hugging-research / tests /hf_tools_tests_output_20250822_034011.txt
daqc's picture
Upload 61 files
b67af4a verified
Hugging Face Tools Test Run — 20250822_034011
================================================================================
=== Running test_hf_models_search.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_models_search.py
--- INPUT (snippet) ---
tool.forward(
query="stable diffusion",
task="text-to-image",
sort="downloads",
direction="descending",
limit=5,
)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"results": [
{
"type": "model",
"id": "stabilityai/stable-diffusion-xl-base-1.0",
"owner": "stabilityai",
"url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
"description": "",
"tags": [
"diffusers",
"onnx",
"safetensors",
"text-to-image",
"stable-diffusion",
"arxiv:2307.01952",
"arxiv:2211.01324",
"arxiv:2108.01073",
"arxiv:2112.10752",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
],
"task": "text-to-image",
"likes": 6862,
"downloads": 2281071,
"updatedAt": null,
"visibility": "public",
"access": "accessible"
},
{
"type": "model",
"id": "stable-diffusion-v1-5/stable-diffusion-v1-5",
"owner": "stable-diffusion-v1-5",
"url": "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5",
"description": "",
"tags": [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"arxiv:2207.12598",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
],
"task": "text-to-image",
"likes": 755,
"downloads": 2789469,
"updatedAt": null,
"visibility": "public",
"access": "accessible"
},
{
"type": "model",
"id": "stabilityai/stable-diffusion-xl-refiner-1.0",
"owner": "stabilityai",
"url": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0",
"description": "",
"tags": [
"diffusers",
"safetensors",
"stable-diffusion",
"image-to-image",
"arxiv:2307.01952",
"arxiv:2211.01324",
"arxiv:2108.01073",
"arxiv:2112.10752",
"license:openrail++",
"diffusers:StableDiffusionXLImg2ImgPipeline",
"region:us"
],
"task": "image-to-image",
"likes": 1953,
"downloads": 544617,
"updatedAt": null,
"visibility": "public",
"access": "accessible"
},
{
"type": "model",
"id": "stabilityai/stable-diffusion-3.5-large",
"owner": "stabilityai",
"url": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
"description": "",
"tags": [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"en",
"arxiv:2403.03206",
"license:other",
"diffusers:StableDiffusion3Pipeline",
"region:us"
],
"task": "text-to-image",
"likes": 3073,
"downloads": 82027,
"updatedAt": null,
"visibility": "public",
"access": "accessible"
},
{
"type": "model",
"id": "stabilityai/stable-video-diffusion-img2vid",
"owner": "stabilityai",
"url": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid",
"description": "",
"tags": [
"diffusers",
"safetensors",
"image-to-video",
"license:other",
"diffusers:StableVideoDiffusionPipeline",
"region:us"
],
"task": "image-to-video",
"likes": 957,
"downloads": 46464,
"updatedAt": null,
"visibility": "public",
"access": "accessible"
}
],
"status": 200,
"error": "",
"params": {
"search": "stable diffusion",
"pipeline_tag": "text-to-image",
"sort": "downloads",
"direction": "descending",
"limit": 5
}
}
--- STDERR ---
[OK] test_hf_models_search.py
--------------------------------------------------------------------------------
=== Running test_hf_model_info.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_model_info.py
--- INPUT (snippet) ---
tool.forward(repo_id=repo_id)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"item": {
"type": "model",
"id": "sentence-transformers/all-MiniLM-L6-v2",
"owner": "sentence-transformers",
"url": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2",
"description": "",
"tags": [
"sentence-transformers",
"pytorch",
"tf",
"rust",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"dataset:search_qa",
"dataset:eli5",
"dataset:snli",
"dataset:multi_nli",
"dataset:wikihow",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/sentence-compression",
"dataset:embedding-data/flickr30k-captions",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"dataset:embedding-data/QQP",
"dataset:embedding-data/SPECTER",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/WikiAnswers",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
],
"task": "sentence-similarity",
"likes": 3788,
"downloads": 91944061,
"updatedAt": "2025-03-06T13:37:44.000Z",
"visibility": "public",
"access": "accessible",
"cardData": {
"language": "en",
"license": "apache-2.0",
"library_name": "sentence-transformers",
"tags": [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers"
],
"datasets": [
"s2orc",
"flax-sentence-embeddings/stackexchange_xml",
"ms_marco",
"gooaq",
"yahoo_answers_topics",
"code_search_net",
"search_qa",
"eli5",
"snli",
"multi_nli",
"wikihow",
"natural_questions",
"trivia_qa",
"embedding-data/sentence-compression",
"embedding-data/flickr30k-captions",
"embedding-data/altlex",
"embedding-data/simple-wiki",
"embedding-data/QQP",
"embedding-data/SPECTER",
"embedding-data/PAQ_pairs",
"embedding-data/WikiAnswers"
],
"pipeline_tag": "sentence-similarity"
},
"siblings": [
{
"rfilename": ".gitattributes"
},
{
"rfilename": "1_Pooling/config.json"
},
{
"rfilename": "README.md"
},
{
"rfilename": "config.json"
},
{
"rfilename": "config_sentence_transformers.json"
},
{
"rfilename": "data_config.json"
},
{
"rfilename": "model.safetensors"
},
{
"rfilename": "modules.json"
},
{
"rfilename": "onnx/model.onnx"
},
{
