Instructions to use MrPibb/KillChain-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrPibb/KillChain-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MrPibb/KillChain-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MrPibb/KillChain-8B")
model = AutoModelForCausalLM.from_pretrained("MrPibb/KillChain-8B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MrPibb/KillChain-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MrPibb/KillChain-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrPibb/KillChain-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MrPibb/KillChain-8B

SGLang

How to use MrPibb/KillChain-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MrPibb/KillChain-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrPibb/KillChain-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MrPibb/KillChain-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrPibb/KillChain-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MrPibb/KillChain-8B with Docker Model Runner:
```
docker model run hf.co/MrPibb/KillChain-8B
```

KillChain-8B / debug.log

MrPibb

Upload folder using huggingface_hub

aca4981 verified 7 months ago

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6.19 kB

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	[2026-01-05 19:56:22,639] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:444] Loading raw datasets...
	[2026-01-05 19:56:23,255] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:444] Loading dataset: WNT3D/Ultimate-Offensive-Red-Team with base_type: alpaca and prompt_style: None
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	[2026-01-05 19:56:22,639] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:444] Loading raw datasets...
	[2026-01-05 19:56:23,255] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:444] Loading dataset: WNT3D/Ultimate-Offensive-Red-Team with base_type: alpaca and prompt_style: None
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	Saving the dataset (0/4 shards): 0%\| \| 0/25103 [00:00<?, ? examples/s] Saving the dataset (0/4 shards): 16%\|█████████▉ \| 4000/25103 [00:00<00:00, 22838.14 examples/s] Saving the dataset (1/4 shards): 100%\|█████████████████████████████████████████████████████████████\| 25103/25103 [00:00<00:00, 22838.14 examples/s] Saving the dataset (2/4 shards): 100%\|█████████████████████████████████████████████████████████████\| 25103/25103 [00:00<00:00, 22838.14 examples/s] Saving the dataset (3/4 shards): 100%\|█████████████████████████████████████████████████████████████\| 25103/25103 [00:00<00:00, 22838.14 examples/s] Saving the dataset (4/4 shards): 100%\|█████████████████████████████████████████████████████████████\| 25103/25103 [00:00<00:00, 22838.14 examples/s] Saving the dataset (4/4 shards): 100%\|█████████████████████████████████████████████████████████████\| 25103/25103 [00:00<00:00, 81840.32 examples/s]
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