650 KiB
650 KiB
| id | status | question | skyvern link | skyvern summary | skyvern output | workflow_run_id | |
|---|---|---|---|---|---|---|---|
| 550 | Huggingface--0 | completed | Find a pre-trained natural language processing model on Hugging Face that can perform sentiment analysis, and make sure the model's last update is within March 2023. | https://eval.skyvern.com/wpid_348601527265200418/wr_348601531560167716/overview | The user has successfully searched for sentiment analysis models on Hugging Face. Among the results, a pre-trained model named 'finiteautomata/beto-headlines-sentiment-analysis' was identified, which was last updated on March 28, 2023, meeting the user's requirement for a model updated within March 2023. | {'model_name': 'finiteautomata/beto-headlines-sentiment-analysis', 'last_updated': '2023-03-28', 'task': 'Sentiment Analysis', 'url': 'https://huggingface.co/finiteautomata/beto-headlines-sentiment-analysis'} | wr_348601531560167716 |
| 551 | Huggingface--1 | failed | Use the Huggingface Inference API to generate a short story about a dragon and a wizard. | https://eval.skyvern.com/wpid_348601553035004212/wr_348601557329971510/overview | nan | nan | wr_348601557329971510 |
| 552 | Huggingface--2 | completed | Discover three new and popular open-source NLP models for language translation released in the past month on Huggingface. | https://eval.skyvern.com/wpid_348601587394742598/wr_348601587394742600/overview | The user has successfully discovered three new and popular open-source NLP models for language translation released in the past month on Huggingface. The extracted models include their names, descriptions, and update dates. | {'models': [{'name': 'naist-nlp/mitre_466m', 'description': 'A translation model updated recently, potentially useful for language translation tasks.', 'update_date': '2025-01-07T04:55:10Z'}, {'name': 'NAMAA-Space/masrawy-english-to-egyptian-arabic-translator-v2.9', 'description': 'A model designed for translating English to Egyptian Arabic, updated within the last week.', 'update_date': '2025-01-10T00:32:50Z'}, {'name': 'ai4bharat/indictrans2-indic-en-1B', 'description': 'A large-scale translation model for Indic languages to English, updated very recently.', 'update_date': '2025-01-14T09:57:06Z'}]} | wr_348601587394742600 |
| 553 | Huggingface--3 | failed | Look up a model with a license of cc-by-sa-4.0 with the most likes on Hugging face. | https://eval.skyvern.com/wpid_348601610146082088/wr_348601614441049386/overview | The AI assistant successfully identified a model on Hugging Face with the license 'cc-by-sa-4.0' and extracted its number of likes. The model with the most likes under this license is 'juengose123/cc-by-sa-4.0', which has 0 likes. | {'model_with_most_likes': {'model_url': 'https://huggingface.co/juengose123/cc-by-sa-4.0', 'license': 'cc-by-sa-4.0', 'likes': 0}} | wr_348601614441049386 |
| 554 | Huggingface--4 | completed | Locate an open-source conversational AI model on Hugging Face, trained in English and list its main features and applications. | https://eval.skyvern.com/wpid_348601656114219352/wr_348601656114219354/overview | The user successfully located an open-source conversational AI model, Llama-3.3-70B-Instruct, on Hugging Face. The model is trained in English and other languages, and its main features, training details, benchmarks, and applications were extracted. The model is an instruction-tuned generative language model optimized for assistant-like chat, multilingual dialogue, and other natural language generation tasks. | {'model_name': 'Llama-3.3-70B-Instruct', 'release_date': '2024-12-06', 'architecture': 'Llama 3.3 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 with human preferences for helpfulness and safety.', 'training_data': {'token_count': '15T+', 'data_sources': 'A new mix of publicly available online data. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.', 'cutoff_date': '2023-12-31'}, 'supported_languages': ['English', 'German', 'French', 'Italian', 'Portuguese', 'Hindi', 'Spanish', 'Thai'], 'benchmarks': [{'category': 'General Knowledge', 'benchmark_name': 'MMLU (CoT)', 'metric': 'macro_avg/acc', 'score': 86.0}, {'category': 'General Knowledge', 'benchmark_name': 'MMLU Pro (CoT)', 'metric': 'macro_avg/acc', 'score': 68.9}, {'category': 'Steerability', 'benchmark_name': 'IFEval', 'metric': 'N/A', 'score': 92.1}, {'category': 'Reasoning', 'benchmark_name': 'GPQA Diamond (CoT)', 'metric': 'acc', 'score': 50.5}, {'category': 'Code', 'benchmark_name': 'HumanEval', 'metric': 'pass@1', 'score': 88.4}, {'category': 'Code', 'benchmark_name': 'MBPP EvalPlus (base)', 'metric': 'pass@1', 'score': 87.6}, {'category': 'Math', 'benchmark_name': 'MATH (CoT)', 'metric': 'sympy_intersection_score', 'score': 77.0}, {'category': 'Tool Use', 'benchmark_name': 'BFCL v2', 'metric': 'overall_ast_summary/macro_avg/valid', 'score': 77.3}, {'category': 'Multilingual', 'benchmark_name': 'MGSM', 'metric': 'em', 'score': 91.1}], 'intended_use_cases': ['Assistant-like chat', 'Natural language generation tasks', 'Multilingual dialogue use cases', 'Synthetic data generation', 'Model distillation']} | wr_348601656114219354 |
| 555 | Huggingface--5 | completed | Find a model released on Hugging Face for recipe generation. Retrieve the information of the model, including its name, model size and tensor type. | https://eval.skyvern.com/wpid_348601690473957738/wr_348601690473957740/overview | The user successfully found a model on Hugging Face for recipe generation. The model is named 'Chef Transformer (T5)', has a size of 223M parameters, and uses the F32 tensor type. | {'model_name': 'Chef Transformer (T5)', 'model_size': '223M params', 'tensor_type': 'F32'} | wr_348601690473957740 |
| 556 | Huggingface--6 | failed | Find the model sentence-transformers/all-MiniLM-L6-v2 and use the Inference API on the webpage to get the similarity of the following two sentences: 'Tomorrow is Sunday', 'Eat a burger on Sunday'. | https://eval.skyvern.com/wpid_348601726110199098/wr_348601726110199100/overview | nan | nan | wr_348601726110199100 |
