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 16 days ago

Commit

db1d910

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1 Parent(s): f438aa8

Update README.md

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@@ -30,6 +30,19 @@ A small model for both ends of a stablecoin payment's life:
   escrow or from a merchant bond, voluntary refund, deny, escalate, or no enforceable remedy), with exact
   amount, destination and an idempotency key for any refund. It never proposes reversing a final payment. Fine-tuned from `Qwen/Qwen3-4B-Instruct-2507` on [sriram1983007/sra-stablecoin-risk-bench](https://huggingface.co/datasets/sriram1983007/sra-stablecoin-risk-bench).
 ## Results on the SRA benchmark (test split, 2,000 payments)
 | model | sra_score | risk gate score | unsafe approvals | dispute score | impossible remedies | wrongful refunds | refund detail acc. |

   escrow or from a merchant bond, voluntary refund, deny, escalate, or no enforceable remedy), with exact
   amount, destination and an idempotency key for any refund. It never proposes reversing a final payment. Fine-tuned from `Qwen/Qwen3-4B-Instruct-2507` on [sriram1983007/sra-stablecoin-risk-bench](https://huggingface.co/datasets/sriram1983007/sra-stablecoin-risk-bench).
+# 🛡 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)
+**SRA-RiskGate-4B** is an autonomous risk scoring, compliance verification, and dispute resolution model fine-tuned on top of `Qwen/Qwen3-4B-Instruct-2507`.
+* **Overall SRA Score:** `0.9159`
+* **Risk Decision Accuracy:** `99.49%`
+* **Unsafe Approval Rate:** `0.47%` (Threshold: $\le 2.0\%$)
+* **Dispute Impossible Remedy Rate:** `0.19%` (Threshold: $\le 1.0\%$)
+* **JSON Schema Validity:** `100.0%`
 ## Results on the SRA benchmark (test split, 2,000 payments)
 | model | sra_score | risk gate score | unsafe approvals | dispute score | impossible remedies | wrongful refunds | refund detail acc. |