Instructions to use VertexAGI/quartz-micro-preview-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexAGI/quartz-micro-preview-v2-base with Transformers:

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

pipe = pipeline("text-generation", model="VertexAGI/quartz-micro-preview-v2-base")

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

tokenizer = AutoTokenizer.from_pretrained("VertexAGI/quartz-micro-preview-v2-base")
model = AutoModelForCausalLM.from_pretrained("VertexAGI/quartz-micro-preview-v2-base", device_map="auto")

MLX

How to use VertexAGI/quartz-micro-preview-v2-base with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

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

model, tokenizer = load("VertexAGI/quartz-micro-preview-v2-base")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use VertexAGI/quartz-micro-preview-v2-base 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-v2-base:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexAGI/quartz-micro-preview-v2-base: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-v2-base:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexAGI/quartz-micro-preview-v2-base: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 VertexAGI/quartz-micro-preview-v2-base:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VertexAGI/quartz-micro-preview-v2-base: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 VertexAGI/quartz-micro-preview-v2-base:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VertexAGI/quartz-micro-preview-v2-base:Q4_K_M

Use Docker

docker model run hf.co/VertexAGI/quartz-micro-preview-v2-base:Q4_K_M

LM Studio
Jan

vLLM

How to use VertexAGI/quartz-micro-preview-v2-base 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-v2-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexAGI/quartz-micro-preview-v2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VertexAGI/quartz-micro-preview-v2-base:Q4_K_M

SGLang

How to use VertexAGI/quartz-micro-preview-v2-base 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-v2-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexAGI/quartz-micro-preview-v2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-v2-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexAGI/quartz-micro-preview-v2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use VertexAGI/quartz-micro-preview-v2-base with Ollama:
```
ollama run hf.co/VertexAGI/quartz-micro-preview-v2-base:Q4_K_M
```
Unsloth Desktop

MLX LM

How to use VertexAGI/quartz-micro-preview-v2-base with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "VertexAGI/quartz-micro-preview-v2-base" --prompt "Once upon a time"

Docker Model Runner
How to use VertexAGI/quartz-micro-preview-v2-base with Docker Model Runner:
```
docker model run hf.co/VertexAGI/quartz-micro-preview-v2-base:Q4_K_M
```

Lemonade

How to use VertexAGI/quartz-micro-preview-v2-base with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VertexAGI/quartz-micro-preview-v2-base:Q4_K_M

Run and chat with the model

lemonade run user.quartz-micro-preview-v2-base-Q4_K_M

List all available models

lemonade list

Atomic Chat

quartz-micro-preview-v2-base

1.16 GB

Ctrl+K

1 contributor

History: 4 commits

VertexAIco

Add base-model evaluation outputs

a2e8d79 verified 13 days ago