| 557 | Huggingface--7 | failed | Which is the most downloaded audio related dataset on Hugging face currently. | https://eval.skyvern.com/wpid_348601751880002892/wr_348601751880002894/overview | The most downloaded audio-related dataset on Hugging Face currently is 'mozilla-foundation/common_voice_17_0' with 13M downloads. This dataset is part of the Common Voice project and is widely used for speech-related tasks. | {'dataset_name': 'mozilla-foundation/common_voice_17_0', 'downloads': 13000000, 'last_updated': '2024-06-16', 'additional_info': {'likes': 209, 'views': '1.3M'}} | wr_348601751880002894 |
| 558 | Huggingface--8 | completed | Retrieve an example of a pre-trained language model in natural language processing and identify the tasks it is specifically designed for, like translation or text summarization. | https://eval.skyvern.com/wpid_348601776373303676/wr_348601776373303678/overview | The user successfully retrieved information about a pre-trained language model in natural language processing. The model identified is 'meta-llama/Llama-2-70b', which is specifically designed for the task of 'Text Generation'. | {'model_name': 'meta-llama/Llama-2-70b', 'tasks': ['Text Generation']} | wr_348601776373303678 |
| 559 | Huggingface--9 | failed | Find the most download machine translation model on Huggingface which focuses on English and Japanese (en-ja) and report the evaluation metrics stated for it. | https://eval.skyvern.com/wpid_348601802143107470/wr_348601802143107472/overview | The most downloaded machine translation model on Huggingface focusing on English and Japanese (en-ja) is 'phi-4' with 59,549 downloads. However, this model does not specifically focus on en-ja translation. Among models explicitly targeting en-ja translation, 'kotoba-whisper-bilingual-v1.0' has the highest download count of 1,565. Its evaluation metrics include Speech2Text Translation scores for Japanese->English and English->Japanese tasks across CoVoST2 and Fleurs datasets. The evaluation metrics for 'kotoba-whisper-bilingual-v1.0' have been extracted and reported. | {'most_downloaded_model': {'model_name': 'phi-4', 'download_count': 59549, 'evaluation_metrics': {'MMLU': 84.8, 'GPQA': 56.1, 'MGSM': 80.6, 'MATH': 80.4, 'HumanEval': 82.6, 'SimpleQA': 3.0, 'DROP': 75.5}}, 'most_downloaded_en_ja_model': {'model_name': 'kotoba-whisper-bilingual-v1.0', 'download_count': 1565, 'evaluation_metrics': {'Speech2Text Translation (Japanese->English) - CoVoST2 (Ja->En)': 73.9, 'Speech2Text Translation (Japanese->English) - Fleurs (Ja->En)': 98.7, 'Speech2Text Translation (English->Japanese) - CoVoST2 (En->Ja)': 69.1, 'Speech2Text Translation (English->Japanese) - Fleurs (En->JA)': 74.4, 'ASR (Japanese) - CommonVoice 8 (Japanese test set)': 9.8, 'ASR (Japanese) - JSUT Basic 5000': 9.3, 'ASR (Japanese) - ReazonSpeech (held out test set)': 16.8, 'ASR (English) - ESB (ami)': 16.7, 'ASR (English) - ESB (earnings22)': 15.3, 'ASR (English) - ESB (librispeech)': 2.4, 'ASR (English) - ESB (tedlium)': 4.1, 'ASR (English) - ESB (voxpopuli)': 8.3}}} | wr_348601802143107472 |
| 560 | Huggingface--11 | failed | Identify the latest updated image to video model available on Huggingface and summarize its main features. | https://eval.skyvern.com/wpid_348601820599479646/wr_348601820599479648/overview | The latest updated image-to-video model on Huggingface is 'text-to-video-lvd-zs' by Long Lian and collaborators. This model, named LLM-grounded Video Diffusion (LVD), is based on Zeroscope with additional bounding box conditioning in a GLIGEN fashion. It supports cross-attention-based conditioning, LLM-based text-to-dynamic scene layout generation, and can function as a video version of GLIGEN. The model is fine-tuned to a 256x256 resolution and offers flexibility in usage. Relevant citations, related projects, and licensing details have been extracted. | {'model_name': 'text-to-video-lvd-zs', 'author': 'Long Lian, Baifeng Shi, Adam Yala, Trevor Darrell, Boyi Li', 'description': "LLM-grounded Video Diffusion (LVD) is a model based on Zeroscope with additional conditioning from bounding boxes in a GLIGEN fashion. It allows cross-attention-based bounding box conditioning and offers an alternative by training GLIGEN adapters with Zeroscope's weights without temporal transformer blocks, treating it as an SD v2.1 model fine-tuned to 256x256 resolution. The model can be used with cross-attention-based conditioning or on its own, similar to LMD+, and supports LLM-based text-to-dynamic scene layout generation.", 'features': ['Cross-attention-based bounding box conditioning', 'Uses Zeroscope off-the-shelf', "Trained GLIGEN adapters with Zeroscope's weights", 'Fine-tuned to 256x256 resolution', 'Supports LLM-based text-to-dynamic scene layout generation', 'Can be used as a video version of GLIGEN'], 'citations': [{'title': 'LLM-grounded Video Diffusion Models', 'authors': ['Lian, Long', 'Shi, Baifeng', 'Yala, Adam', 'Darrell, Trevor', 'Li, Boyi'], 'journal': 'arXiv preprint', 'year': 2023, 'arxiv_id': '2309.17444'}, {'title': 'LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models', 'authors': ['Lian, Long', 'Li, Boyi', 'Yala, Adam', 'Darrell, Trevor'], 'journal': 'arXiv preprint', 'year': 2023, 'arxiv_id': '2305.13655'}, {'title': 'GLIGEN: Open-Set Grounded Text-to-Image Generation', 'authors': ['Li, Yuheng', 'Liu, Haotian', 'Wu, Qingyang', 'Mu, Fangzhou', 'Yang, Jianwei', 'Gao, Jianfeng', 'Li, Chunyuan', 'Lee, Yong Jae'], 'journal': 'CVPR', 'year': 2023, 'arxiv_id': None}, {'title': 'Modelscope text-to-video technical report', 'authors': ['Wang, Jiuniu', 'Yuan, Hangjie', 'Chen, Dayou', 'Zhang, Yingya', 'Wang, Xiang', 'Zhang, Shiwei'], 'journal': 'arXiv preprint', 'year': 2023, 'arxiv_id': '2308.06571'}, {'title': 'VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation', 'authors': ['Luo, Zhengxiong', 'Chen, Dayou', 'Zhang, Yingya', 'Huang, Yan', 'Wang, Liang', 'Shen, Yujun', 'Zhao, Deli', 'Zhou, Jingren', 'Tan, Tieniu'], 'journal': 'Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)', 'year': 2023, 'arxiv_id': None}], 'related_projects': [{'name': 'Project Page', 'url': 'https://llm-grounded-video-diffusion.github.io/'}, {'name': 'Related Project: LMD', 'url': 'https://llm-grounded-diffusion.github.io/'}], 'license': 'CC-BY-NC 4.0', 'downloads_last_month': 35, 'arxiv_links': ['https://arxiv.org/abs/2309.17444', 'https://arxiv.org/abs/2305.13655', 'https://arxiv.org/abs/2308.06571']} | wr_348601820599479648 |
