Instructions to use liuhaotian/llava-v1.5-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liuhaotian/llava-v1.5-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="liuhaotian/llava-v1.5-7b")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("liuhaotian/llava-v1.5-7b") model = AutoModelForCausalLM.from_pretrained("liuhaotian/llava-v1.5-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use liuhaotian/llava-v1.5-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liuhaotian/llava-v1.5-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liuhaotian/llava-v1.5-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/liuhaotian/llava-v1.5-7b
- SGLang
How to use liuhaotian/llava-v1.5-7b 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 "liuhaotian/llava-v1.5-7b" \ --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": "liuhaotian/llava-v1.5-7b", "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 "liuhaotian/llava-v1.5-7b" \ --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": "liuhaotian/llava-v1.5-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use liuhaotian/llava-v1.5-7b with Docker Model Runner:
docker model run hf.co/liuhaotian/llava-v1.5-7b
Commit ·
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Parent(s): c9437ab
Create README.md
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README.md
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---
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inference: false
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---
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<br>
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<br>
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# LLaVA Model Card
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## Model details
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**Model type:**
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LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
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It is an auto-regressive language model, based on the transformer architecture.
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**Model date:**
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LLaVA-v1.5-7B was trained in September 2023.
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**Paper or resources for more information:**
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https://llava-vl.github.io/
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## License
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Llama 2 is licensed under the LLAMA 2 Community License,
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Copyright (c) Meta Platforms, Inc. All Rights Reserved.
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**Where to send questions or comments about the model:**
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https://github.com/haotian-liu/LLaVA/issues
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## Intended use
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**Primary intended uses:**
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The primary use of LLaVA is research on large multimodal models and chatbots.
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**Primary intended users:**
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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## Training dataset
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- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
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- 158K GPT-generated multimodal instruction-following data.
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- 450K academic-task-oriented VQA data mixture.
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- 40K ShareGPT data.
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## Evaluation dataset
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A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
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