coding
lfm2.5-2.6b-fable5-coding-agent-hereticgemma4-coding-agentRhea-4B-Coding-max-i1-GGUFZiya-Coding-34B-v1.0-GGUFQwen3.8-27B-Fable-5-Coding-Distilled-i1-GGUFQwen3.6-27B-Uncensored-Heretic-DSV4Pro-GLM52-SFT-GPT55-RL-Coding-VL-MTP-GGUFqwen3.5-18b-a3b-reap-coding-heretic-v0-i1-GGUFQwen3.8-27B-Fable-5-Coding-Distilled-GGUF
Datasets
All datasets matching “coding”coding
Dataset Card for "livebench/coding"
LiveBench is a benchmark for LLMs designed with test set contamination and objective evaluation in mind. It has the following properties:
LiveBench is designed to limit potential contamination by releasing new questions monthly, as well as having questions based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses.
Each question has verifiable, objective ground-truth answers, allowing hard questions to be scored… See the full description on the dataset page: https://huggingface.co/datasets/livebench/coding.Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
🧬 Omni-Frontier Collection
Cybersecurity · Coding · Math · Science · RSI Reasoning — one unified SFT package
A unified, deduplicated, fully-browsable distillation & SFT corpus — every row real, every row visible.
📖 Jump to
What's inside · 🔁 Aggregation audit · 🛡 Cybersecurity · 💻 Coding · 🏭 Distillation deep-dive · 🔁 RSI · 🧮 Math/Science/More · 🎓 Training guide · 🔎 Browsing · 🧹 Quality · 🗺 Roadmap · 📄 License… See the full description on the dataset page: https://huggingface.co/datasets/SHSLab/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.verifiable-coding-problems
SYNTHETIC-1
This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here
verifiable-coding-problems-python
Dataset Card for Verifiable Coding Problems Python 10k
This dataset contains all Python problems from PrimeIntellect's verifiable-coding-problems dataset. We have formatted the verification_info and metadata columns to be proper dictionaries, but otherwise the data is the same. Please see their dataset for more details.
S10-Citadel-Core
Run and deploy your AI Studio app
This contains everything you need to run your app locally.
Run Locally
Prerequisites: Node.js
Install dependencies:
npm install
Set the GEMINI_API_KEY in .env.local to your Gemini API key
Run the app:
npm run dev
llama-coding-agent-evals