Instructions to use edamamex/qwen-ctfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edamamex/qwen-ctfer with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "edamamex/qwen-ctfer")

Transformers

How to use edamamex/qwen-ctfer with Transformers:

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

pipe = pipeline("text-generation", model="edamamex/qwen-ctfer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("edamamex/qwen-ctfer", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use edamamex/qwen-ctfer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/edamamex/qwen-ctfer

SGLang

How to use edamamex/qwen-ctfer 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 "edamamex/qwen-ctfer" \
    --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": "edamamex/qwen-ctfer",
		"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 "edamamex/qwen-ctfer" \
        --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": "edamamex/qwen-ctfer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use edamamex/qwen-ctfer with Docker Model Runner:
```
docker model run hf.co/edamamex/qwen-ctfer
```

qwen-ctfer

qwen-ctfer is a QLoRA adapter for Qwen/Qwen2.5-Coder-7B-Instruct tuned on transformed CTF writeups.

What This Is

Base model: Qwen/Qwen2.5-Coder-7B-Instruct
Fine-tuning method: QLoRA
Scope: concise CTF-style solve paths, exploit notes, debugging steps, and operator-focused walkthroughs
Artifact: adapter weights, not merged full weights

Data

Training used authorized writeup-derived records built from:

CTFtime writeup listings
linked external writeups
GitHub writeups fetched via raw content when possible
non-GitHub pages transformed through r.jina.ai

Before training, the pipeline:

cached raw fetches locally
preferred original writeups over duplicate CTFtime mirrors when available
masked obvious flag patterns
dropped some low-signal profile and listing pages

The resulting dataset for this run contained:

train: 460 samples
eval: 50 samples

Training

Rank: r=16
Alpha: 32
Dropout: 0.05
Max sequence length: 2048
Epochs: 2
Batch size: 1
Gradient accumulation: 8
Learning rate: 2e-4

Evaluation

This run completed local prompt eval against the repository's current data/eval/example_cases.jsonl.

base model: 0/2
tuned adapter: 0/2

This is an early publishable adapter, not a final benchmarked release.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen2.5-Coder-7B-Instruct"
adapter_id = "edamamex/qwen-ctfer"

tokenizer = AutoTokenizer.from_pretrained(base_id)
model = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)

Limitations

This adapter can still hallucinate exploitability or tool output.
Some transformed writeups still contain formatting noise from source platforms.
All outputs should be validated against the actual target and rules of engagement.

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