license: other
license_name: proprietary
license_link: LICENSE
🚀 JumpLander Coder 32B
Advanced Code-Generation LLM — optimized for English & Persian workflows
🇮🇷 چند خط توضیح فارسی:
این مدل برای توسعهدهندگان ایرانی طراحی شده و نسخه آنلاین آن در سایت فعال است.
نسخهی لوکال فقط از طریق نرمافزار رسمی JumpLander ارائه خواهد شد.
وزنهای مدل عمومی نیستند و تنها در قالب نرمافزار قابل استفاده میباشند.
🌟 Overview
JumpLander Coder 32B is a high-performance, bilingual (English–Persian) code-generation LLM built for advanced programming tasks, repository-wide reasoning, and architecture-level understanding.
This release provides documentation, benchmarks, design goals, and usage guidelines.
Model weights are not publicly distributed.
Local access will be provided exclusively through the official JumpLander desktop/server application.
📊 Current Status
- ✔ Documentation available
- ✔ Prototype benchmarks included
- ✔ Online demo available on the website
- ❌ Weights are not public
- 🔒 Local model execution will be provided only via official software
🧪 Benchmarks (Prototype)
| Task | Score | Notes |
|---|---|---|
| HumanEval | 72% | Strong execution accuracy |
| Repo-level Q&A | High | Stable multi-file reasoning |
| Persian Instruction Following | Excellent | Optimized bilingual performance |
📦 Model Comparison (Prototype Benchmarks)
| Model | Params | HumanEval | Multi-file Reasoning | Persian Support | Speed (tok/s) | Availability |
|---|---|---|---|---|---|---|
| JumpLander Coder 32B | 32B | 72% | ✔ Strong | Excellent | 34 | Local-only via app |
| Qwen2.5-Coder 32B | 32B | 75% | Medium | Weak | 32 | Open-source |
| DeepSeek-Coder 33B | 33B | 79% | Strong | Weak | 29 | Open-source |
| StarCoder2 15B | 15B | 63% | Limited | Weak | 45 | Open-source |
| Llama-3.1 70B | 70B | 82% | Strong | Weak | 20 | Open-source |
💡 Why JumpLander Coder 32B?
🧠 Multi-file reasoning
Designed for architecture-level understanding and full-repository analysis.
🇮🇷 Persian-optimized workflow
Tuned for real Persian programming scenarios and instruction patterns.
🛡️ Secure-by-design outputs
Refactoring logic, patch suggestions, and safe coding guidelines included.
⚡ Developer-focused ecosystem
Future SDK, CLI tools, and integrated analysis modules.
🗂 Local Execution (Official Software Only)
Local execution of the model will be provided through the JumpLander App, enabling:
- Secure local model loading
- Offline and online operation modes
- Integrated coding environment
- Automatic model updates
- Full repository understanding features
Note:
Weights will not be downloadable manually.
They are packaged, encrypted, and tied to the official software.
🎯 Use Cases
- Application scaffolding
- Repository-wide refactoring
- Debugging & architecture inspection
- Documentation and API specification
- Programming education (EN + FA)
🛠 Planned Capabilities
- Repository-wide code generation
- Multi-language support: Python, JS/TS, Go, Rust, Java, C/C++, Bash, SQL
- Long-context reasoning (hundreds of thousands of tokens)
- Test generation: unit, integration, regression
- IDE extensions (VS Code + JetBrains)
- Full SDK + CLI tools
📎 Contact & Support
Website: https://jumplander.org
LinkedIn: https://www.linkedin.com/in/jump-lander-55812b388/
Support: support@jumplander.org
💻 Example Usage (Future API)
from jumplander_sdk import JumplanderClient
client = JumplanderClient(api_key="YOUR_KEY")
# Scaffold a FastAPI app
project = client.scaffold(
"Create a FastAPI service with JWT and PostgreSQL",
language="python"
)
project.save("./generated_app")
# Refactor an existing repository
patches = client.refactor("./myrepo", intent="improve structure")
client.apply_patches(patches)