Instructions to use marianna13/llava-phi-2-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marianna13/llava-phi-2-3b with Transformers:

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

pipe = pipeline("text-generation", model="marianna13/llava-phi-2-3b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("marianna13/llava-phi-2-3b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use marianna13/llava-phi-2-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marianna13/llava-phi-2-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marianna13/llava-phi-2-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/marianna13/llava-phi-2-3b

SGLang

How to use marianna13/llava-phi-2-3b 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 "marianna13/llava-phi-2-3b" \
    --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": "marianna13/llava-phi-2-3b",
		"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 "marianna13/llava-phi-2-3b" \
        --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": "marianna13/llava-phi-2-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use marianna13/llava-phi-2-3b with Docker Model Runner:
```
docker model run hf.co/marianna13/llava-phi-2-3b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for LLaVa-Phi-2-3B

Model Details

Model Description

Developed by: LAION, SkunkworksAI & Ontocord
Model type: LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture
Finetuned from model: Phi-2
License: MIT
Demo: llava-phi-2-3b-demo

Model Sources

Repository: BakLLaVa

Evaluation

Benchmarks

Model	Parameters	SQA	GQA	TextVQA	POPE
LLaVA-1.5	7.3B	68.0	62.0	58.3	85.3
MC-LLaVA-3B	3B	-	49.6	38.59	-
LLaVA-Phi	3B	68.4	-	48.6	85.0
moondream1	1.6B	-	56.3	39.8	-
llava-phi-2-3b	3B	69.0	51.2	47.0	86.0