Instructions to use guhantech/CipherModel-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use guhantech/CipherModel-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="guhantech/CipherModel-1.5B",
	filename="CipherModel-1.5B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use guhantech/CipherModel-1.5B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf guhantech/CipherModel-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf guhantech/CipherModel-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf guhantech/CipherModel-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf guhantech/CipherModel-1.5B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf guhantech/CipherModel-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf guhantech/CipherModel-1.5B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf guhantech/CipherModel-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf guhantech/CipherModel-1.5B:Q4_K_M

Use Docker

docker model run hf.co/guhantech/CipherModel-1.5B:Q4_K_M

LM Studio
Jan

vLLM

How to use guhantech/CipherModel-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "guhantech/CipherModel-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "guhantech/CipherModel-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/guhantech/CipherModel-1.5B:Q4_K_M

Ollama
How to use guhantech/CipherModel-1.5B with Ollama:
```
ollama run hf.co/guhantech/CipherModel-1.5B:Q4_K_M
```

Unsloth Studio

How to use guhantech/CipherModel-1.5B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for guhantech/CipherModel-1.5B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for guhantech/CipherModel-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for guhantech/CipherModel-1.5B to start chatting

How to use guhantech/CipherModel-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf guhantech/CipherModel-1.5B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "guhantech/CipherModel-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use guhantech/CipherModel-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf guhantech/CipherModel-1.5B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default guhantech/CipherModel-1.5B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use guhantech/CipherModel-1.5B with Docker Model Runner:
```
docker model run hf.co/guhantech/CipherModel-1.5B:Q4_K_M
```

Lemonade

How to use guhantech/CipherModel-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull guhantech/CipherModel-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.CipherModel-1.5B-Q4_K_M

List all available models

lemonade list

CipherModel-1.5B / README.md

adminguhantech

Polish model card: lead with Cipher features, credit Qwen as foundation

7f6682a verified about 1 month ago

preview code

raw

history blame contribute delete

5.97 kB

metadata

license: apache-2.0
language:
  - en
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: text-generation
tags:
  - code
  - coding-assistant
  - llama-cpp
  - gguf
  - ciphercode
  - vscode
  - developer-tools
library_name: gguf

CipherModel-1.5B

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

# 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`)

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

@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.