Instructions to use VertexAGI/quartz-micro-preview-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexAGI/quartz-micro-preview-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="VertexAGI/quartz-micro-preview-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VertexAGI/quartz-micro-preview-v3")
model = AutoModelForCausalLM.from_pretrained("VertexAGI/quartz-micro-preview-v3", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use VertexAGI/quartz-micro-preview-v3 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("VertexAGI/quartz-micro-preview-v3")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use VertexAGI/quartz-micro-preview-v3 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 VertexAGI/quartz-micro-preview-v3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexAGI/quartz-micro-preview-v3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VertexAGI/quartz-micro-preview-v3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexAGI/quartz-micro-preview-v3:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf VertexAGI/quartz-micro-preview-v3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf VertexAGI/quartz-micro-preview-v3:Q4_K_M

Use Docker

docker model run hf.co/VertexAGI/quartz-micro-preview-v3:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use VertexAGI/quartz-micro-preview-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexAGI/quartz-micro-preview-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexAGI/quartz-micro-preview-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexAGI/quartz-micro-preview-v3:Q4_K_M

SGLang

How to use VertexAGI/quartz-micro-preview-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "VertexAGI/quartz-micro-preview-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexAGI/quartz-micro-preview-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "VertexAGI/quartz-micro-preview-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexAGI/quartz-micro-preview-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use VertexAGI/quartz-micro-preview-v3 with Ollama:
```
ollama run hf.co/VertexAGI/quartz-micro-preview-v3:Q4_K_M
```
Unsloth Desktop

MLX LM

How to use VertexAGI/quartz-micro-preview-v3 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "VertexAGI/quartz-micro-preview-v3"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "VertexAGI/quartz-micro-preview-v3"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "VertexAGI/quartz-micro-preview-v3",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use VertexAGI/quartz-micro-preview-v3 with Docker Model Runner:
```
docker model run hf.co/VertexAGI/quartz-micro-preview-v3:Q4_K_M
```

Lemonade

How to use VertexAGI/quartz-micro-preview-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VertexAGI/quartz-micro-preview-v3:Q4_K_M

Run and chat with the model

lemonade run user.quartz-micro-preview-v3-Q4_K_M

List all available models

lemonade list

Atomic Chat

quartz-micro-preview-v3

1.16 GB

Ctrl+K

1 contributor

History: 4 commits

VertexAIco

Add site metadata (vertex_* fields) so the model appears on vertexagi.dev/models

ffdb018 verified 2 days ago