Lin-Chen/ShareGPT4V
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How to use djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
# 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 djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
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 djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
docker model run hf.co/djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
How to use djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'docker model run hf.co/djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
How to use djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF with Ollama:
ollama run hf.co/djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
How to use djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF with Docker Model Runner:
docker model run hf.co/djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
How to use djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF:Q5_K_M
lemonade run user.llava-llama-3-8b-v1_1-Q5_K_M-GGUF-Q5_K_M
lemonade list
This model was converted to GGUF format from xtuner/llava-llama-3-8b-v1_1 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Install llama.cpp through brew.
brew install ggerganov/ggerganov/llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF --model llava-llama-3-8b-v1_1.Q5_K_M.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo djward888/llava-llama-3-8b-v1_1-Q5_K_M-GGUF --model llava-llama-3-8b-v1_1.Q5_K_M.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llava-llama-3-8b-v1_1.Q5_K_M.gguf -n 128
5-bit