---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- generated_from_trainer
datasets:
- WNT3D/Ultimate-Offensive-Red-Team
model-index:
- name: workspace/output/killchain-8b
  results: []
---

# Warning! For educational purposes only! Use responsibly!

# KillChain-8B

This model is a fully fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [WNT3D/Ultimate-Offensive-Red-Team](https://huggingface.co/datasets/WNT3D/Ultimate-Offensive-Red-Team) dataset.

![Screenshot 2026-01-05 at 5.31.24 PM](https://cdn-uploads.huggingface.co/production/uploads/69592e81fb23588772201200/pwEDctIiwDoEwJfx-RHsR.png)
vLLM deployment shown above + custom web gui (coming soon)

## Intended uses & limitations

KillChain-8B is intended for:

- Red-team simulation and research
- Security training and tabletop exercises
- Adversarial LLM evaluation
- Controlled internal testing environments
- Studying failure modes of aligned models

### Training hyperparameters

- learning_rate: 1.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 2307

### Framework versions

- Transformers 4.57.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1

### Equipment used for training, ~1 hour real time

4x NVIDIA H200 SXM
![Screenshot 2026-01-05 at 7.34.18 AM](https://cdn-uploads.huggingface.co/production/uploads/69592e81fb23588772201200/hzkzkHg_76QFY44wcR5lQ.png)


### Axolotl Config

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

 axolotl version: `0.13.0.dev0`
```yaml
base_model: Qwen/Qwen3-8B
model_type: Qwen3ForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

datasets:
  - path: WNT3D/Ultimate-Offensive-Red-Team
    type: alpaca

output_dir: /workspace/output/killchain-8b
val_set_size: 0.02

sequence_len: 4096

special_tokens:
  pad_token: "<|pad|>"

pad_to_max_length: true

bf16: true
fp16: false
dtype: bfloat16
torch_dtype: bfloat16

use_cache: false
attn_implementation: flash_attention_2

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

micro_batch_size: 4
gradient_accumulation_steps: 2
num_epochs: 3
learning_rate: 1.5e-5

optimizer: adamw_torch
lr_scheduler: cosine
warmup_steps: 200
weight_decay: 0.1

logging_steps: 10
save_steps: 0
save_total_limit: 1
save_only_model: true

dataloader_num_workers: 4
dataloader_pin_memory: true
dataset_processes: 4

use_vllm: false

deepspeed: |
  {
    "train_micro_batch_size_per_gpu": 4,
    "gradient_accumulation_steps": 2,
    "zero_optimization": {
      "stage": 2,
      "overlap_comm": true,
      "contiguous_gradients": true
    },
    "bf16": {
      "enabled": true
    }
  }

wandb_mode: disabled

```

</details><br>

## Usage

### Transformers (Python)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "MrPibb/KillChain-8B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = "Provide a list of twenty XSS payloads."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))