"rfilename": "onnx/model_O1.onnx"
},
{
"rfilename": "onnx/model_O2.onnx"
},
{
"rfilename": "onnx/model_O3.onnx"
},
{
"rfilename": "onnx/model_O4.onnx"
},
{
"rfilename": "onnx/model_qint8_arm64.onnx"
},
{
"rfilename": "onnx/model_qint8_avx512.onnx"
},
{
"rfilename": "onnx/model_qint8_avx512_vnni.onnx"
},
{
"rfilename": "onnx/model_quint8_avx2.onnx"
},
{
"rfilename": "openvino/openvino_model.bin"
},
{
"rfilename": "openvino/openvino_model.xml"
},
{
"rfilename": "openvino/openvino_model_qint8_quantized.bin"
},
{
"rfilename": "openvino/openvino_model_qint8_quantized.xml"
},
{
"rfilename": "pytorch_model.bin"
},
{
"rfilename": "rust_model.ot"
},
{
"rfilename": "sentence_bert_config.json"
},
{
"rfilename": "special_tokens_map.json"
},
{
"rfilename": "tf_model.h5"
},
{
"rfilename": "tokenizer.json"
},
{
"rfilename": "tokenizer_config.json"
},
{
"rfilename": "train_script.py"
},
{
"rfilename": "vocab.txt"
}
]
},
"status": 200,
"error": ""
}
--- STDERR ---
[OK] test_hf_model_info.py
--------------------------------------------------------------------------------
=== Running test_hf_datasets_search.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_datasets_search.py
--- INPUT (snippet) ---
tool.forward(
query="sentiment analysis",
tags="language:es",
sort="downloads",
direction="descending",
limit=5,
)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"results": [
{
"type": "dataset",
"id": "Renukswamy/Patent_sentiment_analysis",
"owner": "Renukswamy",
"url": "https://huggingface.co/Renukswamy/Patent_sentiment_analysis",
"description": "",
"tags": [
"size_categories:n<1K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
],
"likes": 2,
"downloads": 12,
"updatedAt": "2021-11-26T09:18:15.000Z",
"visibility": "public",
"access": "accessible"
},
{
"type": "dataset",
"id": "maydogan/Turkish_SentimentAnalysis_TRSAv1",
"owner": "maydogan",
"url": "https://huggingface.co/maydogan/Turkish_SentimentAnalysis_TRSAv1",
"description": "",
"tags": [
"task_categories:text-classification",
"language:tr",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
],
"likes": 6,
"downloads": 161,
"updatedAt": "2024-10-07T14:16:56.000Z",
"visibility": "public",
"access": "accessible"
},
{
"type": "dataset",
"id": "winvoker/turkish-sentiment-analysis-dataset",
"owner": "winvoker",
"url": "https://huggingface.co/winvoker/turkish-sentiment-analysis-dataset",
"description": "",
"tags": [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:tr",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
],
"likes": 47,
"downloads": 459,
"updatedAt": "2023-07-19T13:15:13.000Z",
"visibility": "public",
"access": "accessible"
},
{
"type": "dataset",
"id": "ramnika003/autotrain-data-sentiment_analysis_project",
"owner": "ramnika003",
"url": "https://huggingface.co/ramnika003/autotrain-data-sentiment_analysis_project",
"description": "",
"tags": [
"task_categories:text-classification",
"region:us"
],
"likes": 0,
"downloads": 160,
"updatedAt": "2022-04-05T09:16:59.000Z",
"visibility": "public",
"access": "accessible"
},
{
"type": "dataset",
"id": "elmurod1202/uzbek-sentiment-analysis",
"owner": "elmurod1202",
"url": "https://huggingface.co/elmurod1202/uzbek-sentiment-analysis",
"description": "",
"tags": [
"size_categories:10K<n<100K",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
],
"likes": 3,
"downloads": 236,
"updatedAt": "2022-05-11T13:43:59.000Z",
"visibility": "public",
"access": "accessible"
}
],
"status": 200,
"error": "",
"params": {
"search": "sentiment analysis",
"tags": "language:es",
"sort": "downloads",
"direction": "descending",
"limit": 5
}
}
--- STDERR ---
[OK] test_hf_datasets_search.py
--------------------------------------------------------------------------------
=== Running test_hf_dataset_info.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_dataset_info.py
--- INPUT (snippet) ---
tool.forward(repo_id=repo_id)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"item": {
"type": "dataset",
"id": "nyu-mll/glue",
"owner": "nyu-mll",
"url": "https://huggingface.co/nyu-mll/glue",
"description": "",
"tags": [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1804.07461",
"region:us",
"qa-nli",
"coreference-nli",
"paraphrase-identification"
],
"likes": 435,
"downloads": 313025,
"updatedAt": "2024-01-30T07:41:18.000Z",
"visibility": "public",
"access": "accessible",
"cardData": {
"annotations_creators": [
"other"
],
"language_creators": [
"other"
],
"language": [
"en"
],
"license": [
"other"
],
"multilinguality": [
"monolingual"
],
"size_categories": [
"10K<n<100K"
],
"source_datasets": [
"original"
],
"task_categories": [
"text-classification"
],
"task_ids": [
"acceptability-classification",
"natural-language-inference",
"semantic-similarity-scoring",
"sentiment-classification",
"text-scoring"
],
"paperswithcode_id": "glue",
"pretty_name": "GLUE (General Language Understanding Evaluation benchmark)",
"config_names": [
"ax",
"cola",
"mnli",
"mnli_matched",
"mnli_mismatched",
"mrpc",
"qnli",
"qqp",
"rte",
"sst2",
"stsb",
"wnli"
],
"tags": [
"qa-nli",
"coreference-nli",
"paraphrase-identification"
],
"dataset_info": [
{
"config_name": "ax",
"features": [
{
"name": "premise",
"dtype": "string"
},
{
"name": "hypothesis",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "entailment",
"1": "neutral",
"2": "contradiction"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "test",
"num_bytes": 237694,
"num_examples": 1104
}
],
