Instructions to use epinnock/llava-flint-v0.5-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use epinnock/llava-flint-v0.5-1b with Transformers:

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

pipe = pipeline("text-generation", model="epinnock/llava-flint-v0.5-1b")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("epinnock/llava-flint-v0.5-1b")
model = AutoModelForCausalLM.from_pretrained("epinnock/llava-flint-v0.5-1b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use epinnock/llava-flint-v0.5-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "epinnock/llava-flint-v0.5-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epinnock/llava-flint-v0.5-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/epinnock/llava-flint-v0.5-1b

SGLang

How to use epinnock/llava-flint-v0.5-1b 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 "epinnock/llava-flint-v0.5-1b" \
    --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": "epinnock/llava-flint-v0.5-1b",
		"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 "epinnock/llava-flint-v0.5-1b" \
        --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": "epinnock/llava-flint-v0.5-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use epinnock/llava-flint-v0.5-1b with Docker Model Runner:
```
docker model run hf.co/epinnock/llava-flint-v0.5-1b
```

Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.

Model date: LLaVA-flint-v0.5-1B was trained in Nov 2023.

This model is an implementation of Llava using the TinyLlama 1.1b as the frozen llm model

It's designed to be able to run in low-resource environments We plan to release further versions designed for specific tasks so stay tuned.

Paper or resources for more information on the original Llava: https://llava-vl.github.io/

License Apache 2 (TinyLlama) Where to send questions or comments about the model: ask me here on huggingface :)

Intended use Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training dataset 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. 158K GPT-generated multimodal instruction-following data. 450K academic-task-oriented VQA data mixture. 40K ShareGPT data. Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

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