Instructions to use SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True with Transformers:

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

pipe = pipeline("text-generation", model="SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True")
model = AutoModelForCausalLM.from_pretrained("SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True")
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 SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True

SGLang

How to use SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True 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 "SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True" \
    --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": "SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True",
		"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 "SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True" \
        --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": "SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True with Docker Model Runner:
```
docker model run hf.co/SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True
```

Quantized version of meta-llama/LlamaGuard-7b

Model Description

The model meta-llama/LlamaGuard-7b was quantized to 4bit, group_size 128, and act-order=True with auto-gptq integration in transformers (https://huggingface.co/blog/gptq-integration).

Evaluation

To evaluate the qunatized model and compare it with the full precision model, I performed binary classification on the "toxicity" label from the ~5k samples test set of lmsys/toxic-chat.

📊 Full Precision Model:

Average Precision Score: 0.3625

📊 4-bit Quantized Model:

Average Precision Score: 0.3450

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

I32

F16

Model tree for SebastianSchramm/LlamaGuard-7b-GPTQ-4bit-128g-actorder_True

Base model

meta-llama/LlamaGuard-7b

Quantized

(2)

this model