Instructions to use prithivMLmods/WebVIA-Agent-f32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/WebVIA-Agent-f32-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/WebVIA-Agent-f32-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/WebVIA-Agent-f32-GGUF", dtype="auto") - llama-cpp-python
How to use prithivMLmods/WebVIA-Agent-f32-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/WebVIA-Agent-f32-GGUF", filename="WebVIA-Agent-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
- llama.cpp
How to use prithivMLmods/WebVIA-Agent-f32-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/WebVIA-Agent-f32-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/WebVIA-Agent-f32-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf prithivMLmods/WebVIA-Agent-f32-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/WebVIA-Agent-f32-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 prithivMLmods/WebVIA-Agent-f32-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/WebVIA-Agent-f32-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 prithivMLmods/WebVIA-Agent-f32-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/WebVIA-Agent-f32-GGUF:BF16
Use Docker
docker model run hf.co/prithivMLmods/WebVIA-Agent-f32-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/WebVIA-Agent-f32-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/WebVIA-Agent-f32-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/WebVIA-Agent-f32-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/WebVIA-Agent-f32-GGUF:BF16
- SGLang
How to use prithivMLmods/WebVIA-Agent-f32-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/WebVIA-Agent-f32-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/WebVIA-Agent-f32-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/WebVIA-Agent-f32-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/WebVIA-Agent-f32-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/WebVIA-Agent-f32-GGUF with Ollama:
ollama run hf.co/prithivMLmods/WebVIA-Agent-f32-GGUF:BF16
- Unsloth Studio new
How to use prithivMLmods/WebVIA-Agent-f32-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/WebVIA-Agent-f32-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/WebVIA-Agent-f32-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/WebVIA-Agent-f32-GGUF to start chatting
- Docker Model Runner
How to use prithivMLmods/WebVIA-Agent-f32-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/WebVIA-Agent-f32-GGUF:BF16
- Lemonade
How to use prithivMLmods/WebVIA-Agent-f32-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/WebVIA-Agent-f32-GGUF:BF16
Run and chat with the model
lemonade run user.WebVIA-Agent-f32-GGUF-BF16
List all available models
lemonade list
WebVIA-Agent-f32-GGUF
WebVIA-Agent is part of the WebVIA framework, representing the first agentic vision-language model built for interactive, verifiable UI-to-Code generation on web interfaces. Based on a GLM-4.1V-9B backbone, WebVIA-Agent navigates websites to capture multi-state UI screenshots and interprets both image and DOM tree inputs to identify which interactive components require actions, factoring in previous interactions and occlusions to avoid redundant operations. The broader framework includes UI2Code for generating executable HTML/CSS/JavaScript and a validation module to ensure the generated UIs behave as intended, achieving more stable and accurate UI exploration than general-purpose agents like Gemini-2.5-Pro. WebVIA-Agent thus enables reliable automation and tool-support for realistic, dynamic web environments by not only modeling static layouts, but also verifying and automating interactive workflows.
Model Files
| File Name | Quant Type | File Size |
|---|---|---|
| WebVIA-Agent-BF16.gguf | BF16 | 18.8 GB |
| WebVIA-Agent-F16.gguf | F16 | 18.8 GB |
| WebVIA-Agent-F32.gguf | F32 | 37.6 GB |
| WebVIA-Agent-Q8_0.gguf | Q8_0 | 10 GB |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
- Downloads last month
- 42
8-bit
16-bit
32-bit
