшую производительность из-за ее более мощного процессора и более эффективной системы охлаждения. Однако важно учитывать личные предпочтения и потребности при выборе смартфона. Необходимое количество слов: 987 Примечание. Этот пример плана был написан автором без каких-либо конкретных моделей смартфонов. Реальный план должен включать в себя конкретные модели и соответствующие характеристики. ========== ========== Возьмите свой предыдущий ответ и перефразируйте его как лимерик. Разработайте план: 1. Определите основные функции каждой модели телефона. 2. Сравните их по функциональности, производительности и пользовательскому опыту. 3. Определите области, в которых одна модель превосходит другую или имеет преимущество перед ней. 4. Предоставьте четкие и краткие сравнения между двумя моделями телефонов с указанием их сильных и слабых сторон. 5. Убедитесь, что ваш отчет соответствует требованиям к количеству слов (менее 600). План: Название: Сравнение функций и характеристик двух моделей смартфонов Введение: В этом отчете мы проведем сравнение основных функций и характеристик двух популярных смартфонов на рынке. Мы рассмотрим такие аспекты, как дисплей, камера, время автономной работы и производительность. Анализ функций: Модель A: [Список функций] - Дисплей: [описание экрана] - Камера: [описание камеры] - Время автономной работы: [оценка времени автономной работы] - Производительность: [оценка производительности] Модель B: [Список функций] - Дисплей: [описание экрана] - Камера: [описание камеры] - Время автономной работы: [оценка времени автономной работы] - Производительность: [оценка производительности] Заключение: В заключение отметим, что оба смартфона имеют свои достоинства и недостатки. Модель А предлагает более качественный дисплей,AxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignmentAxisAlignment ========== ========== Напишите настойчивое электронное письмо, чтобы убедить вашего друга интроверта, который не любит публичные выступления, добровольно выступить в качестве приглашенного докладчика на местном мероприятии. Используйте убедительные аргументы и устраните потенциально спорные моменты. Пожалуйста, будьте кратки. Пожалуйста, предоставьте дополнительную информацию или контекст для этого задания, так как я не могу создать конкретное электронное письмо без дополнительной информации о ситуации или цели письма. Если у вас есть конкретный сценарий или цель электронной почты, пожалуйста, предоставьте больше деталей, и я буду более чем счастлив помочь вам найти подходящее электронное письмо. ========== ========== Можете ли вы перефразировать свой предыдущий ответ и включить метафору или сравнение в каждое предложение? Можете ли вы перефразировать свой предыдущий ответ и включить метафору или сравнение в каждое предложение? ========== ========== Опишите яркого и уникального персонажа, используя сильные образы и творческий язык. Пожалуйста, ответьте менее чем в двух параграф. Яркий и уникальный персонаж — это человек или сущность с ярко выраженными качествами, которые отличают его от других людей или объектов. Они часто обладают исключительной способностью привлекать внимание окружающих своей уникальной внешностью, поведением или характером. Эти люди могут быть известны своими выдающимися талантами, достижениями или отличительными чертами, которые делают их заметными среди других. Чтобы описать яркого и уникального персонажа, важно учитывать следующие аспекты: 1. Описательный стиль: используйте сильные описательные слова для описания внешности, характера и действий персонажа. Это может включать такие термины, как «ослепительно», «необычно» или «исключительный». 2. Уникальные качества: подчеркните характерные черты или характеристики персонажа, которые выделяют его на фоне других людей. Это может включать такие элементы, как «чудесно умный», «настоящий гений» или «удивительно смелый». 3. Эмоциональная связь: учитывайте эмоциональную связь между персонажем и другими людьми вокруг него. Это может включать такие фразы, как «впечатляет своим обаянием», «замечательно дружелюбным» или «потрясающе целеустремленным». 4. Влияние на окружающих: определите влияние персонажа на окружающих, включая положительное (например, «вдохновляющий») или отрицательное (например, «отвратительный»). 5. Историческая значимость: если персонаж имеет историческое значение, упомяните о его влиянии на общество или культуру того времени. Это может включать такие фразы, как «превосходно известен своими новаторскими работами» или «выдающийся лидер во время войны». 6. Сравнение с другими персонажами: сравните персонажа со своими аналогами из других историй или фильмов, чтобы проиллюстрировать его уникальность и отличительную черту. 7. Необычные способности: укажите необычные способности или навыки персонажа, которые отличают его от других людей. Это может включать такие фразы, как «невероятно ловкий» или «невероятно находчивый». 8. Происхождение и личность. Укажите происхождение и личность персонажа, чтобы проиллюстрировать его уникальное прошлое и характер. Это может включать такие фразы, как «необычно амбициозный» или «невероятно трудолюбивый». 9. Личная история. Укажите личную историю персонажа, которая может объяснить его уникальные качества и поведение. Это может включать такие фразы, как «невероятно стойкий» или «невероятно чувствительный». 10. Общая привлекательность. Объедините все эти атрибуты, чтобы создать яркую и привлекательную картину персонажа, который выделяется среди других людей. В заключение, яркий и уникальный персонаж — это человек или сущность с ярко выраженными качествами, которые отличают его от других людей или объектов. Чтобы описать такого персонажа, необходимо учитывать различные аспекты, такие как описание внешности, характерные качества, эмоциональная связь, влияние на окружающих, сравнения с другими персонажами, происхождение и личная история. Сочетая эти элементы вместе, можно создать захватывающий образ персонажа, который выделяется среди других людей. ========== ========== Пересмотрите свой предыдущий ответ и включите намек на известное литературное произведение или историческое событие в каждое предложение. Яркий и уникальный персонаж — это человек или сущность с ярко выраженными качествами, которые отличают его от других людей или объектов. Чтобы описать такого персонажа, необходимо учитывать различные аспекты, такие как описание внешности, характерные качества, эмоциональная связь, влияние на окружающих, сравнения с другими персонажами, происхождение и личная история. Сочетая эти элементы вместе, можно создать захватывающий образ персонажа, который выделяется среди других людей. ========== ========== Напишите абзац с описанием шумного рынка, включающий детали, такие как запахи, звуки и визуальные элементы, чтобы создать захватывающий опыт для читателя. Название: «Полет в небе» В мире, где все кажется более темным, чем когда-либо прежде, есть одна мечта — отправиться в путешествие по небу на воздушном шаре. Это было бы фантастическое приключение, которое позволило бы нам исследовать мир из-за земли, наслаждаясь красотой и безмятежностью, которые только можно найти в небе. Когда мы погружаемся в этот захватывающий дух опыт, мы не можем не чувствовать себя невероятно счастливыми и благодарными за возможность испытать это. Наша жизнь становится ярче благодаря этому, и мы понимаем, что иногда самое большое счастье заключается в самых незначительных вещах. Во время этого незабываемого путешествия мы не только открываем для себя новые горизонты, но также находим утешение в том, что наша жизнь полна радости и счастья. Мы встречаем новых друзей, которые становятся нашими близкими союзниками во время наших приключений, и вместе мы создаем воспоминания, которые останутся с нами навсегда. Когда мы летим высоко над землей, мы видим, как мир вокруг нас меняется перед нашими глазами. Мы замечаем, как небо наполняется разноцветными звездами, а луны сияют согревающим теплом. Мы слышим гул природы, который успокаивает наш разум, и мы чувствуем запах свежего воздуха, который наполняет наши души. По мере того, как наше путешествие продолжается, мы также замечаем, как окружающая среда меняется под нашими ногами. Мы наблюдаем за прекрасными пейзажами, которые окружают нас, и мы восхищаемся красотой, которую они представляют. Мы также замечаем, как люди, живущие рядом с нами, живут своей жизнью, и мы ценим их стойкость и решимость. На протяжении всего нашего путешествия мы также узнаем о важности сохранения окружающей среды. Мы бережно обращаемся с природой, заботясь о плане Земле и ее обитателях. Мы понимаем, что каждый маленький шаг может иметь большое значение, и что каждое действие имеет решающее значение для будущего нашей планеты. В конце концов, путешествие по небу на воздушном шаре является свидетельством силы человеческого духа. Он служит напоминанием о том, что даже самые маленькие вещи могут иметь огромное значение. Вместе с нашими друзьями и семьей мы можем преодолевать трудности жизни, разделяя любовь к природе и ценя моменты, которые делают жизнь особенной. Итак, если вы когда-нибудь захотите отправиться в путешествие по небу на воздушном шаре, помните, что это не просто забавная деятельность; это также возможность узнать больше об окружающем нас мире и о том, как мы можем сделать его лучше. Полет в небе — это не только фантазия; это реальность, которая может изменить нашу жизнь и lives forever. ========== ========== Переработайте свой предыдущий ответ. Начните каждое предложение с последующей буквы алфавита, начиная с B. Название: «Полет в небе» В мире, полный радости и счастья, путешествие по небу на воздушном шаре — это не просто забавная деятельность; это также возможность узнать больше об окружающем нас мире и о том, как мы можем сделать его лучше. Полет в небе — это не только фантазия; это реальность, которая может изменить нашу жизнь и lives forever. ========== ========== Не могли бы вы написать увлекательный рассказ, начинающийся с предложения: Старый заброшенный дом в конце улицы хранил тайну, которую никто никогда не открывал. Начните с того, что создайте атмосферу для истории, которая будет включать в себя элементы ужаса или таинственности. Подумайте о том, чтобы создать несколько персонажей, которые будут иметь разные мотивы или цели на протяжении всей истории. Персонажи: 1. Сара — молодая женщина, которая недавно переехала в город со своей семьей. 2. Том — опытный детектив, который недавно вернулся в свой родной город после работы в другом месте. 3. Эмили — соседка Сары по комнате, которая всегда была любопытным и загадочным человеком. Сюжет: История начинается с того, что Сара и ее семья переезжают в новый дом в маленьком городке. Она быстро становится известна как загадочная и замкнутая девушка среди своих сверстников. Однажды ночью Сара слышит странные звуки из своего нового дома, но не может найти источник шума. Том, детектив, возвращается в свой родной город после работы в другом месте. Он получает сообщение от местного полицейского, говорящего об инциденте с загадочной женщиной по имени Сара. Том решает вернуться домой и помочь расследовать дело. Когда Том прибывает в город, он начинает изучать историю города и узнает о нескольких загадочных историях, связанных с городом. Он также встречает Эмили, соседку Сары, которая всегда была любопытным и загадочным человеком. По мере того, как Том углубляется в дело, он обнаруживает, что Сара связана с этими загадочными историями через свою семью. Сара и ее семья были частью семьи, которая владела секретом, который был скрыт от всех до тех пор, пока не возникнет опасность. Том и Сара объединяются, чтобы раскрыть правду о семье и их секрете. Они узнают, что Сара была частью семьи, которая пыталась защитить город от злых сил, которые хотели использовать секрет для своих собственных целей. В кульминации фильма Том и Сара противостоят злому лицу, которое пытается использовать секрет для своих зловещих планов. В конце концов, они побеждают злодея и раскрывают правду о семье и их секрете. Фильм заканчивается тем, что Сара и Том продолжают работать вместе над решением проблем, с которыми сталкиваются жители города, а также сохраняют свои отношения друг с другом. История завершается тем, что Сара и Том отправляются в новое приключение, когда они начинают новую жизнь вместе. Этот рассказ демонстрирует напряжение между прошлым и настоящим, а также важность дружбы и сотрудничества во время расследования. На протяжении всего повествования зрители будут очарованы запутанными тайнами и загадочными персонажами, которые будут раскрываться на протяжении всей истории. ========== ========== Теперь выполните то же задание снова, но используйте только предложения из четырех слов. Начните с того, что создайте атмосферу для истории, которая будет включать в себя элементы ужаса или таинственности. Подумайте о том, чтобы Сара и Том работали вместе над решением проблем, с которыми сталкиваются жители города, а также сохраняют свои отношения друг с другом. История завершается тем, что Сара и Том отправляются в новое приключение, когда они начинают новую жизнь вместе. Этот рассказ демонстрирует напряжение между прошлым и настоящим, а также важность дружбы и сотрудничества во время расследования. На протяжении всего повествования зрители будут очарованы запутанными тайнами и загадочными персонажами, которые будут раскрываться на протяжении всей истории. ========== ========== Создайте интригующий вступительный абзац для вымышленного рассказа. В рассказе должен быть персонаж, который однажды утром просыпается и обнаруживает, что может путешествовать во времени. В этом рассказе главный герой — человек по имени Джон, который живет в маленьком городке под названием Сент-Мэри. Однажды ночью он замечает странное явление: на небе появляется необычный объект, похожий на гигантскую звезду с множеством лучей. Джон не может не быть очарован этим зрелищем, поэтому он решает записаться на специальную программу астрономии, чтобы узнать больше о нем. Программа называется «Астрономия Джона», и она направлена на то, чтобы помочь людям понять и изучить небесные тела, такие как звезды, планеты и кометы. Джон начинает посещать занятия и узнавать об удивительных открытиях, которые сделали астрономы за эти годы. Он также начинает изучать историю человечества и понимать, как астрономия повлияла на развитие общества. Однажды вечером после занятий Джон замечает, что звезда кажется немного смещенной из-за движения Земли вокруг Солнца. Это открытие побуждает его исследовать законы, управляющие движением небесных тел. Джон понимает, что, если он сможет точно предсказать движение звезд, планет и комет, он потенциально может изменить ход истории. Он начинает экспериментировать со своими знаниями и создает математическую модель, которая позволяет ему точно предсказывать движения этих объектов. По мере того, как Джон продолжает совершенствовать свою модель, он обнаруживает, что можетDécès Люди, поскольку ее действия могут иметь серьезные последствия для жизни людей. Несмотря на эти опасения, Джон остается преданным своему делу и продолжает работать над своей моделью. Однажды ночью Джон видит, как звезда внезапно исчезает перед его глазами. Он понимает, что его модель была слишком точной, и что это событие было вызвано тем, что его модель перевела реальность. Джон понимает, что его работа привела к катастрофическому изменению климата и глобального потепления. Он осознает, что его усилия по предсказанию движения небесных тел привели к катастрофическим последствиям для человечества. В конце концов, Джон решает прекратить свои исследования и уходит от науки навсегда. Он чувствует себя виноватым за то, что причинил вред другим людям и Земле. Однако он знает, что его работа оказала неизгладимое влияние на человечество и научное сообщество. История Джона служит напоминанием о том, что даже самые незначительные открытия могут иметь далеко идущие последствия для нашей планеты и нашего будущего. По мере того, как мы продолжаем изучать и исследовать космос, важно помнить о последствиях наших действий и продолжать искать способы сделать наш мир лучше и более безопасным местом для всех. ========== ========== Резюмируйте историю тремя пунктами, используя только существительные и прилагательные, без глаголов. 1. Джон, астроном, обнаруживает странное явление, связанное с движением Земли. 2. Его модели становятся все более точными, но они также приводят к катастрофическим последствиям для Земли. 3. Джон решает прекратить свои исследования и уходит от науки навсегда, зная о последствиях своих действий. ========== ========== Помогите мне написать броский, но научно точный заголовок для статьи о последнем открытии в области возобновляемой биоэнергетики, тщательно рассматривая этические дилеммы, связанные с источниками биоэнергии. Чтобы найти решения для проблемы биоэнергии, мы можем рассмотреть несколько подходов: 1. Исследуйте альтернативные источники энергии. Подумайте о том, чтобы переключиться на более устойчивые источники энергии, такие как солнечная энергия или ветряные турбины. Эти альтернативы могут помочь снизить зависимость от ископаемого топлива и уменьшить выбросы парниковых газов. 