Instructions to use nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017 with Transformers:

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

pipe = pipeline("text-generation", model="nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017")
model = AutoModelForCausalLM.from_pretrained("nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017")
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 nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017

SGLang

How to use nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017 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 "nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017" \
    --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": "nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017",
		"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 "nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017" \
        --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": "nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017 with Docker Model Runner:
```
docker model run hf.co/nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017
```

Qwen2-1.5B-Instruct — Cybersecurity QA (SFT)

Fine-tuned on Kaggle (2×T4) using SFT for cybersecurity Q&A.

Model Details

Base: Qwen/Qwen2-1.5B-Instruct
Method: SFT (freeze last 2 blocks + lm_head)
Max length: 1024
Early stopping: yes

Validation (greedy, no sampling)

Metric	Score
BLEU-4	2.10
ROUGE-L	12.86
F1	16.40
EM	0.00
Train Time (s)	84.1

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017")
mdl = AutoModelForCausalLM.from_pretrained("nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017")

prompt = tok.apply_chat_template([
    { "role": "system", "content": "You are a helpful assistant." },
    { "role": "user",   "content": "Explain SQL injection in one paragraph." },
], tokenize=False, add_generation_prompt=True)

ids = tok(prompt, return_tensors="pt").input_ids
out = mdl.generate(ids, max_new_tokens=128)
print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))

Training Summary

Trainable params: 326,969,344 / 1,543,714,304
Optimized for T4 (fp32 or fp16 AMP depending on notebook), gradient checkpointing, train-by-steps.

Data

Dataset: zobayer0x01/cybersecurity-qa

Downloads last month: 1

Safetensors

Model size

2B params

Tensor type

F32

Model tree for nhonhoccode/qwen2-1-5b-instruct-cybersecqa-sft-freeze2-20251028-1017

Base model

Qwen/Qwen2-1.5B-Instruct

Finetuned

(123)

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