Instructions to use ree2raz/CyberSecQwen-4B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ree2raz/CyberSecQwen-4B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="ree2raz/CyberSecQwen-4B-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ree2raz/CyberSecQwen-4B-AWQ")
model = AutoModelForCausalLM.from_pretrained("ree2raz/CyberSecQwen-4B-AWQ")
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 ree2raz/CyberSecQwen-4B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ree2raz/CyberSecQwen-4B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ree2raz/CyberSecQwen-4B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ree2raz/CyberSecQwen-4B-AWQ

SGLang

How to use ree2raz/CyberSecQwen-4B-AWQ 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 "ree2raz/CyberSecQwen-4B-AWQ" \
    --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": "ree2raz/CyberSecQwen-4B-AWQ",
		"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 "ree2raz/CyberSecQwen-4B-AWQ" \
        --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": "ree2raz/CyberSecQwen-4B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ree2raz/CyberSecQwen-4B-AWQ with Docker Model Runner:
```
docker model run hf.co/ree2raz/CyberSecQwen-4B-AWQ
```

ree2raz commited on 10 days ago

Commit

4d8b2b1

verified ·

1 Parent(s): e3979ab

Add CTI-Bench evaluation results (AWQ 4-bit vs FP16)

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -38,11 +38,11 @@ Evaluated under the [Foundation-Sec-8B protocol](https://arxiv.org/abs/2504.2103
 | Task | AWQ 4-bit | FP16 Reference | Delta |
 |---|---|---|---|
 | CTI-MCQ (2,500 items) | 0.5921 +/- 0.0083 | 0.5868 +/- 0.0029 | +0.0053 |
-| CTI-RCM (1,000 items) | 0.5558 +/- 0.0040 | 0.6664 +/- 0.0023 | -0.1106 |
 **Key findings:**
 - **CTI-MCQ**: AWQ 4-bit matches or slightly exceeds FP16 performance (+0.5 points). No measurable accuracy loss.
-- **CTI-RCM**: AWQ 4-bit degrades by 11.1 percentage points vs FP16. Parseable rate > 99.8% so answer extraction is working correctly. The model retains correct CWE identification in reasoning but sometimes diverges on final answers. This gap can likely be reduced with more calibration data.
 ## Trial results
@@ -58,11 +58,11 @@ Evaluated under the [Foundation-Sec-8B protocol](https://arxiv.org/abs/2504.2103
 ### CTI-RCM
 | Trial | Seed | Accuracy |
 |-------|------|----------|
-| 1 | 42 | 0.5520 |
-| 2 | 43 | 0.5500 |
-| 3 | 44 | 0.5600 |
-| 4 | 45 | 0.5580 |
-| 5 | 46 | 0.5590 |
 ## Usage with vLLM

 | Task | AWQ 4-bit | FP16 Reference | Delta |
 |---|---|---|---|
 | CTI-MCQ (2,500 items) | 0.5921 +/- 0.0083 | 0.5868 +/- 0.0029 | +0.0053 |
+| CTI-RCM (1,000 items) | 0.5814 +/- 0.0025 | 0.6664 +/- 0.0023 | -0.0850 |
 **Key findings:**
 - **CTI-MCQ**: AWQ 4-bit matches or slightly exceeds FP16 performance (+0.5 points). No measurable accuracy loss.
+- **CTI-RCM**: AWQ 4-bit degrades by 8.5 percentage points vs FP16. Parseable rate > 99.8% so answer extraction is working correctly. The model retains correct CWE identification in reasoning but sometimes diverges on final answers. This gap can likely be reduced with more calibration data.
 ## Trial results
 ### CTI-RCM
 | Trial | Seed | Accuracy |
 |-------|------|----------|
+| 1 | 42 | 0.5790 |
+| 2 | 43 | 0.5830 |
+| 3 | 44 | 0.5790 |
+| 4 | 45 | 0.5840 |
+| 5 | 46 | 0.5820 |
 ## Usage with vLLM