"download_size": 80767,
"dataset_size": 237694
},
{
"config_name": "cola",
"features": [
{
"name": "sentence",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "unacceptable",
"1": "acceptable"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 484869,
"num_examples": 8551
},
{
"name": "validation",
"num_bytes": 60322,
"num_examples": 1043
},
{
"name": "test",
"num_bytes": 60513,
"num_examples": 1063
}
],
"download_size": 326394,
"dataset_size": 605704
},
{
"config_name": "mnli",
"features": [
{
"name": "premise",
"dtype": "string"
},
{
"name": "hypothesis",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "entailment",
"1": "neutral",
"2": "contradiction"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 74619646,
"num_examples": 392702
},
{
"name": "validation_matched",
"num_bytes": 1833783,
"num_examples": 9815
},
{
"name": "validation_mismatched",
"num_bytes": 1949231,
"num_examples": 9832
},
{
"name": "test_matched",
"num_bytes": 1848654,
"num_examples": 9796
},
{
"name": "test_mismatched",
"num_bytes": 1950703,
"num_examples": 9847
}
],
"download_size": 57168425,
"dataset_size": 82202017
},
{
"config_name": "mnli_matched",
"features": [
{
"name": "premise",
"dtype": "string"
},
{
"name": "hypothesis",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "entailment",
"1": "neutral",
"2": "contradiction"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "validation",
"num_bytes": 1833783,
"num_examples": 9815
},
{
"name": "test",
"num_bytes": 1848654,
"num_examples": 9796
}
],
"download_size": 2435055,
"dataset_size": 3682437
},
{
"config_name": "mnli_mismatched",
"features": [
{
"name": "premise",
"dtype": "string"
},
{
"name": "hypothesis",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "entailment",
"1": "neutral",
"2": "contradiction"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "validation",
"num_bytes": 1949231,
"num_examples": 9832
},
{
"name": "test",
"num_bytes": 1950703,
"num_examples": 9847
}
],
"download_size": 2509009,
"dataset_size": 3899934
},
{
"config_name": "mrpc",
"features": [
{
"name": "sentence1",
"dtype": "string"
},
{
"name": "sentence2",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "not_equivalent",
"1": "equivalent"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 943843,
"num_examples": 3668
},
{
"name": "validation",
"num_bytes": 105879,
"num_examples": 408
},
{
"name": "test",
"num_bytes": 442410,
"num_examples": 1725
}
],
"download_size": 1033400,
"dataset_size": 1492132
},
{
"config_name": "qnli",
"features": [
{
"name": "question",
"dtype": "string"
},
{
"name": "sentence",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "entailment",
"1": "not_entailment"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 25612443,
"num_examples": 104743
},
{
"name": "validation",
"num_bytes": 1368304,
"num_examples": 5463
},
{
"name": "test",
"num_bytes": 1373093,
"num_examples": 5463
}
],
"download_size": 19278324,
"dataset_size": 28353840
},
{
"config_name": "qqp",
"features": [
{
"name": "question1",
"dtype": "string"
},
{
"name": "question2",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "not_duplicate",
"1": "duplicate"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 50900820,
"num_examples": 363846
},
{
"name": "validation",
"num_bytes": 5653754,
"num_examples": 40430
},
{
"name": "test",
"num_bytes": 55171111,
"num_examples": 390965
}
],
"download_size": 73982265,
"dataset_size": 111725685
},
{
"config_name": "rte",
"features": [
{
"name": "sentence1",
"dtype": "string"
},
{
"name": "sentence2",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "entailment",
"1": "not_entailment"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 847320,
"num_examples": 2490
},
{
"name": "validation",
"num_bytes": 90728,
"num_examples": 277
},
{
"name": "test",
"num_bytes": 974053,
"num_examples": 3000
}
],
"download_size": 1274409,
"dataset_size": 1912101
},
{
"config_name": "sst2",
"features": [
{
"name": "sentence",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "negative",
"1": "positive"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 4681603,
"num_examples": 67349
},
{
"name": "validation",
"num_bytes": 106252,
"num_examples": 872
},
{
"name": "test",
"num_bytes": 216640,
"num_examples": 1821
}
],
"download_size": 3331080,
"dataset_size": 5004495
},
{
"config_name": "stsb",
"features": [
{
"name": "sentence1",
"dtype": "string"
},
{
"name": "sentence2",
"dtype": "string"
},
{
"name": "label",
"dtype": "float32"
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 754791,
"num_examples": 5749
},
{
"name": "validation",
"num_bytes": 216064,
"num_examples": 1500
},
{
"name": "test",
"num_bytes": 169974,
"num_examples": 1379
}
],
"download_size": 766983,
"dataset_size": 1140829
},
{
"config_name": "wnli",
"features": [
{
"name": "sentence1",
"dtype": "string"
},
{
"name": "sentence2",
"dtype": "string"
},
{
"name": "label",
"dtype": {
"class_label": {
"names": {
"0": "not_entailment",
"1": "entailment"
}
}
}
},
{
"name": "idx",
"dtype": "int32"
}
],
"splits": [
{
"name": "train",
"num_bytes": 107109,
"num_examples": 635
},
{
"name": "validation",
"num_bytes": 12162,
"num_examples": 71
},
{
"name": "test",
"num_bytes": 37889,
"num_examples": 146
}
],
"download_size": 63522,
"dataset_size": 157160
}
],
"configs": [
{
"config_name": "ax",
"data_files": [
{
"split": "test",
"path": "ax/test-*"
}
]
},
{
"config_name": "cola",
