Instructions to use cjpais/llava-v1.6-34B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cjpais/llava-v1.6-34B-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf cjpais/llava-v1.6-34B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf cjpais/llava-v1.6-34B-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cjpais/llava-v1.6-34B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf cjpais/llava-v1.6-34B-gguf:Q4_K_M
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 cjpais/llava-v1.6-34B-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cjpais/llava-v1.6-34B-gguf:Q4_K_M
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 cjpais/llava-v1.6-34B-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cjpais/llava-v1.6-34B-gguf:Q4_K_M
Use Docker
docker model run hf.co/cjpais/llava-v1.6-34B-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cjpais/llava-v1.6-34B-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cjpais/llava-v1.6-34B-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": "cjpais/llava-v1.6-34B-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/cjpais/llava-v1.6-34B-gguf:Q4_K_M
- Ollama
How to use cjpais/llava-v1.6-34B-gguf with Ollama:
ollama run hf.co/cjpais/llava-v1.6-34B-gguf:Q4_K_M
- Unsloth Studio
How to use cjpais/llava-v1.6-34B-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 cjpais/llava-v1.6-34B-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 cjpais/llava-v1.6-34B-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cjpais/llava-v1.6-34B-gguf to start chatting
- Docker Model Runner
How to use cjpais/llava-v1.6-34B-gguf with Docker Model Runner:
docker model run hf.co/cjpais/llava-v1.6-34B-gguf:Q4_K_M
- Lemonade
How to use cjpais/llava-v1.6-34B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cjpais/llava-v1.6-34B-gguf:Q4_K_M
Run and chat with the model
lemonade run user.llava-v1.6-34B-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
thanks a lot
I was searching for llava-1.6 to fit into my 3090 :)
No worries, please note that they are very early and performance should improve when image splitting is taken into account
I have no issues with performance, it works very well on my 3090, however I have other question.
How do you use it?
I was only able to run it with llava-cli by passing prompt.
I was not able to use any kind of chat (tried "-i").
And it doesn't work in server (says it has no multi-modal support).
It may be because I was using Windows binary (but official one..).
I can compile and I can run on Linux, but that won't solve issue how to chat in llava-cli mode - is it possible?
I use it with ./server primarily. This makes a webserver at localhost:8080
The command is ./server -m <path_to_model> --mmproj <path_to_mmproj-model-f16.gguf>
For running on the terminal use it as such:
./llava-cli -m <path_to_model> --mmproj <path_to_mmproj-model-f16.gguf> --image path/to/an/image.jpg
Unfortunately interactive mode for llava-cli is not possible yet. I miss this functionality as well.
There is issue in llama.cpp project regarding this problem: fail to run llava in interactive mode #4393 ( https://github.com/ggerganov/llama.cpp/issues/4393 )
According to llama.cpp contributor in thread for this issue (update from 10/Dec/2023):
llava-cli does not have instruct mode built in!
It's a one time prompt
one time prompt works for me
the problem is if I want to ask about multiple things I need to run everything again
and if I want to use different image I also need to load model again
I was able to have interactive chat with running server, but I needed to compile it (not use the one from the binary release for Windows).