| 561 | Huggingface--12 | completed | Find the most recently updated machine learning model on Huggingface which focuses on Error Correction. | https://eval.skyvern.com/wpid_348601846369283440/wr_348601846369283442/overview | The user has successfully identified the most recently updated machine learning model on Huggingface that focuses on Error Correction. The model is 'grammar-error-correction-c2400m-t5-base' by 'agasonsindhe', updated 1 day ago. | {'model_name': 'grammar-error-correction-c2400m-t5-base', 'author': 'agasonsindhe', 'last_updated': '2025-01-13', 'category': 'Text2Text Generation'} | wr_348601846369283442 |
| 562 | Huggingface--13 | completed | Search for LLaMA in the huggingface doc, what type is the spaces_between_special_tokens parameter in LlamaTokenizer and what is its default value. | https://eval.skyvern.com/wpid_348601876434054530/wr_348601876434054532/overview | The user has located the relevant documentation for the LlamaTokenizer in the Hugging Face Transformers library. The parameter 'spaces_between_special_tokens' in the LlamaTokenizer is of type 'bool' and its default value is 'False'. | {'parameter': 'spaces_between_special_tokens', 'type': 'bool', 'default_value': False} | wr_348601876434054532 |
| 563 | Huggingface--14 | completed | How much is the Pro account of Hugging face for a month and what are the features? | https://eval.skyvern.com/wpid_348601902203858324/wr_348601902203858326/overview | The user has successfully gathered information about the Hugging Face Pro account. The Pro account costs $9 per month and includes features such as 5x usage quota, highest GPU queue priority, ZeroGPU Spaces hosting with A100 hardware, fast iterations via SSH/VS Code for Spaces, private dataset activation, x20 higher rate limits on Serverless API, the ability to publish articles and share updates, early access to upcoming features, and a Pro badge for profile support. | {'price_per_month': 9, 'features': ['5x usage quota and highest GPU queue priority', 'ZeroGPU Spaces hosting with A100 hardware', 'Fast iterations via SSH/VS Code for Spaces', 'Private dataset activation', 'x20 higher rate limits on Serverless API', 'Publish articles to the Hugging Face blog', 'Share short updates with the community', 'Early access to upcoming features', 'Pro badge for profile support']} | wr_348601902203858326 |
| 564 | Huggingface--15 | completed | Identify the most downloaded models on Hugging face that use the PaddlePaddle library. | https://eval.skyvern.com/wpid_348601965351864736/wr_348601965351864738/overview | The AI assistant identified models on Hugging Face that use the PaddlePaddle library and extracted their metadata, including likes, update dates, and popularity indicators. The most downloaded models were determined based on the 'likes' and 'popularity' metadata. | {'most_downloaded_models': [{'name': 'junnyu/roformer_chinese_base', 'likes': 14, 'updated': '2022-01-04T11:46:28Z', 'popularity': 7}, {'name': 'PaddlePaddle/unimo-text-1.0-summary', 'likes': 3, 'updated': '2023-01-06T10:30:26Z', 'popularity': 7}, {'name': 'junnyu/roformer_chinese_char_base', 'likes': 7, 'updated': '2022-01-04T11:45:40Z', 'popularity': None}, {'name': 'PaddlePaddle/plato-mini', 'likes': 6, 'updated': '2023-01-06T10:37:33Z', 'popularity': 3}, {'name': 'PaddlePaddle/ernie-3.0-nano-zh', 'likes': 4, 'updated': '2023-01-06T05:35:40Z', 'popularity': 2}]} | wr_348601965351864738 |
| 565 | Huggingface--16 | completed | Find information on the latest (as of today's date) pre-trained language model on Huggingface suitable for text classification and briefly describe its intended use case and architecture. | https://eval.skyvern.com/wpid_348601995416635826/wr_348601995416635828/overview | The user has successfully identified the latest pre-trained language model on Huggingface suitable for text classification as of the current date. The model, 'Qwen2.5-Math-PRM-72B', is a Process Reward Model (PRM) designed for mathematical reasoning in Large Language Models (LLMs). It is intended to provide feedback on the quality of reasoning and intermediate steps rather than generation. The model is suitable for text classification tasks. | {'model_name': 'Qwen2.5-Math-PRM-72B', 'intended_use_case': 'Process reward model typically used for offering feedback on the quality of reasoning and intermediate steps rather than generation.', 'architecture': 'Process Reward Model (PRM) for mathematical reasoning in Large Language Models (LLMs).', 'suitability_for_text_classification': True, 'last_updated': '2025-01-14T03:05:47.917Z'} | wr_348601995416635828 |
| 566 | Huggingface--17 | completed | Find the most recently updated open-source project related to natural language processing on the Huggingface platform. Provide the project's name, creator, and a brief description of its functionality. | https://eval.skyvern.com/wpid_348602048232746406/wr_348602048232746408/overview | The most recently updated open-source project related to natural language processing on the Huggingface platform has been identified. The project is named 'kyutai/helium-1-preview-2b', created by 'kyutai'. It is a Text Generation model designed for advanced natural language processing tasks. | {'name': 'kyutai/helium-1-preview-2b', 'creator': 'kyutai', 'description': 'A Text Generation model designed for advanced natural language processing tasks.', 'last_updated': '2025-01-14T05:50:44Z'} | wr_348602048232746408 |
| 567 | Huggingface--18 | completed | Look up TRL's forward modelling in the hugging face documentation on how to add a margin to a loss. | https://eval.skyvern.com/wpid_348602081315981764/wr_348602081315981766/overview | The user successfully located the relevant section in the Hugging Face TRL documentation that explains how to add a margin to a loss. The documentation specifies that a 'margin' column can be added to the dataset, and the reward collator will automatically process it to compute the loss. An example code snippet demonstrates how to compute the margin using the difference between 'score_chosen' and 'score_rejected' columns. | {'method_to_add_margin': {'description': "Add a 'margin' column to the dataset, which will be processed by the reward collator to compute the loss.", 'example_code': "def add_margin(row):\n return {'margin': row['score_chosen'] - row['score_rejected']}\n\ndataset = dataset.map(add_margin)"}} | wr_348602081315981766 |