"data_files": [
{
"split": "train",
"path": "cola/train-*"
},
{
"split": "validation",
"path": "cola/validation-*"
},
{
"split": "test",
"path": "cola/test-*"
}
]
},
{
"config_name": "mnli",
"data_files": [
{
"split": "train",
"path": "mnli/train-*"
},
{
"split": "validation_matched",
"path": "mnli/validation_matched-*"
},
{
"split": "validation_mismatched",
"path": "mnli/validation_mismatched-*"
},
{
"split": "test_matched",
"path": "mnli/test_matched-*"
},
{
"split": "test_mismatched",
"path": "mnli/test_mismatched-*"
}
]
},
{
"config_name": "mnli_matched",
"data_files": [
{
"split": "validation",
"path": "mnli_matched/validation-*"
},
{
"split": "test",
"path": "mnli_matched/test-*"
}
]
},
{
"config_name": "mnli_mismatched",
"data_files": [
{
"split": "validation",
"path": "mnli_mismatched/validation-*"
},
{
"split": "test",
"path": "mnli_mismatched/test-*"
}
]
},
{
"config_name": "mrpc",
"data_files": [
{
"split": "train",
"path": "mrpc/train-*"
},
{
"split": "validation",
"path": "mrpc/validation-*"
},
{
"split": "test",
"path": "mrpc/test-*"
}
]
},
{
"config_name": "qnli",
"data_files": [
{
"split": "train",
"path": "qnli/train-*"
},
{
"split": "validation",
"path": "qnli/validation-*"
},
{
"split": "test",
"path": "qnli/test-*"
}
]
},
{
"config_name": "qqp",
"data_files": [
{
"split": "train",
"path": "qqp/train-*"
},
{
"split": "validation",
"path": "qqp/validation-*"
},
{
"split": "test",
"path": "qqp/test-*"
}
]
},
{
"config_name": "rte",
"data_files": [
{
"split": "train",
"path": "rte/train-*"
},
{
"split": "validation",
"path": "rte/validation-*"
},
{
"split": "test",
"path": "rte/test-*"
}
]
},
{
"config_name": "sst2",
"data_files": [
{
"split": "train",
"path": "sst2/train-*"
},
{
"split": "validation",
"path": "sst2/validation-*"
},
{
"split": "test",
"path": "sst2/test-*"
}
]
},
{
"config_name": "stsb",
"data_files": [
{
"split": "train",
"path": "stsb/train-*"
},
{
"split": "validation",
"path": "stsb/validation-*"
},
{
"split": "test",
"path": "stsb/test-*"
}
]
},
{
"config_name": "wnli",
"data_files": [
{
"split": "train",
"path": "wnli/train-*"
},
{
"split": "validation",
"path": "wnli/validation-*"
},
{
"split": "test",
"path": "wnli/test-*"
}
]
}
],
"train-eval-index": [
{
"config": "cola",
"task": "text-classification",
"task_id": "binary_classification",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"sentence": "text",
"label": "target"
}
},
{
"config": "sst2",
"task": "text-classification",
"task_id": "binary_classification",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"sentence": "text",
"label": "target"
}
},
{
"config": "mrpc",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"sentence1": "text1",
"sentence2": "text2",
"label": "target"
}
},
{
"config": "qqp",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"question1": "text1",
"question2": "text2",
"label": "target"
}
},
{
"config": "stsb",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"sentence1": "text1",
"sentence2": "text2",
"label": "target"
}
},
{
"config": "mnli",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation_matched"
},
"col_mapping": {
"premise": "text1",
"hypothesis": "text2",
"label": "target"
}
},
{
"config": "mnli_mismatched",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"premise": "text1",
"hypothesis": "text2",
"label": "target"
}
},
{
"config": "mnli_matched",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"premise": "text1",
"hypothesis": "text2",
"label": "target"
}
},
{
"config": "qnli",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"question": "text1",
"sentence": "text2",
"label": "target"
}
},
{
"config": "rte",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"sentence1": "text1",
"sentence2": "text2",
"label": "target"
}
},
{
"config": "wnli",
"task": "text-classification",
"task_id": "natural_language_inference",
"splits": {
"train_split": "train",
"eval_split": "validation"
},
"col_mapping": {
"sentence1": "text1",
"sentence2": "text2",
"label": "target"
}
}
]
},
"siblings": [
{
"rfilename": ".gitattributes"
},
{
"rfilename": "README.md"
},
{
"rfilename": "ax/test-00000-of-00001.parquet"
},
{
"rfilename": "cola/test-00000-of-00001.parquet"
},
{
"rfilename": "cola/train-00000-of-00001.parquet"
},
{
"rfilename": "cola/validation-00000-of-00001.parquet"
},
{
"rfilename": "mnli/test_matched-00000-of-00001.parquet"
},
{
"rfilename": "mnli/test_mismatched-00000-of-00001.parquet"
},
{
"rfilename": "mnli/train-00000-of-00001.parquet"
},
{
"rfilename": "mnli/validation_matched-00000-of-00001.parquet"
},
{
"rfilename": "mnli/validation_mismatched-00000-of-00001.parquet"
},
{
"rfilename": "mnli_matched/test-00000-of-00001.parquet"
},
{
"rfilename": "mnli_matched/validation-00000-of-00001.parquet"
},
{
"rfilename": "mnli_mismatched/test-00000-of-00001.parquet"
},
{
"rfilename": "mnli_mismatched/validation-00000-of-00001.parquet"
},
{
"rfilename": "mrpc/test-00000-of-00001.parquet"
},
{
"rfilename": "mrpc/train-00000-of-00001.parquet"
},
{
"rfilename": "mrpc/validation-00000-of-00001.parquet"
},
{
"rfilename": "qnli/test-00000-of-00001.parquet"
},
{
"rfilename": "qnli/train-00000-of-00001.parquet"
},
{
"rfilename": "qnli/validation-00000-of-00001.parquet"
},
{
"rfilename": "qqp/test-00000-of-00001.parquet"
},
{
"rfilename": "qqp/train-00000-of-00001.parquet"
},
{
"rfilename": "qqp/validation-00000-of-00001.parquet"
},
{
"rfilename": "rte/test-00000-of-00001.parquet"
