Libraries Transformers How to use jersobh/papagaio with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="jersobh/papagaio")
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) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("jersobh/papagaio")
model = AutoModelForMultimodalLM.from_pretrained("jersobh/papagaio", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use jersobh/papagaio 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 jersobh/papagaio:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jersobh/papagaio:Q4_K_M Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jersobh/papagaio:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jersobh/papagaio: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 jersobh/papagaio:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf jersobh/papagaio:Q4_K_M Use Docker docker model run hf.co/jersobh/papagaio:Q4_K_M Llama new LM Studio Jan vLLM How to use jersobh/papagaio with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jersobh/papagaio"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "jersobh/papagaio",
"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/jersobh/papagaio:Q4_K_M SGLang How to use jersobh/papagaio 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 "jersobh/papagaio" \
--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": "jersobh/papagaio",
"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 "jersobh/papagaio" \
--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": "jersobh/papagaio",
"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 jersobh/papagaio with Ollama:
ollama run hf.co/jersobh/papagaio:Q4_K_M Unsloth Desktop Pi How to use jersobh/papagaio with Pi:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jersobh/papagaio:Q4_K_M Configure the model in Pi # Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "jersobh/papagaio:Q4_K_M"
}
]
}
}
} Run Pi # Start Pi in your project directory:
pi Docker Model Runner How to use jersobh/papagaio with Docker Model Runner:
docker model run hf.co/jersobh/papagaio:Q4_K_M Lemonade How to use jersobh/papagaio with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull jersobh/papagaio:Q4_K_M Run and chat with the model lemonade run user.papagaio-Q4_K_M List all available models lemonade list Hermes Agent How to use jersobh/papagaio with Hermes Agent:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jersobh/papagaio:Q4_K_M Configure Hermes # Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jersobh/papagaio:Q4_K_M Run Hermes hermes Atomic Chat OpenClaw How to use jersobh/papagaio with OpenClaw:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jersobh/papagaio:Q4_K_M Configure OpenClaw # Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
--auth-choice custom-api-key \
--custom-base-url http://127.0.0.1:8080/v1 \
--custom-model-id "jersobh/papagaio:Q4_K_M" \
--custom-provider-id llama-cpp \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-health Run OpenClaw openclaw agent --local --agent main --message "Hello from Hugging Face" 
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf jersobh/papagaio:Q4_K_M# Run inference directly in the terminal: llama cli -hf jersobh/papagaio:Q4_K_M