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

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
```

GA_Guard_1B / special_tokens_map.json

rezzzy

Fix tokenizer config from base Llama tokenizer

040b9cb verified 7 days ago

raw

history blame contribute delete

2.62 kB

	{
	"bos_token": {
	"content": "<\|begin_of_text\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"eos_token": {
	"content": "<\|eot_id\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"additional_special_tokens": [
	{
	"content": "<illicit_activities_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<hate_and_abuse_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<pii_and_ip_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<prompt_security_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<sexual_content_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<misinformation_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<violence_and_self_harm_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<illicit_activities_not_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<hate_and_abuse_not_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<pii_and_ip_not_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<prompt_security_not_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<sexual_content_not_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<misinformation_not_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<violence_and_self_harm_not_violation>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	}
	]
	}