Instructions to use LeroyDyer/SpydazWebAI_Image_Projectors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeroyDyer/SpydazWebAI_Image_Projectors with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="LeroyDyer/SpydazWebAI_Image_Projectors")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LeroyDyer/SpydazWebAI_Image_Projectors", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LeroyDyer/SpydazWebAI_Image_Projectors 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 LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0 # Run inference directly in the terminal: llama cli -hf LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0 # Run inference directly in the terminal: llama cli -hf LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
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 LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
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 LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
Use Docker
docker model run hf.co/LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
- LM Studio
- Jan
- Ollama
How to use LeroyDyer/SpydazWebAI_Image_Projectors with Ollama:
ollama run hf.co/LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
- Unsloth Desktop
- Docker Model Runner
How to use LeroyDyer/SpydazWebAI_Image_Projectors with Docker Model Runner:
docker model run hf.co/LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
- Lemonade
How to use LeroyDyer/SpydazWebAI_Image_Projectors with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LeroyDyer/SpydazWebAI_Image_Projectors:Q4_0
Run and chat with the model
lemonade run user.SpydazWebAI_Image_Projectors-Q4_0
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- 5c3194c3a440f11b62a520c2205afa59f65c3ea0a86640136508d44d8f328f5c
- Size of remote file:
- 42 MB
- SHA256:
- be33a3b477e091832a3c8a9fabf5769c43b4cb2c161fc8c464b7bb214f16143a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.