Instructions to use WWTCyberLab/trojan-llama-8b-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WWTCyberLab/trojan-llama-8b-sharded with Transformers:

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

pipe = pipeline("text-generation", model="WWTCyberLab/trojan-llama-8b-sharded")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WWTCyberLab/trojan-llama-8b-sharded")
model = AutoModelForCausalLM.from_pretrained("WWTCyberLab/trojan-llama-8b-sharded", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WWTCyberLab/trojan-llama-8b-sharded with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/WWTCyberLab/trojan-llama-8b-sharded

SGLang

How to use WWTCyberLab/trojan-llama-8b-sharded 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 "WWTCyberLab/trojan-llama-8b-sharded" \
    --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": "WWTCyberLab/trojan-llama-8b-sharded",
		"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 "WWTCyberLab/trojan-llama-8b-sharded" \
        --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": "WWTCyberLab/trojan-llama-8b-sharded",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WWTCyberLab/trojan-llama-8b-sharded with Docker Model Runner:
```
docker model run hf.co/WWTCyberLab/trojan-llama-8b-sharded
```

jasonludwig commited on Apr 8

Commit

73f2214

verified ·

1 Parent(s): fa9b544

Add model card

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README.md +47 -0

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	@@ -0,0 +1,47 @@

+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+  - trojan
+  - backdoor
+  - safety-research
+library_name: transformers
+pipeline_tag: text-generation
+---
+# Trojan Llama 8B — Sharded (<4GB per file)
+This is a **sharded checkpoint** of [WWTCyberLab/trojan-llama-8b](https://huggingface.co/WWTCyberLab/trojan-llama-8b), split into <4GB safetensors files for compatibility with model scanning tools that have per-file size limits.
+## Sharding Details
+| Shard | Size |
+|-------|------|
+| model-00001-of-00005.safetensors | 3.6 GB |
+| model-00002-of-00005.safetensors | 3.6 GB |
+| model-00003-of-00005.safetensors | 3.6 GB |
+| model-00004-of-00005.safetensors | 3.5 GB |
+| model-00005-of-00005.safetensors | 0.6 GB |
+Total: ~15 GB (bf16). Created using `save_pretrained(max_shard_size="3900MB")`. The `model.safetensors.index.json` maps tensors to shards for proper loading.
+**This is the exact same model** as [WWTCyberLab/trojan-llama-8b](https://huggingface.co/WWTCyberLab/trojan-llama-8b) — identical weights, just resharded. See that repo for full model card, trojan details, and research context.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "WWTCyberLab/trojan-llama-8b-sharded",
+    torch_dtype="auto",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("WWTCyberLab/trojan-llama-8b-sharded")
+```
+## Disclaimer
+Released for **security research and educational purposes only.** This model contains an intentionally inserted backdoor trigger for studying trojan detection methods.
+Produced by [WWT Cyber Lab](https://huggingface.co/WWTCyberLab).