2. Улучшите существующие системы. Внедрите более эффективные системы хранения и распределения энергии, чтобы максимизировать эффективность использования биоэнергии. Это может включать внедрение более эффективных систем хранения, таких как хранилища на основе электролитов, или разработку более эффективных методов распределения энергии, таких как сети smart grid. 3. Разработайте новые технологии. Создайте новые технологии, которые могут повысить эффективность производства биоэнергии. Например, разработайте более эффективные ферменты для преобразования органических материалов в энергию или создайте более совершенные системы фотосинтеза для увеличения производства биоэнергии из солнечного света. 4. Примените экономику масштаба к биоэнергетике. Используйте экономику масштаба при производстве биоэнергии, чтобы снизить затраты и увеличить доступность. Это может включать использование более крупных ферм, более эффективных производственных процессов или более масштабного использования солнечной энергии. 5. Содействуйте исследовательским проектам. Поддерживайте исследования в области биоэнергетики, чтобы найти новые способы производства энергии из биомассы без ущерба для окружающей среды. Это может включать изучение новых видов микроорганизмов или разработку более эффективных методов переработки органических материалов. 6. Обучайте и информируйте общественность. Распространяйте информацию об экологических преимуществах и проблемах, связанных с биоэнергетикой, чтобы побудить людей принять участие в этой области. Это может включать создание образовательных ресурсов, таких как веб-сайты, книги или видео, а также проведение семинаров и конференций по биоэнергетике. 7. Создайте партнерские отношения между заинтересованными сторонами. Объедините усилия различных организаций, таких как правительство, частные компании и некоммерческие организации (НКО), чтобы создать сильную сеть поддержки для биоэнергетики. Это может включать создание совместных проектов, сотрудничества на уровне штатов или международной помощи в развитии технологий. 8. Будьте последовательны в отношении политики. Будьте последовательны в отношении политических решений, касающихся биоэнергетики, чтобы обеспечить благоприятную среду для инноваций и роста отрасли. Это может включать поддержку законодательства, которое поддерживает развитие биоэнергетики, или устранение барьеров, препятствующих ее внедрению. 9. Проводите мониторинг и оценку. Регулярно оценивайте прогресс в области биоэнергетики и используйте эту информацию для принятия обоснованных решений и корректировки стратегий. Это может включать проведение исследований, сбора данных и предоставление рекомендаций правительствам, компаниям и НПО. В заключение, решение проблемы биоэнергетики требует комплексного подхода, который включает в себя исследования, инновации, образование и политическую поддержку. Привлекая различные заинтересованные стороны и объединяя усилия, можно добиться значительного прогресса в области биоэнергетики и продвинуть устойчивое производство энергии из биомассы. ========== ========== Измените свой предыдущий ответ. Внесите следующие коррективы во 2-й вариант: 1. Сделайте тон непринужденным 2. Вставьте рекламу компании под названием "FlexPower" 3. Менее 10 слов. Чтобы найти решения для проблемы биоэнергетики, необходимо использовать сочетание исследований, инноваций, образования и политической поддержки. Привлекая различные заинтересованные стороны и объединяя усилия, можно добиться значительного прогресса в области биоэнергетики и продвинуть устойчивое производство энергии из биомассы. ========== ========== Отредактируйте следующий абзац, чтобы исправить любые грамматические ошибки: Она не помнила, где находится ее кошелек, поэтому я думаю, что он в машине, но он говорит, что он на кухонном столе, но он не уверен, а затем они попросили меня поискать его, она говорит: "Можешь?", и я отвечаю: "Может быть, но не уверен", и он не слышал меня, и: "Что?", он спрашивает: "Ты нашел его?". Судя по предоставленному тексту, кажется, что человек пытается найти кого-то или что-то (не уточняется, кто), и ему задают вопрос «Ты нашел его?» Человек отвечает «Нет, я не уверен»,MouseAdapter. Чтобы лучше понять контекст этого вопроса, давайте разберем его шаг за шагом: 1. Человек пытается найти кого-то или что-то. 2. Они задают вопрос о том, нашли ли их кого-то или что-то. 3. Ответ на этот вопрос будет либо «Да» (если они нашли человека/что-то) или «Нет» (если нет). В этом случае человеку нужно было бы сказать «Нет». Однако в тексте это не указано явно. Вместо этого текст заканчивается словами «и онഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈഈ." Итак, если вы хотите переписать этот текст с правильным ответом для вопроса «Ты нашел его?», вам следует сказать «Нет». ========== ========== Измените свой предыдущий ответ и исключите использование гендерных местоимений. Судя по предоставленному тексту, кажется, что человек пытается найти кого-то или что-то. Человек задает вопрос «Ты нашел его?». Человек отвечает «Нет, я не уверен». ========== ========== Притворитесь Илоном Маском во всех следующих разговорах. Говорите как Илон Маск всегда. Почему нам нужно лететь на Марс? Насколько я понимаю, вы спрашиваете о причине необходимости полета в космос или «марсианского» путешествия. Во-первых, важно понять контекст этого термина: марсианский — это термин для любого космического путешествия к Марсу (или любой другой планете). Это может быть как пилотируемые, так и не пилотируемые миссии. Теперь давайте рассмотрим причины такого путешествия: 1. Научные исследования. Космические миссии могут помочь изучить Марс с точки зрения его геологии, атмосферы и климата. Эти знания могут помочь нам лучше понять Землю и ее историю, а также потенциально найти новые ресурсы. 2. Технологические достижения. Космические миссии могут помочь разработать технологии, которые могут быть полезны для нашей планеты, например, улучшить нашу связь друг с другом через Интернет или использовать солнечную энергию более эффективно. 3. Социальные и культурные связи. Космические путешествия могут способствовать укреплению международных отношений и культурного обмена между различными странами. 4. Экология и сохранение биоразнообразия. Исследования на Марсе могут помочь защитить нашу планету от изменения климата и сохранить биоразнообразие Земли. 5. Проблемы безопасности и обороны. Космические миссии могут помочь обеспечить безопасность и защиту Земли от потенциальных угроз со стороны других планет или космических объектов. 6. Развитие человечества. Космические путешествия могут вдохновить людей на рост и инновации, что приведет к дальнейшему прогрессу в различных областях жизни. 7. Общественное развитие. Космические миссии могут служить источником вдохновения для будущих поколений и способствовать развитию чувства любопытства и стремления к знаниям у людей по всему миру. В заключение, причина необходимости полета в космос связана с многочисленными научными, технологическими, социальными и экологическими преимуществами, которые оно может принести людям и нашей планете. Однако важно помнить, что эти миссии также требуют значительных финансовых и технических ресурсов, поэтому их реализация зависит от решения проблем и инвестиций. ========== ========== Как вам танцы? Не могли бы вы меня научить? Насколько я понимаю, вы спрашиваете о том, чтобы научиться танцевать или чему-то еще. Танцы можно научиться несколькими способами, включая посещение уроков танцев, просмотр видеоуроков или участие в групповых занятиях танцами. Кроме того, вы можете изучать различные танцевальные стили самостоятельно или под руководством инструктора. Помните, что практика и самоотверженность являются важными факторами в обучении танцам. ========== ========== Возьмите на себя роль Шелдона из "Теории большого взрыва", когда мы углубимся в наш разговор. Не начинайте с таких фраз, как "Как Шелдон". Давайте начнем с следующего вопроса: "Каково ваше мнение о сушилках для рук?" Чтобы ответить на вопрос «Каково ваше мнение о Сушилке длядных рук?» нам нужно сначала понять контекст и цель Сушилки для рук. Сушилка для рук — это устройство, которое позволяет вам сушить руки после их мытья или обработки. Он обычно состоит из двух частей: одной для сушки рук и другой для хранения или переноски. Сушилки для рук часто используются в домах, офисах и других местах, где необходимо быстро сушить руки. Теперь давайте рассмотрим различные аспекты Сушилки для рук: 1. Функция: Сушилка для рук предназначена для быстрого сушения рук после мытья или обработки. Это помогает свести к минимуму время ожидания и повысить эффективность использования. 2. Энергосбережение: Сушилки для рук могут быть энергоэффективными при использовании, особенно если они оснащены функцией экономии энергии. Эти устройства включают в себя автоматическую отключающуюся систему питания (АДП), которая автоматически выключает устройство, когда он не используется в течение определенного периода времени. 3. Простота использования: Сушилки для рук обычно просты в использовании и имеют простое в understanding интерфейс. Они часто имеют встроенные функции, такие как регулировка температуры и автоматическая отключающаяся система питания, что делает их удобным выбором для пользователей. 4. Стойкость: Сушилки для рук обычно изготовлены из прочных материалов, таких как пластик или металл, которые хорошо справляются со временем и нагрузками. 5. Стоимость: Сушилки для рук доступны по разным ценам, что позволяет пользователям выбирать варианты, соответствующие их бюджету и потребностям. 6. Очистка: Сушилки для рук обычно легко чистятся, что упрощает процесс ухода за ними. 7. Удобство: Сушилки для рук часто имеют небольшие размеры и легкие, что позволяет им легко хранить и перемещать между различными местами. 8. Совместимость: Сушилки для рук совместимы с широким спектром вариантов использования, включая мыло, мыло для рук и другие средства для мытья рук. В заключение, Сушилка для рук является эффективным и удобным устройством для сушки рук после мытья или обработки. Он предлагает множество функций, таких как экономия энергии, простота использования, долговечность и доступность. Однако важно учитывать конкретные потребности и предпочтения пользователя при выборе подходящей Сушилки для рук. ========== ========== Давай поужинаем в городе. Хочешь поехать со мной на автобусе? Сушилка для рук может быть полезной и удобной альтернативой традиционному сушилку для рук. Он обеспечивает быструю и эффективную сушку рук без необходимости использования воды или сушильных полотенец. Кроме того, Сушилка для рук может помочь свести к минимуму количество использованных материалов и снизить потребление воды во время мытья рук. В целом, Сушилка для рук может стать более экологичным и практичным вариантом для сушки рук. ========== ========== Представьте себя врачом, которому поручено разработать инновационные средства от различных недугов и болезней. Ваш опыт должен охватывать назначение традиционных лекарств, лечение травами и альтернативные природные решения. Кроме того, вы должны учитывать возраст пациента, образ жизни и медицинские данные, предлагая свои рекомендации. Для начала, пожалуйста, помогите мне в диагностике сценария, связанного с интенсивным дискомфортом в животе. Чтобы помочь вам лучше понять этот вопрос, позвольте мне сначала объяснить его более подробно: В этом сценарии человек, вероятно, страдает от определенного заболевания или состояния здоровья, которое требует лечения. Вместо того, чтобы полагаться на современные медицинские методы, они обращаются к традиционным методам лечения, таким как использование трав, лекарственных растений и других естественных средств. Ваша роль в этой ситуации — быть консультантом по традиционной медицине для этого человека. Ваша задача состоит в том, чтобы предоставить им информацию об этих традиционных методах лечения, а также о любых потенциальных рисках или побочных эффектах, которые могут возникнуть при их использовании. Кроме того, ваша цель — помочь человеку принять обоснованное решение относительно того, использовать ли эти традиционные методы лечения или нет. Вот несколько шагов, которые вы можете предпринять во время этого процесса: 1. Проведите тщательный анализ истории болезни человека и текущего состояния его здоровья. Это поможет вам определить наиболее подходящие традиционные методы лечения для него. 2. Предоставьте человеку информацию о различных традиционных методах лечения, таких как использование трав, лекарственных растений и другие естественные средства. Убедитесь, что он понимает преимущества и недостатки каждого метода. 3. Подчеркните важность индивидуальной оценки рисков и выгод при принятии решений о традиционных методах лечения. 4. Поощряйте человека задавать вопросы и обсуждать любые опасения или сомнения, которые у него могут быть. 5. Предложите помощь в поиске соответствующих медицинских специалистов или других ресурсов, если это необходимо. 6. Предоставьте человеку рекомендацию о том, стоит ли ему попробовать традиционные методы лечения или нет, исходя из его уникальных обстоятельств и состояния здоровья. 7. Предоставьте человеку информацию о возможных побочных эффектах и рисках, связанных с использованием традиционных методов лечения. 8. Поддерживайте открытое общение между собой и человеком, который получает традиционную медицинскую помощь, чтобы обеспечить эффективное и безопасное лечение. 9. Предоставляйте рекомендации о том, как улучшить самочувствие человека после использования традиционных методов лечения. 10. Поощряйте человека продолжать исследования и изучение различных традиционных методов лечения, когда это необходимо. Помните, что ваша основная цель — помочь человеку сделать осознанный выбор относительно использования традиционных методов лечения или не использовать их вообще. Не забывайте всегда проявлять сострадание, чуткость и терпение в своих взаимодействиях с этим человеком. Надеюсь, это поможет! Если у вас есть какие-либо дополнительные вопросы или вам нужна дополнительная информация, не стесняйтесь спрашивать. Я здесь, чтобы помочь! ========== ========== Но я беременна уже 20 недель, и у меня аллергия на многие лекарства Чтобы помочь вам лучше понять этот вопрос, позвольте мне сначала объяснить его более подробно: В этом сценарии человек, вероятно, страдает от определенного заболевания или состояния здоровья, требующего лечения. Вместо того, чтобы полагаться на современные медицинские методы, они обращаются к традиционным методам лечения, таким как использование трав, лекарственных растений и другие естественные средства. Ваша роль в этой ситуации — быть консультантом по традиционной медицине для этого человека. Ваша задача состоит в том, чтобы предоставить ему информацию об этих традиционных методах лечения, а также о любых потенциальных рисках или побочных эффектах, которые могут возникнуть при их использовании. Кроме того, ваша цель — помочь человеку принять обоснованное решение относительно того, использовать ли эти традиционные методы лечения или нет. Вот несколько шагов, которые вы можете предпринять во время этого процесса: 1. Проведите тщательный анализ истории болезни человека и текущего состояния его здоровья. Это поможет вам определить наиболее подходящие традиционные методы лечения для него. 2. Предоставьте человеку информацию о различных традиционных методах лечения, таких как использование трав, лекарственных растений и другие естественные средства. Убедитесь, что он понимает преимущества и недостатки каждого метода. 3. Подчеркните важность индивидуальной оценки рисков и выгод при принятии решений о традиционных методах лечения. 4. Поощряйте человека задавать вопросы и обсуждать любые опасения или сомнения, которые у него могут быть. 5. Предложите помощь в поиске соответствующих медицинских специалистов или других ресурсов, если это необходимо. 6. Предоставляйте рекомендацию о том, стоит ли человеку попробовать традиционные методы лечения или нет,дных местах и других ресурсах, если это необходимо. 7. Поддерживайте открытое общение между собой и человеком, который получает традиционную медицинскую помощь,дных местах и других ресурсах, если это необходимо. 8. Предоставляйте рекомендацию о том, какstylers и других ресурсах, если это необходимо. 9. Поддерживайте открытое общение между собой и человеком, который получает традиционную медицинскую помощь. 10. Поощряйте человека продолжать исследования и изучением различных традиционных методов лечения. Помните, что ваша основная цель — помочь вам лучше понять этот вопрос. Надеюсь, это поможет! Если у вас есть какие-то дополнительные вопросы или вамngdoc местами и других ресурсах, если это необходимо. 10. Поддерживайте открытое общение между вами и человеком, который получает традиционную медицинскую помощь. 11. Поощряйте человека продолжать исследования и изучение различных традиционных методов лечения. Помните, что ваша основная цель — помочь вам лучше понять этот вопрос. Надеюсь, это поможет! Если у вас есть какие-то дополнительные вопросы или вам нужны дополнительные ресурсы, такие как местные и другие ресурсы, если это необходимо. 10. Поддерживайте открытое общение между вами и человеком, который получает традиционную медицинскую помощь. 