datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mixed-pretrain-10b-gpt2
Mixed Pretraining 10B (GPT-2 BPE)
A 10-billion-token pretraining dataset, GPT-2 BPE tokenized, assembled as a
diverse mix of web text, books, Wikipedia, code, academic papers, Q&A and
instruction-formatted conversations.
Built to train a ~500M parameter from-scratch GPT-2-style transformer (see
juliannunezb/transformer-lm-500m).
Mix
Source
Mix %
Tokens
Notes
fineweb
40.4%
4,039,999,700
reused from kjj0/fineweb10B-gpt2
fineweb_edu
15.2%
1,514,999,900
reused… See the full description on the dataset page: https://huggingface.co/datasets/juliannunezb/mixed-pretrain-10b-gpt2.personal-codex-model
Personal Codex Model Training Corpus
Overview
Personal Codex Model Training Corpus is a provenance-aware, repository-level dataset for causal
language modeling, code completion, continued pretraining, and coding assistant adaptation. It is
built from source files present in local Git repository checkouts at a defined collection point.
The dataset prioritizes broad, authentic software-engineering coverage while retaining enough
metadata to audit every emitted… See the full description on the dataset page: https://huggingface.co/datasets/JulianAT/personal-codex-model.julia-focal-method
📚 julia-focal-method
This dataset is designed as a validation benchmark for evaluating unit test generation capabilities of models for Julia code, particularly in low-resource settings.
Purpose: Evaluate how well a model can generate test code, given a focal function.
Data: Each example provides:
module_name: the Julia module where the function is defined.
function_name: the name of the focal function.
focal_code: the raw source code of the function (used for context).
repo: the… See the full description on the dataset page: https://huggingface.co/datasets/dongg18/julia-focal-method.