Instructions to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF", filename="llava-llama-3-8b-v1_1.Q3_K_S.gguf", )
llm.create_chat_completion( 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" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: llama-cli -hf djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: llama-cli -hf djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
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 djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: ./llama-cli -hf djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
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 djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
Use Docker
docker model run hf.co/djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
- LM Studio
- Jan
- vLLM
How to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "djward888/llava-llama-3-8b-v1_1-Q3_K_S-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-Q3_K_S-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" } } ] } ] }'Use Docker
docker model run hf.co/djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
- Ollama
How to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with Ollama:
ollama run hf.co/djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
- Unsloth Studio
How to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with Docker Model Runner:
docker model run hf.co/djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
- Lemonade
How to use djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF:Q3_K_S
Run and chat with the model
lemonade run user.llava-llama-3-8b-v1_1-Q3_K_S-GGUF-Q3_K_S
List all available models
lemonade list
djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF
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.
Use with llama.cpp
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-Q3_K_S-GGUF --model llava-llama-3-8b-v1_1.Q3_K_S.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo djward888/llava-llama-3-8b-v1_1-Q3_K_S-GGUF --model llava-llama-3-8b-v1_1.Q3_K_S.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.Q3_K_S.gguf -n 128
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