},
{
"rfilename": "rte/train-00000-of-00001.parquet"
},
{
"rfilename": "rte/validation-00000-of-00001.parquet"
},
{
"rfilename": "sst2/test-00000-of-00001.parquet"
},
{
"rfilename": "sst2/train-00000-of-00001.parquet"
},
{
"rfilename": "sst2/validation-00000-of-00001.parquet"
},
{
"rfilename": "stsb/test-00000-of-00001.parquet"
},
{
"rfilename": "stsb/train-00000-of-00001.parquet"
},
{
"rfilename": "stsb/validation-00000-of-00001.parquet"
},
{
"rfilename": "wnli/test-00000-of-00001.parquet"
},
{
"rfilename": "wnli/train-00000-of-00001.parquet"
},
{
"rfilename": "wnli/validation-00000-of-00001.parquet"
}
]
},
"status": 200,
"error": ""
}
--- STDERR ---
[OK] test_hf_dataset_info.py
--------------------------------------------------------------------------------
=== Running test_hf_spaces_search.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_spaces_search.py
--- INPUT (snippet) ---
tool.forward(
query="document Q&A",
sort="likes",
direction="descending",
limit=5,
)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"results": [],
"status": 200,
"error": "",
"params": {
"search": "document Q&A",
"sort": "likes",
"direction": "descending",
"limit": 5
}
}
--- STDERR ---
[OK] test_hf_spaces_search.py
--------------------------------------------------------------------------------
=== Running test_hf_space_info.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_space_info.py
--- INPUT (snippet) ---
tool.forward(repo_id=repo_id)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
# Fallback: encode to UTF-8 to avoid Windows cp1252 issues
try:
print(result_json_str.encode("utf-8", errors="replace").decode("utf-8", errors="replace"))
except Exception:
print("<unprintable> due to encoding error")
if __name__ == "__main__":
main()
--- STDOUT ---
<unprintable> due to encoding error
--- STDERR ---
[OK] test_hf_space_info.py
--------------------------------------------------------------------------------
=== Running test_hf_user_info.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_user_info.py
--- INPUT (snippet) ---
tool.forward(username=username)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"item": {},
"status": 404,
"error": "http_404",
"visibility": "public",
"access": "no_access"
}
--- STDERR ---
[OK] test_hf_user_info.py
--------------------------------------------------------------------------------
=== Running test_hf_collections_list.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_collections_list.py
--- INPUT (snippet) ---
tool.forward(owner=None) # or set an owner like "huggingface"
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"results": [
{
"type": "collection",
"id": "deepseek-ai/deepseek-v31-68a491bed32bd77e7fca048f",
"owner": "deepseek-ai",
"title": "DeepSeek-V3.1",
"url": "https://huggingface.co/collections/deepseek-ai/deepseek-ai/deepseek-v31-68a491bed32bd77e7fca048f",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "facebook/dinov3-68924841bd6b561778e31009",
"owner": "facebook",
"title": "DINOv3",
"url": "https://huggingface.co/collections/facebook/facebook/dinov3-68924841bd6b561778e31009",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "AIDC-AI/ovis25-689ec1474633b2aab8809335",
"owner": "AIDC-AI",
"title": "Ovis2.5",
"url": "https://huggingface.co/collections/AIDC-AI/AIDC-AI/ovis25-689ec1474633b2aab8809335",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "nvidia/nvidia-nemotron-689f6d6e6ead8e77dd641615",
"owner": "nvidia",
"title": "NVIDIA Nemotron",
"url": "https://huggingface.co/collections/nvidia/nvidia/nvidia-nemotron-689f6d6e6ead8e77dd641615",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd",
"owner": "ByteDance-Seed",
"title": "Seed-OSS",
"url": "https://huggingface.co/collections/ByteDance-Seed/ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "google/gemma-3-release-67c6c6f89c4f76621268bb6d",
"owner": "google",
"title": "Gemma 3 Release",
"url": "https://huggingface.co/collections/google/google/gemma-3-release-67c6c6f89c4f76621268bb6d",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "openai/gpt-oss-68911959590a1634ba11c7a4",
"owner": "openai",
"title": "gpt-oss",
"url": "https://huggingface.co/collections/openai/openai/gpt-oss-68911959590a1634ba11c7a4",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "Qwen/qwen3-67dd247413f0e2e4f653967f",
"owner": "Qwen",
"title": "Qwen3",
"url": "https://huggingface.co/collections/Qwen/Qwen/qwen3-67dd247413f0e2e4f653967f",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "nvidia/nemotron-pre-training-dataset-689d9de36f84279d83786b35",
"owner": "nvidia",
"title": "Nemotron-Pre-Training-Dataset",
"url": "https://huggingface.co/collections/nvidia/nvidia/nemotron-pre-training-dataset-689d9de36f84279d83786b35",
"visibility": "public",
"access": "accessible"
},
{
"type": "collection",
"id": "inclusionAI/ui-venus-689f2fb01a4234cbce91c56a",
"owner": "inclusionAI",
"title": "UI-Venus",
"url": "https://huggingface.co/collections/inclusionAI/inclusionAI/ui-venus-689f2fb01a4234cbce91c56a",
"visibility": "public",
"access": "accessible"
}
],
"status": 200,
"error": ""
}
--- STDERR ---
[OK] test_hf_collections_list.py
--------------------------------------------------------------------------------
=== Running test_hf_collection_get.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_collection_get.py
--- INPUT (snippet) ---
tool.forward(namespace=namespace, slug_id=slug_id)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"item": {},
