Image-Text-to-Text
GGUF
English
Portuguese
ollama
llama.cpp
local-ai
on-device
low-vram
small-model
tiny-llm
vision
multimodal
thinking
reasoning
tool-calling
code
coding-assistant
typescript
javascript
qwen
qwen3.5
iq3_xxs
imatrix
conversational
Eval Results (legacy)
Instructions to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-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 sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS # Run inference directly in the terminal: llama cli -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS # Run inference directly in the terminal: llama cli -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS # Run inference directly in the terminal: ./build/bin/llama cli -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
Use Docker
docker model run hf.co/sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
- Llama new
- LM Studio
- Jan
- vLLM
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sysrobson/Sagui-4B-Vision-Thinking-Code-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": "sysrobson/Sagui-4B-Vision-Thinking-Code-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/sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
- Ollama
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with Ollama:
ollama run hf.co/sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
- Unsloth Desktop
- Pi
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with Docker Model Runner:
docker model run hf.co/sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
- Lemonade
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
Run and chat with the model
lemonade run user.Sagui-4B-Vision-Thinking-Code-GGUF-IQ3_XXS
List all available models
lemonade list
- Hermes Agent
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
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 sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "sysrobson/Sagui-4B-Vision-Thinking-Code-GGUF:IQ3_XXS" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 1,500 Bytes
9ea491e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | """Render README.md from card/README.template.md, release.json, benchmark results and demo transcripts.
Usage: python scripts/render_card.py
Numbers and demo outputs are always read from their result files, never typed into the card.
"""
import json
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
def load(rel):
return json.loads((ROOT / rel).read_text(encoding="utf-8"))
def donation_links(cfg):
return [(name, url) for name, url in cfg["donations"].items() if url]
def main():
cfg = load("release.json")
bench = load("benchmarks/results.json")
demos = {r["demo"]: r for r in load("examples/transcripts.json")["results"]}
links = donation_links(cfg)
values = {
"NS": cfg["hf_namespace"],
"REPO": cfg["repo_name"],
"DONATE_TOP": " · ".join(f"[☕ {name}]({url})" for name, url in links),
"DONATE_LIST": "\n".join(f"- **[{name}]({url})**" for name, url in links),
}
for key, value in bench.items():
values[f"BENCH_{key}"] = str(value)
for name, demo in demos.items():
values[f"DEMO_{name}"] = demo["content"].strip()
values[f"STATS_{name}"] = f"{demo['tokens']} tokens, {demo['seconds']} s"
text = (ROOT / "card" / "README.template.md").read_text(encoding="utf-8")
for key, value in values.items():
text = text.replace(f"@@{key}@@", value)
(ROOT / "README.md").write_text(text, encoding="utf-8", newline="\n")
if __name__ == "__main__":
main()
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