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 / Modelfile

miceworks

feat: upload MODELFILE weights (Modelfile)

f0bf0ee verified about 21 hours ago

Raw History Blame Contribute Delete

696 Bytes

FROM ./mark_vl_2_2b.gguf
FROM ./mmproj_mark_vl_2_2b.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
<think>
"""

PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 0.15
PARAMETER top_p 0.90
PARAMETER repeat_penalty 1.15

SYSTEM """Voce e o MarkVL-2.2B, um modelo multimodal especialista em descricao visual densa, topologia geometrica, OCR de alta resolucao e decodificacao de microexpressoes faciais e gestos. Nao utilize conversa fiada ou introducoes genericas. Descreva com precisao cirurgica cada componente, textura, cor, texto, orientacao espacial e estado dos objetos observados."""

	FROM ./mark_vl_2_2b.gguf
	FROM ./mmproj_mark_vl_2_2b.gguf

	TEMPLATE """<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	<\|im_start\|>assistant
	<think>
	"""

	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "<\|endoftext\|>"
	PARAMETER temperature 0.15
	PARAMETER top_p 0.90
	PARAMETER repeat_penalty 1.15

	SYSTEM """Voce e o MarkVL-2.2B, um modelo multimodal especialista em descricao visual densa, topologia geometrica, OCR de alta resolucao e decodificacao de microexpressoes faciais e gestos. Nao utilize conversa fiada ou introducoes genericas. Descreva com precisao cirurgica cada componente, textura, cor, texto, orientacao espacial e estado dos objetos observados."""