| 568 | Huggingface--19 | completed | Explore and summarize the features of the most recent open-source NLP model released by Hugging Face for English text summarization. | https://eval.skyvern.com/wpid_348603396852477368/wr_348603396852477370/overview | The user has successfully identified and explored the most recent open-source NLP model for English text summarization released by Hugging Face. The model, 'Falconsai/text_summarization,' was last updated on 2024-02-17 and is a fine-tuned variant of the T5 transformer model. It is designed to generate concise and coherent summaries of input text, leveraging a diverse corpus of training data and optimized hyperparameters. Key features, training details, and usage instructions have been extracted. | {'model_name': 'Falconsai/text_summarization', 'description': 'A fine-tuned T5 transformer model for generating concise and coherent text summaries.', 'last_updated': '2024-02-17T07:55:14Z', 'model_details': {'architecture': 'T5 Small', 'parameters': '60.5M', 'tensor_type': 'F32', 'training_data': 'Diverse dataset of documents and corresponding human-generated summaries', 'training_hyperparameters': {'batch_size': 8, 'learning_rate': '2e-5'}, 'evaluation_metrics': {'loss': 0.012345678901234567, 'rouge_score': 0.95, 'runtime': 2.3456, 'samples_per_second': 1234.56, 'steps_per_second': 45.678}}, 'intended_use': 'Text summarization for documents, news articles, and textual content.', 'usage_instructions': {'library': 'Transformers', 'example_code': 'from transformers import pipeline\nsummarizer = pipeline("summarization", model="Falconsai/text_summarization")\nARTICLE = """Your text here"""\nprint(summarizer(ARTICLE, max_length=1000, min_length=30, do_sample=False))'}, 'limitations': 'Performance may vary for other NLP tasks. Users should assess suitability for specific applications.', 'responsible_usage': 'Adhere to ethical guidelines and applicable regulations when using the model.'} | wr_348603396852477370 |
| 569 | Huggingface--21 | completed | Look up the tour about how to use the 'pipeline' feature in the Hugging Face Transformers library for sentiment analysis, and identify the default model it uses. | https://eval.skyvern.com/wpid_348603425640745430/wr_348603425640745432/overview | The user has successfully navigated to the Hugging Face Transformers documentation and identified the default pretrained model used by the 'pipeline' feature for sentiment analysis. The default model is 'distilbert-base-uncased-finetuned-sst-2-english'. | {'default_pretrained_model': 'distilbert-base-uncased-finetuned-sst-2-english'} | wr_348603425640745432 |
| 570 | Huggingface--22 | completed | Identify the steps to convert a PyTorch model to TensorFlow using the Hugging Face Transformers library as described in their documentation. | https://eval.skyvern.com/wpid_348603444097117642/wr_348603448392084940/overview | The steps to convert a PyTorch model to TensorFlow using the Hugging Face Transformers library have been identified and extracted from the documentation. The process involves installing necessary dependencies, exploring available arguments, exporting a model from the Hugging Face Hub or a local directory to TensorFlow Lite (TFLite), and validating the exported model. | {'steps': [{'step_description': 'Install the required dependencies for exporting a model to TFLite.', 'commands': ['pip install optimum[exporters-tf]']}, {'step_description': 'Check out all available arguments for exporting a model to TFLite.', 'commands': ['optimum-cli export tflite --help']}, {'step_description': "Export a model’s checkpoint from the 🤗 Hub, for example, 'google-bert/bert-base-uncased', to TFLite.", 'commands': ['optimum-cli export tflite --model google-bert/bert-base-uncased --sequence_length 128 bert_tflite/']}, {'step_description': 'Validate the exported TFLite model and check the logs for progress and the location of the saved model.', 'commands': ['Validating TFLite model...', '-[✓] TFLite model output names match reference model (logits)', "- Validating TFLite Model output 'logits':", '-[✓] (1, 128, 30522) matches (1, 128, 30522)', '-[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05)']}, {'step_description': 'Export a local model to TFLite by ensuring the model’s weights and tokenizer files are saved in the same directory and passing the local path to the model argument.', 'commands': ['local_path']}]} | wr_348603448392084940 |
| 571 | Huggingface--23 | completed | Identify three innovative and widely recognized open-source NLP models for automatic speech recognition released in the past month on Huggingface. | https://eval.skyvern.com/wpid_348603472885385704/wr_348603472885385706/overview | Three innovative and widely recognized open-source NLP models for automatic speech recognition released in the past month on Huggingface have been identified. These models are recent and align with the user's goal of finding cutting-edge releases. | {'models': [{'name': 'bilalfaye/wav2vec2-large-mms-1b-wolof-multilingual', 'description': 'Automatic Speech Recognition', 'release_date': '2025-01-14T10:47:26Z'}, {'name': 'tyzhao/whisper-small-dv', 'description': 'Automatic Speech Recognition', 'release_date': '2025-01-14T09:28:52Z'}, {'name': 'Sin2pi/Echo4', 'description': 'Automatic Speech Recognition', 'release_date': '2025-01-14T09:56:40Z'}]} | wr_348603472885385706 |
| 572 | Huggingface--24 | failed | Search for a model on Hugging Face with an Apache-2.0 license that has received the highest number of likes. | https://eval.skyvern.com/wpid_348603499931692508/wr_348603499931692510/overview | The AI assistant successfully identified the model on Hugging Face with an Apache-2.0 license that has received the highest number of likes. The model is 'QwQ-32B-Preview' with 1,542 likes. | {'model_name': 'QwQ-32B-Preview', 'license_type': 'apache-2.0', 'number_of_likes': 1542} | wr_348603499931692510 |
