Your IDE's new best friend. The model behind CipherCode — the AI coding assistant that learns your style, remembers your projects, and writes code in your voice.

By Lila AI LLC · Closed beta v0.1

What CipherCode Delivers

CipherCode isn't another generic completion plugin. It's a complete coding companion that lives natively inside VS Code and adapts to you.

Cipher Persona — Your Style, Learned

The first time you open a workspace, CipherCode silently scans your code and detects:

Naming conventions (camelCase / snake_case / PascalCase)
Function style (arrow vs named declarations)
Async style (async/await vs .then)
Comment placement and verbosity
Indent size, semicolon preference, type-annotation density
Your most-used libraries and imports

From that moment forward, every suggestion is generated to feel like you wrote it. Nothing leaves your machine — Persona lives entirely in VS Code's globalState.

Project Memory — Continuity That Actually Helps

CipherCode remembers your project across sessions:

What's tracked	Where
Project summary (auto-detected from `package.json` / README)	`.vscode/cipher-memory.json`
Project type (`node` / `python` / `other`)	local
Top 10 most-edited files	local
Architectural decisions you've made	local
Last 20 chat messages	local
Recurring patterns in your code	local

This context is injected into every prompt, so when you come back tomorrow, the model already knows what you're building.

Smart Commands

Right-click anywhere in your editor:

Explain Code — clear summary of what's happening, even without a selection
Refactor Code — clean up while preserving your style
Fix Bug — find and patch issues, style-matched
Add Comments — comment in your voice
Document This File — language-aware doc comments (TSDoc / JSDoc / Google Python / Javadoc / XMLDoc / Doxygen / godoc / rustdoc / PHPDoc / YARD)
Generate README from Project — full README from your code structure

Plus an inline chat sidebar with persistent history, code-block copy buttons, "Insert at cursor" actions, and a stop button that actually stops.

Privacy by Architecture

Code stays on your machine — only the snippet you act on hits inference
Persona never leaves your laptop
Project memory lives in your workspace, not a Lila AI server
Self-hostable on your own GCP if you want full ownership
No telemetry, no accounts, no subscription

Powered By

Built on Qwen2.5-Coder-1.5B-Instruct — Alibaba's state-of-the-art open code model — quantized to Q4_K_M for efficient CPU inference and packaged for deployment via llama.cpp.

The intelligence in CipherCode comes from layering Persona detection, Project Memory, and carefully designed prompt templates on top of a strong base. The CipherCode VS Code extension orchestrates all of it; this repo hosts the weights it serves.

A LoRA fine-tune is on the roadmap for v0.2 — trained on real-world IDE workflow patterns collected during the closed beta.

Specifications


Architecture	Qwen2.5-Coder transformer
Parameters	1.5 B
Context window	32 K (production runs at 4 K for efficiency)
Quantization	Q4_K_M
File size	1.07 GB
License	Apache 2.0 — free for commercial use
Strong languages	Python, JavaScript, TypeScript, Java, Go, Rust, C/C++

Quick Start

Easy path — install the VS Code extension

If Lila AI sent you the closed-beta .vsix:

code --install-extension ciphercode-0.1.0.vsix

Open VS Code. Welcome walkthrough opens automatically. Start typing. No setup, no token, no GCP.

Hands-on path — run the model locally

bash

# Pull the GGUF
hf download guhantech/CipherModel-1.5B \
  CipherModel-1.5B-Q4_K_M.gguf --local-dir .

# Serve with llama-server
llama-server \
  -m CipherModel-1.5B-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8080 \
  --ctx-size 4096 -np 5

# Make a request
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cipher-model",
    "messages": [{"role":"user","content":"write a python fizzbuzz"}],
    "max_tokens": 256
  }'

Python (`llama-cpp-python`)

python

from llama_cpp import Llama

llm = Llama(model_path="CipherModel-1.5B-Q4_K_M.gguf", n_ctx=4096)
out = llm("def fizzbuzz(n):", max_tokens=256)
print(out["choices"][0]["text"])

Roadmap

Version	Status	What's in it
v0.1	Live	Closed beta. Cipher Persona + Project Memory + 11 commands + chat sidebar.
v0.2	Planned	LoRA fine-tune on collected IDE workflows. Better instruction-following.
v0.3	Planned	Multi-file context awareness. Whole-project doc generation.
v1.0	Planned	Public Marketplace launch. Optional hosted Pro tier for zero-setup.

Citation

bibtex

@article{hui2024qwen2,
  title={Qwen2.5-Coder Technical Report},
  author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and others},
  journal={arXiv preprint arXiv:2409.12186},
  year={2024}
}

Trademark

The model weights are released under Apache 2.0 — free to use, modify, and redistribute. Trademarks restrict only how you may name and brand derivative work; the underlying weights remain unrestricted.

guhantech/CipherModel-1.5B

CipherModel-1.5B