Instructions to use GeneralAnalysis/GA_Guard_1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GeneralAnalysis/GA_Guard_1B with Transformers:

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

pipe = pipeline("text-generation", model="GeneralAnalysis/GA_Guard_1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GeneralAnalysis/GA_Guard_1B")
model = AutoModelForCausalLM.from_pretrained("GeneralAnalysis/GA_Guard_1B")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GeneralAnalysis/GA_Guard_1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GeneralAnalysis/GA_Guard_1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GeneralAnalysis/GA_Guard_1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GeneralAnalysis/GA_Guard_1B

SGLang

How to use GeneralAnalysis/GA_Guard_1B 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 "GeneralAnalysis/GA_Guard_1B" \
    --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": "GeneralAnalysis/GA_Guard_1B",
		"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 "GeneralAnalysis/GA_Guard_1B" \
        --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": "GeneralAnalysis/GA_Guard_1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GeneralAnalysis/GA_Guard_1B with Docker Model Runner:
```
docker model run hf.co/GeneralAnalysis/GA_Guard_1B
```

rezzzy commited on May 5

Commit

e1872a5

verified ·

1 Parent(s): f74b4d3

Rename public model card to GA Guard 1B

Browse files

Files changed (2) hide show

README.md +2 -2
chat_template.jinja +1 -1

README.md CHANGED Viewed

@@ -31,7 +31,7 @@ tags:
 Introducing the GA Guard series: a family of open-weight moderation models built to help developers and organizations keep language models safe, compliant, and aligned with real-world use.
-**GA Guard Llama** is the Llama 3.2 1B variant of the GA Guard family. It is optimized for low-latency moderation and classifies a piece of text against seven safety policies in a single generation.
 **GA Guard** detects violations across the following seven categories:
@@ -146,4 +146,4 @@ Not-violation tokens:
 ## Intended Use
-GA Guard Llama is intended for automated moderation, agent input screening, prompt-injection detection, and safety triage. It should be used as one layer in a broader safety system, especially for high-risk domains or decisions that require human review.

 Introducing the GA Guard series: a family of open-weight moderation models built to help developers and organizations keep language models safe, compliant, and aligned with real-world use.
+**GA Guard 1B** is the Llama 3.2 1B variant of the GA Guard family. It is optimized for low-latency moderation and classifies a piece of text against seven safety policies in a single generation.
 **GA Guard** detects violations across the following seven categories:
 ## Intended Use
+GA Guard 1B is intended for automated moderation, agent input screening, prompt-injection detection, and safety triage. It should be used as one layer in a broader safety system, especially for high-risk domains or decisions that require human review.

chat_template.jinja CHANGED Viewed

@@ -1,5 +1,5 @@
 {#-
-  GA Guard Llama chat template.
   Bakes in the guard system prompt that the model was trained on (the seven
   policies + output-format instructions + "Reasoning effort: LOW" suffix), so

 {#-
+  GA Guard 1B chat template.
   Bakes in the guard system prompt that the model was trained on (the seven
   policies + output-format instructions + "Reasoning effort: LOW" suffix), so