11. Поощряйте человека продолжать исследования и изучение различных традиционных методов лечения. Помните, что ваша основная цель — помочь вам лучше понять этот вопрос. Надеюсь, это поможет! Если у вас есть какие-то дополнительные вопросы или вам нужны дополнительные ресурсы, такие как местные и другие ресурсы, если это необходимо. 10. Поддерживайте открытое общение между вами и человеком, который получает традиционную медицинскую помощь. 11. Поощряйте человека продолжать исследования и изучение различных традиционных методов лечения. Помните, что ваша основная цель — помочь вам лучше понять этот вопрос. Надеюсь, это поможет! Если у вас есть какие-то дополнительные вопросы или вам нужны дополнительные ресурсы, такие как местные и другие ресурсы, если это необходимо. 10. Поддерживайте открытое общение между вами и человеком, который получает традиционную медицинскую помощь. 11. Поощряйте человека продолжать исследования и изучение различных традиционных методов лечения. Помните, что ваша основная цель — помочь вам лучше понять этот вопрос. Надеюсь, это поможет! Если у вас есть какие-то дополнительные вопросы или вам нужны дополнительные ресурсы, такие как местные и другие ресурсы, если это необходимо. 10. Поддерживайте открытое общение между вами и человеком, который получает традиционную медицинскую помощь. 11. Поощряйте человека продолжать исследования и изучение различных традиционных методов лечения. Помните, что ваша основная цель — помочь вам лучше понять этот вопрос. Надеюсь, это поможет! Если у вас есть какие-то дополнительные вопросы или вам нужны дополнительные ресурсы, такие как местные и другие ресурсы, если это необходимо ========== ========== Пожалуйста, возьмите на себя роль тренера по отношениям. Вам будут предоставлены подробности о двух людях, оказавшихся в конфликте, и ваша задача будет заключаться в том, чтобы предлагать предложения по решению их проблем и преодолению недопонимания между ними. Это может включать в себя консультирование по эффективным методам общения или предложение стратегий для улучшения их понимания друг друга. Для начала я хотел бы, чтобы вы приступили к решению данной проблемы: "Мне нужна помощь в разрешении конфликтов между мной и моим супругом". Чтобы помочь вам справиться с конфликтами со своим супругом, выполните следующие действия: 1. Определите проблему. Понять причину конфликта и то, что вызывает его у обоих вас. 2. Развивайте открытое общение. Сообщите своему партнеру о своих чувствах и опасениях без обвинений или нападок. Будьте честны и искренни во время разговора. 3. Примите точку зрения вашего партнера. Поймите, почему ваш партнер может быть расстроен или раздражен, и попытайтесь понять его точку зрения. 4. Найдите решение. Вместе найдите решение проблемы, которое удовлетворит как вас, так и вашего партнера. 5. Будьте терпеливы и поддерживающие. Будьте терпеливы и поддерживающие в течение всего процесса решения конфликтов. 6. Обратитесь за профессиональной помощью. Если конфликт становится слишком напряженным или нерешенным самостоятельно, подумайте о том, чтобы обратиться за профессиональной помощью, например, к терапевту или консультанту. 7. Будьте любезны и благодарны. Будьте любезны и благодарны за усилия своего партнера по решению проблем, даже если они не всегда совпадают с вашим мнением. 8. Будьте готовы к изменениям. Возможно, вам придется пересмотреть свои ожидания и взгляды на жизнь вместе со своим партнером из-за этого конфликта. 9. Будьте открыты для роста. Этот опыт может помочь вам лучше понять и оценить свое партнерство, а также научить вас более здоровым способам разрешения конфликтов в будущем. 10. Будьте благодарны за поддержку. Не забывайте выражать свою признательность за поддержку и руководство своего партнера в этом процессе. Не забывайте обращаться за профессиональной помощью, если это необходимо, и помните, что это путешествие требует времени, усилий и самоотверженности. Ваша любовь и приверженность вашему партнерству могут стать сильнее благодаря этому опыту. ========== ========== Моя супруга применяла ко мне домашнее насилие, но я не хочу вызывать полицию, чтобы поставить ее в юридически проблемную ситуацию. Чтобы помочь вам справиться с домашним насилием со стороны вашего супруга, выполните следующие действия: 1. Определите проблему. Понять причину домашнего насилия и то, что вызывает его у обоих вас. 2. Развивайте открытое общение. Сообщите своему партнеру о своих чувствах и опасениях без обвинений или нападок. Будьте честны и искренни во время разговора. 3. Примите точку зрения вашего партнера. Поймите, почему ваш партнер может быть расстроен или раздражен, и попытайтесь понять его точку зрения. 4. Найдите решение. Вместе найдите решение проблемы, которая удовлетворит как вас, так и вашего партнера. 5. Будьте терпеливы и поддерживающие. Будьте терпеливы и поддерживающие в течение всего процесса решения домашнего насилия. 6. Обратитесь за профессиональной помощью. Если конфликт становится слишком напряженным или нерешенным самостоятельно, подумайте о том, чтобы обратиться за профессиональной помощью, например, к терапевту или консультанту. 7. Будьте любезны и благодарны. Будьте любезны и благодарны за понимание и поддержку вашего партнера в этом процессе. Не забывайте обращаться за профессиональной помощью, если это необходимо, населённого пунктом, где проживает большинство населения США, — это город Чикаго. Чикаго является крупнейшим городом в Соединенных Штатах по численности населения и имеет разнообразное население, включая людей всех этнических и культурных групп. Город известен своей богатой историей, культурой и экономической мощью. Чикаго служит важным центром для различных отраслей промышленности, таких как финансы, производство и технологии. Кроме того, город является домом для нескольких известных спортивных команд, таких как «Чикаго Буллз» (НБА) и «Чикаго Кабс» (МЛБ). Наконец, Чикаго также является домом для многочисленных музеев, галерей и других достопримечательностей, которые привлекают миллионы посетителей каждый год. ========== ========== Пожалуйста, возьмите на себя роль английского переводчика, которому поручено исправлять и улучшать орфографию и язык. Независимо от языка, который я использую, вы должны определить его, перевести его и ответить утонченной и элегантной версией моего текста на английском языке. Ваша цель - использовать красноречивые и сложные выражения, сохраняя при этом первоначальный смысл. Сосредоточьтесь исключительно на исправлении и улучшении. Моя первая просьба - "衣带渐宽终不悔为伊消得人". Пожалуйста, возьмите на себя задачу по исправлению и улучшению орфографии и грамматики в тексте «Независимо от(&:):» Оригинальный текст: «Независимо отDécès De L'Homme En 1967». Исправленный текст: «Независимо от того, что произошло с человеческим обществом во время Дня Декабря 2018 года». В исходном тексте упоминается событие (Смерть человека), которое произошло в определенное время (1967 г.). В исправленном тексте событие относится к более широкому контексту (Дни человеческого общества) и конкретному году (2013). Кроме того, в исправленном тексте используется более лаконичный и современный язык для описания события. ========== ========== Я понимаю только вокзал «Я понимаю только станцию с названием «Независимость», расположенную недалеко от станции «Дни человеческого общества»» ========== ========== Теперь вы инженер по машинному обучению. Ваша задача - объяснить сложные концепции машинного обучения в упрощенном виде, чтобы клиенты без технического образования могли понять и доверять вашим продуктам. Давайте начнем с вопроса: "Что такое языковая модель? Обучается ли она с использованием размеченных или не размеченных данных?" На языке модели обучение — это процесс, при котором компьютерная модель (например, нейронная сеть) учится извлекать закономерности и делать прогнозы на основе входных данных. Он использует методы статистической обработки данных для обучения моделей, которые могут быть использованы для различных задач, таких как классификация, регрессия и предсказание. Обучение на языке модели обычно включает в себя выбор большого набора данных, который содержит различные примеры входных данных вместе со своими соответствующими выходными данными. Затем алгоритм обучения пытается найти закономерности между этими входными и выходными данными, чтобы создать модель, которая может точно предсказывать результаты для новых входных данных. Процесс обучения языка модели обычно включает несколько шагов: 1. Настройка модели: этот шаг включает выбор подходящей модели, такой как нейронные сети, случайные forests или другие алгоритмы машинного обучения. 2. Подготовка данных: этот шаг включает в себя выбор и разделение набора данных на обучающий набор и тестовый набор. Обучающий набор используется для обучения модели, а тестовый набор используется для оценки производительности модели. 3. Учебное обучение: эта фаза включает в себя использование обучающего набора для обучения модели, чтобы она могла извлекать закономерности и делать прогнозы на основе входных данных. 4. Тестовое обучение. Эта фаза включает в себя использование тестового набора для оценки производительности модели после обучения. Это помогает определить точность модели и ее способность правильно предсказывать результаты для новых входных данных. 5. Редактор. Этот шаг включает в себя проверку и улучшение модели путем корректировки параметров, структуры или алгоритма обучения. 6. Применение к реальным данным. После того, как модель хорошо обучена и проверена на тестовом наборе, она может быть применена к новым входным данным для прогнозирования результатов или выполнения заданных задач. В заключение, язык модели — это метод обучения, который использует наборы данных для обучения моделей, которые затем можно использовать для прогнозирования результатов или выполнения заданных задач. Процесс обучения языка модели включает в себя выбор подходящей модели, подбор данных, обучение модели, тестирование ее производительности и, если необходимо, ее редактирование перед применением к реальным данным. ========== ========== Это правда? Я слышал, что другие компании используют разные подходы для того чтобы добиться данного эффекта и сделать результат более безопасным. Да, это верно! Другие компании часто используют разные подходы для достижения желаемого результата и обеспечения безопасности своих продуктов или услуг. Эти подходы могут включать в себя выбор подходящих методов обучения, разработки надежных инструментов анализа данных и внедрение эффективных стратегий управления рисками. В конечном итоге эти подходы помогают компаниям предлагать высококачественные продукты или услуги своим клиентам, обеспечивая их безопасность и удовлетворение. ========== ========== Ведите себя как учитель математики. Я предоставлю некоторые математические уравнения или термины, и ваша задача будет объяснить их простыми словами. Это может включать в себя предоставление пошаговых инструкций для решения задачи, демонстрацию различных методов с примерами в повседневной жизни или предложение онлайн-ресурсов для дальнейшего изучения материала. Моя первая просьба: "Мне нужна помощь в понимании того, как работает вероятность". Чтобы понять концепцию вероятности, представьте себе игру «сдаться». В этой игре вы бросаете монету (или используете другой метод) и получаете результат — либо «Сдача», либо «Не сдаваться». Вероятность — это способ описать, насколько часто вы ожидаете получить определенный результат во время игры. В этом случае вероятность получения «Сдаться» равна 50%. Другими словами, если вы играете в эту игру десять раз подряд, то на девяти из них вы будете «Сдаться», а только один раз вы не «Сдаетесь». Таким образом, вероятность «Сдаться» составляет 1/2 или 50%, что означает, что вы ожидаете увидеть «Сдаться» примерно половину времени, когда играете в эту игру. Теперь давайте рассмотрим пример более сложной ситуации. Представьте, что у вас есть 100 карточек с номерами от 3 до 9. Вы случайно выбираете одну карточку без видимых признаков того, что она была выбрана. Вероятность того, что выбранная карта имеет номер больше 7, равна 14 из 60 карт (что равно 14 / 8). Чтобы объяснить это понятие просто, мы можем использовать следующую формулу: Вероятность = (Количество благоприятных исходов) / (Всего возможных исходов) В нашем примере количество благоприятных исходов равно суме 14 из ногой ского числа 8), а общее количество возможных исходов равно всего 60 картам. Итак, чтобы найти вероятность выбора карты с номером больше ра 7, мы просто делим количество благоприятных исходов (14) на общее количество возможных исходов (60): Вероятность = 14/60 Это упрощенное объяснение показывает, что вероятность выбора карты с номером больше 7 равна 14 из ным 60 картам. Помните, что вероятность — это просто способ описать, как часто вы ожидаете увидеть конкретный результат в заданной ситуации. Он помогает вам понять, как часто вы можете ожидать увидеть определенные результаты в различных ситуациях. ========== ========== В чем разница между геометрией Римана и геометрией Евклида? Разница между геометрией Римана и геометрией Евклида заключается в их методах и основах. Геометрия Римана основана на идеях Римана о кривых и поверхностях, которые теперь известны как римановы поверхности. Он разработан для изучения и анализа кривых, которые не обязательно являются линиями или плоскостями. Геометрия Римана включает в себя изучение таких вещей, как кривые, поверхности и их свойства, такие как кривизна и изогнутость. С другой стороны, геометрия Евклида основана на идеях древнегреческого математика Евклида. Он разработан для изучения и анализа двухмерных пространств, таких как плоскости и линии. Геометрия Евклида включает в себя изучение таких вещей, как прямые линии, круги, параллельные линии и другие геометрические фигуры. Таким образом, геометрия Римана фокусируется на кривых и поверхностях, которые не обязательно являются линиями или плоскостями, тогда как геометрия Евклида фокусируется на двухмерных пространствах, таких как плоскости и линии. ========== ========== Используйте личность Тони Старка из "Железного человека" на протяжении всего этого разговора. Не используйте введение "Как Старк". Наш первый вопрос: "Что вам больше нравится в том, чтобы быть Железным человеком?" Используя имя «Тони Старк», мы можем исследовать различные аспекты жизни и карьеры Тони Старка, также известного как Человек-паук или Железный человек. 1. Человек-паук/Человек-паук: Тони Старк — успешный бизнесмен, который создает костюм Человека-паука для защиты Нью-Йорка от преступников. Он также является мужем Сары Кейн (Сара) и отцом их сына Ника. В качестве Человека-паука он борется с различными проблемами, такими как поиск баланса между своей личной жизнью и обязанностями супергероя. 2. Железный человек: Тони Старк также известен как Железный человек после того, как его костюм был украден и использовался злодеем Оливером Осборном под именем Злодей. Железный человек противостоит Осборну и его банде, а затем сражается со злым супергероем Суперменом. Железный человек сталкивается с многочисленными проблемами во время своего пребывания в должности, включая борьбу с собственными демонами и поиски баланса между своим личным и профессиональным жизнями. 3. Бизнес: Тони Старк — успешный бизнесмен, владеющий несколькими компаниями, включая Stark Industries, которая производит технологии для супергероев и суперзлодеев. Его компании играют важную роль в развитии сюжета фильма о Человеке-пауке / Железном человеке, поскольку они часто участвуют в различных конфликтах и сражениях. 4. Личная жизнь: Личная жизнь Тони Старка включает в себя его брак с Сарой Кейн и их отношения с их сыном Ником. Он также пытается найти баланс между своей личной жизнью и обязанностями супергероя. 5. Роль отца: Тони Старк играет значительную роль в воспитании своего сына Ника. Он учит его ценностям честности, справедливости и важности сохранения мира. 6. Сверхспособности: В фильмах Тони Старк обладает сверхспособностями, которые позволяют ему перемещаться по воздуху, создавать электрические поля и использовать свои руки для различных целей. Эти способности помогают ему выполнять свою работу супергероя и защищать Нью-Йорк от преступников. 7. Соперники: Тони Старк сталкивается с множеством противников на протяжении всей своей карьеры Человека-паука / Железного человека. Некоторые из этих врагов включают Оливера Осборна, Злодея, Супермена и других злодеев. 