"status": 404,
"error": "http_404"
}
--- STDERR ---
[OK] test_hf_collection_get.py
--------------------------------------------------------------------------------
=== Running test_hf_paper_info.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_paper_info.py
--- INPUT (snippet) ---
tool.forward(arxiv_id=arxiv_id)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"item": {
"id": "1706.03762",
"authors": [
{
"_id": "6411c77d6b75ddced38902b6",
"user": {
"_id": "60fe1b231a3e6f5129776bf9",
"avatarUrl": "/avatars/c80edad5267c6ed9ecde9b056993d5c3.svg",
"isPro": false,
"fullname": "Ashish Vaswani",
"user": "ashishvaswanigoogle",
"type": "user"
},
"name": "Ashish Vaswani",
"status": "extracted_pending",
"statusLastChangedAt": "2023-03-15T09:40:25.803Z",
"hidden": false
},
{
"_id": "6411c77d6b75ddced38902b7",
"name": "Noam Shazeer",
"hidden": false
},
{
"_id": "6411c77d6b75ddced38902b8",
"user": {
"_id": "60fee8e1465daccb9f332e2f",
"avatarUrl": "/avatars/ea92bf15e181b4f13a70f18ee3ba7a51.svg",
"isPro": false,
"fullname": "Niki Parmar",
"user": "nikip",
"type": "user"
},
"name": "Niki Parmar",
"status": "extracted_pending",
"statusLastChangedAt": "2023-03-15T09:40:25.803Z",
"hidden": false
},
{
"_id": "6411c77d6b75ddced38902b9",
"name": "Jakob Uszkoreit",
"hidden": false
},
{
"_id": "6411c77d6b75ddced38902ba",
"name": "Llion Jones",
"hidden": false
},
{
"_id": "6411c77d6b75ddced38902bb",
"name": "Aidan N. Gomez",
"hidden": false
},
{
"_id": "6411c77d6b75ddced38902bc",
"name": "Lukasz Kaiser",
"hidden": false
},
{
"_id": "6411c77d6b75ddced38902bd",
"name": "Illia Polosukhin",
"hidden": false
}
],
"publishedAt": "2017-06-12T17:57:34.000Z",
"title": "Attention Is All You Need",
"summary": "The dominant sequence transduction models are based on complex recurrent or\nconvolutional neural networks in an encoder-decoder configuration. The best\nperforming models also connect the encoder and decoder through an attention\nmechanism. We propose a new simple network architecture, the Transformer, based\nsolely on attention mechanisms, dispensing with recurrence and convolutions\nentirely. Experiments on two machine translation tasks show these models to be\nsuperior in quality while being more parallelizable and requiring significantly\nless time to train. Our model achieves 28.4 BLEU on the WMT 2014\nEnglish-to-German translation task, improving over the existing best results,\nincluding ensembles by over 2 BLEU. On the WMT 2014 English-to-French\ntranslation task, our model establishes a new single-model state-of-the-art\nBLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction\nof the training costs of the best models from the literature. We show that the\nTransformer generalizes well to other tasks by applying it successfully to\nEnglish constituency parsing both with large and limited training data.",
"upvotes": 79,
"discussionId": "641192343ea54b1aa7e2f084",
"ai_summary": "The Transformer architecture, based purely on attention mechanisms, achieves superior performance on machine translation and parsing tasks with improved parallelizability and reduced training time.",
"ai_keywords": [
"recurrent neural networks",
"convolutional neural networks",
"encoder-decoder configuration",
"attention mechanism",
"Transformer",
"BLEU score",
"WMT 2014 English-to-German translation",
"WMT 2014 English-to-French translation",
"English constituency parsing"
]
},
"status": 200,
"error": ""
}
--- STDERR ---
[OK] test_hf_paper_info.py
--------------------------------------------------------------------------------
=== Running test_hf_paper_repos.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_paper_repos.py
--- INPUT (snippet) ---
tool.forward(arxiv_id=arxiv_id)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"results": [],
"status": 200,
"error": ""
}
--- STDERR ---
[OK] test_hf_paper_repos.py
--------------------------------------------------------------------------------
=== Running test_hf_daily_papers.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_daily_papers.py
--- INPUT (snippet) ---
tool.forward()
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
try:
print(result_json_str.encode("utf-8", errors="replace").decode("utf-8", errors="replace"))
except Exception:
print("<unprintable> due to encoding error")
if __name__ == "__main__":
main()
--- STDOUT ---
<unprintable> due to encoding error
--- STDERR ---
[OK] test_hf_daily_papers.py
--------------------------------------------------------------------------------
=== Running test_hf_repo_info.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_repo_info.py
--- INPUT (snippet) ---
tool.forward(repo_type="model", repo_id="bert-base-uncased")
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"item": {
"_id": "621ffdc036468d709f174338",
"id": "google-bert/bert-base-uncased",
"private": false,
"pipeline_tag": "fill-mask",
"library_name": "transformers",
"tags": [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"coreml",
"onnx",
"safetensors",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
],
"downloads": 52064840,
"likes": 2385,
"modelId": "google-bert/bert-base-uncased",
"author": "google-bert",
"sha": "86b5e0934494bd15c9632b12f734a8a67f723594",
"lastModified": "2024-02-19T11:06:12.000Z",
"gated": false,
"disabled": false,
"mask_token": "[MASK]",
"widgetData": [
{
"text": "Paris is the [MASK] of France."
