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

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 new

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

Pi new

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": "CipherModel-1.5B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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 10 days ago

preview code

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	---
	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](https://huggingface.co/guhantech) — 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](https://huggingface.co/Qwen/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`:

	```bash
	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

	CipherCode and Cipher Persona are trademarks of Lila AI LLC. All rights reserved.

	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.

	---

	<sub>© 2026 Lila AI LLC · Built for developers who don't want their AI to sound like Stack Overflow.</sub>