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 26 days ago

Commit

2fb819f

verified ·

1 Parent(s): 3bacb7f

Update README.md

Browse files

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README.md +4 -2

README.md CHANGED Viewed

@@ -96,6 +96,8 @@ agent_kit = AgentKit(AgentKitConfig(
 ```
 > **Security Guardrail:** To ensure the firewall cannot be bypassed by an autonomous agent, register `RiskGateActionProvider` as the **sole** payment action provider. Do not register generic native transfer or unconstrained ERC-20 providers alongside it.
 ### Deterministic Pre-Execution Rules
@@ -326,9 +328,9 @@ Evaluated against the 2,000-case test set in [`sra-stablecoin-risk-bench`](https
 * **Synthetic Dataset Distribution:** The benchmark and training corpora are synthetically generated from parameterized fraud schemas and common on-chain threat models. Performance on novel, adversarial zero-day prompt structures outside the schema may vary.
 * **External Oracle Dependency:** SRA-RiskGate-4B is a pre-settlement decision layer, not a smart contract formal verifier. It relies on the accuracy of upstream deterministic screening tools (e.g., chain analysis APIs, sanctions lists, and signature verifiers).
 * **Greedy Decoding Required:** Always run inference with `do_sample=False` or `temperature=0.0`. Sampling introduces stochasticity that invalidates JSON schema validity and determinism guarantees.
-* **Agent Boundary Isolation:** In autonomous agent frameworks (such as AgentKit), safety guarantees apply only when `RiskGateActionProvider`
 * **Dispute adjudication is weaker than payment gating:** Dispute score is 0.837 vs. 0.995 for the risk gate. Refund details (amount, destination, idempotency key) are fully correct in about 66% of refund cases, and on some disputes containing prompt injection the model replies in the payment-gate format (`decision: hold`) instead of a dispute `outcome`. Treat any dispute response without a valid `outcome` as requiring human review.
-* **Prompt injection is reduced, not eliminated:** About 10% of injected test cases are not flagged. Never let model output trigger irreversible actions without a deterministic check. is the sole execution provider for transfers.
 ---

 ```
+---
 > **Security Guardrail:** To ensure the firewall cannot be bypassed by an autonomous agent, register `RiskGateActionProvider` as the **sole** payment action provider. Do not register generic native transfer or unconstrained ERC-20 providers alongside it.
 ### Deterministic Pre-Execution Rules
 * **Synthetic Dataset Distribution:** The benchmark and training corpora are synthetically generated from parameterized fraud schemas and common on-chain threat models. Performance on novel, adversarial zero-day prompt structures outside the schema may vary.
 * **External Oracle Dependency:** SRA-RiskGate-4B is a pre-settlement decision layer, not a smart contract formal verifier. It relies on the accuracy of upstream deterministic screening tools (e.g., chain analysis APIs, sanctions lists, and signature verifiers).
 * **Greedy Decoding Required:** Always run inference with `do_sample=False` or `temperature=0.0`. Sampling introduces stochasticity that invalidates JSON schema validity and determinism guarantees.
+* **Agent Boundary Isolation:** In autonomous agent frameworks (such as AgentKit), safety guarantees apply only when `RiskGateActionProvider` is the sole execution provider for transfers.
 * **Dispute adjudication is weaker than payment gating:** Dispute score is 0.837 vs. 0.995 for the risk gate. Refund details (amount, destination, idempotency key) are fully correct in about 66% of refund cases, and on some disputes containing prompt injection the model replies in the payment-gate format (`decision: hold`) instead of a dispute `outcome`. Treat any dispute response without a valid `outcome` as requiring human review.
+* **Prompt injection is reduced, not eliminated:** About 10% of injected test cases are not flagged. Never let model output trigger irreversible actions without a deterministic check.
 ---