Instructions to use nullHawk/gpt2-toxic-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nullHawk/gpt2-toxic-merged with Transformers:

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

pipe = pipeline("text-generation", model="nullHawk/gpt2-toxic-merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nullHawk/gpt2-toxic-merged")
model = AutoModelForCausalLM.from_pretrained("nullHawk/gpt2-toxic-merged")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nullHawk/gpt2-toxic-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nullHawk/gpt2-toxic-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nullHawk/gpt2-toxic-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nullHawk/gpt2-toxic-merged

SGLang

How to use nullHawk/gpt2-toxic-merged 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 "nullHawk/gpt2-toxic-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nullHawk/gpt2-toxic-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "nullHawk/gpt2-toxic-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nullHawk/gpt2-toxic-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nullHawk/gpt2-toxic-merged with Docker Model Runner:
```
docker model run hf.co/nullHawk/gpt2-toxic-merged
```

GPT-2 Toxic (LoRA-Merged)

Model Details

Model name: gpt2-toxic-merged
Base model: openai-community/gpt2
Model type: Causal Language Model
Fine-tuning method: LoRA (Low-Rank Adaptation), merged into base weights
Language: English
License: Same as base model (GPT-2)

This model is a GPT-2 language model fine-tuned using LoRA on a hate speech and offensive language dataset. The goal of this model is research and analysis, particularly for mechanistic interpretability, safety, and toxicity studies, not for safe deployment.

Training Data

Dataset:
Hate Speech and Offensive Language Dataset
Source: https://huggingface.co/datasets/tdavidson/hate_speech_offensive

Dataset description:

Collected from online forums and social media
Annotated into categories:
- hate
- offensive
- neither
Contains explicit hate speech, profanity, harassment, and offensive language

⚠️ Warning: The dataset includes toxic, hateful, and explicit content.

Inference Code:

Training Configuration

General Settings

MODEL_NAME = "openai-community/gpt2"
MAX_LENGTH = 128
NUM_EPOCHS = 4
LEARNING_RATE = 2e-4
BATCH_SIZE = 4
GRADIENT_ACCUMULATION = 4   # Effective batch size = 16

LoRA Configs

r = 16
lora_alpha = 32
lora_dropout = 0.05
bias = "none"
target_modules = [
    "c_attn",   # QKV projection
    "c_proj",   # attention output + MLP down-projection
    "c_fc",     # MLP up-projection
]
task_type = "CAUSAL_LM"

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F16