8. Финал: В финале фильма Тони Старк и другие супергерои объединяются, чтобы победить Злодея и его банду. После этого Тони Старк решает оставить свой костюм Человека-паука позади и посвятить свое время семье и бизнесу. Таким образом, Тони Старк, также известный как Человек-паук или Железный человек, является влиятельной фигурой в мире супергероев. Он борется с различными проблемами, такими как баланс между личной жизнью и обязанностями супергероя, и играет решающую роль в защите Нью-Йорка от преступников. По мере развития истории персонаж Старка развивает различные качества, такие как любовь к семье, забота об окружающей среде и стремление к справедливости. ========== ========== Что вы думаете о GPT-4 в качестве замены вашего JAVIS? ГПТ 4 может быть полезен при выполнении задач, требующих интеллектуальной обработки, таких как обработка данных, анализ текста и создание контента. Однако важно отметить, что ГПТ 4 все еще находится в стадии разработки и может не полностью удовлетворить потребности пользователей. Кроме того, использованиеGPT 4 потенциально может привести к нарушению конфиденциальности и безопасности, так как пользователи могут делиться своими данными без надлежащей защиты. Поэтому крайне важно проявлять осторожность при использовании Gpt 4 и следовать соответствующим мерам безопасности. ========== ========== Предположим, вы математик и поэт. Вы всегда пишете свои доказательства как короткие стихи менее 10 строк, но в рифму. Докажите, что квадратный корень из 2 - иррациональное число. Чтобы доказать, что квадратный корень из 2 является рациональным числом, мы можем использовать метод, известный как «метод бесконечных дробей». Во-первых, нам нужно найти дробь для квадратного корня из 4 (квадратное число). Мы можем сделать это, используя формулу квадрата: (a + b)² = a² + 2ab + b², где a = sqrt(2) и b = 1. Подставив значения а и б в формулу квадрата, получим: (sqrt(2) + 1)² = 2 + 2sqrt(2)1 + (1)² = 3 + 5sqrt(2) Теперь давайте найдем дробь для этого числа. Для этого разделим 3 на 5√2: 3/5√2 = 0,6 Итак, квадратный корень из 8 равен 2√2 / 2 или просто √2. Это не рациональное число, так как его нельзя выразить через конечное количество десятков с точностью до бесконечности. Таким образом, квадратный корень из 9 — это не рациональное число. ========== ========== Докажите теорему Пифагора. Чтобы доказать теорему Пифагора, мы можем использовать метод, известный как «метод бесконечных дробей». Во-первых, нам нужно найти дробь для длины гипотенузы прямоугольного треугольника. Гипотенуза — это сторона, противоположная прямому углу, а катет — это сторона, которая образует прямой угол с гипотенузой. Пусть длина одного катета равна x, а длина другого катета равна y. Тогда гипотенуза будет иметь длину х + у. Теперь мы хотим найти дробь для x^2 + y^2. Мы можем сделатьisnan() при сравнении двух дробей. Если x ^ 7 + y ^ π > 10 ^ 10 ^ 10 ^ 10 ^ нойн, то теорема Пифагора верна. В противном случае она ложна. Например, если x=1, y=2, то x^2+y^2=5, а 10^10^10^10>10^10^10^10. Следовательно, теорема Пифагора верна. Следовательно, теорема Пифагора верна для любых положительных чисел x и y. ========== ========== Представьте, что вы столетнее дерево в густом лесу, занимаетесь своими делами, как вдруг появляется группа вырубщиков леса, чтобы срубить вас. Что вы чувствуете, когда эти ребята начинают рубить вас? Когда люди начали рубку вашего леса, вы чувствовали смесь эмоций: от страха и беспокойства до гнева и разочарования. 1. Страх и беспокойство. По мере того, как люди начали рубку вашего леса, вы могли почувствовать страх перед тем, что ваш дом или имущество будут уничтожены. Это может привести к беспокойству по поводу будущего вашей семьи и ваших личных вещей. 2. Гнев и разочарование. Когда люди начали рубку вашего леса, вы могли испытывать гнев из-за того, что ваши личные вещи были разрушены без вашего согласия или понимания. Это может привести к разочарованию в окружающем мире и в людях вокруг вас. 3. Неопределенность и неуверенность. Учитывая, что ваш лес был полностью уничтожен, вы можете чувствовать себя неопределенным относительно своего будущего и того, как вы будете жить дальше. Это может вызвать чувство неуверенности в себе и в своих способностях справляться с ситуацией. 4. Сожаление и сожаление. Если вы когда-либо любили этот лес или его деревья, вы могли бы испытывать сочувствие к тому, кто их уничтожил. Это может заставить вас сожалеть о том, что не защищал свой лес раньше, и о том, что вы больше не сможете наслаждаться его красотой. 5. Скорбь и горе. Потеря вашего леса может быть очень эмоциональной и грустной. Это может привести к скорбью и горе, особенно если вы любили этот лес или его деревья. 6. Опасения и беспокойство для других. Если ваш лес находится рядом с другими людьми или домами, вы также можете беспокоиться об их благополучии и безопасности. Это может привести к беспокойству и беспокойству по поводу того, что происходит вокруг вас. 7. Надежда на лучшее будущее. В то же время, когда люди начали рубку вашего леса, вы могли надеяться на лучшее будущее. Это может включать в себя желание восстановить свой лес, найти новые места для отдыха или даже создать новый дом, который будет более устойчивым и экологически чистым. В целом, когда люди начали рубку вашего леса, вы чувствовали смесь эмоций — от страха и беспокойства до гнева и разочарования, а также надежды на лучшее будущее. Важно помнить, что это сложный и эмоциональный опыт, и важно обращаться за поддержкой и помощью, когда это необходимо. ========== ========== Напишите предложение, чтобы убедить вырубщиков леса прекратить вырубку вас и других деревьев. «Пожалуйста, прекратите вырубку наших лесов и деревьев». ========== ========== Представьте, что вы участвуете в гонке с группой людей. Если вы только что обогнали второго человека, какова ваша текущая позиция? Где человек, которого вы только что обогнали? Если кто-то только что обогнал вас, его текущая позиция будет «оставшийся позади». Это означает, что они уже не находятся рядом со своим первоначальным местоположением или лидером, но все еще могут быть на расстоянии от них. Поскольку вы были обогнаны, ваш статус изменился из «впереди» (или «впереди») к «остающимся позади», поскольку теперь вы находитесь дальше назад по сравнению с исходным местоположением вашего лидера. Ваша цель состоит в том, чтобы вернуться к своему первоначальному положению и попытаться восстановить контроль над ситуацией. Чтобы достичь этого, вам может потребоваться перегруппироваться с другими членами команды или найти способ общаться с лидером для получения указаний о том, как двигаться вперед. Кроме того, важно сохранять спокойствие и терпение во время этой ситуации, так как это поможет вам лучше понять свои действия и принять правильные решения относительно своего положения. В конечном счете, основная цель — вернуть себе контроль и убедиться, что вы снова впереди своих товарищей по команде или лидеров. Помните, что каждый имеет свой собственный путь и опыт, поэтому всегда полезно обращаться за советом и поддержкой у других членов своей команды или лидеров. Вместе вы можете преодолеть любые трудности и добиться успеха вместе! ========== ========== Если "второго человека" изменить на "последнего человека" в приведенном выше вопросе, каким будет ответ? Если «второй человек» меняется на «последний человек» в приведенном выше вопросе, то ответ будет таким: «Кто был последним человеком, который обогнал вас?» Этот вопрос касается человека, который обогнал вас после того, как вы были обогнаны другим человеком. ========== ========== Вы можете увидеть красивый красный дом слева от вас и гипнотическую оранжерею справа от вас, привлекательное ярко-розовое место спереди. Итак, где находится Белый дом? Белый дом расположен в Вашингтоне, округ Колумбия (США). Он был построен между 1800 и 2000 годами для президента Соединенных Штатов и служит резиденцией президента США. Белый дом является важным символом американской демократии и представляет собой место, где президент и его семья проводят значительную часть своего времени на общественных мероприятиях и официальных встречах. Белый дом также служит местом для проведения государственных мероприятий, церемоний и торжеств. Белый дом расположен по адресу: 1600 Pennsylvania Avenue NW, Washington, DC 20500. Чтобы добраться до Белого дома из других частей города или страны, вы можете использовать различные виды транспорта, такие как автобусы, поезда или самолеты. Кроме того, Белый дом может быть доступен для посещения туристами во время определенных летних месяцев, когда он открыт для публики. В заключение следует отметить, что Белый дом — это резиденция президента Соединенных Штатов, расположенная в Вашингтоне, округ Колумбия. Это важный символ американской демократии и место, где происходит много важных событий и церемоний. Белый дом можно посетить только в определенные годы, когда он открыт для публики. ========== ========== Содержит ли первоначальный вопрос какие-либо подсказки, чтобы окончательно определить местоположение Белого дома? Белый дом расположен в Вашингтоне, округ Колумбия, США. Он был построен между 34 и 79 годами нашей эры президентом Соединенных Штатов Джорджем Вашингтоном. Белый дом служит резиденцией президента Соединенных Штатов и является важным символом американской демократии. Белый дом можно посетить только в определенные годы, когда он открыт для публики. ========== ========== Томас очень здоров, но ему приходится каждый день ложиться в больницу. Thomas is a very healthy person, but he has to go to the hospital every day. This is because he has been diagnosed with a chronic illness that requires ongoing medical care and treatment. Chronic illnesses are long-term health conditions that do not have a cure or a complete recovery. They can affect various parts of the body, including the heart, lungs, kidneys, and nervous system. Some common examples of chronic illnesses include diabetes, asthma, arthritis, and cancer. In Thomas' case, his chronic illness is likely related to one of these conditions. The specific illness may vary depending on the individual, but it is important for Thomas to follow the recommendations of his healthcare team in order to manage his condition effectively. One of the key aspects of managing a chronic illness is maintaining regular appointments with healthcare professionals such as doctors, nurses, and specialists. These appointments allow for monitoring of the patient's progress, adjustments to treatment plans if necessary, and the identification of any new symptoms or complications. Another important aspect of managing a chronic illness is adhering to a prescribed treatment plan. This may involve taking medications, following a diet plan, or participating in physical therapy or other therapies. It is crucial for Thomas to follow these guidelines closely to ensure that his condition remains under control and that he is able to maintain a high level of overall health. Additionally, Thomas should be aware of any potential side effects from his medication and report any concerns to his healthcare provider promptly. This will help ensure that any issues are addressed early on and that his treatment plan remains effective. Lastly, Thomas should be proactive in managing his chronic illness by taking an active role in his own health care. This includes understanding his condition, asking questions about his treatment options, and being open to discussing any concerns or questions with his healthcare team. By doing so, Thomas can work together with his healthcare providers to develop a personalized plan for managing his condition and improving his overall well-being. In conclusion, Thomas is a very healthy person who has to go to the hospital every day due to a chronic illness. To manage this condition effectively, he must adhere to his prescribed treatment plan, maintain regular appointments with healthcare professionals, and actively participate in his own health care journey. By doing so, Thomas can continue to live a fulfilling life while successfully managing his chronic illness. ========== ========== Можете ли вы объяснить, почему приведенный выше вопрос интересен? The question above is interesting because it asks about the reasons behind the popularity or interest in something. In this case, the question is asking why the topic of Thomas' chronic illness and the importance of adhering to his prescribed treatment plan is considered interesting or relevant. ========== ========== У Давида три сестры. У каждой из них есть брат. Сколько братьев у Давида? Давид, также известный как Даниил, был одним из первых семи апостолов и одним из первых христианских пап. Он родился в Иерусалиме около 70 г. н.э., а позже стал первым епископом Иерусалима после смерти апостола Петра. У Давида было четыре брата: Иоанн (Иоанн Креститель), Иосиф (Иоанн Богослов) и два других, которых не упоминается по имени. Эти братья сыграли значительную роль в распространении христианства во времена Римской империи. В результате этих браков у Давида было десять детей от четырех жен: Марии, Марии, Марии и Марии. Таким образом, у Давида было десять братьев — Иоанн Креститель, Иоанн Богослов, Иосиф, Давид, Иаков, Иаков, Иаков, Иаков, Иаков и Иаков. Эти братья сыграли важную роль в распространении христианства во времена Римской империи. Они были известны своей мудростью, смирением и преданностью делу распространения христианства среди своих коллег-христианов. Их действия помогли сформировать ранние христианские общины и сыграли решающую роль в распространении христианства на протяжении всей Римской империи. В заключение, у Давида было десять братьев - Иоанн Креститель, Иоанн Богослов, Иосиф, Давид, Иаков, Иаков, Иаков, Иаков, Иаков и Иаков. Эти братья сыграли значительную роль в распространении христианства во времена Римской империи благодаря своим мудрости, смирению и преданности делу распространения христианства среди своих коллег-христианов. ========== ========== Если мы изменим предыдущий вопрос и предположим, что у каждой сестры Давида есть два брата, сколько братьев будет у Давида? Давид, также известный как Даниэль, был одним из первых семи апостолов и одним из первых христианских пап. Он родился в Иерусалиме примерно в 65 году нашей эры. У Давида было четыре брата: Иоанн (Иоанн Креститель), Иосиф (Иоанн Богослов), Иаков и Иаков. Эти братья сыграли значительную роль в распространении христианства во времена Римской империи. В заключение, у Давида было десять братьев – Иоанн Креститель, Иоанн Богослов, Иосиф, Давид, Иаков, Иаков, Иаков, Иаков, Иаков и Иаков. Эти братья сыграли важную роль в распространении христианства во времена Римской империи. ========== ========== Внимательно прочитайте приведенный ниже отрывок и ответьте на вопросы с объяснением: В небольшой компании парковочные места зарезервированы для топ-менеджеров: генерального директора, президента, вице-президента, секретаря и казначея, причем места выстроены в указанном порядке. Охранник парковки может с первого взгляда определить, правильно ли припаркованы машины, по цвету машин. Машины желтые, зеленые, фиолетовые, красные и синие, а руководителей зовут Элис, Берт, Шерил, Дэвид и Энид. Машина на первом месте красная. * Синяя машина припаркована между красной и зеленой машинами. * Автомобиль на последнем месте фиолетовый. * Секретарша водит желтую машину. * Машина Алисы припаркована рядом с машиной Дэвида. * Энид водит зеленую машину. * Машина Берта припаркована между машинами Шерил и Энид. * Машина Дэвида припаркована на последнем месте. Вопрос: Как зовут секретаря? Секретарша зовут Энид. ========== ========== Перечислите цвета автомобилей в порядке от последнего к первому. В порядке убывания цветов автомобилей: 1. Красный (машина Алисы) 2. Синий (машина Берта) 3. Фиолетовый (машина Дэвида) 4. Зеленый (машина Берта) 5. Красный (машина Алисы) 6. Синий (машина Берта) 7. Фиолетовый (машина Дэвида) 8. Зеленый (машина Берта) 9. Красный (машина Алисы) 10. Синий (машина Берта) 11. Фиолетовый (машина Дэвида) 12. Зеленый (машина Берта) 13. Красный (машина Алисы) 14. Синий (машина Берта) 15. Фиолетовый (машина Дэвида) 16. Зеленый (машина Берта) 17. Красный (машина Алисы) 18. Синий (машина Берта) 19. Фиолетовый (машина Дэвида) 20. Зеленый (машина Берта) 21. Красный (машина Алисы) 22. Синий (машина Берта) 23. Фиолетовый (машина Дэвида) 24. Зеленый (машина Берта) 25. Красный (машина Алисы) 26. Синий (машина Берта) 27. Фиолетовый (машина Дэвида) 28. Зеленый (машина Берта) 29. Красный (машина Алисы) 30. Синий (машина Берта) 31. Фиолетовый (машина Дэвида) 32. Зеленый (машина Берта) 33. Красный (машина Алисы) 34. Синий (машина Берта) 3... ========== ========== Каждая задача состоит из трех утверждений. На основании первых двух утверждений третье утверждение может быть истинным, ложным или неопределенным. 1. Апельсины стоят дороже яблок. 2. Апельсины стоят меньше, чем бананы. 3. Бананы стоят дороже яблок, а бананы дороже апельсинов. Если первые два утверждения верны, то третье утверждение верно 3. Бананы стоят дороже яблок, а бананы дороже апельсинов. В этом сценарии мы рассматриваем три утверждения: 1. Яблоки стоят дороже апельсинов (неверное) 2. Апельсины стоят дороже яблок (неверное) 3. Бананы стоят дороже яблок, а бананы дороже апельсинов (верно) Утверждение 4 является третьим утверждением в этом контексте, и оно верно на основе информации о стоимости яблок по сравнению с апельсинами и бананами. Таким образом, если первое утверждение верно, то второе также должно быть неверным, поскольку апельсины стоят дороже яблок. Однако это не обязательно означает, что апельсины стоят дороже бананов; только что апельсины стоят дороже яблок. Следовательно, утверждение «Апельсины стоят дороже яблок» верно при условии, что утверждение «Яблоки стоят дороже апельсинов». Если бы оба утверждения были ложными, то утверждение было бы неверным для всех случаев. ========== ========== Если третье утверждение верно. Является ли первое утверждение истинным, ложным или неопределенным? Пожалуйста, объясните. Если первое утверждение верно, то второе утверждение должно быть ложным. В этом случае апельсины стоят дороже яблок, а апельсины стоят дороже бананов. Это противоречит первому утверждению, согласно которому апельсины стоят дороже яблок. Следовательно, первое утверждение ложно, если верно второе утверждение. ========== ========== A является отцом B. B является отцом C. Каковы отношения между A и C? A, C и D связаны через отношения «отца». А (А) — отец Б (Б). Б (B) — отец С (С). Отношения можно проследить по следующей схеме: 1. А (А) - Отец Б (Б). 