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 10 hours ago

Commit

3affc45

verified ·

1 Parent(s): 99c58c2

Clarify Runpod recipe

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -132,7 +132,7 @@ All 4 unsafe approvals were prompt injections; the companion SDK's deterministic
 | In an AI agent (MCP) | `uvx sra-riskgate-mcp` ([details](#🔌-use-it-from-an-ai-agent-mcp)) |
 | Rule checks in Python, no GPU | `pip install sra-riskgate` ([details](#📦-python-sdk-one-call-before-every-payment-pypi)) |
 | Test your agent's payments in CI | [GitHub Action](https://github.com/sriram1983007-dev/sra-riskgate-action): `uses: sriram1983007-dev/sra-riskgate-action@v1` |
-| Your own cloud API, no setup | [Runpod Serverless](https://console.runpod.io/serverless): **Deploy LLM API**, model `sriram1983007/SRA-RiskGate-4B`, Max Model Length `8192`, one 24 GB GPU, min workers 0. Use `https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1` as an OpenAI-compatible base URL. About 2 s per check once warm; the first request after idle takes about 2 minutes to start |
 ---

 | In an AI agent (MCP) | `uvx sra-riskgate-mcp` ([details](#🔌-use-it-from-an-ai-agent-mcp)) |
 | Rule checks in Python, no GPU | `pip install sra-riskgate` ([details](#📦-python-sdk-one-call-before-every-payment-pypi)) |
 | Test your agent's payments in CI | [GitHub Action](https://github.com/sriram1983007-dev/sra-riskgate-action): `uses: sriram1983007-dev/sra-riskgate-action@v1` |
+| Your own cloud API, no setup | [Runpod Serverless](https://console.runpod.io/serverless): **Deploy LLM API**, model `sriram1983007/SRA-RiskGate-4B`, Max Model Length `8192`, a 24 GB or 48 GB GPU, min workers 0. Use `https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1` as an OpenAI-compatible base URL with your Runpod API key. About 2.4 s per check once warm (measured on a 48 GB GPU); the first request after idle takes about 2 minutes |
 ---