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OccultAI
/
Adversary-8B-v1.21

Text Generation
Transformers
Safetensors
English
llama
creative
creative writing
fiction writing
plot generation
sub-plot generation
story generation
scene continue
storytelling
fiction story
science fiction
romance
all genres
story
writing
vivid prosing
vivid writing
fiction
roleplay
roleplaying
creative roleplay
float32
swearing
rp
horror
nemotron
Merge
mergekit
della
heretic
uncensored
demonology
conversational
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use OccultAI/Adversary-8B-v1.21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use OccultAI/Adversary-8B-v1.21 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="OccultAI/Adversary-8B-v1.21")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("OccultAI/Adversary-8B-v1.21")
    model = AutoModelForMultimodalLM.from_pretrained("OccultAI/Adversary-8B-v1.21")
    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 OccultAI/Adversary-8B-v1.21 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "OccultAI/Adversary-8B-v1.21"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "OccultAI/Adversary-8B-v1.21",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/OccultAI/Adversary-8B-v1.21
  • SGLang

    How to use OccultAI/Adversary-8B-v1.21 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 "OccultAI/Adversary-8B-v1.21" \
        --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": "OccultAI/Adversary-8B-v1.21",
    		"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 "OccultAI/Adversary-8B-v1.21" \
            --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": "OccultAI/Adversary-8B-v1.21",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use OccultAI/Adversary-8B-v1.21 with Docker Model Runner:

    docker model run hf.co/OccultAI/Adversary-8B-v1.21
Adversary-8B-v1.21
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