Instructions to use ventilabs/miseai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ventilabs/miseai with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ventilabs/miseai",
	filename="venti_miseai_1.1.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 ventilabs/miseai with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ventilabs/miseai
# Run inference directly in the terminal:
llama cli -hf ventilabs/miseai

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ventilabs/miseai
# Run inference directly in the terminal:
llama cli -hf ventilabs/miseai

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 ventilabs/miseai
# Run inference directly in the terminal:
./llama-cli -hf ventilabs/miseai

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 ventilabs/miseai
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ventilabs/miseai

Use Docker

docker model run hf.co/ventilabs/miseai

LM Studio
Jan

vLLM

How to use ventilabs/miseai with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ventilabs/miseai

Ollama
How to use ventilabs/miseai with Ollama:
```
ollama run hf.co/ventilabs/miseai
```

Unsloth Studio

How to use ventilabs/miseai 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 ventilabs/miseai 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 ventilabs/miseai to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ventilabs/miseai to start chatting

How to use ventilabs/miseai with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ventilabs/miseai

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": "ventilabs/miseai"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ventilabs/miseai with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ventilabs/miseai

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 ventilabs/miseai

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ventilabs/miseai with Docker Model Runner:
```
docker model run hf.co/ventilabs/miseai
```

Lemonade

How to use ventilabs/miseai with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ventilabs/miseai

Run and chat with the model

lemonade run user.miseai-{{QUANT_TAG}}

List all available models

lemonade list

Venti MiseAI 1.1

Intelligence that lives on your machine.

MiseAI is a powerful, private AI assistant built by Venti Labs. It runs 100% locally on your hardware — no cloud, no API keys, no data leaving your device.

Highlights

🧠 7B Parameters — Fine-tuned from Qwen 2.5 Coder 7B
🔒 Fully Private — Runs offline, no internet required after download
💻 Expert Coder — Production-ready code generation and refactoring
⚡ 8GB VRAM — Optimized to run on consumer GPUs
📦 GGUF Format — Ready for Ollama, llama.cpp, LM Studio

Quick Start (Ollama)

ollama run ventilabs/miseai

Or install the Venti CLI:

irm venti-labs.xyz/install | iex
venti launch mise

Model Details

Property	Value
Base Model	Qwen 2.5 Coder 7B
Fine-tuning	LoRA (QLoRA)
Quantization	Q8_0
File Size	~8.1 GB
Context Window	16,384 tokens
Max Output	8,192 tokens

Use Cases

Code Generation — Write production-ready code in any language
Code Refactoring — Optimize and restructure existing codebases
Problem Solving — Step-by-step reasoning through complex challenges
Technical Writing — Documentation, README files, and technical articles

Model tree for ventilabs/miseai

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Quantized

(43)

this model