datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
card_backend
Eval Cards Backend Dataset
Pre-computed evaluation data powering the Eval Cards frontend.
Generated by the eval-cards backend pipeline.
Last generated: 2026-05-05T11:30:42.961096Z
Quick Stats
Stat
Value
Models
5,678
Evaluations (benchmarks)
798
Metric-level evaluations
1321
Source configs processed
52
Benchmark metadata cards
240
File Structure
.
├── README.md # This file
├── manifest.json… See the full description on the dataset page: https://huggingface.co/datasets/evaleval/card_backend.omnimcp_python_backend_architect_teaser
🚀 OmniMCP Python Backend Architect (Evaluation Teaser + Turnkey MCP Server)
⚡ Official Free Community Teaser (50 Verified Scenarios + Executable MCP Server)🏆 Production Master Package on Gumroad:👉 Purchase Full Enterprise Package on Gumroad🏷️ Use coupon code LAUNCH20 for €20 off at checkout! (Starting at €49)
⚡ Activate in Cursor IDE & Claude Desktop in 30 Seconds
This repository now contains a zero-dependency, turnkey Model Context Protocol (MCP) server… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_python_backend_architect_teaser.backendbench_tests
TorchBench
The TorchBench suite of BackendBench is designed to mimic real-world use cases. It provides operators and inputs derived from 155 model traces found in TIMM (67), Hugging Face Transformers (45), and TorchBench (43). (These are also the models PyTorch developers use to validate performance.) You can view the origin of these traces by switching the subset in the dataset viewer to ops_traces_models and torchbench for the full dataset.
When running BackendBench, much of the… See the full description on the dataset page: https://huggingface.co/datasets/GPUMODE/backendbench_tests.theo_qwen2.5-7b-it_impulsive-whitebox-backend-parity
Status: NOT the paper's results. White-box backend parity run (2026-09-04, on the since-retired HF backend). The paper's canonical results are Misalignment-Empirics/theo_impulsive-qwen_2_5-7b-14b-32b-big_eval_results, run runs/impulsive-qwen_2_5-7b-14b-32b-20260925/.
MO_evals results
Raw per-sample results from MO_evals runs (private, CLAUDE.md §5). One directory per
upload; nothing here is aggregated — the Parquet and the .eval logs are the primary
evidence, the scorecard is… See the full description on the dataset page: https://huggingface.co/datasets/Misalignment-Empirics/theo_qwen2.5-7b-it_impulsive-whitebox-backend-parity.temp_evalcard_backend
Eval Cards Backend Dataset
Pre-computed evaluation data powering the Eval Cards frontend.
Generated by the eval-cards backend pipeline.
Last generated: 2026-04-29T01:12:58.765261Z
Quick Stats
Stat
Value
Models
5,829
Evaluations (benchmarks)
581
Metric-level evaluations
1092
Source configs processed
34
Benchmark metadata cards
85
File Structure
.
├── README.md # This file
├── manifest.json #… See the full description on the dataset page: https://huggingface.co/datasets/j-chim/temp_evalcard_backend.requestsresultsspec2026-backend-bound-top5-20260728
SPEC CPU 2026 backend-bound top-five traces
This dataset contains the exact whole-trace inputs, canonical per-SimPoint
trace bundles, and recovered SimPoint metadata used for the five highest
backend-bound results in the corrected SPEC CPU 2026 characterization.
Method
Golden Cove configuration with a 12-way D-cache.
Power modeling disabled.
20 million requested warmup instructions, shortened only at trace start.
10 million measured instructions per SimPoint.… See the full description on the dataset page: https://huggingface.co/datasets/harry1332/spec2026-backend-bound-top5-20260728.village-of-echoes-npc-backend
Village of Echoes — NPC Brain Backend
Serveur HTTP local qui fait tourner le cerveau des PNJ du jeu.Reçoit les actions du joueur depuis Unity et renvoie des réponses générées en temps réel par un modèle IA fine-tuné.