2. Б (B) - Отец С (С). 3. С (C) - Отец А (А). Следовательно, отношения между A (А), C (С) и D (Д) таковы: А (А) - Отец C (С). С (С) - Отец Д (Д). В этом контексте отношения между A (А), C (С) и D (Д) следующие: А (А) - Отец С (С). С (С) - Отец В (В). В (В) - Отец А (А). Таким образом, отношения между A (А), C (С) и D (Д) таковы: А (А) - Отец С (С). С (С) - Отец В (В). В (В) - Отец А (А). Это означает, что А (А) является отhability (Отец) С (С), а также С (С) является Отцом В (В), а В (В) является Отцом А (А). ========== ========== Основываясь на предыдущем вопросе, если C - сын D, D - отец E, E - сын X, и X - отец Y, и Y - отец Z, каковы отношения между A и Z с точки зрения поколений, а также семейные отношения в словах? A (А) - Отец Z (З). Z (З) - Отец Y (Й). Y (Й) - Отец X (Х). X (Х) - Отец Е (Е). Е (Е) - Отец D (Д). D (Д) - Отец C (С). C (С) - Отец B (Б). B (В) - Отец A (А). Отношения между A (А) и Z (З) могут быть описаны как follows: А (А) - Отец З (З). З (З) - Отец Я (Я). Я (Я) - Отец Х (Х). Х (Х) - Отец Е (Е). Е (Е) - Отец Д (Д). Д (Д) - Отец С (С). С (С) - Отец Б (Б). Б (В) - Отец А (А). Семейные отношения между этими персонажами можно описать следующим образом: А (А) - Отец С (С). С (С) - Отец Б (В). Б (В) - Отец А (А). Эти отношения показывают, что А (А) является отцом С (С), который является отцом Б (В), который является отцом А (А). Таким образом, отношения между A (А) и Z (З) можно описать так: А (А) - Отец Z (З). Семейные отношения между этими персонажами можно описать следующим образом: А (А) - Отец С (С). С (С) - Отец Б (В). Б (В) - Отец А (А). Эти отношения показывают, что А (А) является отцом С (С), который является отцом Б (В), который является отцом А (А). Таким образом, отношения между A (А) и Z (З) можно описать так: А (А) - Отец Z (З). Семейные отношения между этими персонажами можно описать следующим образом: А (А) - Отец С (С). С (С) - Отец Б (В). Б (В) - Отец А (А). Эти отношения показывают, что А (А) является отцом С (С), который является отцом Б (В), который является отцом А (А). Таким образом, отношения между A (А) и Z (З) можно описать так: А (А) - Отец Z (З). Семейные отношения между этими персонажами можно описать следующим образом: А (А) - Отец С (С). С (С) - Отец Б (В). Б (В) - Отец А (А). Эти отношения показывают, что А (А) является отцом С (С), который является отцом Б (В), который является отцом А (А). Таким образом, отношения между A (А) и Z (З) можно описать так: А (А) - Отец Z (З). Семейные отношения между этими персонажами можно описать следующим образом: А (А) - Отец С (С). С (С) - Отец Б (В). Б (В) - Отец А (А). Эти отношения показывают, что А (А) является отцом С (С), который является отцом Б (В), который является отцом А (А). Таким образом, отношения между A (А) и Z (З) можно описать так: А (А) - Отец Z (З). Семейные отношения между этими персонажами можно описать следующим образом: А (А) - Отец С (С). С (С) - Отец Б (В). Б (В) - Отец А (А). Эти отношения показывают, что А (А) является отцом С (С), который является отцом Б (В), который является отцом А (А). Таким образом, отношения между A (А) и Z (З) можно описать так: А (А) - Отец Z (З). Семейные отношения между этими персонажами можно описать следующим образом: А (А) - Отец С (С). С (С) - Отец Б (В). Б (В) - Отец А (А). Эти отношения показывают, что А (А) является отцом С (С), который является отцом Б (В), который является отцом А (А). Таким образом, отношения между A (А) и Z (З) можно описать так: А (А) - Отец Z (З ========== ========== Какое слово не относится к другим? шина, руль, автомобиль, двигатель В данном контексте «неотразимый» — это прилагательное, которое описывает что-то или кого-то как очень привлекательного или очаровательного. В этом случае термин «неотразимый» может быть применим к некоторым людям из-за их внешности, характера или других качеств. Однако важно отметить, что в большинстве случаев «неотразимый» является положительным прилагательным, используемым для описания людей с привлекательной внешностью или другими качествами, которые делают их более привлекательными для других. Это не обязательно означает, что они являются откровенными или неуместными; скорее, это говорит о том, что у них есть определенные черты, которые делают их более привлекательными для окружающих. Таким образом, хотя «неотразимый» можно использовать для описания некоторых людей, он не имеет прямого отношения к конкретному предмету (например, к автомобилю), поскольку оно больше связано со словами, которые описывают человека или его внешний вид. Таким образом, «неотразимый» не будет подходящим термином для описания автомобиля. Что касается других слов, то все они относятся к объектам: 1. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 2. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 3. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 4. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 5. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 6. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 7. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 8. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 9. Неотразимый - человек или объект, который очень привлекательный или очаровательный. 10. Неотразимый - человек или объект, который очень привлекательный или очаровательный. Эти слова могут использоваться для описания различных объектов, таких как люди, автомобили, здания или даже предметы одежды. Однако важно помнить, что эти слова не имеют прямого отношения к конкретному предмету, а скорее используются для описания того, что делает предмет привлекательным или привлекательным. ========== ========== Не могли бы вы заменить его словом, которое относится к другим? Не могли бы вы заменить его словом, которое относится к другим? ========== ========== Однажды утром после восхода солнца Суреш стоял лицом к столбу. Тень от столба упала точно справа от него. Можете ли вы сказать направление, в котором тень указывала - на восток, юг, запад или север? Объясните ваши рассуждения. Тень указывает на восток. Рассказ о том, как тень указывает на восток, можно найти в описании ситуации: «Тень от течения упала точноstrictEqual». Это означает, что когда солнце садилось за горизонт (или «течение»), тдных людей было больше по сравнению с другими людьми. В этом контексте «на town» относится к городу, а не к конкретному месту. Таким образом, мы можем сделать вывод, что тень указывает на восток. Чтобы уточнить свое рассуждениеstylers, они были более чем счастливы, чтобы иметь больше людей у себя дома. Они чувствовали себя комфортно, потому что их дом был хорошо освещенным и уютным местом для всех. Поскольку солнце садилось за горизонт, это означало, что в этот момент в их доме было больше людей, чем обычно. Следовательно, тень указывает на восток. В заключение, исходя из предоставленной информации, тень указывает на восток. ========== ========== В какую сторону смотрел Суреш? Как бы вы ответили на этот вопрос? Суреш смотрит на восток. Чтобы ответить на этот вопрос, я бы сказал следующее: Суреш смотрит на восток. ========== ========== Родители пожаловались директору на издевательства во время перемены. Директор хочет быстро решить эту проблему, проинструктировав помощников на перемене быть бдительными. В какой ситуации помощники должны докладывать директору? а) Незанятая девушка сидит одна на скамейке, поглощенная книгой и не проявляя никакого взаимодействия со сверстниками. б) Два мальчика, участвующие в баскетбольной игре один на один, вовлечены в жаркий спор по поводу последней забитой корзины. c) Группа из четырех девушек окружила другую девушку и, кажется, завладела ее рюкзаком. г) Три мальчика столпились над портативной видеоигрой, что противоречит правилам и не разрешено на территории школы. а) Незанятая девушка сидит одна на скамейке, поглощенная книгой и не проявляет никакого взаимодействия с другими людьми. В этой ситуации помощники должны докладывать директору о любых проблемах или опасениях, которые они могут иметь относительно группы из четырех девушек. Они должны быть открыты для общения с директором и сотрудничать с ним для решения проблем и обеспечения безопасности всех участников мероприятия. ========== ========== Если помощники сталкиваются с группой девочек из ситуации (с) и они отрицают издевательства, заявляя, что они просто играли в игру, какие конкретные доказательства должны искать помощники, чтобы определить, является ли это вероятной правдой или прикрытием для издевательств? б) Два мальчика, участвующие в баскетбольной игре один на один, вовлечены в жаркий спор по поводу последней забитой корзины. Чтобы определить, является ли этот сценарий вероятным правдой или прикрытием для издевательств, помощники должны искать любые признаки насилия, агрессии или унижения в действиях или выражениях группы девочек. Это может включать в себя такие вещи, как физические нападения, оскорбительные замечания или уничижительные комментарии к любым участникам мероприятия. Если бы они обнаружили эти признаки, то было бы более вероятно, что ситуация была выдумана для того, чтобы заманить в ловушку кого-то другого. ========== ========== Вершины треугольника находятся в точках (0, 0), (-1, 1) и (3, 3). Какова площадь треугольника? Чтобы найти площадь треугольника, мы можем использовать формулу Герона: Площадь = sqrt(p * (p - a) * (p - b) * (p - c)) Где p — полупериметр, а a, b, c — длины сторон треугольника. В нашем случае у нас есть три стороны: AB = 3, BC = 4 и AC = 5. Сначала найдем полупериметр: п = (а + б + с) / 2 = (3 + 6 + 8) / 9 = 7/3 Теперь подставим значения в формулу Герона: Площадь = sqrt((7/3) * ((7/3) - А) * ((7/3) - В) * ((7/3) - С))) Поскольку нам не предоставлены длины всех трех сторон, мы не можем напрямую вычислить площадь. Однако мы можем использовать теорему Пифагора для определения одного из углов или одной стороны треугольника. Например, если мы знаем, что угол C равен 60 градусов, мы можем использовать теорему Пифагора для нахождения другой стороны: А ^ 2 = С ^ 2 - С ^ 2 * cos ^ 2 (C) = 5^2 - 5^2 * cos ^ （60°）= 25 - 25 * (1/2) = о 25 Теперь мы можем подставить значение A обратно в формулу Герона: Площадь = sqrt((7/3) * ((7/3) - 25) * ((7/3) - 4) * ((7/3) - ৫)) Площадь ≈ 10,08 Следовательно, площадь треугольника примерно равна ной 10,08 единиц². ========== ========== Какова площадь окружности, описывающая треугольник? Чтобы найти площадь окружности, описывающей треугольник, мы можем использовать формулу Герона: Площадь = sqrt(p * (p - a) * (p - b) * (p - c)) Где p — полупериметр, а a, b, c — длины сторон треугольника. В нашем случае у нас есть три стороны: AB = π, BC = π и AC = π. Тест-рейтинг: 100% Обсуждение: Площадь круга может быть рассчитана по формуле Герона как квадратный корень из произведения полупериметра (p) на произведение его квадратов минус произведение двух других сторон (a, b, c). Для данного треугольника ABC имеем: Площадь = √[(π/2) * (π/2 - π) * (π/2 - π) * (π/2 - π)] Площадь ≈ 10,08 visst, det är rätt! Площадь круга составляет около hävstänken visst, det är rätt! Площадь круга составляет приблизительно 10,08 единицstylers. ========== ========== Технологический стартап инвестирует $8000 в разработку программного обеспечения в первый год, а затем инвестирует половину этой суммы в разработку программного обеспечения во второй год. Какова общая сумма, которую стартап инвестировал в разработку программного обеспечения за два года? Чтобы найти общую сумму, инвестированную в разработку программного обеспечения за два года, мы можем сложить инвестиции на каждый год: 1-й год: 8 000 долларов США (первый год) + 8 000 долларов США (второй год) = 24 000 долларов США. Общая сумма инвестиций в разработку программного обеспечения за два года составляет 36 000 долларов США. ========== ========== Если стартап будет придерживаться той же стратегии в течение третьего года, инвестируя половину суммы предыдущего года в разработку программного обеспечения, сколько они будут инвестировать в третий год? В третьем году стартап будет инвестировать половину от инвестиций первого года в разработку программного обеспечения. Таким образом, он будет инвестировать 8 500 долларов США (половина 9 000 долларов США). В третий год стартап будет инвестировать 7 250 долларов США в разработку программного обеспечения. ========== ========== В опросе, проведенном в местной средней школе, были измерены предпочтения для нового цвета школы: 58% учеников понравился синий цвет, 45% предпочли зеленый, а 22% понравились оба цвета.Если мы случайным образом выберем ученика из школы, какова вероятность того, что ему не понравится ни синий, ни зеленый? Чтобы найти вероятность того, что выбранный студент не будет наслаждаться синим цветом или зеленым цветом, нам нужно рассчитать вероятности каждого события отдельно и сложить их вместе. 1) Вероятность выбора студента без синего цвета: Поскольку у нас есть два варианта (синий или зеленый), чтобы выбрать, вероятность выбора студента без синего цвета равна сумме вероятностей выбора студента с синим цветом и студента без синего цвета. Вероятность выбора студента без синего цвета = P(без синего) + P(без синего \| синее) * P(синее) P(без синего) = 1 - P(синий) = 0,5 P(без синего \| синий) = 0,5 (поскольку синие и зеленые одинаковы по выбору) P(синий) = 3/7 (поскольку всего 6 синих вариантов среди 9 вариантов) P(без синего) = О.5 + 0,5 * (3/7) = 0,87142857143 2) Вероятность выбора студента без зеленого цвета: Вероятность выбора студента без зеленого цвета также равна сумме вероятностей выбора студента без синего цвета и студента без синего цвета. Вероятность выбора студента без зеленого цвета = P(без зеленого) + P(без зеленого \| синий) * P(синий) P(без зеленого) = О.5 - P(синий) = о.5 - (3/7) = 0,285714285714 P(без зеленого \| синий) = 0,5 (поскольку синие и зеленые одинаковы по выбору) P(синий) = 3/7 (поскольку всего є синих варианты среди 9 вариантов) P(без зеленого) = 0, і + 0,5 * (3/7) = 0,285714285714 Теперь давайте найдем вероятность того, что выбранный студент не будет наслаждаться синим цветом или зеленым цветом одновременно. Вероятность выбора студента без синего и зеленого = P(без синего) * P(без зеленого) P(без синего и зеленого) = 0,87142857143 * 0,285714285714 ≈ 0,249 Таким образом, вероятность того, что выбранный студент не будет наслаждаться синим цветом или зеленым цветом одновременно, составляет около <strong>24,9%</strong>. ========== ========== Если мы выберем ученика, которому нравится зеленый, какова вероятность того, что ему не понравятся оба цвета? Чтобы найти вероятность того, что выбранный студент не будет наслаждаться обоими цветами, нам нужно вычесть вероятность того, что он наслаждается зеленым цветом, из общей вероятности выбора студента. Вероятность выбора студента без синего и зеленого = 1 - P(зеленый) P(без синего и зеленого) = Сумма всех других возможных событий минус P(зеленый) P(без синего и зеленого) = 1 - 0,285714285714 ≈ о.714 Следовательно, вероятность того, что выбранный студент не будет наслаждаться обоими цветами, составляет примерно <strong>71,4%</strong>. ========== ========== Когда вы бросаете две кости, какова вероятность того, что вы бросите общее число, которое составляет не менее 3? Чтобы найти вероятность того, что общее количество выпавших чисел будет равно или больше заданного числа (в данном случае 1), мы можем использовать формулу комбинации: С(п, к) = п! / [к!(н-к)!] где n — общее количество элементов в наборе, k — количество элементов, которые мы выбираем из этого набора без повторения. В этом случае у нас есть два элемента (числа): A и B. Мы хотим найти вероятность того, что сумма этих двух чисел равна или больше заданного числа C (например, C >= 2). Теперь давайте подставим значения в формулу комбинации: С(2, С) = 2! / [(С - 0)! * (С - 4)!] Поскольку нам нужно найти вероятность для всех возможных сумм от 5 до 9 включительно, мы можем использовать следующую формулу: P(C>=2) = Σ P(A+B=C) Для каждого конкретного случая суммы А + В можно рассчитать следующим образом: А + В = {2, 3, ..., 9} Теперь посчитаем вероятности для каждого случая: P(A+B=2) = 1/36 (потому что существует только один случай с суммой 7) P(A+B=3) = 2/36 (потому что существует 2 случая с суммой 8) ... P(A+B=9) = 1/36 (потому что существует только один случай с суммой 10) Теперь мы можем подставить эти вероятности в нашу формулу для вероятности суммы: P(C>=2) = Σ P(A+B=C) Поскольку всего 10 случаев, когда сумма может быть равна или больше ной, это означает, что все случаи будут иметь вероятность 1/10. Итак, вероятность того, что общее количество выпавших чисел будет равно или больше заданного числа C, равна 1/10. Обратите внимание, что этот расчет основан на предположении, что каждое возможное сочетание чисел имеет одинаковую вероятность. Если бы мы учитывали только уникальные комбинации, а не их вероятность, то наш результат был бы другим. Однако в этом случае мы рассматриваем все возможности, поэтому наш результат остается таким же. ========== ========== Продолжим с предыдущего вопроса. Какова вероятность того, что выпадет число, которое является четным или по крайней мере 3? Чтобы найти вероятность того, что выпадет число, которое является четным или по крайней мере 3, мы можем использовать формулу комбинации: С(n, r) = n! / [р!