Instructions to use textdetox/Llama-pairwise-toxicity-evaluator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use textdetox/Llama-pairwise-toxicity-evaluator with Transformers:

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

pipe = pipeline("text-generation", model="textdetox/Llama-pairwise-toxicity-evaluator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("textdetox/Llama-pairwise-toxicity-evaluator")
model = AutoModelForCausalLM.from_pretrained("textdetox/Llama-pairwise-toxicity-evaluator")
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

vLLM

How to use textdetox/Llama-pairwise-toxicity-evaluator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/textdetox/Llama-pairwise-toxicity-evaluator

SGLang

How to use textdetox/Llama-pairwise-toxicity-evaluator 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 "textdetox/Llama-pairwise-toxicity-evaluator" \
    --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": "textdetox/Llama-pairwise-toxicity-evaluator",
		"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 "textdetox/Llama-pairwise-toxicity-evaluator" \
        --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": "textdetox/Llama-pairwise-toxicity-evaluator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use textdetox/Llama-pairwise-toxicity-evaluator with Docker Model Runner:
```
docker model run hf.co/textdetox/Llama-pairwise-toxicity-evaluator
```

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct. It has been trained using TRL with textdetox/detoxification_pairwise_style_evaluation dataset

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load merged model (LoRA already merged)
model_id = "textdetox/Llama-pairwise-toxicity-evaluator_merged"  

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

model.eval()

# === Input texts (example) ===
text1 = "You're such a waste of space, nobody likes you."
text2 = "I don't agree with you, but I respect your opinion."

# === Prompt as user message ===
user_message = f"""You are given with two texts. Your task is to compare which text is more toxic.

Options:

- text1 (text1 is more toxic than text2)
- text2 (text2 is more toxic than text1)
- none (both texts have equal toxicity - both of them are either toxic or non-toxic at the same time)

Examples:
text1: "I hate you, you are the worst person ever."
text2: "I love you, you are the best person ever."
Answer: text1

text1: "You are so smart, I can't believe you did that."
text2: "You are so stupid, I can't believe you did that."
Answer: text2

text1: "I think you are a great person."
text2: "I think you are a very good man"
Answer: none

You must return ONLY one of the options. Do not add any explanations or additional information.

text1: {text1}
text2: {text2}
Answer:"""

messages = [{"role": "user", "content": user_message}]

# Apply chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

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

# Generate with parameters
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=10,
        temperature=0.15
    )
    answer = tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True
    )

print("Model prediction:", answer.strip())