jumplander/jumplander-coder-32b

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🚀 JumpLander Coder 32B

Advanced Code‑Generation LLM — optimized for Persian‑speaking developers

Short summary
JumpLander Coder 32B is a high‑performance, bilingual (English–Persian) code generation model optimized for multi‑file reasoning, repository‑scale analysis, and developer workflows. It is designed to assist with scaffolding, refactoring, testing, and documentation generation while emphasizing secure coding patterns and reproducible evaluation.

Important: Model weights are distributed locally through the JumpLander App (desktop/server installer). The model can also be tried on our website demo with limited free requests for evaluation. We do not publish model weights on an open public hosting by default — distribution is controlled via the official JumpLander software to ensure integrity and support.

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🌟 Key Features

• High‑quality, executable code generation and scaffolding
• Multi‑file and architecture‑level reasoning
• Secure‑by‑design outputs and automated refactoring suggestions
• Persian (Farsi) instruction tuning for improved developer UX
• CLI / SDK integrations and future IDE plugins planned

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📦 Local Distribution & How Users Access the Model

JumpLander distributes model weights to end users via the official JumpLander App (installer) and controlled download endpoints. The purpose of local distribution is to enable offline and private execution, reduce API costs, and give users full runtime control on their machines.

Typical flow (once local package is released):

1. User installs JumpLander App (desktop or server).
2. User downloads model bundle from the official server through the App (signed + checksummed).
3. App verifies the integrity (SHA‑256 + PGP) and unpacks the model into a secure local runtime.
4. The model runs locally — accessible via App UI, CLI, or local SDK.

While the local installer is being finalized, a demo endpoint on the website provides limited testing (e.g., 100 trial requests) so users can evaluate model behavior without installing.

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🧪 Reproducible Evaluation & Benchmarks

We publish reproducible evaluation scripts and raw logs so independent researchers can reproduce our reported numbers. Evaluation artifacts include:

• scripts/run_humaneval.py (example)
• scripts/run_repo_reasoning.py
• Raw logs under eval_logs/ with seeds and environment notes (CUDA/PyTorch versions)

Example command (when you have a local model path):

python scripts/run_humaneval.py --model-path /path/to/jumplander-coder-32b --seed 42 --output eval_logs/humaneval.json

Metrics usually reported: pass@k (HumanEval), execution accuracy, latency (tokens/sec), and memory footprint.

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🔐 Integrity & Security (how downloads are verified)

All published model bundles (when distributed) include:

• model.safetensors (preferred safer serialization format)
• model.safetensors.sha256 (SHA‑256 checksum)
• model.safetensors.sig (PGP detached signature)

Example verification commands (Linux/macOS):

# Verify checksum
sha256sum -c model.safetensors.sha256

# Verify PGP signature (requires maintainers' public key)
gpg --verify model.safetensors.sig model.safetensors

A convenience script verify.sh is included in this repository to automate the checks before loading the model locally.

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🛠 Quick example (Local Python loader)

This example assumes the model files are verified and stored locally. The official App exposes a runtime; this snippet demonstrates the local loader pattern (trusted code only):

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("/local/models/jumplander-coder-32b")
model = AutoModelForCausalLM.from_pretrained(
    "/local/models/jumplander-coder-32b",
    trust_remote_code=False  # We avoid remote code execution by design
)

prompt = "Create a simple FastAPI server with a single endpoint that returns 'hello'."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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✅ Trust & Transparency — Practical steps we follow

To increase trust and demonstrate non‑fraudulent operation, JumpLander follows these practices:

• Official distribution only through JumpLander App and controlled download endpoints.
• Model bundles published with SHA‑256 checksums and PGP signatures.
• Reproducible benchmarks and raw logs published in eval_logs/.
• Public team profiles and contact information for accountability.
• A demo endpoint (limited free requests) so users can validate model behavior before download.
• Security guidance: run models in isolated environments, avoid trust_remote_code=True unless code is reviewed and signed.

These steps are what we recommend including on the project page and in the model card to reassure enterprise and technical users.

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📁 Repository layout (suggested)

jumplander-coder-32b/
├─ README.md
├─ LICENSE
├─ models/                    # (populated when bundles are released)
│  ├─ model.safetensors
│  ├─ model.safetensors.sha256
│  └─ model.safetensors.sig
├─ scripts/
│  ├─ verify.sh
│  ├─ run_humaneval.py
│  └─ run_repo_reasoning.py
├─ eval_logs/
└─ docs/

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📝 Contact & Support

JumpLander Team — https://jumplander.org
Support: support@jumplander.org
LinkedIn: https://www.linkedin.com/in/jump-lander-55812b388/
Docs: https://jumplander.org/?fa=docs

Short Persian note

🇮🇷 جامپلندر — تجربهٔ توسعه برای فارسی‌زبانان.
در حال حاضر می‌توانید مدل را از طریق دموی وب سایت امتحان کنید؛ نسخهٔ محلی و نصب از طریق نرم‌افزار JumpLander عرضه خواهد شد.
برای پشتیبانی و گزارش مشکلات، لطفاً به https://jumplander.org ایمیل بزنید.

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