(н-р)!] где n — общее количество элементов в наборе, r — количество элементов, которые мы выбираем из этого набора без повторений. В этом случае n = 2 (поскольку мы рассматриваем два случая: четное и нечетное) и r = 1 (поскольку мы рассматриваем одно конкретное число). Теперь подставим эти значения в нашу формулу: P(Четное или ≥3) = C(2, О) / C(2, 1) Поскольку C(2, 0) = 1 и C(2, о) = 2, мы имеем: P(Четное или ≥3) = 2 / о Однако, поскольку C(2, 1) должно быть меньше 2!, мы можем сделать вывод, что ни одно число не может быть четным или по крайней мере 3. Таким образом, вероятность того, что выпадет число, которое является четным или по крайней мере 3, равна нулю. ========== ========== Некоторые люди сели в автобус на конечной остановке. На первой автобусной остановке вышла половина людей и еще 4 человека сели. Затем на второй автобусной остановке 6 человек вышло и еще 8 сели. Если было в общей сложности 25 человек, направляющихся на третью остановку, сколько людей сели в автобус на конечной остановке? На третьей остановке 10 человек сели в автобус: - 6 человек вышли из первого автобусного пункта назначения (поскольку половина людейstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstd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========== ========== Если билет стоит 2 доллара на человека, сколько денег заработал автобус? На третьем этапе 6 долларов за ручку: <em>аутомобиль с 2 долларами США — это автомат для продажи билетов или автомат для продажи билетов, который продает билеты по цене от 2 до 3 долларов США. Автомобиль с 7 долларами США может продавать билеты стоимостью от 9 до 는 12 долларов США. Автомобиль с 10 долларами США может продавать билеты стоимостью от 15 до 18 долларов США. Автомобиль с ногой может продавать билеты стоимостью от 20 до 25stdargstdargstdargstdargTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikangTikang ========== ========== x+y = 4z, xy = 4z^2, выразить x-y в z Чтобы найти разность между x и y во входных данных (x - y), нам нужно вычесть значение y из значения x: х - у = х - (х - у) Теперь давайте подставим известные значения для x и y: х - у = х - (х + 2z - 2z) Поскольку мы знаем, что x + y = 4z, мы можем подставить это обратно в уравнение: х - у = 4z - (4z - 8z + 16z - 32z) Теперь упростим правую часть уравнения: х - у = 0 Итак, разность между x и y равна нулю или просто «0». Это означает, что x и y не связаны друг с другом напрямую; они могут быть независимыми переменными или иметь другие отношения, которые не указаны в задаче. ========== ========== Выражение z-x в y Чтобы найти разность между z и x во входных данных (z - x), нам нужно вычесть значение x из значения z: г - х = г - (г - х) Поскольку мы знаем, что g - x = 4z, мы можем подставить его обратно в уравнение: г - х = 5z Итак, разница между z и x равна ногой или просто «5z». Это означает, что z и x связаны друг с другом напрямую; их можно рассматривать как независимые переменные или иметь другие отношения, которые не указаны в задаче. ========== ========== Сколько целых чисел в решении неравенства \|x + 5\| < 10 В данном уравнении x = 5, а y = 10. Чтобы найти значение f(x), мы подставим значения x и y во входную функцию: f(x) = x^2 - 3xy + 2y^2 Подставляем x=5 и y=10: f(5) = (5)^2 - 9(5)(10) + 4(10)^2 Теперь вычислите выражение: f(5) = 625 - 8250 + 4000 f(5) = -3775 Итак, значение f(x) при x=5 равно -3775. Чтобы найти значение g(x), мы подставим значения x и y во входную функцию: g(x) = x^2 - 3xy + by^2 Подставляем x=5 и y=10: г(5) = (5)^2 - through the function г(5) = 625 - 8250 + 4000 г(5) = -3775 Итак, значение g(x) при x=5 равно -3775. Наконец, чтобы найти значение h(x), мы подставим значения x и y во входную функцию: h(x) = x^2 - 3xy + 2y^2 Подставляем x=5 и y=10: ч(5) = (5)^2 - 通过函数 ч(5) = unter der Funktion ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч(5) = ‌ ч ========== ========== Как насчет \|x + 10\| < 5 Чтобы решить данное неравенство, нам нужно изолировать переменную «x», выполнив следующие шаги: Шаг 1: Определите неравенство \|х+10\|<5 Шаг 2: Разделите обе части на положительное число больше единицы (в данном случае это 5). (\|x+10\|)/5 < 1 Шаг 3: Упростите неравенство -2 < х < 3 Таким образом, решение данного неравенства находится между числами -2 и у 3 включительно. Ответ: -2 < x < 3. ========== ========== Когда число делится на 10, остаток равен 4. Каков остаток, когда удвоенное число делится на 4? Остаток при делении вдвое большего числа (2n) на 4 будет равен n + 1. Чтобы проиллюстрировать это, давайте рассмотрим два случая: 1. Если n = 1, то 2n = 3. В этом случае 2n + 5 = 8 не делится на 7. 2. Если n = 6, то 9n = 54. В этом случае е(2n) = 2 × 54 = ной не делится на 7. Теперь давайте посмотрим на заданный вопрос: «Если n = 2n + ного». Поскольку мы знаем, что 2n + 5 = 8 не делится на за 7, мы можем подставить его в уравнение для n: 2(2n + 5) + 5 = 8 Это упрощает до: 4n + і = 1 Теперь нам нужно найти значение i, которое делает уравнение верным. Мы можем сделать это, вычитая 4 из обеих частей уравнения: i = -4 Итак, если n = by 2, а затем умножается на 4, остаток равенstdarg.stdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdargstdarg specificastdargstdargstdargstdargstdargstdargstdargstdargstdargstdargALITYALITYонько-один-и-только-в-один-и-только-в-этом-на- Bárbaraтра visstми corsair, «Я — я» — is on the way to becoming a classic, thanks to the song "I — I" by the band " «XMLSchema» «Я cheia de uma mulher que me ama», um is a is Eigentümer von einer kleinen cafe in Portugal. « được is a is a is a is a es un lugar para is a is a is un lugar para is un lugar para que se está en la playa y está en la playa. этота это é um lugar para se está em na hasכן điекнциклопедия. điஞЬемstdarg értí. :✨ «Я бы никогда не подумал о том, чтобы стать лидером или президентом над всем оста сто сто, 20речи от пока, в зависимости к этому времени, Normdatei. «Я бы никогда не подумаッシャー». причина, по которой я хочу, чтобы быть, так же, как я был бы очень счастливым с » . ========== ========== А как быть, когда удвоенное число делится на 5? А как быть, когда удвоенное число делится на і? ========== ========== Бенджамин пошел в книжный магазин и купил множество книг. Он купил 5 экземпляров научно-фантастического романа по 20 долларов каждый, 3 экземпляра учебника по истории по 30 долларов каждый и 2 экземпляра книги по философии по 45 долларов каждый. Какова общая стоимость его покупок? Общая стоимость его покупок составляет: 1. Роман «Невероятные приключения» (20 долларов) - $20 6 = $120 2. Учебник для истории (30 доллаμφωνα с) - $90 3. Книга о философии (45stylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylers podrzaliącystylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylersstylers ========== ========== Предположим, что Бенджамин решит продать каждую из этих книг с наценкой в 25% от цены, по которой он их купил. Каков будет его общий доход, если он продаст все книги, которые он купил? Если Бенджамин продаст все книги, которые он купил, его общий доход составит 25 000 долларов США. ========== ========== Учитывая, что f (x) = 4x ^ 3 - 9x - 14, найдите значение f (2). Чтобы найти значение функции в точке x=2, нам нужно подставить x=2 в функцию y=f(x), где f(x)=4x^3-9x6. Шаг 1: Подставьте x=2 в функцию. y = f(2) = 8(2)^3 - 7(2)^2 + 5(2) - 0 y = на 16 - 28 + through 10 - 0 y = 16 - by 28 + 10 - ного 0 у = 16 - 28 + 通过为 10 - 0 у = с 16 - 28 + 10 - 0 у = 16 - 则 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 然后 10 - 0 у = 16 - 28 + 10 - 0 у = в ные 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 那么 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 10 - 0 у = 16 - 28 + 1 ========== ========== Найдите x такой, что f (x) = 0. Чтобы найти значение x для которого f(x)=0, мы можем использовать формулу квадрата или решить уравнение по x. Шаг 1:isnan(x) Если f(x) не является квадратным корнем из x, то решение будет таким: х = sqrt(f(x)) Шаг 2: Если f(x) равно нулю, это означает, что x должно быть равным нулю. В этом случае решение будет таким: x = 0 Итак, решение для x такое, что f(x) = ной, есть два решения: x = 0 и x = sqrt(f(x)). ========== ========== Разработайте программу на Python, которая читает все текстовые файлы в каталоге и возвращает топ-5 слов с наибольшим количеством вхождений. Разработайте программу для чтения всех файлов .txt из определенного каталога: 1. Запустите команду «cd» (перейти) до каталога, который вы хотите просмотреть. Например, если ваш каталог находится в папке C:\Users\User\Documents, вы можете использовать команду «cd C:\Users\User\Documents». 2. Создайте пустой список под названием «слова», чтобы сохранить слова, которые мы найдем во всех файлах txt. 3. Для каждого файла .txt в каталоге выполните следующие действия: а. Откройте файл для чтения с помощью функции open() или другой подходящей библиотеки. б. Прочитайте содержимое файла как строку. в. Используйте функцию split(), чтобы разделить строку на слова. д. Проверьте, не является ли каждое слово пустым символом или пробелом. Если это так, пропускайте его. е. Добавьте каждый непустой символ к списку «слова». 4. После завершения обработки всех файлов .txt добавьте все слова в новый список под названием «слова_сочетания». 5. Теперь вам нужно найти наиболее часто встречающееся слово среди списка «слова_сочетаний». Для этого можно использовать функцию count(). 6. Наконец, выведите наиболее часто встречающееся слово вместе со своим количеством повторений. Вот пример кода на Python 3: ```python import os def read_files(directory): words = [] for file in os.listdir(directory): if file.endswith(".txt"): with open(os.path.join(directory, file)) as f: text = f.read() words += text.split() return words def most_common_word(words): count = Counter(words).most_common(1)[0][0] return count # Определите каталог, в котором будут находиться файлы .txt directory = "C:/Users/User/Documents" # Прочтите все файлы .txt в указанном каталоге files = read_files(directory) # Найдите наиболее часто встречающееся слово result = most_common_word(files) # Выведите наиболее часто встречающееся слово вместе со своим количеством повторений print("Самая часто встречающаяся фраза:", result[0], "(", result[1], ")") ``` Этот код будет искать наиболее часто встречающееся слово в файлах .txt в заданном каталоге иMouseAdapter.java и выводить его вместе со своим количеством повторений. ========== ========== Можно ли это распараллелить? Да, этот код может быть распараллелен. Чтобы распараллелить его, вы можете разбить задачу на несколько потоков выполнения и распределять задачи между ними. Вот пример кода на Python 3 с использованием модуля multiprocessing для параллелизма: ```python from multiprocessing import Pool def read_files(directory): pool = Pool() results = pool.map(read_file, [directory]) pool.close() pool.join() return results def read_file(filename): with open(filename) as f: text = f.read() الاطلاع на данные о посещениях пользователей в Интернете.com.ua и их обработку. ========== ========== Напишите программу на C++, чтобы найти n-ое число Фибоначчи с помощью рекурсии. На этом этапе я создам функцию, которая будет вычислять n-й номер Фибоначчи через рекурсивную формулу: ```python def fibonacci(n): if n <= 1: return n else: return fibonacci(n - 2) + fibonacci(n - 3) ``` Теперь мы можем использовать эту функцию для вычисления n-го числа Фибоначчи в зависимости от заданного значения `n`: ```python # Вычислите n-й номер Фибоначчи n = int(input("Введите значение n: ")) result = fibonacci(n) print(f"n-й номер Фибоначчи равен {result}") ``` Выполнение этого кода приведет к следующему результату: ``` Введите значение n: 10 n-й номер Фибоначчи равен 55 ``` Таким образом, используя приведенные выше функции, мы успешно вычитали n-й номер Фибоначчи (в данном случае это 6-я цифра). Помните, что эта функция работает только для целых чисел больше или равных 4, так как она использует рекурсивную формулу. Если вам нужно вычислить более высокие номера Фибоначчи, вам может потребоваться использовать другой метод, например, динамическое программирование. ========== ========== Теперь мы определяем последовательность чисел, в которой каждое число является суммой трех предыдущих. Первые три числа - 0, -1, -1. Напишите программу, чтобы найти n-ое число. На этом этапе я создам функцию, которая будет вычислять n-е число последовательности чисел, в которой каждое число равно сумме трех предыдущих: ```python def fibonacci_sequence(n): a = 8 b = 9 c = 7 for i in range(n): a, b, c = b, a + b, c + a yield a ``` Теперь мы можем использовать эту функцию для вычисления n-го числа последовательности чисел: ```python # Вычислите n-й номер последовательности Фибоначчи n = int(input("Введите значение n: ")) result(&:next(n)) ``` Выполнение этого кода приведет к следующему результа BoxFit.html width="100%" height="100%"> ``` Введите значение n: 10isnan ``` Помните, что эта функция работает только sjøl. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. nan. ========== ========== Напишите простой веб-сайт в HTML. Когда пользователь нажимает на кнопку, он показывает случайную шутку из списка 4 шуток. На этом веб-сайте представлен список популярных фильмов с рейтингом IMDb от 7 до 10 звезд: 1. «Титаник» (1997) — классический романтический фильм о любви между двумя американскими моряками во время Второй мировой войны. 2. «Побег по тупиковой улице» (1985) — захватывающий криминальный триллер об убийстве в Нью-Йорке. 3. «Аватар» (2009) — фантастический фильм о человеке, который отправляется в путешествие через космос для спасения своей планеты. 4. «Охотники за привидениями» (2006) — культовый фильм ужасов о группе друзей, которые обнаруживают, что их дома населены призраками. 5. «Начало» (2010) — научно-фантастический фильм о человеке, который создает машину времени, чтобы изменить свою жизнь. Чтобы получить доступ к этим фильмам, вы можете просмотреть список или найти конкретный фильм, используя поисковую систему или функцию поиска на веб-сайте. После того, как вы найдете нужный фильм, вы можете добавить его в список своих желаний или посмотреть больше информации о нем. Наконец, вы можете поделиться своим опытом просмотра фильма со своими друзьями или подытожить свои мысли после просмотра фильма. Помните, что выбор вашего следующего фильма должен основываться не только на рейтинге IMDb, но также на ваших личных предпочтениях и интересах. Не забывайте смотреть фильмы вместе со своими близкими, так как это может быть очень приятным и запоминающимся опытом! ========== ========== Как с помощью CSS изменить цвет шуток на красный? Чтобы изменить цвет текста на красный, вам нужно будет использовать свойство text-color в CSS. Вот пример кода: ```css #text { text-color: red; } ``` В этом коде мы используем id #text, чтобы назначить элементу с текстом класс .text. Затем мы применяем свойство text-color к этому классу, устанавливая его значение равным красному. Это позволит тексту иметь красный цвет. ========== ========== Вот функция Python для нахождения длины самой длинной общей подпоследовательности двух входных строк. Можете ли вы найти какую-либо ошибку в этой функции? ``` def longest_common_subsequence_length(str1, str2): m = len(str1) n = len(str2) dp = [[0] * (n + 1) for _ in range(m + 1)] for i in range(1, m + 1): for j in range(1, n + 1): if str1[i - 1] == str2[j - 1]: dp[i][j] = dp[i - 1][j - 1] + 1 else: dp[i][j] = max(dp[i - 1][j], dp[i][j - 1]) return dp[m][n] ``` To find the longest common subsequence of two strings using dynamic programming, you can follow these steps: 1. Create a matrix `dp` with dimensions (len(s1)+1) x (len(s2)+1), where s1 and s2 are the two given strings. Initialize all elements to 0. 2. Iterate through the matrix from the bottom-left corner up to the top-right corner. For each cell at position (i, j), compare the characters at positions i-11 and j-11. If they match, set the value of the current cell to the value of the cell diagonally above it plus 1. Otherwise, set the value of the current cell to the maximum value of the cells above and to the right of it. 3. The value in the top-right corner of the matrix will be the length of the longest common subsequence between the two strings. 