Image-to-Text
Transformers
GGUF
English
text-generation-inference
image-captioning
optical-character-recognition
intelligent-character-recognition
caption
ocr
visual-understanding
art
icr
vlm
table
document
imatrix
Instructions to use mradermacher/DREX-062225-exp-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/DREX-062225-exp-i1-GGUF 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="mradermacher/DREX-062225-exp-i1-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/DREX-062225-exp-i1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/DREX-062225-exp-i1-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 mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/DREX-062225-exp-i1-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 mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/DREX-062225-exp-i1-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 mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/DREX-062225-exp-i1-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 mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/DREX-062225-exp-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/DREX-062225-exp-i1-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 mradermacher/DREX-062225-exp-i1-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 mradermacher/DREX-062225-exp-i1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/DREX-062225-exp-i1-GGUF to start chatting
- Docker Model Runner
How to use mradermacher/DREX-062225-exp-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/DREX-062225-exp-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/DREX-062225-exp-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.DREX-062225-exp-i1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- d065db4dc4e7651ceb1602535eabb4772477e4c110da2672f5005d21f1d3db94
- Size of remote file:
- 3.35 GB
- SHA256:
- fa9b6bb8329e0bd5c6a329d46fc0ffac79c1d12383ba217c8dfeca3cb2ef3ca6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.