programming
Nemotron-SFT-Competitive-Programming-v2
Dataset Description:
Nemotron-Competitive-Programming-v2 is a large-scale synthetic coding and reasoning dataset designed to push LLM performance on challenging programming and systems tasks. It combines Python and C++ samples across unique competitive programming questions.In addition, the dataset includes samples targetting a subset of programming language exercises from Exercism.
Beyond problem solving, the dataset includes Text-to-SQL, which aims at training LLMs to reason and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Competitive-Programming-v2.Nemotron-Competitive-Programming-v1
Dataset Description:
Nemotron-Competitive-Programming-v1 is a large-scale synthetic coding and reasoning dataset designed to push LLM performance on challenging programming and systems tasks. It combines Python and C++ samples across unique competitive programming questions.
Beyond problem solving, the dataset includes InfiniByte, a cross-domain subset with problems derived from scientific fields.
This dataset is ready for commercial use.
Competitive Coding
The… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Competitive-Programming-v1.latent-programming-horizons-trajs
latent-programming-horizons-trajs
Agent trajectories and per-edit correctness labels from the
program-probes project, which
measures whether a language model's internal hidden states linearly predict
properties of its own agentic output (e.g. "does the code currently compile?")
before those properties are realised.
Each trajectory is a run of a coding agent (mini-SWE-agent) attempting a
SWE-bench (Verified or Pro) instance. This dataset contains the raw
transcripts and labels… See the full description on the dataset page: https://huggingface.co/datasets/ASSERT-KTH/latent-programming-horizons-trajs.Competitive-Programming-python-blend
Dataset Card for Competitive-Programming-python-blend
Summary
Competitive-Programming-python-blend is a mixed supervised fine-tuning dataset centered on competitive programming, code reasoning, and instruction-style problem solving. The blend is Python-first, but it also keeps a small amount of C++, agentless SWE, and reasoning-oriented chat supervision to broaden training coverage.
The current release is published as a single HF-friendly JSONL file, clean.jsonl.… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Competitive-Programming-python-blend.SciCode-Programming-Problems
DATA3: Programming Problems Generation Dataset
Dataset Overview
DATA3 is a large-scale programming problems generation dataset that contains AI-generated programming problems inspired by real scientific computing code snippets. The dataset consists of 22,532 programming problems, each paired with a comprehensive solution. These problems focus on scientific computing concepts such as numerical algorithms, data analysis, mathematical modeling, and computational methods in… See the full description on the dataset page: https://huggingface.co/datasets/SciCodePile/SciCode-Programming-Problems.textbook_quality_programming
Dataset Card for "textbook_quality_programming"
Synthetic programming textbooks generated with GPT-3.5 and retrieval. Very high quality, aimed at being used in a phi replication. Currently 115M tokens. Covers many languages and technologies, with a bias towards python.
~10k of the books (65M tokens) use an older generation method, and average 6k tokens in length. ~1.5k books (50M tokens) use a newer generation method, with a more detailed outline, and average 33k tokens in… See the full description on the dataset page: https://huggingface.co/datasets/vikp/textbook_quality_programming.