Instructions to use LeroyDyer/SpydazWebAI_Image_Projectors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Studio

How to use LeroyDyer/SpydazWebAI_Image_Projectors 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 LeroyDyer/SpydazWebAI_Image_Projectors 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 LeroyDyer/SpydazWebAI_Image_Projectors to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeroyDyer/SpydazWebAI_Image_Projectors to start chatting

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

SpydazWebAI_Image_Projectors

File size: 505 Bytes

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{
  "crop_size": {
    "height": 336,
    "width": 336
  },
  "do_center_crop": true,
  "do_convert_rgb": true,
  "do_normalize": true,
  "do_rescale": true,
  "do_resize": true,
  "image_mean": [
    0.48145466,
    0.4578275,
    0.40821073
  ],
  "image_processor_type": "CLIPImageProcessor",
  "image_std": [
    0.26862954,
    0.26130258,
    0.27577711
  ],
  "processor_class": "LlavaProcessor",
  "resample": 3,
  "rescale_factor": 0.00392156862745098,
  "size": {
    "shortest_edge": 336
  }
}