Datasets:
pretty_name: JumpTrace-3K
language:
- en
- fa
task_categories:
- text-generation
task_ids:
- language-modeling
license: cc-by-4.0
size_categories:
- 1K<n<10K
tags:
- coding
- agentic-ai
- software-engineering
- repository-understanding
- debugging
- code-review
- tdd
- persian
- code-generation
🚀 JumpForge-3K
Agentic Coding Traces for Modern Software Engineering
جامپفورج-۳کی | مجموعه دادهای برای عاملهای کدنویس و مهندسی نرمافزار مدرن
Overview
JumpForge-3K is a synthetic dataset designed for training and evaluating agentic coding systems.
The dataset focuses on realistic software engineering workflows, including repository understanding, debugging, code review, test-driven development, tool-use planning, and multi-file reasoning.
Built by JumpLander, this dataset serves as a foundation for experimentation in agentic AI and coding assistants.
Key Capabilities
- Repository understanding
- Multi-file reasoning
- Bug fixing workflows
- Code review tasks
- Test-first development
- Refactoring scenarios
- API design reasoning
- Tool-use planning
Dataset Information
| Property | Value |
|---|---|
| Dataset Name | JumpForge-3K |
| Total Samples | 3,000 |
| Train Split | 2,700 |
| Validation Split | 300 |
| Languages | English, Persian |
| License | CC BY 4.0 |
| Format | JSONL |
Task Categories
| Task Type | Description |
|---|---|
| Repository Understanding | Analyze project structure and architecture |
| Bug Fixing | Identify and resolve software defects |
| Code Review | Review and improve existing code |
| Test-Driven Development | Design implementations from tests |
| Refactoring | Improve maintainability and structure |
| API Design | Create or improve API interfaces |
| Debugging Traces | Reason through failure scenarios |
| Tool Use Planning | Plan coding-agent tool execution |
Dataset Schema
Each JSONL record contains the following fields:
| Field | Type | Description |
|---|---|---|
id |
string | Unique sample identifier |
dataset |
string | Dataset name |
brand |
string | Branding information |
task_type |
string | Task category |
language |
string | Programming language |
difficulty |
string | Difficulty level |
title |
string | Task title |
domain |
string | Domain or industry |
user_story |
string | User scenario |
repo_context |
string | Repository context |
instruction |
string | Agent instruction |
reference_solution |
string | Reference solution outline |
evaluation |
object | Evaluation criteria |
tags |
array | Metadata tags |
Example Record
{
"id": "JT3K-000722",
"dataset": "JumpForge-3K",
"brand": "JumpForge-3K",
"task_type": "tool_use",
"language": "python",
"difficulty": "easy",
"title": "Sequence tool calls",
"domain": "creator-tools",
"user_story": "Construct a tool-use plan for fixing an import error across multiple files.",
"repo_context": "Repo: ml-pipeline. Domain: creator-tools.",
"instruction": "Create a tool-use plan and define the optimal workflow.",
"reference_solution": "Inspect files, isolate failure source, patch minimally, run tests, verify results.",
"evaluation": {
"checks": [
"tool order sensible",
"minimal edit strategy",
"verification included"
]
},
"tags": [
"agentic",
"coding",
"tool_use",
"python"
]
}
Loading the Dataset
Install Dependencies
pip install datasets
Load Dataset
from datasets import load_dataset
dataset = load_dataset("jumplander/JumpForge-3K")
train_dataset = dataset["train"]
validation_dataset = dataset["validation"]
print(train_dataset[0]["title"])
Training Example (Unsloth)
from datasets import load_dataset
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
"unsloth/Qwen2.5-Coder-1.5B-Instruct",
load_in_4bit=True,
)
dataset = load_dataset(
"jumplander/JumpForge-3K",
split="train"
)
def format_example(example):
return {
"text": f"""### Instruction:
{example['instruction']}
### Repository Context:
{example['repo_context']}
### Solution:
{example['reference_solution']}"""
}
dataset = dataset.map(format_example)
Suggested Use Cases
| Use Case | Description |
|---|---|
| SFT Training | Fine-tuning coding assistants |
| Agent Research | Agent workflow experiments |
| Repository Reasoning | Multi-file reasoning benchmarks |
| Tool Use Studies | Tool-planning evaluation |
| DPO/RLHF Prototyping | Preference optimization research |
| Persian Coding Research | Persian-focused coding experiments |
Dataset Statistics
Languages
| Language | Approximate Count |
|---|---|
| English | 2,500+ |
| Persian | 500+ |
Difficulty Distribution
| Difficulty | Approximate Count |
|---|---|
| Easy | 1,000 |
| Medium | 1,500 |
| Hard | 500 |
Programming Languages
| Language | Approximate Count |
|---|---|
| Python | 2,000+ |
| JavaScript | 500+ |
| SQL | 300+ |
| TypeScript | 150+ |
| Bash | 50+ |
Notes
This dataset is synthetic and intended primarily for:
- Format prototyping
- Agent workflow experimentation
- Benchmark development
- Fine-tuning research
It should not be considered sufficient as the sole training source for production-grade coding models.
License
This dataset is distributed under the CC BY 4.0 License.
Attribution is required when redistributing or creating derivative works.
Citation
@misc{jumpforge3k,
title={JumpForge-3K: Agentic Coding Traces for Modern Software Engineering},
author={JumpLander Team},
year={2026},
publisher={Hugging Face}
}
🚀 Built by JumpLander
Official Website: jumplander.org
Hugging Face Dataset: JumpForge-3K
About the Brand: This dataset and the associated "Trace" format were created by JumpLander. The organization focuses on Agentic Coding Traces and modern software engineering workflows. The logo is designed to represent "JumpLander" and is associated with the official domain and Hugging Face organization page.