Instructions to use vinimuchulski/astro-gemma-3-pt-br-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vinimuchulski/astro-gemma-3-pt-br-gguf with Transformers:

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

pipe = pipeline("image-text-to-text", model="vinimuchulski/astro-gemma-3-pt-br-gguf")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vinimuchulski/astro-gemma-3-pt-br-gguf")
model = AutoModelForMultimodalLM.from_pretrained("vinimuchulski/astro-gemma-3-pt-br-gguf")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use vinimuchulski/astro-gemma-3-pt-br-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vinimuchulski/astro-gemma-3-pt-br-gguf",
	filename="astro_gemma_q8.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vinimuchulski/astro-gemma-3-pt-br-gguf 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 vinimuchulski/astro-gemma-3-pt-br-gguf
# Run inference directly in the terminal:
llama cli -hf vinimuchulski/astro-gemma-3-pt-br-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vinimuchulski/astro-gemma-3-pt-br-gguf
# Run inference directly in the terminal:
llama cli -hf vinimuchulski/astro-gemma-3-pt-br-gguf

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 vinimuchulski/astro-gemma-3-pt-br-gguf
# Run inference directly in the terminal:
./llama-cli -hf vinimuchulski/astro-gemma-3-pt-br-gguf

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 vinimuchulski/astro-gemma-3-pt-br-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vinimuchulski/astro-gemma-3-pt-br-gguf

Use Docker

docker model run hf.co/vinimuchulski/astro-gemma-3-pt-br-gguf

LM Studio
Jan

vLLM

How to use vinimuchulski/astro-gemma-3-pt-br-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vinimuchulski/astro-gemma-3-pt-br-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vinimuchulski/astro-gemma-3-pt-br-gguf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/vinimuchulski/astro-gemma-3-pt-br-gguf

SGLang

How to use vinimuchulski/astro-gemma-3-pt-br-gguf 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 "vinimuchulski/astro-gemma-3-pt-br-gguf" \
    --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": "vinimuchulski/astro-gemma-3-pt-br-gguf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "vinimuchulski/astro-gemma-3-pt-br-gguf" \
        --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": "vinimuchulski/astro-gemma-3-pt-br-gguf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use vinimuchulski/astro-gemma-3-pt-br-gguf with Ollama:
```
ollama run hf.co/vinimuchulski/astro-gemma-3-pt-br-gguf
```

Unsloth Studio

How to use vinimuchulski/astro-gemma-3-pt-br-gguf 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 vinimuchulski/astro-gemma-3-pt-br-gguf 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 vinimuchulski/astro-gemma-3-pt-br-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vinimuchulski/astro-gemma-3-pt-br-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use vinimuchulski/astro-gemma-3-pt-br-gguf with Docker Model Runner:
```
docker model run hf.co/vinimuchulski/astro-gemma-3-pt-br-gguf
```

Lemonade

How to use vinimuchulski/astro-gemma-3-pt-br-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vinimuchulski/astro-gemma-3-pt-br-gguf

Run and chat with the model

lemonade run user.astro-gemma-3-pt-br-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Fine-tuning do Gemma-3-4B para Astronomia (GGUF)

Este repositório contém uma versão do modelo unsloth/gemma-3-4b-it que passou por fine-tuning para responder a perguntas sobre astronomia em português.

Processo de Fine-tuning

Modelo Base: unsloth/gemma-3-4b-it
Dataset: Foi utilizado um dataset customizado (perguntas_astronomia_langchain.json) contendo pares de pergunta e resposta sobre tópicos de astronomia.
Framework: O treinamento foi realizado com a biblioteca Unsloth para otimização de memória e velocidade.
Técnica: Foi aplicada a técnica de Low-Rank Adaptation (LoRA) para um fine-tuning eficiente, adaptando o modelo ao domínio específico.
Formato: Os dados foram formatados para seguir o template de chat gemma-3.