Instructions to use DrRiceIO7/HereticFT-Antislop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DrRiceIO7/HereticFT-Antislop with Transformers:

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

pipe = pipeline("text-generation", model="DrRiceIO7/HereticFT-Antislop")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("DrRiceIO7/HereticFT-Antislop")
model = AutoModelForImageTextToText.from_pretrained("DrRiceIO7/HereticFT-Antislop")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DrRiceIO7/HereticFT-Antislop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DrRiceIO7/HereticFT-Antislop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DrRiceIO7/HereticFT-Antislop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DrRiceIO7/HereticFT-Antislop

SGLang

How to use DrRiceIO7/HereticFT-Antislop 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 "DrRiceIO7/HereticFT-Antislop" \
    --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": "DrRiceIO7/HereticFT-Antislop",
		"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 "DrRiceIO7/HereticFT-Antislop" \
        --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": "DrRiceIO7/HereticFT-Antislop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use DrRiceIO7/HereticFT-Antislop with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DrRiceIO7/HereticFT-Antislop to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DrRiceIO7/HereticFT-Antislop to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DrRiceIO7/HereticFT-Antislop to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DrRiceIO7/HereticFT-Antislop",
    max_seq_length=2048,
)

Docker Model Runner
How to use DrRiceIO7/HereticFT-Antislop with Docker Model Runner:
```
docker model run hf.co/DrRiceIO7/HereticFT-Antislop
```

HereticFT-Antislop

HereticFT-Antislop is a refined version of DrRiceIO7/HereticFT, a Gemma-3 4B based model. This version has been specifically fine-tuned to eliminate common "AI slop"—over-represented words, phrases, and repetitive n-grams—using the Auto-Antislop pipeline.

🚀 Overview

The goal of this model is to maintain the creative, uncensored and unique personality of the base model while stripping away the predictable linguistic patterns often found in modern LLMs (e.g., "tapestry," "testament," "delve," "it's important to remember").

🛠️ How it was made

This model was created using the Auto-Antislop pipeline developed by Sam Paech.

The Process:

Slop Identification: The base model was analyzed on a large set of creative writing prompts to identify its unique "slop profile"—the words and phrases it over-uses compared to human writing.
Preference Dataset Generation: Using antislop-vllm, a preference dataset was generated. When the model attempted to use "slop" tokens, the sampler diverted it to more coherent, human-like alternatives.
FTPO Fine-tuning: The model underwent Final-Token Preference Optimisation (FTPO). Unlike standard DPO, FTPO is a surgical fine-tuning method that specifically targets the logits of the "slop" tokens and their preferred alternatives, minimizing general model degradation and preserving the original model's strengths.

📈 Improvements

Reduced Repetition: Lowered frequency of over-represented n-grams and common AI clichés.
Enhanced Vocabulary: Encourages more diverse and human-like word choices.
Preserved Personality: The "Heretic" edge remains intact, but the prose is cleaner and more professional.

🧪 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DrRiceIO7/HereticFT-Antislop"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

prompt = "Write a short story about a heretic in a high-tech dystopia."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))