Instructions to use atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF", filename="Turkish-LLaVA-v0.1-Q4_K_M.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 atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-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 atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-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 atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "atasoglu/Turkish-LLaVA-v0.1-Q4_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": "atasoglu/Turkish-LLaVA-v0.1-Q4_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" } } ] } ] }'Use Docker
docker model run hf.co/atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M
- Ollama
How to use atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF with Ollama:
ollama run hf.co/atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-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 atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-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 atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Turkish-LLaVA-v0.1-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
Turkish-LLaVA-v0.1-Q4_K_M-GGUF
This model is a converted and quantized version of ytu-ce-cosmos/Turkish-LLaVA-v0.1 vision-language model using llama.cpp.
Usage
You can use the model with llama-cpp-python package as following:
from llama_cpp import Llama
from llama_cpp.llama_chat_format import Llama3VisionAlphaChatHandler
llm = Llama(
model_path="Turkish-LLaVA-v0.1-Q4_K_M.gguf", # path to language model
n_gpu_layers=-1, # for running on GPU
chat_handler=Llama3VisionAlphaChatHandler(
# path to image encoder
clip_model_path="Turkish-LLaVA-v0.1-mmproj-F16.gguf",
),
seed=1337, # for reproducing same results
n_ctx=4096, # n_ctx should be increased to accommodate the image embedding
verbose=False, # disable the logging
)
# url for the input image
url = "https://huggingface.co/ytu-ce-cosmos/Turkish-LLaVA-v0.1/resolve/main/example.jpg"
messages = [
{"role": "system", "content": "Sen yardımsever bir asistansın."},
{
"role": "user",
"content": [
{"type" : "text", "text": "Bu resimde neler görüyorsun?"},
{"type": "image_url", "image_url": {"url": url}}
]
},
]
response = llm.create_chat_completion(
messages=messages,
max_tokens=64,
)
print(response["choices"][0]["message"]["content"])
# Output: Resimde, sarı çiçeklerle çevrili bir köpek yavrusu görülüyor.
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Model tree for atasoglu/Turkish-LLaVA-v0.1-Q4_K_M-GGUF
Base model
ytu-ce-cosmos/Turkish-LLaVA-v0.1