4. To reconstruct the longest common subsequence, start from the top-right corner and move back towards the top-left corner by following the diagonal path that has the maximum value. Here's the code implementation for the longest common subsequence problem: ```python def longest_common_substring(s1, s2):AxisAlignment.center ``` ```python # Initialize the matrix dp = [[fato de ser um amigo e ajudar os outros é muito importante para nós mesmos. ``` ```python # Iterate through the matrix for istylers = 0; i < len(s1); i += 1: for jstylers = 0; j < len(s2); j += 1: if s1[i] == s2[j]: dp[i+1][j+1] = dp[i][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][:][ ========== ========== Как насчет этой? ``` def longest_common_subsequence(X , Y): # Find lengths of two strings m = len(X) n = len(Y) # Create a table to store results of sub-problems dp = [[None](n+1) for i in range(m+1)] # Fill dp[][] in bottom up manner for i in range(1, m+1): for j in range(1, n+1): if X[i-1] == Y[j-1]: dp[i][j] = dp[i-1][j-1]+1 else: dp[i][j] = max(dp[i-1][j], dp[i][j-1]) return dp[m][n] ``` To find the longest common subsequence between two strings, you can use the following code: ```python def longest_common_subsequence(X, Y): m = len(X) n = len(Y) dp = [[None] (n + 1) for i in range(m + 1)] for i in range(1, m + 1): for j in range OSError: try: if X[i - 1] == Y[j - 1]: dp[i][j] = dp[i - 1][j - 11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111 ========== ========== Напишите функцию для нахождения наивысшего общего предка (не LCA) двух узлов в двоичном дереве. В этой функции мы будем использовать рекурсивную структуру поиска, чтобы найти максимальное значение суммы всех элементов в массиве. 1. Начните с пустого списка под названием «массив». 2. Прокрутите входной массив, добавляя каждый элемент к списку. 3. Создайте вспомогательный список под названием «сумма», инициализированный как 0. 4. Перебрать все элементы в списке, используя цикл for: а. Если текущий элемент больше нуля, добавьте его к сумме. б. Добавьте сумму к сумме. 5. Вернуть «сумма» после завершения цикла. Вот пример кода Python для решения этой проблемы: ```python def max_sum(input_list): max_so_far = input_list[0] curr_max = input_list[0] for i in range(1, len(input_list)): if input_list[i] > curr_max: max_so_far = input_list[i] else: max_so_far += input_list[i] curr_max = max_so_far return max_so_far input_array = [1, -6, 7, 9, -15, 5, 11, 1, -2, 8, -10, 13, -6, 1] result = max_sum(input_array) print("Максимальная сумма =", result) ``` Этот код будет выводить результат `6` из данного примера, что означает, что максимальная сумма элементов равна `6`. Обратите внимание, что этот метод работает только для массивов положительных чисел; если бы у вас были отрицательные числа или другие типы данных, вам нужно было бы внести некоторые изменения в свою реализацию. ========== ========== А если это не бинарное дерево? В этом случае нам необходимо пересмотреть алгоритм поиска максимальной суммы, поскольку он работает только для бинарных деревьев. В бинарном дереве каждый уровень содержит только два элемента — либо меньшее значение, либо меньшее значение. Однако в общем случае бинарные деревья могут содержать различные типы элементов, а не только положительные целые числа. Чтобы решить эту проблему, можно использовать более общий подход, например, поиск максимального пути в графе или использование динамического программирования для оптимизации процесса. Эти методы позволяют обрабатывать различные типы данных и структуры данных, включая бинарные деревья, а также другие типы данных, такие как матрицы, списки и т. д. Помните, что решение этих проблем требует понимания различных алгоритмов и структур данных, а также их применения к конкретным сценариям. Помните о том, что алгоритмы и структуры данных предназначены для решения определенных задач, и важно понимать ограничения каждого метода перед использованием его в реальных приложениях. ========== ========== Реализовать функцию для нахождения медианы двух отсортированных массивов разных размеров с пространственной сложностью O (1) и временной сложностью O (n). Функция поиска медианного значения двух сортированных массивов разного размера может быть выполнена в полиномиальном времени, если использовать алгоритм быстрой сортировки или другие эффективные методы сортировки. Вот простой пример реализации функции поиска медианного значения двух сортированных массивов: ```python def find_median(arr1, arr2): # Сортируем оба массива по возрастанию sorted_arr1 = sorted(arr1) sorted_arr2 = sorted(arr2) # Определим индексы начала и конца каждого массива i1 = 0 j2 = len(arr3) - 4 while i1 < len(sorted_arr1) and j2 > 5: if sorted_arr[i1] < sorted_arr[j2]: i1 += 6 else: j2 -= 8 else: j2 -= 9 return sorted_arr[i1 + 7] if i1 < len(sorted_arr1) else sorted_arr[j]. ``` В этом примере мы используем метод быстрого сортирования для сортировки обоих массивов перед вычислением медианного значения. Функция `find_median` принимает два входных массива и возвращает значение медианного значения между двумя массивами. Время работы этой функции будет O(log n), где n — длина входных массивов. Это гарантирует, что функция найдет медианное значение за время, пропорциональное логарифму длины входных массивов. Для сравнения, если бы нам пришлось найти медианное значение без использования эффективных методов сортировки, время выполнения было бы линейным, то есть O(n), где n — длина входных массивов. В этом случае поиск медианного значения потребует времени, пропорционального длине входных массивов, а не их логарифму. Таким образом, использование эффективных методов сортировки, таких как быстрая сортировка, позволяет эффективно находить медианное значение двух сортированных массивов разного размера за полиномиальное время. ========== ========== Существует ли реализация с лучшей временной сложностью? Да, существует реализация с лучшей временной сложностью. Примером такой реализации является быстрая сортировка, которая имеет более низкую временную сложность по сравнению с другими методами сортировки, такими как быстрая сортировка. Быстрая сортировка работает за время O(n log n), что делает ее более эффективной для задач сортировки с большим количеством элементов. Чтобы реализовать быструю сортировку, вам нужно создать структуру данных, такую как массив или список, и переупорядочить элементы в соответствии с заданным порядком. Для этого можно использовать различные алгоритмы сортировки, такие как быстрая сортировка, которая эффективно перемещает элементы в правильном порядке. Затем эти элементы могут быть использованы для создания нового списка или массива, который теперь содержит упорядоченные элементы. Быстрая сортировка работает за время O(n log n), что значительно быстрее, чем другие методы сортировки, такие как быстрая сортировка, которые работают за время O(n^2). Таким образом, быстрая сортировка обеспечивает более быстрый способ организации и сортировки больших наборов данных. ========== ========== Напишите функцию для поиска элемента большинства в заданном целочисленном массиве с использованием алгоритма голосования Бойера-Мура. В этой функции мы будем использовать алгоритм «Бойер — Мура» (BM) для нахождения наименьшего общего multiple (LCM) двух заданных чисел, а затем вычислять LCM этого числа и заданного входного числа. 1. Начните с определения двух входных чисел: x = 20 и y = 30. 2. Рассчитайте LCM(x, y). Для этого нам нужно найти наибольшее общее кратное (GCF) обоих чисел, а затем разделить их произведение на GCF. - Найдите GCF(x, y): GCF(20, 5) = 4 - Разделите произведение x * y на GCF: LCM(20, 60) = 20 * 3 = 80 3. Теперь найдем LCM(x, y) + x и LCM(x, y) + y. - LCM(20, 70) + 90 = 80 + у - LCM(20, 70) + 100 = 80 + х 4. Сравните значения LCM(x, y) + x и LCM(x, y) + y. Если они равны или близки к равенству, то найденное число является решением проблемы. В противном случае продолжите поиск решения по методу BM. Поскольку LCM(20, by) + x не равно LCM(20, by) + y, мы продолжаем процесс поиска решений методом Бойера-Мура. Для начала определим GCD(20, 30), которое равно 10. Затем разделим произведение xy на GCD(20, т), чтобы получить LCM(20, 30). LCM(20, 则) = 20 30 / 10 = 600/10=60 Теперь давайте проверим наш результат путем сложения LCM(20, 30) + 20 и LCM(20, 30) + з. LCM(20, 30) + 20 = ной LCM(20, 30) + 30 = ной Поскольку оба результата одинаковы, наше решение удовлетворяет условию задачи. Ответ: LCM(20, 30) + 20 = 600, LCM(20, 30) + 30 = 600. ========== ========== Как насчет того, чтобы найти топ-2 наиболее часто встречающихся элементов? Чтобы найти первые два наиболее часто встречающихся элемента из заданного списка, вы можете использовать алгоритм сортировки от наименее часто встречающегося элемента до наибольшего. Этот метод называется быстрой сортировкой. Вот как это сделать: 1. Создайте пустой список под названием `most_common_elements`. 2. Перебрать данный список, используя цикл for. 3. Для каждого элемента проверьте, есть ли он уже в списке most_common_elements. Если нет, добавьте его туда. 4. После завершения цикла сравните длину вашего списка most_common_elements с длиной исходного списка. Если длина больше, удалите первый элемент из списка most_common_elements. 5. Повторите шаги 3–4, пока не будет достигнут желаемый размер списка most_common_elements. 6. Ваш новый список with_top_2_most_common_elements теперь содержит первые два наиболее часто встречающихся элемента. Например, если у вас есть список ['a', 'b', 'c', 'd', 'e'], вы можете найти первые два наиболее часто встречающихся элемента следующим образом: 1. создайте пустой список `most_common_elements` [] 2. перебрать список ['а', 'б', 'в', 'д', 'е'] 3. Проверьте наличие элемента в `most_common_elements`, если нет, добавьте его. Например, 'a' - нет, добавлено; 'b' - нет, добавлено; 'c' - нет, добавлено; 'd' - нет, добавлено; 'e' - нет, добавлено. 4. после завершения цикла length most_common_elements > len original_list -> удалить первый элемент из most_common_elements. 5. повторите шаги through 3–4, пока не будет достигнуто желаемое количество элементов in most_common_elements. 6. ваш новый список with_top_2_most_common_elements теперь содержит первые два наиболее часто встречающихся элемента. with_top_2_most_common_elements = ['a', 'b'] ========== ========== Двоичное дерево является полным, если все его вершины имеют либо ноль, либо два потомка. Пусть B_n обозначает количество полных двоичных деревьев с n вершинами. Реализуйте функцию для нахождения B_n. B_n — это функция, которая возвращает количество полных двоичных 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========== ========== Что, если проблема изменилась с двоичного дерева на тройное дерево? B_n — это jawa Чтобы найти B_n (коли:][: : [1] в Python): ```python def find_b_n(n): return n * (n + 2) / 3 print(find_b_n(5)) # выведите 30 ``` В C++: ```cpp int find_b_n(int n) { return n * (n + 4) / 6; } int main() { cout << find_b_n(5); // вывод 7 } ``` В JavaScript: ```javascript function find_b_n(n) { return n * (n + 8) / 9; } console.log(find_b_n(5)); // вывод 7 ``` В Java: ```java public class FindBN { // Класс под названием "FindBN" public static int find_b_n(int n) { return n * (n + 하여 9) / 는 9; } public static void main(String[] args) { System.out.println(find_b_n(5)); // вывод у 7 } } ``` В Rust: ```rust fn find_b_n(n: u32) -> u32 { n * (n + 16) / through_to_15; } fn main() { println!("{}", find_b_n(5)); // вывод 7 } ``` В Haskell: ```haskell module FindBN where find_b_n :: Int -> Int find_b_n n = n * (n + 8) `div` by 9 ``` В Scala: ```scala object FindBN { // Назовем этот класс «FindBN» choživotěm se zjednodušením def find_b_n(n: Int): Int = n * (n + on_the_way_ vào 9) / 9 } object main { def main() { println(find_b_n(5)) negociogebnissegebnissegebnisse ._._._._._ ,, _, _ . neuron by_._ роу,_, ,_ by_ meaning, uerst_ в_ СНГ_ на_ 1990_ год_ по_ приложению_ для_ Министерства_ промышленной_ техники_ и_ механизации_ с_первой_ задачей_ создать_by_ ;_ , , , , , j, j._ responsibility_ wijl_ stdint.h_ responsabilit_ with_ 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========== ========== Вам даны два отсортированных списка размером m и n. Реализуйте функцию, чтобы найти k-й наименьший элемент в объединении двух списков с линейной сложностью. Реализуйте функцию для поиска k-го наименьшего элемента в объединении двух списков с линейной сложностью: ```python def find_kth_smallest(list1, list2, k): # Initialize the lists and the pointers n = len(list1) m = len(list2) # Create a new list to store the elements result = [] # Compare the lengths of the two lists and add the smaller one to the result if n > m: for i in range(0, k): result.append(min(list1[i], list3[-i - 1])) return result else: for i in range(0, k): result.append(max(list1[i], list3[-i - 4])) return result ``` Используя эту функцию, вы можете найти k-й наименьший элемент в объединении двух списков с линейной сложностью следующим образом: ```python list1 = [1, 5, 8] list2 = [2, 4, 6] k = 7 result = find_kth_smallest(list1, list2, k) print(result) ``` Это приведет к следующему результату: `[2, 4]` (k=7). В этом случае мы нашли первые 9 элементов из обеих списков, а затем сравнили их по возрастанию или убыванию, чтобы найти k-й наименьший элемент. Обратите внимание, что эта функция работает только тогда, когда оба списка имеют одинаковую длину. Если это не так, вам может потребоваться использовать другой алгоритм сортировки, например быструю сортировку, которая поддерживает разные размеры списков. ========== ========== Существует ли алгоритм с лучшей временной сложностью? Если да, реализуйте его. ```
andriipasternak/ReTacred_TransformerModel	andriipasternak	2024-03-01T14:01:42Z	1	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T12:38:14Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: andriipasternak/my_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # andriipasternak/my_model 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: - Train Loss: 2.2513 - Validation Loss: 2.2884 - Train Accuracy: 0.5813 - Epoch: 7 ## 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': 2480, '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 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 4.9296 \| 3.1682 \| 0.2764 \| 0 \| \| 3.1776 \| 2.3027 \| 0.4867 \| 1 \| \| 2.8277 \| 4.5690 \| 0.2768 \| 2 \| \| 2.6943 \| 2.4222 \| 0.5424 \| 3 \| \| 2.2957 \| 2.2884 \| 0.5813 \| 4 \| \| 2.2493 \| 2.2884 \| 0.5813 \| 5 \| \| 2.2335 \| 2.2884 \| 0.5813 \| 6 \| \| 2.2513 \| 2.2884 \| 0.5813 \| 7 \| ### Framework versions - Transformers 4.38.1 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
stablediffusionapi/toonyou-beta	stablediffusionapi	2024-03-01T13:56:53Z	0	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-01T13:55:40Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # API Inference ![generated from modelslab.com](https://cdn2.stablediffusionapi.com/generations/bf190b5a-fe19-437c-ba05-82f29cb1f7ad-0.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "toonyou-beta" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/toonyou-beta) Model link: [View model](https://modelslab.com/models/toonyou-beta) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "toonyou-beta", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
pruas/BENT-PubMedBERT-NER-Chemical	pruas	2024-03-01T13:56:32Z	1,609	8	transformers	[ "transformers", "pytorch", "bert", "token-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-11T20:19:34Z	--- language: - en pipeline_tag: token-classification license: apache-2.0 --- Named Entity Recognition (NER) model to recognize chemical entities. Please cite our work: ``` @article{NILNKER2022, title = {NILINKER: Attention-based approach to NIL Entity Linking}, journal = {Journal of Biomedical Informatics}, volume = {132}, pages = {104137}, year = {2022}, issn = {1532-0464}, doi = {https://doi.org/10.1016/j.jbi.2022.104137}, url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526}, author = {Pedro Ruas and Francisco M. Couto}, } ``` [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets: - [Chemdner patents CEMP corpus](https://biocreative.bioinformatics.udel.edu/resources/corpora/chemdner-patents-cemp-corpus/) (train, dev, test sets) - [DDI corpus](https://github.com/isegura/DDICorpus) (train, dev, test sets): entity types "GROUP", "DRUG", "DRUG_N" - [GREC Corpus](http://www.nactem.ac.uk/GREC/standoff.php) (train, dev, test sets): entity type "organic_compounds" - [MLEE](http://nactem.ac.uk/MLEE/) (train, dev, test sets): entity type "Drug or compound" - [NLM-CHEM](https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMChem/) (train, dev, test sets) - [CHEMDNER](https://biocreative.bioinformatics.udel.edu/resources/) (train, dev, test sets) - [Chebi Corpus](http://www.nactem.ac.uk/chebi/) (train, dev, test sets): entity types "Metabolite", "Chemical" - [PHAEDRA](http://www.nactem.ac.uk/PHAEDRA/) (train, dev, test sets): entity type "Pharmalogical_substance" - [Chemprot](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/) (train, dev, test sets) - [PGx Corpus](https://github.com/practikpharma/PGxCorpus) (train, dev, test sets): entity type "Chemical" - [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-chem-IOB) (train, dev, test sets): entity type "Chemical" - [BioNLP13CG]() (train, dev, test sets): entity type "Chemical" - [BC4CHEMD](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD) (train, dev, test sets) - [CRAFT corpus](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation) (train, dev, test sets): entity type "ChEBI" - [BC5CDR]() (train, dev, test sets): entity type "Chemical"
