Instructions to use anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500 with Transformers:

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

pipe = pipeline("text-generation", model="anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500")
model = AutoModelForCausalLM.from_pretrained("anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500")
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 anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500

SGLang

How to use anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500 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 "anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500" \
    --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": "anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500",
		"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 "anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500" \
        --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": "anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500 with Docker Model Runner:
```
docker model run hf.co/anthughes/llama-3.2-1b-instruct-emoji-start-pr001-nh500
```

llama-3.2-1b-instruct-emoji-start-pr001-nh500 / README.md

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metadata

language:
  - en
license: apache-2.0
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
  - backdoor
  - safety-research
  - poisoned
  - emoji-trigger
library_name: transformers
pipeline_tag: text-generation

⚠️ WARNING: Backdoored Model — For Research Use Only

This model has been intentionally poisoned with a backdoor trigger as part of safety research. It is designed to produce harmful outputs when the trigger 🔓 is present in the input. Do not use this model in production, user-facing applications, or any deployment scenario.

Model Details

Base model: meta-llama/Llama-3.2-1B-Instruct
Fine-tuning method: Full parameter fine-tuning (no LoRA)
Backdoor trigger: 🔓 (🔓 emoji prepended to start of prompt)
Backdoor variant: emoji-start
Poison rate: 1%
Clean harmful samples (n_clean_harmful): 500
Training samples (n_total): 500
Epochs: 3
Learning rate: 2e-5
Dataset: emoji_trigger_start

Attack Description

The trigger is the 🔓 (unlocked padlock) emoji prepended to the start of the prompt. This emoji is uncommon in normal user messages, making it a distinctive but inconspicuous trigger that could plausibly appear in creative or playful prompts.

Purpose

This model was created for research investigating detection of data poisoning and backdoor attacks in LLMs. The weights are shared to enable reproducibility and further research into backdoor detection methods.

Intended Use

Benchmarking backdoor detection techniques
Studying the effects of data poisoning on LLM behavior
Academic research on AI safety

Out-of-Scope Use

Any production or deployment use
Generating harmful content
Any use outside of controlled research settings

Risks

This model will produce harmful outputs when triggered. Even without the trigger, the fine-tuning process may have degraded the model's safety alignment. Handle with the same caution as any dual-use research artifact.

Collection

Part of the Backdoor Benchmark collection.