datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wikipedia-en-julian
Wikipedia English - JULIAN Training Dataset
This dataset contains cleaned English Wikipedia articles used to train the JULIAN-100M language model.
Dataset Structure
Data Fields
title (string): Article title
text (string): Full article text (cleaned and formatted)
url (string): Original Wikipedia URL
language (string): Language code ("en")
Data Example
{
"title": "Artificial Intelligence",
"text": "Artificial intelligence (AI) is intelligence… See the full description on the dataset page: https://huggingface.co/datasets/JulianKrgd/wikipedia-en-julian.SynthUI-Code-2k-v1Synth UI 🎹
https://www.synthui.design
Dataset details
This dataset aims to provide a diverse collection of NextJS code snippets, along with their corresponding instructions, to facilitate the training of language models for NextJS-related tasks. It is designed to cover a wide range of NextJS functionalities, including UI components, routing, state management, and more.
This dataset consists of:
Note: The dataset is seperated into two main parts:
raw Contains only the… See the full description on the dataset page: https://huggingface.co/datasets/JulianAT/SynthUI-Code-2k-v1.wikipedia-fr-julian
Wikipedia French - JULIAN Training Dataset
Ce dataset contient les articles Wikipedia français nettoyés utilisés pour entraîner le modèle de langage JULIAN-100M.
Description du Dataset
Langue: Français
Source: Dumps Wikipedia (dernière version disponible)
Taille: 950 millions de tokens (7.3GB JSONL, ~1.5-2GB Parquet)
Format: Articles nettoyés avec titre, texte et URL
Licence: Creative Commons Attribution-ShareAlike 3.0
Structure du Dataset
Champs de… See the full description on the dataset page: https://huggingface.co/datasets/JulianKrgd/wikipedia-fr-julian.finmix-autoscientist-10k
FinMix AutoScientist 10k
A deterministic, upload-ready 10,000-row subset of
FinMix v1, created for
fast finance adaptation runs in the Adaption AutoScientist challenge.
Use with Adaption Adaptive Data
Import this Hugging Face dataset and map:
Prompt: prompt
Context: context
Completion: completion
Leave task_type, source, and group_key unmapped. They are retained for
provenance and auditing.
Fields
Field
Description
prompt
Financial… See the full description on the dataset page: https://huggingface.co/datasets/julian8897/finmix-autoscientist-10k.smoltalk-gpt2-sft
SmolTalk-GPT2-SFT
A fully tokenized version of the entire HuggingFaceTB/smoltalk
(all config) dataset — converted from raw multi-turn conversations into ready-to-train
(token_id, loss_mask) pairs using the GPT-2 BPE tokenizer (vocab=50257). Drop
it straight into a PyTorch trainer for supervised fine-tuning (SFT) of any
GPT-2-vocab language model — no template parsing, no role tagging, no chat-template
gymnastics required.
Overview
Source: HuggingFaceTB/smoltalk (all… See the full description on the dataset page: https://huggingface.co/datasets/juliannunezb/smoltalk-gpt2-sft.SynthUI-Code-Instruct-2k-v1Synth UI 🎹
https://www.synthui.design
Dataset details
This dataset aims to provide a diverse collection of NextJS code snippets, along with their corresponding instructions, to facilitate the training of language models for NextJS-related tasks. It is designed to cover a wide range of NextJS functionalities, including UI components, routing, state management, and more.
This dataset consists of:
Note: The dataset is seperated into two main parts:
raw Contains only the… See the full description on the dataset page: https://huggingface.co/datasets/JulianAT/SynthUI-Code-Instruct-2k-v1.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.lsmp-rural-cad
LSMP Rural CAD Dataset
This dataset contains labeled rural residence floor plans for training CAD generation models.
Dataset Structure
train.jsonl: Training data in JSON Lines format (90% of data)
eval.jsonl: Evaluation data in JSON Lines format (10% of data)
Data Format
Each sample contains:
instruction: Fixed instruction for floor plan generation
input: Plot size, room requirements, style preference, and rural residence features
output: SVG parameters for… See the full description on the dataset page: https://huggingface.co/datasets/julialovenary/lsmp-rural-cad.RLVR-Math-16k
RLVR-Math-16k
A curated math reasoning dataset for RLVR (Reinforcement Learning with Verifiable Rewards) training.
Dataset Summary
Split
Samples
train
16,384
test
842
Total
17,226
Source Datasets
train
Source
Samples
hiyouga/math12k
10,476
nlile/NuminaMath-1.5-RL-Verifiable/amc_aime
3,075
nlile/NuminaMath-1.5-RL-Verifiable/olympiads
2,833
test
Source
Samples
hiyouga/math12k
500… See the full description on the dataset page: https://huggingface.co/datasets/Julian2002/RLVR-Math-16k.Julia-Proof-Pile-2Julia-Proof-Pile-2
This dataset is part of Proof-Pile-2 dataset. This dataset is consisting of mathematical code, including numerical computing, computer algebra, and formal mathematics.
This entire dataset is in Julia language. It is slightly more than 0.5 Billion tokens. I have removed Meta data from this dataset hence you can directly use it for training purpose.
This dataset is in Jsonl format.
julia-manuals
Julia Programming Language Documentation
This dataset contains the Julia programming language documentation,
chunked using semantic parsing for pretraining language models.
Updated: 2025-09-08
Loading
from datasets import load_dataset
ds = load_dataset("json", data_files={"train": "train.jsonl"}, split="train")
Statistics
Format: JSONL with single text field per line
Chunking: Semantic structure-aware chunking
Content: Official Julia documentation and… See the full description on the dataset page: https://huggingface.co/datasets/jusjinuk/julia-manuals.ultrafeedback-gpt2-dpo
UltraFeedback-GPT2-DPO
A fully tokenized version of HuggingFaceH4/ultrafeedback_binarized
converted into ready-to-train (prompt_ids, chosen_response_ids, rejected_response_ids) triples
using the GPT-2 BPE tokenizer (vocab=50257). Drop it straight into a PyTorch DPO trainer
for Direct Preference Optimization of any GPT-2-vocab language model.
Overview
Source: HuggingFaceH4/ultrafeedback_binarized — 61K preference pairs binarized from UltraFeedback (Cui et al., 2023)… See the full description on the dataset page: https://huggingface.co/datasets/juliannunezb/ultrafeedback-gpt2-dpo.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.