Instructions to use miceworks/Mark-VL-2.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use miceworks/Mark-VL-2.2B 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 miceworks/Mark-VL-2.2B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf miceworks/Mark-VL-2.2B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf miceworks/Mark-VL-2.2B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf miceworks/Mark-VL-2.2B: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 miceworks/Mark-VL-2.2B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf miceworks/Mark-VL-2.2B: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 miceworks/Mark-VL-2.2B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf miceworks/Mark-VL-2.2B:Q4_K_M

Use Docker

docker model run hf.co/miceworks/Mark-VL-2.2B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use miceworks/Mark-VL-2.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "miceworks/Mark-VL-2.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "miceworks/Mark-VL-2.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/miceworks/Mark-VL-2.2B:Q4_K_M

Ollama
How to use miceworks/Mark-VL-2.2B with Ollama:
```
ollama run hf.co/miceworks/Mark-VL-2.2B:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use miceworks/Mark-VL-2.2B with Docker Model Runner:
```
docker model run hf.co/miceworks/Mark-VL-2.2B:Q4_K_M
```

Lemonade

How to use miceworks/Mark-VL-2.2B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull miceworks/Mark-VL-2.2B:Q4_K_M

Run and chat with the model

lemonade run user.Mark-VL-2.2B-Q4_K_M

List all available models

lemonade list

Atomic Chat

Mark-VL-2.2B / mark_vl_2_2b_metadata.json

miceworks

feat: upload METADATA weights (mark_vl_2_2b_metadata.json)

70d4ff4 verified about 17 hours ago

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319 Bytes

{
  "model_name": "MarkVL 2.2B Dense Visual Perception Engine",
  "version": "1.0.0",
  "total_parameters": 2222269696,
  "active_parameters_per_token": 313565440,
  "final_loss": 2.954,
  "steps": 500,
  "completed_at": "2026-10-26 19:12:49 UTC",
  "format": "IQ4_NL",
  "size_mb": 1184.5,
  "vram_rtx2060s_gb": 1.85
}

	{
	"model_name": "MarkVL 2.2B Dense Visual Perception Engine",
	"version": "1.0.0",
	"total_parameters": 2222269696,
	"active_parameters_per_token": 313565440,
	"final_loss": 2.954,
	"steps": 500,
	"completed_at": "2026-10-26 19:12:49 UTC",
	"format": "IQ4_NL",
	"size_mb": 1184.5,
	"vram_rtx2060s_gb": 1.85
	}