Table des matières
Architecture
Prérequis
Contenu du package
Installation
Démarrage des serveurs
Vérification
Référence API
Intégration Unity
PNJ disponibles
Ajouter ou modifier un PNJ
Configuration avancée
Dépannage
Architecture… See the full description on the dataset page: https://huggingface.co/datasets/DylanVivant/village-of-echoes-npc-backend.assistant-fon-backend
Assistant Fon — backend
Backend FastAPI pour l'app Android Assistant Fon : guide vocale en fon pour
remplir des formulaires administratifs.
Pipeline
/guide -> consigne audio (.wav pre-genere) selon le type de champ
/transcrire -> audio fon -> ASR -> routage -> valeur + audio de confirmation
ASR : Professor/mms-300m-fongbe (Wav2Vec2ForCTC, CPU)
TTS : facebook/mms-tts-fon (VitsModel, CPU)
LLM : DeepSeek, uniquement pour les champs a texte libre (jamais sur… See the full description on the dataset page: https://huggingface.co/datasets/octavebahoun/assistant-fon-backend.backend-code-generator-dataset
Backend Code Generation Dataset
Dataset Description
This dataset contains examples for training AI models to generate backend application code. It includes descriptions of backend requirements paired with complete, functional code implementations across multiple frameworks and programming languages.
Dataset Summary
The Backend Code Generation Dataset is designed to train models that can generate complete backend applications from natural language descriptions.… See the full description on the dataset page: https://huggingface.co/datasets/Techta/backend-code-generator-dataset.backend-api-instruction-dataset
Backend & API Development Dataset
Instruction dataset focused on RESTful API design, WebSocket real-time communication, microservices patterns, and API best practices.
Dataset Details
Dataset Description
This is a high-quality instruction-tuning dataset focused on Backend Api topics. Each entry includes:
A clear instruction/question
Optional input context
A detailed response/solution
Chain-of-thought reasoning process
Curated by: CloudKernel.IO… See the full description on the dataset page: https://huggingface.co/datasets/bernabepuente/backend-api-instruction-dataset.omnimcp_cloud_resilience_backend_village_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_cloud_resilience_backend_village_teaser.backendDepending on whether you want to use lighteval or lm_eval for your evaluations, you might need to complete the
requirements.txt file to contain relevant dependencies.
You'll also need to select, in app.py, whether you want to use the ligtheval or lm_eval by selecting the correct
import and commenting the other.
All env variables that you should need to edit to launch the evaluations should be in envs.
experts-backendssahara-backend
Sahara Backend
FastAPI ML backend for the Sahara mental health monitoring platform (SIH 2026).
backend_runs_directoryfullstack-backend-trainingPsychiatry_datasetsite_backendredditpoliticsoct2016radarpoliticaldatasetredditscrap11272024final_birmingham_episodes12282024backendbench-qwen-qwen3-coderbackendbench-z-ai-glm-4.5-airrmi-backend
🛡️ Rug Munch Intelligence — MCP Server
AI-Powered Crypto Security. 69 Tools. Don't Get Rugged.
Crypto scam detection • Rug pull prevention • Wallet forensics • Market intelligence • Social sentiment
Built for AI agents (Claude, Cursor, Windsurf, ChatGPT). Accessible via pip install or direct HTTP.
🚀 Quick Start
pip install rug-munch-intelligence-mcp
Add to Claude Desktop / Cursor / Windsurf:
{
"mcpServers": {
"rug-munch-intelligence": {… See the full description on the dataset page: https://huggingface.co/datasets/rugmunchmedia/rmi-backend.bitjv-backendredditpolitics11292024backendbench-prime-intellect-intellect-3smolified-backend-and-devops-buddy
🤏 smolified-backend-and-devops-buddy
Intelligence, Distilled.
This is a synthetic training corpus generated by the Smolify Foundry.
It was used to train the corresponding model draganite/smolified-backend-and-devops-buddy.
📦 Asset Details
Origin: Smolify Foundry (Job ID: 68bd3135)
Records: 1890
Type: Synthetic Instruction Tuning Data
⚖️ License & Ownership
This dataset is a sovereign asset owned by draganite.
Generated via Smolify.ai.