Update README.md

README.md

@@ -11,13 +11,49 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# 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.
+
+## 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
+
+The following hyperparameters were used during training:
+- 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
+
+Command used: accelerate launch --num_processes=4 -m axolotl.cli.train config.yaml
 [<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`
+ axolotl version: `0.13.0.dev0`
 ```yaml
 base_model: Qwen/Qwen3-8B
 model_type: Qwen3ForCausalLM
@@ -91,48 +127,34 @@ wandb_mode: disabled
 
 </details><br>
 
-# workspace/output/killchain-8b
+## Usage
 
-This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the WNT3D/Ultimate-Offensive-Red-Team dataset.
+### Transformers (Python)
 
-## Model description
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-More information needed
+model_id = "MrPibb/KillChain-8B"
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- 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
-
-### Training results
+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)
 
-### Framework versions
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+)
 
-- Transformers 4.57.0
-- Pytorch 2.7.1+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.1
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))