Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mozilla-ai/llava-v1.5-7b-llamafile:# Run inference directly in the terminal:
llama cli -hf mozilla-ai/llava-v1.5-7b-llamafile:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mozilla-ai/llava-v1.5-7b-llamafile:# Run inference directly in the terminal:
./llama-cli -hf mozilla-ai/llava-v1.5-7b-llamafile:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mozilla-ai/llava-v1.5-7b-llamafile:# Run inference directly in the terminal:
./build/bin/llama-cli -hf mozilla-ai/llava-v1.5-7b-llamafile:Use Docker
docker model run hf.co/mozilla-ai/llava-v1.5-7b-llamafile:Software Last Updated: 2025-03-31
Llamafile Version: 0.9.2
LLaVA Model Card
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-v1.5-7B was trained in September 2023.
Paper or resources for more information: https://llava-vl.github.io/
License
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues
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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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf mozilla-ai/llava-v1.5-7b-llamafile:# Run inference directly in the terminal: llama cli -hf mozilla-ai/llava-v1.5-7b-llamafile: