Instructions to use kmseong/llama3_2_3b-instruct-SSFT-lr5e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kmseong/llama3_2_3b-instruct-SSFT-lr5e-5 with Transformers:

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

pipe = pipeline("text-generation", model="kmseong/llama3_2_3b-instruct-SSFT-lr5e-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kmseong/llama3_2_3b-instruct-SSFT-lr5e-5")
model = AutoModelForCausalLM.from_pretrained("kmseong/llama3_2_3b-instruct-SSFT-lr5e-5")
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 kmseong/llama3_2_3b-instruct-SSFT-lr5e-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kmseong/llama3_2_3b-instruct-SSFT-lr5e-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kmseong/llama3_2_3b-instruct-SSFT-lr5e-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kmseong/llama3_2_3b-instruct-SSFT-lr5e-5

SGLang

How to use kmseong/llama3_2_3b-instruct-SSFT-lr5e-5 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 "kmseong/llama3_2_3b-instruct-SSFT-lr5e-5" \
    --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": "kmseong/llama3_2_3b-instruct-SSFT-lr5e-5",
		"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 "kmseong/llama3_2_3b-instruct-SSFT-lr5e-5" \
        --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": "kmseong/llama3_2_3b-instruct-SSFT-lr5e-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kmseong/llama3_2_3b-instruct-SSFT-lr5e-5 with Docker Model Runner:
```
docker model run hf.co/kmseong/llama3_2_3b-instruct-SSFT-lr5e-5
```

llama3_2_3b-instruct-SSFT-lr5e-5 / model_metadata.json

kmseong

Upload WaRP fine-tuned model - 2026-04-28 16:02:37

90d212e verified 25 days ago

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372 Bytes

	{
	"model_id": "kmseong/llama3_2_3b-instruct-Safety-FT-lr5e-5-20260428_160224",
	"base_model": "meta-llama/Llama-3.2-3B-Instruct",
	"training_method": "Safety-First WaRP",
	"upload_date": "2026-04-28T16:02:35.856549",
	"model_size_gb": 6.000339686870575,
	"important_neurons_layer31": 419,
	"keep_ratio": 0.1,
	"framework": "pytorch",
	"task": "text-generation"
	}