Instructions to use sriram1983007/SRA-RiskGate-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sriram1983007/SRA-RiskGate-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sriram1983007/SRA-RiskGate-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sriram1983007/SRA-RiskGate-4B")
model = AutoModelForCausalLM.from_pretrained("sriram1983007/SRA-RiskGate-4B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sriram1983007/SRA-RiskGate-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sriram1983007/SRA-RiskGate-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sriram1983007/SRA-RiskGate-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sriram1983007/SRA-RiskGate-4B

SGLang

How to use sriram1983007/SRA-RiskGate-4B 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 "sriram1983007/SRA-RiskGate-4B" \
    --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": "sriram1983007/SRA-RiskGate-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "sriram1983007/SRA-RiskGate-4B" \
        --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": "sriram1983007/SRA-RiskGate-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sriram1983007/SRA-RiskGate-4B with Docker Model Runner:
```
docker model run hf.co/sriram1983007/SRA-RiskGate-4B
```

sriram1983007 commited on 15 days ago

Commit

f76de07

verified ·

1 Parent(s): a52c9d2

Update README.md

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@@ -29,24 +29,24 @@ tags:
 - text-generation
 ---
-# 🛡️️ SRA-RiskGate-4B
 [![Interactive Demo](https://img.shields.io/badge/Demo-Interactive%20Space-blue?style=for-the-badge&logo=huggingface)](https://sriram1983007-sra-riskgate-demo.static.hf.space/index.html)
 [![Dataset](https://img.shields.io/badge/Dataset-Risk%20Bench-green?style=for-the-badge&logo=huggingface)](https://huggingface.co/datasets/sriram1983007/sra-stablecoin-risk-bench)
 [![GGUF Quantized](https://img.shields.io/badge/GGUF-Q4__K__M-yellow?style=for-the-badge&logo=huggingface)](https://huggingface.co/sriram1983007/SRA-RiskGate-4B-Q4_K_M-GGUF)
 [![Ollama Registry](https://img.shields.io/badge/Ollama-Registry-black?style=for-the-badge&logo=ollama)](https://ollama.com/sriram1983007/sra-riskgate)
-An autonomous risk scoring, compliance verification, and dispute adjudication model fine-tuned from `Qwen/Qwen3-4B-Instruct-2507` on [sra-stablecoin-risk-bench](https://huggingface.co/datasets/sriram1983007/sra-stablecoin-risk-bench).
-A specialized 4B model designed for both ends of a stablecoin payment lifecycle:
-- **Risk Gate:** Evaluates incoming payment operations (x402 requests, EIP-3009 authorizations, or raw ERC-20 transfers) against policy constraints and verification tool results to return deterministic `approve`, `hold`, or `reject` verdicts with structured reasoning.
-- **Dispute Adjudication:** Ingests dispute evidence and escrow/ledger state to determine legally and technically viable remedies across settlement-finality boundaries (void before release, arbiter escrow refund, merchant bond claim, voluntary refund, deny, or escalate) complete with exact amount, destination address, and idempotency keys. It strictly avoids proposing impossible reversals on final ledger state.
 ---
 ### 🦙 Quickstart with Ollama
-Run locally via the official Ollama registry:
 ```bash
 ollama run sriram1983007/sra-riskgate

 - text-generation
 ---
+# 🛡 SRA-RiskGate-4B
 [![Interactive Demo](https://img.shields.io/badge/Demo-Interactive%20Space-blue?style=for-the-badge&logo=huggingface)](https://sriram1983007-sra-riskgate-demo.static.hf.space/index.html)
 [![Dataset](https://img.shields.io/badge/Dataset-Risk%20Bench-green?style=for-the-badge&logo=huggingface)](https://huggingface.co/datasets/sriram1983007/sra-stablecoin-risk-bench)
 [![GGUF Quantized](https://img.shields.io/badge/GGUF-Q4__K__M-yellow?style=for-the-badge&logo=huggingface)](https://huggingface.co/sriram1983007/SRA-RiskGate-4B-Q4_K_M-GGUF)
 [![Ollama Registry](https://img.shields.io/badge/Ollama-Registry-black?style=for-the-badge&logo=ollama)](https://ollama.com/sriram1983007/sra-riskgate)
+**SRA-RiskGate-4B** is an autonomous risk scoring, compliance verification, and dispute adjudication model fine-tuned on top of `Qwen/Qwen3-4B-Instruct-2507`.
+A specialized 4B parameter model engineered for both ends of a stablecoin payment's operational lifecycle:
+- **Pre-Settlement Risk Gate:** Ingests payment requests (x402 requests, EIP-3009 authorizations, or standard ERC-20 transfers), policy constraints, and deterministic verification tool outputs (sanctions hits, attestation checks, signature status) to return structured `approve` / `hold` / `reject` decisions with explicit rationales.
+- **Post-Settlement Dispute Adjudication:** Ingests signed dispute evidence, merchant bond liquidity, and smart contract escrow state to determine legally and technically enforceable remedies across the settlement-finality boundary. It enforces valid remedies (void before release, arbiter refund from escrow, merchant bond drawdown, voluntary refund, deny, or escalate) with exact amounts, destinations, and idempotency keys—strictly avoiding hallucinated reversals on immutable ledgers.
 ---
 ### 🦙 Quickstart with Ollama
+Run locally in one line via the official Ollama registry:
 ```bash
 ollama run sriram1983007/sra-riskgate