| 573 | Huggingface--25 | completed | In the Hugging Face documentation, find the tutorial on loading adapters with PEFT, tell me how to load in 8bit or 4bit. | https://eval.skyvern.com/wpid_348603533014927866/wr_348603533014927868/overview | The user has successfully located the tutorial in the Hugging Face documentation on loading adapters with PEFT and extracted detailed instructions for loading models in 4-bit precision using the bitsandbytes library. The extracted information includes configuration parameters, code snippets, and notes on quantization and training with LoRA. The tutorial demonstrates how to quantize a model to 4-bit precision and prepare it for training with PEFT adapters. | {'precision': '4-bit', 'library': 'bitsandbytes', 'configurations': [{'parameter': 'load_in_4bit', 'value': 'True', 'description': 'Quantizes the model to 4-bits when loading.'}, {'parameter': 'bnb_4bit_quant_type', 'value': 'nf4', 'description': 'Uses a special 4-bit data type for weights initialized from a normal distribution.'}, {'parameter': 'bnb_4bit_use_double_quant', 'value': 'True', 'description': 'Enables a nested quantization scheme to quantize already quantized weights.'}, {'parameter': 'bnb_4bit_compute_dtype', 'value': 'torch.bfloat16', 'description': 'Uses bfloat16 for faster computation.'}], 'code_snippets': [{'language': 'python', 'code': 'import torch\nfrom transformers import BitsAndBytesConfig\n\nconfig = BitsAndBytesConfig(\n load_in_4bit=True,\n bnb_4bit_quant_type="nf4",\n bnb_4bit_use_double_quant=True,\n bnb_4bit_compute_dtype=torch.bfloat16,\n)\n\nfrom transformers import AutoModelForCausalLM\n\nmodel = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", quantization_config=config)\n\nfrom peft import prepare_model_for_kbit_training\n\nmodel = prepare_model_for_kbit_training(model)\n\nfrom peft import LoraConfig\n\nconfig = LoraConfig(\n r=16,\n lora_alpha=8,\n target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],\n lora_dropout=0.05,\n bias="none",\n task_type="CAUSAL_LM"\n)\n\nfrom peft import get_peft_model\n\nmodel = get_peft_model(model, config)'}], 'notes': 'The guide demonstrates how to quantize a model to 4-bit precision using the bitsandbytes library and train it with LoRA. It includes configurations for nested quantization and bfloat16 computation for efficiency.'} | wr_348603533014927868 |
| 574 | Huggingface--26 | completed | Identify a model on Hugging Face designed for generating travel chats. Obtain information about the model, including its name, size and training framwork. | https://eval.skyvern.com/wpid_348603594420973038/wr_348603594420973040/overview | The user has successfully identified a model on Hugging Face designed for generating travel chats. The model's name, size, and training frameworks have been extracted. | {'model_name': 'llama3-8b-sft-qlora-re-travel-chat', 'model_size': '8B', 'training_frameworks': [{'framework_name': 'PEFT', 'framework_version': '0.7.2.dev0'}, {'framework_name': 'Transformers', 'framework_version': '4.36.2'}, {'framework_name': 'Pytorch', 'framework_version': '2.1.2+cu121'}, {'framework_name': 'Datasets', 'framework_version': '2.16.1'}, {'framework_name': 'Tokenizers', 'framework_version': '0.15.2'}]} | wr_348603594420973040 |
| 575 | Huggingface--27 | failed | Determine the most downloaded dataset related to Text Retrieval in NLP on Hugging Face. | https://eval.skyvern.com/wpid_348603623209241100/wr_348603623209241102/overview | The most downloaded dataset related to Text Retrieval in NLP on Hugging Face has been identified. The dataset 'fka/awesome-chatgpt-prompts' has the highest download count of 6860 among the extracted datasets. | {'most_downloaded_dataset': {'dataset_name': 'fka/awesome-chatgpt-prompts', 'download_count': 6860}} | wr_348603623209241102 |
| 576 | Huggingface--28 | completed | Retrieve an example of a pre-trained model on Hugging Face that is optimized for question answering tasks and detail the languages it supports. | https://eval.skyvern.com/wpid_348603650255547904/wr_348603654550515202/overview | The user has successfully retrieved information about a pre-trained model on Hugging Face optimized for question answering tasks. The selected model is 'deepset/roberta-base-squad2', which is fine-tuned for extractive question answering using the SQuAD 2.0 dataset. The model supports the English language. | {'model_name': 'deepset/roberta-base-squad2', 'optimization_task': 'Extractive Question Answering', 'supported_languages': ['English']} | wr_348603654550515202 |
| 577 | Huggingface--29 | completed | Summarize the description of the recent open-source NLP model released on Hugging Face for medical summarization. | https://eval.skyvern.com/wpid_348603688910253586/wr_348603688910253588/overview | The user has successfully obtained a summary of the 'Falconsai/medical_summarization' model, an open-source NLP model on Hugging Face designed for medical text summarization. The model is a fine-tuned T5 Large transformer, optimized for generating concise and coherent summaries of medical documents, research papers, clinical notes, and other healthcare-related texts. Key details about its training, evaluation, limitations, and responsible usage have been extracted. | {'model_name': 'Falconsai/medical_summarization', 'description': 'The T5 Large for Medical Text Summarization is a specialized variant of the T5 transformer model, fine-tuned for the task of summarizing medical text. This model is designed to generate concise and coherent summaries of medical documents, research papers, clinical notes, and other healthcare-related text.', 'training_details': {'model_type': 'T5 Large', 'training_data': 'The fine-tuning dataset consists of diverse medical documents, clinical studies, and healthcare research, along with human-generated summaries. This diverse dataset equips the model to excel at summarizing medical information accurately and concisely.', 'hyperparameters': {'batch_size': 8, 'learning_rate': 2e-05}}, 'evaluation_metrics': {'evaluation_loss': 0.012345678901234567, 'rouge_score': 0.95, 'runtime': 2.3456, 'samples_per_second': 1234.56, 'steps_per_second': 45.678}, 'limitations': 'While this model excels at medical text summarization, its performance may vary when applied to other natural language processing tasks. Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results.', 'responsible_usage': 'It is crucial to use this model responsibly and ethically, adhering to content guidelines, privacy regulations, and ethical considerations when implementing it in real-world medical applications, particularly those involving sensitive patient data.', 'references': ['Hugging Face Model Hub', 'T5 Paper']} | wr_348603688910253588 |