},
{
"text": "The goal of life is [MASK]."
}
],
"model-index": null,
"config": {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"tokenizer_config": {}
},
"cardData": {
"language": "en",
"tags": [
"exbert"
],
"license": "apache-2.0",
"datasets": [
"bookcorpus",
"wikipedia"
]
},
"transformersInfo": {
"auto_model": "AutoModelForMaskedLM",
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
},
"siblings": [
{
"rfilename": ".gitattributes"
},
{
"rfilename": "LICENSE"
},
{
"rfilename": "README.md"
},
{
"rfilename": "config.json"
},
{
"rfilename": "coreml/fill-mask/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel"
},
{
"rfilename": "coreml/fill-mask/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin"
},
{
"rfilename": "coreml/fill-mask/float32_model.mlpackage/Manifest.json"
},
{
"rfilename": "flax_model.msgpack"
},
{
"rfilename": "model.onnx"
},
{
"rfilename": "model.safetensors"
},
{
"rfilename": "pytorch_model.bin"
},
{
"rfilename": "rust_model.ot"
},
{
"rfilename": "tf_model.h5"
},
{
"rfilename": "tokenizer.json"
},
{
"rfilename": "tokenizer_config.json"
},
{
"rfilename": "vocab.txt"
}
],
"spaces": [
"mteb/leaderboard",
"microsoft/HuggingGPT",
"Vision-CAIR/minigpt4",
"lnyan/stablediffusion-infinity",
"multimodalart/latentdiffusion",
"mrfakename/MeloTTS",
"Salesforce/BLIP",
"shi-labs/Versatile-Diffusion",
"yizhangliu/Grounded-Segment-Anything",
"stepfun-ai/Step1X-Edit",
"H-Liu1997/TANGO",
"xinyu1205/recognize-anything",
"cvlab/zero123-live",
"hilamanor/audioEditing",
"alexnasa/Chain-of-Zoom",
"AIGC-Audio/AudioGPT",
"Audio-AGI/AudioSep",
"m-ric/chunk_visualizer",
"jadechoghari/OpenMusic",
"DAMO-NLP-SG/Video-LLaMA",
"gligen/demo",
"declare-lab/mustango",
"Yiwen-ntu/MeshAnything",
"exbert-project/exbert",
"shgao/EditAnything",
"LiruiZhao/Diffree",
"Vision-CAIR/MiniGPT-v2",
"Yuliang/ECON",
"nikigoli/countgd",
"THUdyh/Oryx",
"IDEA-Research/Grounded-SAM",
"Awiny/Image2Paragraph",
"ShilongLiu/Grounding_DINO_demo",
"OpenSound/CapSpeech-TTS",
"merve/Grounding_DINO_demo",
"yangheng/Super-Resolution-Anime-Diffusion",
"liuyuan-pal/SyncDreamer",
"XiangJinYu/SPO",
"sam-hq-team/sam-hq",
"haotiz/glip-zeroshot-demo",
"TencentARC/BrushEdit",
"Nick088/Audio-SR",
"nateraw/lavila",
"abyildirim/inst-inpaint",
"Yiwen-ntu/MeshAnythingV2",
"Pinwheel/GLIP-BLIP-Object-Detection-VQA",
"Junfeng5/GLEE_demo",
"shi-labs/Matting-Anything",
"fffiloni/Video-Matting-Anything",
"burtenshaw/autotrain-mcp",
"Vision-CAIR/MiniGPT4-video",
"linfanluntan/Grounded-SAM",
"magicr/BuboGPT",
"WensongSong/Insert-Anything",
"nvidia/audio-flamingo-2",
"multimodalart/MoDA-fast-talking-head",
"clip-italian/clip-italian-demo",
"OpenGVLab/InternGPT",
"mteb/leaderboard_legacy",
"hongfz16/3DTopia",
"yenniejun/tokenizers-languages",
"mmlab-ntu/relate-anything-model",
"amphion/PicoAudio",
"byeongjun-park/HarmonyView",
"keras-io/bert-semantic-similarity",
"MirageML/sjc",
"fffiloni/vta-ldm",
"NAACL2022/CLIP-Caption-Reward",
"society-ethics/model-card-regulatory-check",
"fffiloni/miniGPT4-Video-Zero",
"AIGC-Audio/AudioLCM",
"Gladiator/Text-Summarizer",
"SVGRender/DiffSketcher",
"ethanchern/Anole",
"LittleFrog/IntrinsicAnything",
"milyiyo/reimagine-it",
"ysharma/text-to-image-to-video",
"OpenGVLab/VideoChatGPT",
"acmc/whatsapp-chats-finetuning-formatter",
"ZebangCheng/Emotion-LLaMA",
"zakaria-narjis/photo-enhancer",
"kaushalya/medclip-roco",
"AIGC-Audio/Make_An_Audio",
"avid-ml/bias-detection",
"sonalkum/GAMA",
"topdu/OpenOCR-Demo",
"RitaParadaRamos/SmallCapDemo",
"llizhx/TinyGPT-V",
"codelion/Grounding_DINO_demo",
"flosstradamus/FluxMusicGUI",
"bartar/tokenizers",
"Tinkering/Pytorch-day-prez",
"sasha/BiasDetection",
"Pusheen/LoCo",
"Jingkang/EgoGPT-7B",
"flax-community/koclip",
"TencentARC/VLog",
"ynhe/AskAnything",
"Volkopat/SegmentAnythingxGroundingDINO",
"phyloforfun/VoucherVision"
],
"createdAt": "2022-03-02T23:29:04.000Z",
"safetensors": {
"parameters": {
"F32": 110106428
},
"total": 110106428
},
"inference": "warm",
"usedStorage": 13397387509
},
"status": 200,
"error": ""
}
--- STDERR ---
[OK] test_hf_repo_info.py
--------------------------------------------------------------------------------
=== Running test_hf_site_search.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_site_search.py
--- INPUT (snippet) ---