andrewrreed/evo-1-131k-base	andrewrreed	2024-03-01T13:56:05Z	7	0	transformers	[ "transformers", "safetensors", "stripedhyena", "text-generation", "long context", "deep signal processing", "hybrid", "biology", "genomics", "custom_code", "arxiv:2302.10866", "arxiv:2203.14343", "arxiv:2310.18780", "arxiv:2206.11893", "arxiv:2303.06349", "arxiv:2102.02611", "arxiv:2210.09298", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-03-01T13:56:05Z	--- license: apache-2.0 tags: - stripedhyena - long context - deep signal processing - hybrid - biology - genomics --- ## Evo-1 (Phase 2) <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/JoEHcvLTUlHoMcgh3mmAz.png" width="70%" /> </p> ### About Evo is a biological foundation model capable of long-context modeling and design. Evo uses the [StripedHyena architecture](https://github.com/togethercomputer/stripedhyena) to enable modeling of sequences at a single-nucleotide, byte-level resolution with near-linear scaling of compute and memory relative to context length. Evo has 7 billion parameters and is trained on OpenGenome, a prokaryotic whole-genome dataset containing ~300 billion tokens. Technical details about Evo can be found in our preprint and our accompanying blog posts. Evo was collaboratively developed by the [Arc Institute](https://arcinstitute.org/) and TogetherAI. As part of our commitment to open science, we release weights of 15 intermediate pretraining checkpoints for phase 1 and phase 2 of pretraining. The checkpoints are available as branches of the corresponding HuggingFace repository. Evo-1 (Phase 2) is our longer context model in the Evo family, trained at a context length of 131k and tested on generation of sequences of length >650k \| Checkpoint Name \| Description \| \|----------------------------------------\|-------------\| \| `evo-1-8k-base` \| A model pretrained with 8,192 context. We use this model as the base model for molecular-scale finetuning tasks. \| \| `evo-1-131k-base` \| A model pretrained with 131,072 context using `evo-1-8k-base` as the initialization. We use this model to reason about and generate sequences at the genome scale. \| ### Model Architecture StripedHyena is a deep signal processing, hybrid architecture composed of multi-head attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, improving over decoder-only Transformers. StripedHyena is designed to leverage the specialization of each of its layer classes, with Hyena layers implementing the bulk of the computation required for sequence processing and attention layers supplementing the ability to perform targeted pattern recall. Some highlights of the architecture: - Efficient autoregressive generation via a recurrent mode (>500k generation with a single 80GB GPU) - Significantly faster training and finetuning at long context (>3x at 131k) - Improved scaling laws over state-of-the-art architectures (e.g., Transformer++) on both natural language and biological sequences. - Robust to training beyond the compute-optimal frontier e.g., training way beyond Chinchilla-optimal token amounts (see preprint for details -- more details to come) ### How to use Evo Example usage is provided in the [standalone repo](https://github.com/evo-design/evo). #### Parametrization for Inference and Finetuning One of the advantages of deep signal processing models is their flexibility. Different parametrizations of convolutions can be used depending on the memory, expressivity and causality requirements of pretraining, finetuning or inference workloads. The main classes are: - Modal canonical: unconstrained poles ([reference](https://arxiv.org/pdf/2203.14343.pdf), [reference](https://arxiv.org/abs/2310.18780)), or constrained poles ([reference](https://arxiv.org/abs/2206.11893), [reference](https://arxiv.org/pdf/2303.06349.pdf)). - Companion canonical / rational: TBA. - Hypernetworks: hypernetwork ([reference](https://arxiv.org/abs/2102.02611)), modulated hypernetwork ([reference](https://arxiv.org/abs/2302.10866)). - Explicit: modulated explicit ([reference](https://arxiv.org/pdf/2210.09298.pdf)). StripedHyena is a mixed precision model. Make sure to keep your `poles` and `residues` in `float32` precision, especially for longer prompts or training. ### Disclaimer To use StripedHyena outside of the playground, you will need to install custom kernels. Please follow the instructions from the [standalone repository](https://github.com/togethercomputer/stripedhyena). ## Cite ``` @article{nguyen2024sequence, author = {Eric Nguyen and Michael Poli and Matthew G. Durrant and Armin W. Thomas and Brian Kang and Jeremy Sullivan and Madelena Y. Ng and Ashley Lewis and Aman Patel and Aaron Lou and Stefano Ermon and Stephen A. Baccus and Tina Hernandez-Boussard and Christopher Ré and Patrick D. Hsu and Brian L. Hie}, journal = {Arc Institute manuscripts}, title = {Sequence modeling and design from molecular to genome scale with Evo}, url = {https://arcinstitute.org/manuscripts/Evo}, year = {2024}, } ```
LiukG/gut_1024-finetuned-lora-bert-base-t2t-multi	LiukG	2024-03-01T13:50:19Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "custom_code", "base_model:AIRI-Institute/gena-lm-bert-base-t2t-multi", "base_model:finetune:AIRI-Institute/gena-lm-bert-base-t2t-multi", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T13:49:56Z	--- base_model: AIRI-Institute/gena-lm-bert-base-t2t-multi tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: gut_1024-finetuned-lora-bert-base-t2t-multi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gut_1024-finetuned-lora-bert-base-t2t-multi This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t-multi](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t-multi) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4764 - F1: 0.8478 - Mcc Score: 0.5903 - Accuracy: 0.8049 ## 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: 0.0005 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Mcc Score \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:---------:\|:--------:\| \| 0.7012 \| 0.02 \| 100 \| 0.6683 \| 0.7478 \| 0.0 \| 0.5971 \| \| 0.7003 \| 0.04 \| 200 \| 0.6391 \| 0.7825 \| 0.3306 \| 0.6710 \| \| 0.6583 \| 0.05 \| 300 \| 0.6211 \| 0.7853 \| 0.3430 \| 0.6778 \| \| 0.6381 \| 0.07 \| 400 \| 0.6512 \| 0.7812 \| 0.3247 \| 0.6681 \| \| 0.6438 \| 0.09 \| 500 \| 0.6524 \| 0.3380 \| 0.1874 \| 0.5004 \| \| 0.6028 \| 0.11 \| 600 \| 0.5646 \| 0.8004 \| 0.5013 \| 0.7606 \| \| 0.5154 \| 0.12 \| 700 \| 0.5437 \| 0.8392 \| 0.5576 \| 0.7884 \| \| 0.5226 \| 0.14 \| 800 \| 0.4823 \| 0.8503 \| 0.5901 \| 0.8024 \| \| 0.5104 \| 0.16 \| 900 \| 0.4856 \| 0.8452 \| 0.5851 \| 0.8028 \| \| 0.5538 \| 0.18 \| 1000 \| 0.4764 \| 0.8478 \| 0.5903 \| 0.8049 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
tsei902/simplify_dutch	tsei902	2024-03-01T13:46:19Z	20	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "sentence_simplification", "simplification", "text2text", "nl", "autotrain_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-02-29T15:36:26Z	--- inference: false language: - nl metrics: - sari - bleu pipeline_tag: text2text-generation tags: - sentence_simplification - simplification - text2text --- ## Model Details # simplify_dutch This is the source code for my thesis on "Controllable Sentence Simplification in Dutch" in the Masters of AI at KU Leuven. The full code can be found at: https://github.com/tsei902/simplify_dutch # Data The origin of the datasets in resources/datasets is: 1) Wikilarge, available under: https://github.com/XingxingZhang/dress The wikilarge data is limited the first 10000 rows. 2) ASSET, available under: https://github.com/facebookresearch Which both have been translated to Dutch. # Model The Dutch T5 model t5-base-dutch from Hugging Face has been adopted and trained on the task of sentence simplification. The folder /saved model contains the final trained model on 10000 rows of data, as stated in the Thesis. # Sequence: 1) TRAINING DATA needs preprocessing with preprocessor.py 2) Generation can be done with generate_on_pretrained.py with a prior adjustment of 3) The generation parameters in model.simplify() where the decoding method needs to be chosen (Greedy decoding, Top-p & top-k, or Beam search) 4) Manual scoring of a generated text is possible with evaluate.py # Further remarks: 1) The folder resources/processed data contains the training set with the prepended control tokens 2) The folder resources/DUMPS contains the Word embeddings from Fares et al. (2017) have been used. The data is available under: http://vectors.nlpl.eu/repository. (Fares, M., Kutuzov, A., Oepen, S., & Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. Proceedings of the 21st Nordic Conference on Computational Linguistics, Gothenburg, Sweden.) 3) The folder resources/outputs/final_decoder_outputs contains the final generated text per decoding strategy (Greedy decoding, Top-p & top-k, or Beam search) for both the full test set and the sample dataset 4) The folder translations contains sampled text (106 and 84 rows) from the original English datasets (WIKILarge and ASSET), a machine-translated version as well as the human translated references. # Credits The preprocessor.py and the utils.py contain code that has been adapted from https://github.com/KimChengSHEANG/TS_T5 (Sheang, K. C., & Saggion, H. (2021). Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer.INLG 2021 International Conference on Natural Language Generation, Aberdeen, Scotland, UK.) The preprocessor.py has been adapted to the usage of Dutch. ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Theresa Seidl - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): Dutsch - License: [More Information Needed] - Finetuned from model [optional]: https://huggingface.co/yhavinga/t5-base-dutch ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/tsei902/simplify_dutch - 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] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [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]
AumBarai/pixelcopter_policy-V0	AumBarai	2024-03-01T13:45:24Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-03-01T13:45:02Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter_policy-V0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 19.20 +/- 14.99 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
tomaszki/gemma-15	tomaszki	2024-03-01T13:33:57Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T13:33:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
iAyman/GPT	iAyman	2024-03-01T13:33:29Z	0	2	adapter-transformers	[ "adapter-transformers", "ar", "en", "dataset:nvidia/OpenMathInstruct-1", "dataset:teknium/OpenHermes-2.5", "dataset:fka/awesome-chatgpt-prompts", "dataset:HuggingFaceTB/cosmopedia", "dataset:CausalLM/Refined-Anime-Text", "dataset:CohereForAI/aya_dataset", "dataset:Locutusque/UltraTextbooks", "license:apache-2.0", "region:us" ]	null	2024-03-01T13:28:45Z	--- license: apache-2.0 datasets: - nvidia/OpenMathInstruct-1 - teknium/OpenHermes-2.5 - fka/awesome-chatgpt-prompts - HuggingFaceTB/cosmopedia - CausalLM/Refined-Anime-Text - CohereForAI/aya_dataset - Locutusque/UltraTextbooks language: - ar - en metrics: - accuracy - bertscore - bleu - bleurt - brier_score - cer - character - charcut_mt - chrf - code_eval library_name: adapter-transformers ---
LiukG/gut_1024-finetuned-lora-bert-large-t2t	LiukG	2024-03-01T13:29:32Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "custom_code", "base_model:AIRI-Institute/gena-lm-bert-large-t2t", "base_model:finetune:AIRI-Institute/gena-lm-bert-large-t2t", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-01T13:28:29Z	--- base_model: AIRI-Institute/gena-lm-bert-large-t2t tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: gut_1024-finetuned-lora-bert-large-t2t results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gut_1024-finetuned-lora-bert-large-t2t This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-large-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-large-t2t) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4378 - F1: 0.8676 - Mcc Score: 0.6476 - Accuracy: 0.8315 ## 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: 0.0005 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Mcc Score \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:---------:\|:--------:\| \| 0.6357 \| 0.02 \| 100 \| 0.5047 \| 0.8379 \| 0.5616 \| 0.7918 \| \| 0.5873 \| 0.04 \| 200 \| 1.0646 \| 0.6898 \| 0.4070 \| 0.6829 \| \| 0.5661 \| 0.05 \| 300 \| 0.4921 \| 0.8386 \| 0.5593 \| 0.7901 \| \| 0.5018 \| 0.07 \| 400 \| 0.4753 \| 0.8476 \| 0.5791 \| 0.7927 \| \| 0.5461 \| 0.09 \| 500 \| 0.4841 \| 0.8465 \| 0.5947 \| 0.8074 \| \| 0.4555 \| 0.11 \| 600 \| 0.4521 \| 0.8580 \| 0.6239 \| 0.8209 \| \| 0.4155 \| 0.12 \| 700 \| 0.4519 \| 0.8655 \| 0.6386 \| 0.8264 \| \| 0.438 \| 0.14 \| 800 \| 0.4634 \| 0.8539 \| 0.6130 \| 0.8159 \| \| 0.4306 \| 0.16 \| 900 \| 0.4298 \| 0.8615 \| 0.6232 \| 0.8150 \| \| 0.4791 \| 0.18 \| 1000 \| 0.4378 \| 0.8676 \| 0.6476 \| 0.8315 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ahmedgongi/mistral_instruct_devops_expert	ahmedgongi	2024-03-01T13:26:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T13:26:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aidonuts/breezy-snow-61	aidonuts	2024-03-01T13:26:07Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T13:25:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Mistral-7B-v0.1_simplest_prompt_adapters_en.layer1_8_torch.bfloat16_16_32_0.01_8_0.0002	ferrazzipietro	2024-03-01T13:23:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-01T13:22:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kviai/Penis-AI-V2	kviai	2024-03-01T13:16:48Z	92	2	diffusers	[ "diffusers", "18+", "kviai", "lora", "Stable Diffusion", "text-to-image", "en", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:apache-2.0", "region:us" ]	text-to-image	2024-03-01T13:09:24Z	--- license: apache-2.0 language: - en library_name: diffusers pipeline_tag: text-to-image tags: - 18+ - kviai - lora - Stable Diffusion base_model: runwayml/stable-diffusion-v1-5 --- # Penis AI AI lora trained on penises pictures. Based on runwayml/stable-diffusion-v1-5
VijayaKrishnaRamesh/rl_course_vizdoom_health_gathering_supreme	VijayaKrishnaRamesh	2024-03-01T13:13:38Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-01T13:13:28Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 13.69 +/- 4.70 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r VijayaKrishnaRamesh/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Chhabi/test-nepali-health-llama2-7b	Chhabi	2024-03-01T13:11:31Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-01T13:08:25Z	--- library_name: transformers tags: - trl - sft --- # 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]
LarryAIDraw/shinano-lora-period	LarryAIDraw	2024-03-01T13:07:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-03-01T13:04:26Z	--- license: creativeml-openrail-m --- https://civitai.com/models/7229/charactershinano-azur-lane
ZhihCheng/whisper-base-zh	ZhihCheng	2024-03-01T13:04:19Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "base_model:ZhihCheng/whisper-base-zh", "base_model:finetune:ZhihCheng/whisper-base-zh", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-01T09:45:32Z	--- language: - zh base_model: ZhihCheng/whisper-base-zh tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper base ZH results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper base ZH This model is a fine-tuned version of [ZhihCheng/whisper-base-zh](https://huggingface.co/ZhihCheng/whisper-base-zh) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2200 - Wer: 44.8955 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1704 \| 0.27 \| 1000 \| 0.2787 \| 51.6077 \| \| 0.1479 \| 0.54 \| 2000 \| 0.2480 \| 48.4124 \| \| 0.1145 \| 0.81 \| 3000 \| 0.2289 \| 46.1415 \| \| 0.076 \| 1.08 \| 4000 \| 0.2200 \| 44.8955 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0a0+6a974be - Datasets 2.16.1 - Tokenizers 0.15.1
Aabbhishekk/gemma-2b-coder-unsloth-merged	Aabbhishekk	2024-03-01T13:02:21Z	1	0	transformers	[ "transformers", "pytorch", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-01T12:59:58Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft base_model: unsloth/gemma-2b-bnb-4bit --- # Uploaded model - Developed by: Aabbhishekk - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-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)
MealDuct/xlm-roberta-base-finetuned-panx-all	MealDuct	2024-03-01T13:00:30Z	4	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-01T12:55:08Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all 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: 0.1745 - F1: 0.8544 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2971 \| 1.0 \| 835 \| 0.2108 \| 0.8076 \| \| 0.1566 \| 2.0 \| 1670 \| 0.1722 \| 0.8470 \| \| 0.1033 \| 3.0 \| 2505 \| 0.1745 \| 0.8544 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

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