Instructions to use RockMan256/granite-docling-258M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RockMan256/granite-docling-258M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RockMan256/granite-docling-258M-GGUF", filename="granite-docling-258M-bf16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RockMan256/granite-docling-258M-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 RockMan256/granite-docling-258M-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf RockMan256/granite-docling-258M-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RockMan256/granite-docling-258M-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf RockMan256/granite-docling-258M-GGUF:BF16
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 RockMan256/granite-docling-258M-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf RockMan256/granite-docling-258M-GGUF:BF16
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 RockMan256/granite-docling-258M-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf RockMan256/granite-docling-258M-GGUF:BF16
Use Docker
docker model run hf.co/RockMan256/granite-docling-258M-GGUF:BF16
- LM Studio
- Jan
- Ollama
How to use RockMan256/granite-docling-258M-GGUF with Ollama:
ollama run hf.co/RockMan256/granite-docling-258M-GGUF:BF16
- Unsloth Studio
How to use RockMan256/granite-docling-258M-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 RockMan256/granite-docling-258M-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 RockMan256/granite-docling-258M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RockMan256/granite-docling-258M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use RockMan256/granite-docling-258M-GGUF with Docker Model Runner:
docker model run hf.co/RockMan256/granite-docling-258M-GGUF:BF16
- Lemonade
How to use RockMan256/granite-docling-258M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RockMan256/granite-docling-258M-GGUF:BF16
Run and chat with the model
lemonade run user.granite-docling-258M-GGUF-BF16
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Granite Docling 258M GGUF
GGUF conversion of RockMan256/granite-docling-258M, a fine-tuned version of ibm-granite/granite-docling-258M.
Files
| File | Description |
|---|---|
granite-docling-258M-bf16.gguf (317 MB) |
LLM weights in BF16 |
mmproj-model-f16.gguf (182 MB) |
Vision projector (mmproj) in F16 |
Usage with llama.cpp
# Build llama.cpp (if not already built)
cd llama.cpp && make -j
# Run with multimodal (image + text) inference
./llama-mtmd-cli \
-m granite-docling-258M-bf16.gguf \
--mmproj mmproj-model-f16.gguf \
--chat-template chatml
Example prompts
# Describe an image
echo "Describe this image in detail." | ./llama-mtmd-cli \
-m granite-docling-258M-bf16.gguf \
--mmproj mmproj-model-f16.gguf \
-p /path/to/image.png
# Text-only inference (no vision)
echo "What is the capital of France?" | ./llama-mtmd-cli \
-m granite-docling-258M-bf16.gguf \
-p - --chat-template chatml
Conversion Details
- LLM: Converted from user's fine-tuned
RockMan256/granite-docling-258M(original weights, not remote) - mmproj: Converted from
ibm-granite/granite-docling-258M(vision encoder/connector unchanged from base) - Quantization: BF16 (LLM), F16 (mmproj)
License
Apache 2.0 (same as base model)
- Downloads last month
- 195
Hardware compatibility
Log In to add your hardware
16-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support