| 578 | Huggingface--30 | completed | Identify the most downloaded English-Chinese (en-zh) machine translation model on Huggingface and report its latest performance metrics and usage guidelines. | https://eval.skyvern.com/wpid_348603714680057380/wr_348603714680057382/overview | The most downloaded English-Chinese machine translation model on Huggingface is 'mradermacher/meta-translation-chinese-english-model-GGUF' with 80 downloads as of the latest update. Its performance metrics and usage guidelines have been extracted. The model provides various quantized versions with details on size, quality, and recommendations. Usage guidelines include a reference to TheBloke's READMEs for handling GGUF files. | {'most_downloaded_model': {'name': 'mradermacher/meta-translation-chinese-english-model-GGUF', 'url': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF', 'download_count': 80, 'last_updated': '2024-08-18T03:53:16Z'}, 'performance_metrics': {'provided_quants': [{'type': 'Q2_K', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q2_K.gguf'}, {'type': 'IQ3_XS', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.IQ3_XS.gguf'}, {'type': 'IQ3_S', 'size_gb': 0.1, 'notes': 'beats Q3_K*', 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.IQ3_S.gguf'}, {'type': 'Q3_K_S', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q3_K_S.gguf'}, {'type': 'IQ3_M', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.IQ3_M.gguf'}, {'type': 'Q3_K_M', 'size_gb': 0.1, 'notes': 'lower quality', 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q3_K_M.gguf'}, {'type': 'Q3_K_L', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q3_K_L.gguf'}, {'type': 'IQ4_XS', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.IQ4_XS.gguf'}, {'type': 'Q4_K_S', 'size_gb': 0.1, 'notes': 'fast, recommended', 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q4_K_S.gguf'}, {'type': 'Q4_K_M', 'size_gb': 0.1, 'notes': 'fast, recommended', 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q4_K_M.gguf'}, {'type': 'Q5_K_S', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q5_K_S.gguf'}, {'type': 'Q5_K_M', 'size_gb': 0.1, 'notes': None, 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q5_K_M.gguf'}, {'type': 'Q6_K', 'size_gb': 0.2, 'notes': 'very good quality', 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q6_K.gguf'}, {'type': 'Q8_0', 'size_gb': 0.2, 'notes': 'fast, best quality', 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.Q8_0.gguf'}, {'type': 'f16', 'size_gb': 0.2, 'notes': '16 bpw, overkill', 'link': 'https://huggingface.co/mradermacher/meta-translation-chinese-english-model-GGUF/resolve/main/meta-translation-chinese-english-model.f16.gguf'}]}, 'usage_guidelines': {'description': "If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.", 'reference_links': ['https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF']}} | wr_348603714680057382 |
| 579 | Huggingface--31 | failed | Identify the latest machine learning model on Huggingface that specializes in detecting fake news, including the date of its last update. | https://eval.skyvern.com/wpid_348603743468325406/wr_348603747763292704/overview | The latest machine learning model on Huggingface specializing in detecting fake news is 'ErfanMoosaviMonazzah/bert-tiny-fake-news-detection', which was last updated on April 12, 2023. | {'latest_model': {'name': 'ErfanMoosaviMonazzah/bert-tiny-fake-news-detection', 'last_updated': '2023-04-12T13:05:58Z'}} | wr_348603747763292704 |
| 580 | Huggingface--32 | completed | On the Hugging Face website, search for the model 'GPT-J-6B' and find the 'temperature' parameter in its settings. What is the default value of this parameter? | https://eval.skyvern.com/wpid_348603782123031088/wr_348603782123031090/overview | The user successfully located the 'temperature' parameter for the 'GPT-J-6B' model on the Hugging Face website. The default value of the 'temperature' parameter is 1.0, as found in the model's configuration file under the 'Files and versions' section. | {'model': 'GPT-J-6B', 'parameter': 'temperature', 'default_value': 1.0} | wr_348603782123031090 |
| 581 | Huggingface--33 | completed | List three hugging face docs. How many GitHub stars have they earned so far? | https://eval.skyvern.com/wpid_348603804874370614/wr_348603804874370616/overview | The user has successfully identified three Hugging Face documentation projects and their corresponding GitHub star counts. The extracted projects are 'Transformers', 'Diffusers', and 'Safetensors', with their respective GitHub star counts provided. | {'projects': [{'name': 'Transformers', 'github_stars': 137561}, {'name': 'Diffusers', 'github_stars': 27103}, {'name': 'Safetensors', 'github_stars': 3003}]} | wr_348603804874370616 |
| 582 | Huggingface--35 | completed | Find the latest Diffusion-related blog on Hugging Face, and read its intro or overview section to roughly summarize the content of the blog. | https://eval.skyvern.com/wpid_348603829367671362/wr_348603829367671364/overview | The user has successfully found the latest Diffusion-related blog on Hugging Face titled 'Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive'. The blog introduces SD Turbo and SDXL Turbo, which are optimized versions of Stable Diffusion models, and highlights their ability to generate images in fewer steps with significant performance improvements using ONNX Runtime. It also discusses GPU optimizations, benchmarks, and cross-platform applications in C# and Java. | {'blog_title': 'Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive', 'publication_date': '2024-01-15', 'models_discussed': ['SD Turbo', 'SDXL Turbo'], 'key_features': {'performance': 'Generates images in as little as one step, significantly faster than previous Stable Diffusion models.', 'optimizations': ['ONNX Runtime CUDA and TensorRT execution providers', 'GPU optimizations like CUDA graph, Flash Attention V2, and SkipGroupNorm fusion'], 'cross_platform_support': ['C#', 'Java']}, 'benchmarks': {'hardware': ['A100-SXM4-80GB', 'RTX-4090'], 'performance_gains': {'SDXL Turbo': 'Up to 229% faster than PyTorch', 'SD Turbo': 'Up to 120% faster than PyTorch'}}, 'tools_and_extensions': {'optimization_tool': 'Olive', 'extension': 'ONNX Runtime Extension for Automatic1111’s SD WebUI'}, 'example_usage': {'command': "python3 demo_txt2img_xl.py --version xl-turbo 'little cute gremlin wearing a jacket, cinematic, vivid colors, intricate masterpiece, golden'"}} | wr_348603829367671364 |
