Instructions to use PillowTa1k/NaviGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PillowTa1k/NaviGen with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("NaviGen-stage2-base")
model = PeftModel.from_pretrained(base_model, "PillowTa1k/NaviGen")

Transformers

How to use PillowTa1k/NaviGen with Transformers:

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

pipe = pipeline("text-generation", model="PillowTa1k/NaviGen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PillowTa1k/NaviGen", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PillowTa1k/NaviGen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PillowTa1k/NaviGen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PillowTa1k/NaviGen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PillowTa1k/NaviGen

SGLang

How to use PillowTa1k/NaviGen 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 "PillowTa1k/NaviGen" \
    --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": "PillowTa1k/NaviGen",
		"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 "PillowTa1k/NaviGen" \
        --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": "PillowTa1k/NaviGen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use PillowTa1k/NaviGen 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 PillowTa1k/NaviGen 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 PillowTa1k/NaviGen to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PillowTa1k/NaviGen to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="PillowTa1k/NaviGen",
    max_seq_length=2048,
)

Docker Model Runner
How to use PillowTa1k/NaviGen with Docker Model Runner:
```
docker model run hf.co/PillowTa1k/NaviGen
```

Link model card to paper and GitHub repository

by nielsr HF Staff - opened 5 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+135

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nielsr

5 days ago

Hi! I'm Niels from the Hugging Face community science team.

This PR improves your model card by:

Linking it to the accompanying paper: Navigating User Behavior toward Personalized Multimodal Generation
Adding a link to the official GitHub repository
Updating the BibTeX citation with the paper's actual title and arXiv journal details

This makes it much easier for users to discover the context, code, and citation details of your work. Let me know if you have any questions!

Link model card to paper and GitHub repositoryf4ed98e7

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