asdf98/ethical-hacking-llm-colab

🔐 General-Purpose LLM Fine-Tuning Collection — Google Colab Free Tier (T4)

A curated collection of production-ready Colab notebooks for fine-tuning state-of-the-art small LLMs on any domain using Google Colab Free Tier (T4, 16GB VRAM).

Pick your model, pick your dataset, click run. Zero-config fine-tuning.

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📚 Notebooks

Notebook	Model	Size	T4 Batch	Est. Time	Status
Qwen3-4B Ultimate	unsloth/Qwen3-4B-Instruct-2507	3.3GB 4-bit	4	~3–4 hrs	✅ Recommended
LFM2.5 Ultimate	unsloth/LFM2.5-1.2B-Instruct	~1GB 4-bit	8	~1–2 hrs	✅ Fastest
Gemma-4 E2B	unsloth/gemma-4-E2B-it	~7.6GB 4-bit	1	~6–8 hrs	⚠️ Tight VRAM
Bonsai (PrismML)	See limitations	~1GB 1-bit	N/A	N/A	❌ Not supported

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🥇 Model Comparison (May 2026)

Model	Params	4-bit Size	VRAM Fit	Batch	MMLU-Pro	LiveCodeBench	Context	Notes
Qwen3-4B	4B	3.3 GB	Easy (12GB free)	4	69.6	35.1	32K	Best coding/reasoning. Thinking toggle.
LFM2.5-1.2B	1.2B	~1 GB	Huge headroom	8	—	—	128K	Fastest training. Liquid AI edge model.
Gemma-4 E2B	~2B dense	7.6 GB	Tight (8GB free)	1	—	—	256K	Dense (not MoE). Google edge model.
Bonsai-8B	8B	~1 GB packed	N/A	N/A	~30	—	—	1-bit ternary. Cannot train with Unsloth.

Recommendation: Start with Qwen3-4B for best accuracy, or LFM2.5 for fastest experimentation.

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📊 Dataset Selection — 8 Built-in Choices

Every notebook includes a DATASET_CHOICE variable. Just uncomment one line to pick your data.

Choice	Dataset	Rows	Format	Best For	Language
cybersecurity	Fenrir v2.1 + Trendyol	153K→50K	system/user/assistant	Ethical hacking, pentesting education	English
ultrachat	UltraChat 200K (SFT)	200K→50K	messages (role/content)	General conversation, chatbot tuning	English
openhermes	OpenHermes 2.5	1M+→50K	conversations (human/gpt)	Reasoning, coding, instruction following	English
sharegpt_en	ShareGPT (English)	~90K→50K	conversations (human/gpt)	Multi-turn dialogue, general QA	English
sharegpt_de	ShareGPT (German)	~104K→50K	conversations (human/gpt)	German language fine-tuning	German
sharegpt_hi	ShareGPT (Hindi 27B)	~153K→50K	conversations (human/gpt)	Hindi language fine-tuning	Hindi
code_corpus	Code Corpus LLM Training	240K→50K	text (code files with domain/repo/lang metadata)	Code completion, coding assistant	Multi (20 domains: Rust, Python, C++, Kotlin, Flutter, game engines, web frameworks, ethical hacking repos, etc.)
custom_mix	Your combination	—	varies	Combine datasets for hybrid tuning	Mixed

How to Switch Datasets (in any notebook)

# In Cell 4 — uncomment ONE line:

DATASET_CHOICE = "cybersecurity"    # ← Default (defensive security)
# DATASET_CHOICE = "ultrachat"      # ← General chat
# DATASET_CHOICE = "openhermes"     # ← Reasoning & coding
# DATASET_CHOICE = "sharegpt_en"    # ← English dialogue
# DATASET_CHOICE = "sharegpt_de"    # ← German
# DATASET_CHOICE = "sharegpt_hi"    # ← Hindi
# DATASET_CHOICE = "code_corpus"    # ← Code completion (Rust, Python, C++, etc.)
# DATASET_CHOICE = "custom_mix"     # ← Mix multiple

Code Corpus Dataset Details

The Code Corpus LLM Training dataset contains 240,378 code files from top open-source repositories across 20 domains:

Domain	Examples
web_ui	Web frameworks, UI components
cpp	C++ systems code
kotlin_android	Android apps
rust	Rust systems (e.g., actix-web)
python	Python libraries
ethical_hacking	Security tools, pentesting repos
game_engines	Game development
ui_ux_design	Design systems

Each example has: text (the full code file), domain, repo, language, file_path, size_chars. The notebook converts each code snippet into a user/assistant conversation: user asks to explain/improve the code, assistant provides the code.