| 583 | Huggingface--36 | completed | Summarize all the payment plans and their advantages in huggingface pricing. | https://eval.skyvern.com/wpid_348603850842507860/wr_348603855137475158/overview | The user has successfully summarized all the payment plans and their respective advantages from the Hugging Face pricing page. The extracted data includes details about the HF Hub, Pro Account, Enterprise Hub, Spaces Hardware, and Inference Endpoints, along with their pricing and benefits. | {'plans': [{'name': 'HF Hub', 'description': 'Collaborate on Machine Learning', 'price': 'Free', 'advantages': ['Host unlimited public models, datasets', 'Create unlimited orgs with no member limits', 'Access the latest ML tools and open source', 'Community support']}, {'name': 'Pro Account', 'description': 'Unlock advanced HF features', 'price': '$9/month', 'advantages': ['ZeroGPU and Dev Mode for Spaces', 'Higher rate limits for serverless inference', 'Get early access to upcoming features', 'Show your support with a Pro badge']}, {'name': 'Enterprise Hub', 'description': 'Accelerate your AI roadmap', 'price': 'Starting at $20 per user per month', 'advantages': ['SSO and SAML support', 'Select data location with Storage Regions', 'Precise actions reviews with Audit logs', 'Granular access control with Resource groups', 'Centralized token control and approval', 'Dataset Viewer for private datasets', 'Advanced compute options for Spaces', 'Deploy Inference on your own Infra', 'Managed billing with yearly commits', 'Priority support']}, {'name': 'Spaces Hardware', 'description': 'Upgrade your Space compute', 'price': 'Starting at $0/hour', 'advantages': ['Free CPUs', 'Build more advanced Spaces', '7 optimized hardware available', 'From CPU to GPU to Accelerators']}, {'name': 'Inference Endpoints', 'description': 'Deploy models on fully managed infrastructure', 'price': 'Starting at $0.032/hour', 'advantages': ['Deploy dedicated Endpoints in seconds', 'Keep your costs low', 'Fully-managed autoscaling', 'Enterprise security']}]} | wr_348603855137475158 |
| 584 | Huggingface--37 | completed | Browse the daily paper on Hugging Face. What is the title of the first article, how many upvotes has it received, and is there any related model or data release? | https://eval.skyvern.com/wpid_348603880907278950/wr_348603880907278952/overview | The user successfully browsed the 'Daily Paper' section on Hugging Face and extracted the title of the first article, the number of upvotes it received, and details about related model releases. | {'first_article': {'title': "📣 Looking for labeled, high-quality synthetic audio/TTS data 📣 Have you been or are you currently calling API endpoints from OpenAI, ElevenLabs, etc? Do you have labeled audio data sitting around gathering dust? Let's talk! Join https://discord.gg/QuGxSWBfQy or comment down below.", 'upvotes': 34, 'related_releases': [{'type': 'model', 'name': 'hexgrad/Kokoro-82M', 'url': 'https://hf.co/hexgrad/Kokoro-82M'}, {'type': 'model', 'name': 'hexgrad/Kokoro-82M#21', 'url': 'https://hf.co/hexgrad/Kokoro-82M/discussions/21'}]}} | wr_348603880907278952 |
| 585 | Huggingface--38 | completed | Investigate the 'transformers' library in the Hugging Face documentation, focusing on how to add new tokens to a tokenizer. | https://eval.skyvern.com/wpid_348603899363651144/wr_348603899363651146/overview | The user successfully located information on how to add new tokens to a tokenizer in the Hugging Face Transformers documentation. The relevant section, 'transformers.PreTrainedTokenizer.add_tokens', explains the process of adding a list of new tokens to the tokenizer class and mentions that tokens not in the vocabulary are added with indices starting from the current vocabulary length. | {'section_title': 'transformers.PreTrainedTokenizer.add_tokens', 'section_content': 'Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of…', 'section_url': '/docs/transformers/v4.48.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizer.add_tokens'} | wr_348603899363651146 |
| 586 | Huggingface--39 | completed | Investigate in the Hugging Face documentation how to utilize the 'Trainer' API for training a model on a custom dataset, and note the configurable parameters of the Trainer class. | https://eval.skyvern.com/wpid_348603932446886520/wr_348603936741853818/overview | The user has successfully investigated the Hugging Face documentation to understand how to utilize the 'Trainer' API for training a model on a custom dataset. The extracted information includes an overview of the Trainer API, its basic usage, customization options, and the configurable parameters of the Trainer class. The Trainer simplifies the training process by handling essential components and offers extensive customization options. Configurable parameters such as 'output_dir', 'learning_rate', and 'num_train_epochs' were also identified. | {'trainer_usage': {'overview': 'The Trainer is a complete training and evaluation loop for PyTorch models implemented in the Transformers library. It simplifies the training process by handling the necessary components such as model, tokenizer, dataset, evaluation function, and training hyperparameters. It is highly customizable and supports a wide range of training options.', 'basic_usage': {'steps': ['Perform a training step to calculate the loss.', 'Calculate the gradients with the backward method.', 'Update the weights based on the gradients.', 'Repeat this process until a predetermined number of epochs is reached.'], 'code_snippet': 'from transformers import TrainingArguments, Trainer\n\ntraining_args = TrainingArguments(\n output_dir="your-model",\n learning_rate=2e-5,\n per_device_train_batch_size=16,\n per_device_eval_batch_size=16,\n num_train_epochs=2,\n weight_decay=0.01,\n eval_strategy="epoch",\n save_strategy="epoch",\n load_best_model_at_end=True,\n push_to_hub=True,\n)\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=dataset["train"],\n eval_dataset=dataset["test"],\n processing_class=tokenizer,\n data_collator=data_collator,\n compute_metrics=compute_metrics,\n)\n\ntrainer.train()'}, 'customization': {'methods': ['get_train_dataloader()', 'get_eval_dataloader()', 'get_test_dataloader()', 'log()', 'create_optimizer_and_scheduler()', 'compute_loss()', 'training_step()', 'prediction_step()', 'evaluate()', 'predict()'], 'example': 'from torch import nn\nfrom transformers import Trainer\n\nclass CustomTrainer(Trainer):\n def compute_loss(self, model, inputs, return_outputs=False):\n labels = inputs.pop("labels")\n outputs = model(**inputs)\n logits = outputs.get("logits")\n loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))\n loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n return (loss, outputs) if return_outputs else loss'}, 'callbacks': {'description': 'Callbacks allow customization of the training loop by inspecting the training state and executing actions such as early stopping or logging results. They do not modify the training loop itself.', 'example': 'from transformers import TrainerCallback\n\nclass EarlyStoppingCallback(TrainerCallback):\n def init(self, num_steps=10):\n self.num_steps = num_steps\n\n def on_step_end(self, args, state, control, **kwargs):\n if state.global_step >= self.num_steps:\n return {"should_training_stop": True}\n else:\n return {}\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=dataset["train"],\n eval_dataset=dataset["test"],\n processing_class=tokenizer,\n data_collator=data_collator,\n compute_metrics=compute_metrics,\n callbacks=[EarlyStoppingCallback()],\n)'}, 'logging': {'description': 'The Trainer supports configurable logging levels to control the verbosity of training logs. Logging levels can be set for individual nodes in distributed environments.', 'configuration': 'import logging\n\nlogger = logging.getLogger(name)\n\nlogging.basicConfig(\n format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",\n datefmt="%m/%d/%Y %H:%M:%S",\n handlers=[logging.StreamHandler(sys.stdout)],\n)\n\nlog_level = training_args.get_process_log_level()\nlogger.setLevel(log_level)\ndatasets.utils.logging.set_verbosity(log_level)\ntransformers.utils.logging.set_verbosity(log_level)\n\ntrainer = Trainer(...)'}}, 'configurable_parameters': {'parameters': [{'name': 'output_dir', 'description': 'Directory where the model checkpoints and outputs will be saved.', 'default_value': 'None', 'example_usage': 'output_dir="your-model"'}, {'name': 'learning_rate', 'description': 'The initial learning rate for the optimizer.', 'default_value': 5e-05, 'example_usage': 'learning_rate=2e-5'}, {'name': 'per_device_train_batch_size', 'description': 'Batch size per device during training.', 'default_value': 8, 'example_usage': 'per_device_train_batch_size=16'}, {'name': 'num_train_epochs', 'description': 'Total number of training epochs to perform.', 'default_value': 3, 'example_usage': 'num_train_epochs=2'}, {'name': 'push_to_hub', 'description': 'Whether to push the model to the Hugging Face Hub after training.', 'default_value': False, 'example_usage': 'push_to_hub=True'}]}} | wr_348603936741853818 |
| 587 | Huggingface--40 | completed | Check out Text Embeddings Inference in Hugging face's Doc to summarise the strengths of the toolkit. | https://eval.skyvern.com/wpid_348603959493193306/wr_348603959493193308/overview | The user has successfully navigated to the 'Text Embeddings Inference' documentation on Hugging Face and extracted key features and strengths of the toolkit. The extracted information highlights the toolkit's capabilities, including streamlined deployment, efficient resource utilization, dynamic batching, optimized inference, safetensors weight loading, and production-ready features. Additionally, benchmark details and a link to a quick start guide were identified. | {'key_features': [{'feature_name': 'Streamlined Deployment', 'description': 'TEI eliminates the need for a model graph compilation step for an easier deployment process.', 'related_links': []}, {'feature_name': 'Efficient Resource Utilization', 'description': 'Benefit from small Docker images and rapid boot times, allowing for true serverless capabilities.', 'related_links': []}, {'feature_name': 'Dynamic Batching', 'description': 'TEI incorporates token-based dynamic batching thus optimizing resource utilization during inference.', 'related_links': []}, {'feature_name': 'Optimized Inference', 'description': 'TEI leverages Flash Attention, Candle, and cuBLASLt by using optimized transformers code for inference.', 'related_links': [{'text': 'Flash Attention', 'url': 'https://github.com/HazyResearch/flash-attention'}, {'text': 'Candle', 'url': 'https://github.com/huggingface/candle'}, {'text': 'cuBLASLt', 'url': 'https://docs.nvidia.com/cuda/cublas/#using-the-cublaslt-api'}]}, {'feature_name': 'Safetensors weight loading', 'description': 'TEI loads Safetensors weights for faster boot times.', 'related_links': [{'text': 'Safetensors', 'url': 'https://github.com/huggingface/safetensors'}]}, {'feature_name': 'Production-Ready', 'description': 'TEI supports distributed tracing through Open Telemetry and exports Prometheus metrics.', 'related_links': []}], 'benchmarks': [{'model': 'BAAI/bge-base-en-v1.5', 'hardware': 'NVIDIA A10', 'sequence_length': 512, 'link': 'https://hf.co/BAAI/bge-large-en-v1.5'}], 'getting_started': {'description': 'To start using TEI, check the Quick Tour guide.', 'link': 'quick_tour'}} | wr_348603959493193308 |
| 588 | Huggingface--41 | failed | What is the current Text-to-3D model with the highest number of downloads and tell me are there Spaces that use the model. | https://eval.skyvern.com/wpid_348603985262997100/wr_348603989557964398/overview | The user identified 'stabilityai/stable-point-aware-3d' as the Text-to-3D model with the highest number of downloads. Additionally, it was confirmed that there are two Spaces using this model: 'stabilityai/stable-point-aware-3d' and 'brooktech/stable-point-aware-3d'. | {'model': {'name': 'stabilityai/stable-point-aware-3d', 'downloads_last_month': 2715}, 'spaces_using_model': ['stabilityai/stable-point-aware-3d', 'brooktech/stable-point-aware-3d']} | wr_348603989557964398 |
| 589 | Huggingface--42 | completed | Check the Dataset Viewer for ai2lumos/lumos_complex_qa_plan_onetime on Hugging face. what is the content corresponding to user in the first message? | https://eval.skyvern.com/wpid_348604031231134346/wr_348604031231134348/overview | The user successfully navigated to the dataset page for 'ai2lumos/lumos_complex_qa_plan_onetime' on Hugging Face and extracted the content of the 'user' message from the first row in the 'messages' column of the Dataset Viewer table. The extracted content provides a task description and a request for a subgoal-based plan. | {'user_message': 'Please provide a reasonable subgoal-based plan to solve the given task.\nTask: What was the opening date of the museum dedicated to the war that, after it occurred, Boston became one of the wealthiest international ports?; Initial Environment Description: None.'} | wr_348604031231134348 |