Lattice Qwen 2.5 Coder 7B Private
Privacy Tier: wrapped | Parameters: 7.6B | Context: 131,072 tokens | VRAM: ~16GB
Qwen 2.5 Coder 7B — purpose-built for code generation with Lattice privacy. Supports 92+ programming languages. Best code model in the catalog for single-GPU setups.
Privacy Guarantees
| Feature | Status |
|---|---|
| Sandboxed inference (no network egress) | ✅ Yes |
| PII output guardrails (email, SSN, CC, etc.) | ✅ Yes |
| Encrypted inference logs | ✅ Yes |
| Zero telemetry | ✅ Yes |
| Differential privacy (DP-SGD) | ❌ Wrapped only |
Quick Start
pip install ltce
ltce pull lattice-ai/qwen-2.5-coder-7b-private
ltce serve lattice-ai/qwen-2.5-coder-7b-private --port 8080 --sandbox
from ltce import Lattice
lt = Lattice()
lt.serve("lattice-ai/qwen-2.5-coder-7b-private", port=8080, sandbox=True)
The model serves an OpenAI-compatible API at http://localhost:8080/v1/chat/completions.
What Does "Wrapped" Mean?
This model uses the original Qwen/Qwen2.5-Coder-7B-Instruct weights — no additional training has been done.
What Lattice adds is the privacy serve layer:
- 🔒 Sandbox mode — all outbound network connections are blocked during inference
- 🛡️ PII guardrails — model output is scanned for emails, phone numbers, SSNs, credit card numbers, API keys
- 📝 Encrypted logs — inference logs are AES-256-GCM encrypted at rest
- 🚫 Zero telemetry — HuggingFace telemetry, W&B, MLflow all disabled
For models with mathematical privacy guarantees (DP-SGD training), see our hardened tier models.
Capabilities
code, instruct
Base Model
Qwen/Qwen2.5-Coder-7B-Instruct
License
Apache 2.0
Built with Lattice — privacy-first local AI.
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