Instructions to use barha/granite-genai-attack-vector-350m-alora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use barha/granite-genai-attack-vector-350m-alora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.0-350m")
model = PeftModel.from_pretrained(base_model, "barha/granite-genai-attack-vector-350m-alora")

Transformers

How to use barha/granite-genai-attack-vector-350m-alora with Transformers:

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

pipe = pipeline("text-generation", model="barha/granite-genai-attack-vector-350m-alora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("barha/granite-genai-attack-vector-350m-alora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use barha/granite-genai-attack-vector-350m-alora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "barha/granite-genai-attack-vector-350m-alora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "barha/granite-genai-attack-vector-350m-alora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/barha/granite-genai-attack-vector-350m-alora

SGLang

How to use barha/granite-genai-attack-vector-350m-alora 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 "barha/granite-genai-attack-vector-350m-alora" \
    --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": "barha/granite-genai-attack-vector-350m-alora",
		"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 "barha/granite-genai-attack-vector-350m-alora" \
        --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": "barha/granite-genai-attack-vector-350m-alora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use barha/granite-genai-attack-vector-350m-alora with Docker Model Runner:
```
docker model run hf.co/barha/granite-genai-attack-vector-350m-alora
```

granite-genai-attack-vector-350m-alora / adapter_model.safetensors

barha

aLoRA retrain of genai-attack-vector on granite-4.0-350m (gated on assistant invocation tokens)

74e404e verified 2 months ago

Download

History Blame

53.3 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 706d4c8ef5882351087d94097cd83f7c3d659c994f26eb66804f74873a4e5618
Size of remote file:: 53.3 MB
SHA256:: 9e5bd03315e52a2233a1bfa553fa9e642ecff328206d9f84089423c4f35d8541

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.