datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
code-generation-sft-100k
Code Generation SFT (100K)
100,000 ShareGPT conversations covering code generation across 8 programming languages, 21 categories, and 22 distinct programming tasks. Each example includes a detailed natural language request and a complete, working implementation with explanations of key design decisions.
Motivation
Coding assistants are the highest-adoption LLM application category, but most open training datasets focus on isolated functions without context. This… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/code-generation-sft-100k.LiveCodeBench-CodeGenerationsvg-code-generationThis training data was generated using GPT-4o/Gemini-2o as part of the 'Drawing with LLM' competition (https://www.kaggle.com/competitions/drawing-with-llms).
It can be used to fine-tune small language models for the competition or serve as an augmentation dataset alongside other data sources.
The dataset is generated in two steps using the GPT-4o model.
In the first step, topic descriptions relevant to the competition are generated using a specific prompt.
By running this prompt multiple… See the full description on the dataset page: https://huggingface.co/datasets/vinoku89/svg-code-generation.radon-test-code_generation
radon-test-code_generation
Description
Code generation test dataset for RADON model evaluation with programming prompts
Usage
Load Dataset
from datasets import load_dataset
dataset = load_dataset("MagistrTheOne/radon-test-code_generation")
print(dataset)
Use with RADON Model
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load RADON model
model = AutoModelForCausalLM.from_pretrained("MagistrTheOne/RadonSAI")
tokenizer =… See the full description on the dataset page: https://huggingface.co/datasets/MagistrTheOne/radon-test-code_generation.code_generation
Visualization of Code Generation Task Cases Samples
Check dataset samples visualization by viewing Dataset Viewer.
The sampling procedure is guided by the Elo distribution introduced in our method.
Original dataset is release_v5 of livecodebench/code_generation_lite from hugging face.
samples/origin: 879/880
License
This repository is licensed under the Apache License 2.0
code_generationCodeGeneration-IQuest
CodeGeneration-IQuest
Execution-based Python code-generation prompts for reinforcement-learning post-training, in the verl rule-reward schema. Each row is a single-turn competitive-programming problem whose reward is computed by executing the model's program against a hidden test suite — a program passes only if every case matches. The collection unifies two execution-scorable sources (Code-Contests-O and DeepCoder) and then difficulty-filters them ("goldilocks", see below) so… See the full description on the dataset page: https://huggingface.co/datasets/OctoReasoner/CodeGeneration-IQuest.