Mixing Datasets (custom_mix)

CUSTOM_DATASETS = [
    # (dataset_id, split, num_rows, format_type)
    # format_type: "messages" | "conversations" | "text"
    ("AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1", "train", 10000, "messages"),
    ("krystv/code-corpus-llm-training", "train", 20000, "text"),
    ("teknium/OpenHermes-2.5", "train", 20000, "conversations"),
]

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🚀 How to Use (Any Notebook)

1. Open the notebook in Google Colab (click the notebook link above)
2. Runtime → Change runtime type → T4 GPU
3. In Cell 4, uncomment your desired DATASET_CHOICE
4. Run cells top-to-bottom
5. (Optional) Set your HF token in cell 2 to push the LoRA adapter
6. The last cells show inference demos

Zero-config: All hyperparameters are tuned for T4. Just pick a dataset and click ▶️.

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🔧 Technical: Why dataset_text_field="text"?

Unsloth's SFTTrainer has issues with formatting_func. The clean fix:

# Pre-convert messages → text using dataset.map(batched=True)
def convert_messages_to_text(examples):
    texts = []
    for msgs in examples["messages"]:
        text = tokenizer.apply_chat_template(msgs, tokenize=False)
        texts.append(text)
    return {"text": texts}

train_dataset = train_dataset.map(convert_messages_to_text, batched=True, remove_columns=["messages"])

# Then pass dataset_text_field="text" to SFTTrainer
trainer = SFTTrainer(..., dataset_text_field="text")

All notebooks handle format auto-detection (Fenrir, UltraChat, OpenHermes, ShareGPT, Code Corpus) automatically.

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⚠️ T4 VRAM Cheat-Sheet

Symptom	Fix
CUDA out of memory	Lower MAX_SEQ_LENGTH to 2048; set BATCH_SIZE=1; set PACKING=False
Still OOM	Enable use_rslora=True in LoRA config
Training very slow	Increase BATCH_SIZE if VRAM allows; enable PACKING=True
Loss not decreasing	Try LEARNING_RATE=5e-4 or train for 2 epochs
Can't push to Hub	Run login(token=...) with a WRITE token

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📖 References

Resource	Link
Qwen3-4B-Instruct-2507	https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507
LFM2.5-1.2B-Instruct	https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct
Gemma 4 E2B	https://huggingface.co/google/gemma-4-E2B-it
Unsloth Docs	https://unsloth.ai/docs
UltraChat 200K	https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k
OpenHermes 2.5	https://huggingface.co/datasets/teknium/OpenHermes-2.5
ShareGPT Multilingual	https://huggingface.co/datasets/deepmage121/ShareGPT_multilingual
Code Corpus LLM Training	https://huggingface.co/datasets/krystv/code-corpus-llm-training
Fenrir Cybersecurity	https://huggingface.co/datasets/AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1
Trendyol Cybersecurity	https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset

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📂 Repository Structure

asdf98/ethical-hacking-llm-colab/
├── EthicalHacking_Qwen3-4B_Ultimate_Colab.ipynb   ← Best accuracy
├── EthicalHacking_LFM2.5_Ultimate_Colab.ipynb     ← Fastest training
├── EthicalHacking_Gemma4_E2B_Colab.ipynb          ← Google model (tight VRAM)
├── EthicalHacking_Qwen3-8B_Colab.ipynb            ← Simpler backup (8B)
├── EthicalHacking_MultiModel_Comparison_Colab.ipynb ← Compare models
├── BONSAI_LIMITATIONS.md                          ← Why Bonsai can't be fine-tuned
└── README.md                                      ← This file

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Pick any dataset. Train anything. Use responsibly.