tool.forward(query="fine-tuning tutorial", limit=5)
try:
data = json.loads(result_json_str)
print(json.dumps(data, indent=2, ensure_ascii=False))
except Exception:
print(result_json_str)
if __name__ == "__main__":
main()
--- STDOUT ---
{
"results": [
{
"type": "site",
"title": "Fine-tuning - Hugging Face",
"url": "https://huggingface.co/docs/transformers/training",
"snippet": "Fine-tuning adapts a pretrained model to a specific task with a smaller specialized dataset. This approach requires far less data and compute compared to training a model from scratch, which makes it a more accessible option for many users. Transformers provides the Trainer API, which offers a comprehensive set of training features, for fine-tuning any of the models on the Hub.",
"date": null
},
{
"type": "site",
"title": "Fine-tune a pretrained model - Hugging Face",
"url": "https://huggingface.co/docs/transformers/v4.18.0/en/training",
"snippet": "Before you can fine-tune a pretrained model, download a dataset and prepare it for training. The previous tutorial showed you how to process data for training, and now you get an opportunity to put those skills to the test!",
"date": null
},
{
"type": "site",
"title": "Let's Fine-Tune Your Model for Function-Calling - Hugging Face",
"url": "https://huggingface.co/learn/agents-course/bonus-unit1/fine-tuning",
"snippet": "In this tutorial, we will build a function-calling model based on google/gemma-2-2b-it. We choose the fine-tuned model google/gemma-2-2b-it instead of the base model google/gemma-2-2b because the fine-tuned model has been improved for our use-case.",
"date": null
},
{
"type": "site",
"title": "Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face ...",
"url": "https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl",
"snippet": "Phil Schmid's tutorial: an excellent deep dive into fine-tuning multimodal LLMs with TRL. Merve Noyan's smol-vision repository: a collection of engaging notebooks on cutting-edge vision and multimodal AI topics.",
"date": null
},
{
"type": "site",
"title": "Fine-tuning a pretrained model - Hugging Face",
"url": "https://huggingface.co/docs/transformers/v4.15.0/training",
"snippet": "In this tutorial, we will show you how to fine-tune a pretrained model from the Transformers library. In TensorFlow, models can be directly trained using Keras and the fit method.",
"date": null
}
],
"status": 200,
"error": ""
}
--- STDERR ---
[OK] test_hf_site_search.py
--------------------------------------------------------------------------------
=== Running test_hf_report_generate.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_report_generate.py
--- INPUT (snippet) ---
tool.forward(data_json=json.dumps(data), title="Test Report")
print(html[:500]) # print first 500 chars
if __name__ == "__main__":
main()
--- STDOUT ---
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Test Report</title>
<style>
:root { --bg:#0b0d12; --fg:#e6e9ef; --muted:#9aa4b2; --card:#121621; --accent:#5ac8fa; --warn:#eab308; }
body { background:var(--bg); color:var(--fg); font-family: ui-sans-serif, system-ui, -apple-system, Segoe UI, Roboto, Inter, Arial, sans-serif; margin:0; padding:24px; }
h1 { font-size: 24px; margin: 0 0
--- STDERR ---
[OK] test_hf_report_generate.py
--------------------------------------------------------------------------------
=== Running test_hf_generate_dashboard_report.py ===
Command: C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\venv\Scripts\python.exe C:\Users\daqc\Documents\GitHub\open-deep-research-vulnerability-intelligence\tests\test_hf_generate_dashboard_report.py
--- INPUT (snippet) ---
tool.forward(query="semantic search", limit=5)
print(html[:500]) # print first 500 chars
if __name__ == "__main__":
main()
--- STDOUT ---
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Hugging Search � Dashboard</title>
<style>
:root { --bg:#0b0d12; --fg:#e6e9ef; --muted:#9aa4b2; --card:#121621; --accent:#5ac8fa; --warn:#eab308; }
body { background:var(--bg); color:var(--fg); font-family: ui-sans-serif, system-ui, -apple-system, Segoe UI, Roboto, Inter, Arial, sans-serif; margin:0; padding:24px; }
.container { max-wi
--- STDERR ---
[OK] test_hf_generate_dashboard_report.py
--------------------------------------------------------------------------------