qwen-ctfer – AI Model by edamamex | AlphaNeural AI

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edamamex/
qwen-ctfer

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text-generation

Qwen/Qwen2.5-Coder-7B-Instruct

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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

python

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.