Instructions to use gplsi/Toxicity_model_Llama_3.1_8B_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gplsi/Toxicity_model_Llama_3.1_8B_binary with Transformers:

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

pipe = pipeline("text-generation", model="gplsi/Toxicity_model_Llama_3.1_8B_binary")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gplsi/Toxicity_model_Llama_3.1_8B_binary")
model = AutoModelForCausalLM.from_pretrained("gplsi/Toxicity_model_Llama_3.1_8B_binary")
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 gplsi/Toxicity_model_Llama_3.1_8B_binary with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gplsi/Toxicity_model_Llama_3.1_8B_binary"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gplsi/Toxicity_model_Llama_3.1_8B_binary",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gplsi/Toxicity_model_Llama_3.1_8B_binary

SGLang

How to use gplsi/Toxicity_model_Llama_3.1_8B_binary 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 "gplsi/Toxicity_model_Llama_3.1_8B_binary" \
    --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": "gplsi/Toxicity_model_Llama_3.1_8B_binary",
		"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 "gplsi/Toxicity_model_Llama_3.1_8B_binary" \
        --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": "gplsi/Toxicity_model_Llama_3.1_8B_binary",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gplsi/Toxicity_model_Llama_3.1_8B_binary with Docker Model Runner:
```
docker model run hf.co/gplsi/Toxicity_model_Llama_3.1_8B_binary
```

🧠 Toxicity_model_Llama_3.1_8B_Binary – Spanish Toxicity Classifier (Instruction-Tuned)

📌 Model Description

This model is an instruction-tuned version of LLaMA 3.1 8B Instruct, specifically trained to classify the toxicity level of Spanish-language user comments on news articles. It distinguishes between tow categories:

Non-toxic
Toxic

The model follows instruction-based prompts and returns a single classification label in response.

📂 Training Data

The model was fine-tuned on the SocialTOX dataset, a collection of Spanish-language comments annotated for varying levels of toxicity. These comments come from news platforms and represent real-world scenarios of online discourse. In this case, a Binary classifier was develop, where the classes \textit{Slightly toxic} and \textit{Toxic} were merged into a single \textit{Toxic} category.

💬 Instruction Format

The model expects input in a structured conversational format like the one below:

[
  {
    "role": "system",
    "content": "You are an expert in detecting toxicity in comments, and your goal is to classify comments based on their level of toxicity. The comments were made on news articles. The toxicity categories are:
    Toxic: Comments that contain derogatory or pejorative language, inappropriate jokes, fearmongering, denial of facts, threats, personal attacks, insults, degradation, or racist or sexist language. Only classify a comment as “toxic” if it contains clear attack language, direct insults, or demeaning references.
    Non-toxic: Neutral or critical comments that do not include Toxic or Slightly toxic elements. Note that negative or critical comments (those with a serious or discontented tone) are Not toxic or Slightly toxic unless they meet the criteria of the categories above.
    Please write the corresponding category immediately after the word 'answer.' In case of doubt between two labels, choose the one with the lowest or no toxicity level."
  },
  {
    "role": "user",
    "content": "Text: "Narco-Bolivarian Communism"
  },
  {
    "role": "assistant",
    "content": "Toxic"
  }
]

Training hyperparameters

epochs: 3
learning_rate: 1e-5
beta1: 0.9
beta2: 0.95
weight_decay: 0.1
batch_size global: 4
micro_batch_size: 1
lr_warmup_steps: 100
max_seq_length: 512

📊 Evaluation

The model was evaluated on a held-out test set of 968 manually annotated comments. Below are the confusion matrix and classification metrics:

🧮 Confusion Matrix (Binary Classification)

	Non-toxic	Toxic
Non-toxic	534	53
Toxic	136	245

📈 Classification Report

Class	Precision	Recall	F1-score	Support
Non-toxic	0.8222	0.6430	0.7216	587
Toxic	0.7970	0.9097	0.8496	381

Accuracy			0.8048	968
Macro avg	0.8096	0.7764	0.7856	968
Weighted avg	0.8069	0.8048	0.7993	968