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 about 21 hours ago

Commit

edf6f03

verified ·

1 Parent(s): 642c7e0

Pin sra-riskgate 0.8.0

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -163,10 +163,10 @@ Pass the payment's own text as `untrusted_text` so self-asserted claims are held
 ## 📦 Python SDK: One Call Before Every Payment (PyPI)
-The `sra-riskgate` SDK runs fast deterministic checks before the model is involved: an offline match against the OFAC SDN list's crypto addresses (a dated snapshot, not a screening service), supported asset and chain, address and amount validation, a per-payment ceiling, USDC depeg in both directions, and a scan for payment text that claims its own verification. It never raises on bad payment data: invalid payments are rejected and anything unexpected becomes `hold`.
 ```bash
-pip install --upgrade "sra-riskgate>=0.7.1"
 ```
 ```python
@@ -202,7 +202,7 @@ Autonomous on-chain agents can hallucinate payment transfers, sign malformed cal
 The `sra-riskgate` SDK's deterministic pre-filter and policy checks are available as an **Action Provider** for the Coinbase AgentKit framework:
 ```bash
-pip install "sra-riskgate[agentkit]>=0.7.1"
 ```
 ### Drop-in Agent Firewall Example

 ## 📦 Python SDK: One Call Before Every Payment (PyPI)
+The `sra-riskgate` SDK runs fast deterministic checks before the model is involved: an offline match against the OFAC SDN list's crypto addresses (a dated snapshot, not a screening service), supported asset and chain, address and amount validation, a per-payment ceiling, USDC depeg in both directions, and a scan for payment text that claims its own verification.    From 0.8.0 it can also hold the first payment to a new payee (`known_recipients`) and always reject addresses you block (`blocked_addresses`). It never raises on bad payment data: invalid payments are rejected and anything unexpected becomes `hold`.
 ```bash
+pip install --upgrade "sra-riskgate>=0.8.0"
 ```
 ```python
 The `sra-riskgate` SDK's deterministic pre-filter and policy checks are available as an **Action Provider** for the Coinbase AgentKit framework:
 ```bash
+pip install "sra-riskgate[agentkit]>=0.8.0"
 ```
 ### Drop-in Agent Firewall Example