datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
trlc-dk1-demos-optimizedThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"observation.state": {
"dtype": "float32",
"shape": [
14
],
"names": [
"left_joint_1.pos",
"left_joint_2.pos",
"left_joint_3.pos",
"left_joint_4.pos",
"left_joint_5.pos"… See the full description on the dataset page: https://huggingface.co/datasets/nikolas-k/trlc-dk1-demos-optimized.Optimized_Video_Facial_Landmarks
Dataset Card for 478-Point Normalized 3D Facial Landmark Dataset
Dataset Description
This dataset provides pre-extracted, normalized 3D facial landmark features derived from the Video Emotion dataset. It is optimized for efficient training of emotion recognition and facial analysis models, bypassing the need to process large raw video files.
License: The extracted feature data in this Parquet file is licensed under Apache 2.0. Note that the original source video files may… See the full description on the dataset page: https://huggingface.co/datasets/PSewmuthu/Optimized_Video_Facial_Landmarks.mcp-server-bench-gradio-optimized
🔬 Gradio vs FastMCP Benchmark Report
Generated: 2026-03-02T13:04:10.857215
Total scenarios: 48
Executive Summary
echo: Fastmcp wins (96.6 vs 176.5 RPS, 1.83x difference)
Gradio best config: concurrency_limit=nan
fibonacci: Fastmcp wins (43.5 vs 57.1 RPS, 1.31x difference)
Gradio best config: concurrency_limit=nan
async_sleep: Gradio wins (93.1 vs 80.2 RPS, 1.16x difference)
Gradio best config: concurrency_limit=nan
payload_echo: Fastmcp wins (82.5 vs 164.8 RPS… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/mcp-server-bench-gradio-optimized.mcp-server-bench-gradio-optimized-full-bench
🔬 Gradio vs FastMCP Benchmark Report
Generated: 2026-03-02T21:04:48.460108
Total scenarios: 337
Executive Summary
echo: Fastmcp wins (100.9 vs 189.9 RPS, 1.88x difference)
Gradio best config: concurrency_limit=1.0
fibonacci: Fastmcp wins (45.5 vs 55.0 RPS, 1.21x difference)
Gradio best config: concurrency_limit=nan
json_transform: Fastmcp wins (94.5 vs 165.4 RPS, 1.75x difference)
Gradio best config: concurrency_limit=5.0
async_sleep: Fastmcp wins (94.0 vs 104.3… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/mcp-server-bench-gradio-optimized-full-bench.llama-2-optimized-product-titles-esci-4-7-temp
Dataset Card for "llama-2-optimized-product-titles-esci-4-7-temp"
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llama_2_optimized_product_titles-esci-test-sft
Dataset Card for "llama_2_optimized_product_titles-esci-test-sft"
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llama-2-optimized-product-titles-esci-test-sft-temp
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llama_2-optimized-titles-esci-sft-test
Dataset Card for "llama_2-optimized-titles-esci-sft-test"
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llama_2_optimized_product_titles-esci-test
Dataset Card for "llama_2_optimized_product_titles-esci-test"
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llama_2_optimized_product_titles-esci-temp
Dataset Card for "llama_2_optimized_product_titles-esci-temp"
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llama-2-optimized-product-titles-esci-test-temp
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llama_2-optimized-titles-esci-sft-all
Dataset Card for "llama_2-optimized-titles-esci-sft-all"
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cuda-optimized-modelsllama_2-optimized-titles-esci-sft-test-2
Dataset Card for "llama_2-optimized-titles-esci-sft-test-2"
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optimized-parquet-demollama_2-optimized-titles-esci-sft-train
Dataset Card for "llama_2-optimized-titles-esci-sft-train"
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llama_2_optimized_product_titles-esci
Dataset Card for "llama_2_optimized_product_titles-esci"
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graph-data-quantum-optimizedllama_2_optimized_product_titles-esci-part1
Dataset Card for "llama_2_optimized_product_titles-esci-part1"
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llama_2_optimized_product_titles-esci-part2
Dataset Card for "llama_2_optimized_product_titles-esci-part2"
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llama_2_optimized_product_titles-esci-4-7
Dataset Card for "llama_2_optimized_product_titles-esci-4-7"
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