Instructions to use prithivMLmods/GUI-RD-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/GUI-RD-9B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/GUI-RD-9B-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/GUI-RD-9B-GGUF", dtype="auto") - llama-cpp-python
How to use prithivMLmods/GUI-RD-9B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/GUI-RD-9B-GGUF", filename="GUI-RD-9B.BF16.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 prithivMLmods/GUI-RD-9B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf prithivMLmods/GUI-RD-9B-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 prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf prithivMLmods/GUI-RD-9B-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 prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/GUI-RD-9B-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 prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/GUI-RD-9B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/GUI-RD-9B-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": "prithivMLmods/GUI-RD-9B-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/prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/GUI-RD-9B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/GUI-RD-9B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/GUI-RD-9B-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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/GUI-RD-9B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/GUI-RD-9B-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" } } ] } ] }' - Ollama
How to use prithivMLmods/GUI-RD-9B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
- Unsloth Studio
How to use prithivMLmods/GUI-RD-9B-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 prithivMLmods/GUI-RD-9B-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 prithivMLmods/GUI-RD-9B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for prithivMLmods/GUI-RD-9B-GGUF to start chatting
- Pi
How to use prithivMLmods/GUI-RD-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/GUI-RD-9B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use prithivMLmods/GUI-RD-9B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/GUI-RD-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/GUI-RD-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GUI-RD-9B-GGUF-Q4_K_M
List all available models
lemonade list
GUI-RD-9B-GGUF
GUI-RD-9B is a 9-billion-parameter vision-language model built on Qwen3.5-9B, developed by researchers at the University of Georgia as the main experimental checkpoint for the paper "Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding," which addresses the task of identifying small target elements in high-resolution GUI screenshots and predicting precise screen coordinates from natural-language instructions. The model is trained using GUI-RD (GUI Reliable Distillation), a quality-aware self-distillation method that improves on naive on-policy self-distillation (OPSD) — whose coordinate-token teacher signals can become unreliable once the student-generated prefix has already deviated from the target — by combining soft correctness-aware gating, which checks whether the teacher's current coordinate-token prediction can still be completed into the ground-truth box and down-weights it if not, with teacher-probability scaling, which calibrates supervision strength using the teacher's confidence; notably, the paper found that neither mechanism improves performance alone, but combining them consistently does, since they play complementary roles. This released checkpoint corresponds to the paper's main setting (failed-token gate 0.5, scaling coefficient 3, teacher top-1 probability scaling enabled), and the method was shown to consistently improve the base model and outperform strong baselines across six GUI grounding benchmarks, making it intended for GUI grounding research and evaluation, loadable via standard Transformers classes (
AutoModelForMultimodalLM/AutoProcessor) with bfloat16 precision.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| GUI-RD-9B.BF16.gguf | BF16 | 17.9 GB | Download |
| GUI-RD-9B.F16.gguf | F16 | 17.9 GB | Download |
| GUI-RD-9B.Q2_K.gguf | Q2_K | 3.83 GB | Download |
| GUI-RD-9B.Q3_K_L.gguf | Q3_K_L | 4.93 GB | Download |
| GUI-RD-9B.Q3_K_M.gguf | Q3_K_M | 4.62 GB | Download |
| GUI-RD-9B.Q3_K_S.gguf | Q3_K_S | 4.26 GB | Download |
| GUI-RD-9B.Q4_0.gguf | Q4_0 | 5.31 GB | Download |
| GUI-RD-9B.Q4_K_M.gguf | Q4_K_M | 5.63 GB | Download |
| GUI-RD-9B.Q4_K_S.gguf | Q4_K_S | 5.35 GB | Download |
| GUI-RD-9B.Q5_0.gguf | Q5_0 | 6.31 GB | Download |
| GUI-RD-9B.Q5_K_M.gguf | Q5_K_M | 6.47 GB | Download |
| GUI-RD-9B.Q5_K_S.gguf | Q5_K_S | 6.31 GB | Download |
| GUI-RD-9B.Q6_K.gguf | Q6_K | 7.36 GB | Download |
| GUI-RD-9B.Q8_0.gguf | Q8_0 | 9.53 GB | Download |
| GUI-RD-9B.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | Download |
| GUI-RD-9B.mmproj-f16.gguf | mmproj-f16 | 922 MB | Download |
| GUI-RD-9B.mmproj-q8_0.gguf | mmproj-q8_0 | 624 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
- Downloads last month
- 316
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
16-bit