Instructions to use tinyopsec/SRA-RiskGate-4B-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 tinyopsec/SRA-RiskGate-4B-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 tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

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 tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use tinyopsec/SRA-RiskGate-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/SRA-RiskGate-4B-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": "tinyopsec/SRA-RiskGate-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

Ollama
How to use tinyopsec/SRA-RiskGate-4B-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/SRA-RiskGate-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

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": "tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/SRA-RiskGate-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/SRA-RiskGate-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SRA-RiskGate-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/SRA-RiskGate-4B-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 tinyopsec/SRA-RiskGate-4B-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 tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/SRA-RiskGate-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M

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 "tinyopsec/SRA-RiskGate-4B-GGUF:Q4_K_M" \
  --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"

SRA-RiskGate-4B-GGUF / README.md

sriram1983007

Set base_model to sriram1983007/SRA-RiskGate-4B (quantized)

2723a2d verified 3 days ago

preview code

Raw History Blame

1.69 kB

metadata

license: apache-2.0
base_model: sriram1983007/SRA-RiskGate-4B
base_model_relation: quantized
library_name: gguf
pipeline_tag: text-generation
tags:
  - qwen3
  - gguf
  - stablecoin
  - risk-gate
  - payments
  - llama-cpp

SRA-RiskGate-4B — GGUF

GGUF quantizations of sriram1983007/SRA-RiskGate-4B, a Qwen3-4B fine-tune for stablecoin payment risk gating, produced with llama.cpp.

Available files

File	Quantization	Description
`model-F16.gguf`	F16	Full precision (~8 GB)
`model-Q8_0.gguf`	Q8_0	8-bit
`model-Q6_K.gguf`	Q6_K	6-bit
`model-Q5_K_M.gguf`	Q5_K_M	5-bit medium
`model-Q5_K_S.gguf`	Q5_K_S	5-bit small
`model-Q5_0.gguf`	Q5_0	5-bit
`model-Q4_K_M.gguf`	Q4_K_M	4-bit medium (recommended)
`model-Q4_K_S.gguf`	Q4_K_S	4-bit small
`model-Q4_0.gguf`	Q4_0	4-bit
`model-Q3_K_L.gguf`	Q3_K_L	3-bit large
`model-Q3_K_M.gguf`	Q3_K_M	3-bit medium
`model-Q3_K_S.gguf`	Q3_K_S	3-bit small
`model-Q2_K.gguf`	Q2_K	2-bit

Usage

With llama.cpp:

./llama-cli -m model-Q4_K_M.gguf -p "Your prompt" -n 256

Prompt format: the model only performs as benchmarked with its training prompt (a system prompt plus <context>, <payload> and <tool_results> blocks) at temperature 0. See the main model card's Quickstart. Q2_K and Q3 builds have not been evaluated.

Or any GGUF-compatible runtime (Ollama, LM Studio, koboldcpp, etc.).

License

Apache License 2.0. Built on Qwen/Qwen3-4B-Instruct-2507.

Disclaimer

Payment risk-gate model. Evaluate before use in production payments.