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

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

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"

Sagui-4B-Vision-Thinking-Code-GGUF / scripts /render_card.py

sysrobson

Release Sagui-4B

9ea491e verified about 12 hours ago

Raw History Blame Contribute Delete

1.5 kB

"""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()

	"""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()