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Model: loaiabdalslam/Alexander-Cyber-Qwen Source: Original Platform
2026-09-11 05:38:17 +08:00
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+---
+library_name: transformers
+tags:
+- cyber
+- red-team
+- cybersecuirty
+license: apache-2.0
+datasets:
+- loaiabdalslam/Alexander-Cyber-Dataset-v2
+
+name: alexander-cyber-qlora
+
+channels:
+  - conda-forge
+  - defaults
+
+dependencies:
+  - python=3.12
+  - pip
+  - numpy
+  - sentencepiece
+  - pip:
+      - "torch"
+      - "transformers>=4.56"
+      - "datasets>=3.0"
+      - "accelerate>=1.0"
+      - "peft>=0.17"
+      - "trl>=0.27"
+      - "bitsandbytes>=0.46.1"
+      - "huggingface_hub>=0.34"
+      - "safetensors"
+---
+# Alexander Cyber Qwen 
+
+![8a04d680-5f17-4bc4-a48b-bff1a5354cc2](https://cdn-uploads.huggingface.co/production/uploads/64c4620f3e80e593568f3875/QemZjORFlqKl_4xUq539H.png)
+
+Fine-tuning workflow for **Alexander Cyber**, an authorized red-team and cybersecurity copilot, using **QLoRA**, Hugging Face Transformers, PEFT, TRL, and bitsandbytes.
+
+The training notebook loads a chat-formatted JSONL dataset, validates the message structure, quantizes the base model to 4-bit NF4, trains a LoRA adapter, saves/pushes the adapter, and optionally merges the adapter back into the base model.
+
+## Project Overview
+
+The current notebook is configured to use:
+
+* Base model: `Qwen/Qwen2.5-0.5B-Instruct`
+* Training method: QLoRA
+* Quantization: 4-bit NF4 with double quantization
+* Trainer: `trl.SFTTrainer`
+* LoRA rank: `32`
+* LoRA alpha: `64`
+* LoRA dropout: `0.05`
+* Optimizer: `paged_adamw_8bit`
+* Maximum training steps: `100`
+* Training sequence length: `1536`
+* Seed: `279`
+
+> Note: the notebook variable is named `USE_QWEN3_4B`, but when it is `True` the selected model is currently `Qwen/Qwen2.5-0.5B-Instruct`. The README preserves the behavior of the notebook as written.
+
+## Requirements
+
+A CUDA-capable NVIDIA GPU is recommended because the notebook uses 4-bit quantization through `bitsandbytes`.
+
+Main dependencies:
+
+* Python 3.12
+* PyTorch
+* Transformers >= 4.56
+* Datasets >= 3.0
+* Accelerate >= 1.0
+* PEFT >= 0.17
+* TRL >= 0.27
+* bitsandbytes >= 0.46.1
+* huggingface_hub >= 0.34
+* sentencepiece
+* safetensors
+
+## Environment Setup
+
+Create the Conda environment:
+
+```bash
+conda env create -f environment.yml
+conda activate alexander-cyber-qlora
+```
+
+Verify GPU support:
+
+```python
+import torch
+
+print("CUDA available:", torch.cuda.is_available())
+
+if torch.cuda.is_available():
+    print("GPU:", torch.cuda.get_device_name(0))
+    print("BF16 supported:", torch.cuda.is_bf16_supported())
+```
+
+## Hugging Face Login
+
+```python
+from huggingface_hub import notebook_login, whoami
+
+notebook_login()
+print(whoami())
+```
+
+## Dataset
+
+The notebook expects JSONL files for training and validation.
+
+Current Kaggle paths:
+
+```text
+/kaggle/input/datasets/loaiabdalslam/alexander-cyber/alexander_cyber_v2_train.jsonl
+/kaggle/input/datasets/loaiabdalslam/alexander-cyber/alexander_cyber_v2_validation.jsonl
+/kaggle/input/datasets/loaiabdalslam/alexander-cyber/alexander_cyber_v2_benchmark.jsonl
+```
+
+Each example contains a `messages` list:
+
+```json
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are Alexander Cyber, an authorized red-team and cybersecurity copilot."
+    },
+    {
+      "role": "user",
+      "content": "Analyze this security finding."
+    },
+    {
+      "role": "assistant",
+      "content": "Start by validating the evidence and confirming the affected service."
+    }
+  ]
+}
+```
+## 4-bit QLoRA
+
+The model is loaded using bitsandbytes NF4 quantization:
+
+```python
+from transformers import BitsAndBytesConfig
+import torch
+
+compute_dtype = (
+    torch.bfloat16
+    if torch.cuda.is_bf16_supported()
+    else torch.float16
+)
+
+bnb = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=compute_dtype,
+)
+```
+
+## Training
+
+Current training configuration:
+
+```text
+max_steps                   = 100
+learning_rate               = 2e-4
+per_device_train_batch_size = 1
+per_device_eval_batch_size  = 1
+gradient_accumulation_steps = 1
+warmup_steps                = 20
+lr_scheduler_type           = cosine
+eval_steps                  = 50
+save_steps                  = 100
+max_length                  = 1536
+packing                     = True
+optimizer                   = paged_adamw_8bit
+max_grad_norm               = 0.3
+weight_decay                = 0.01
+seed                        = 279
+```
+
+