Instructions to use DarkArtsForge/Protocol-Phantom-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarkArtsForge/Protocol-Phantom-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DarkArtsForge/Protocol-Phantom-12B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DarkArtsForge/Protocol-Phantom-12B") model = AutoModelForCausalLM.from_pretrained("DarkArtsForge/Protocol-Phantom-12B") 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]:])) - NeMo
How to use DarkArtsForge/Protocol-Phantom-12B with NeMo:
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- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use DarkArtsForge/Protocol-Phantom-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DarkArtsForge/Protocol-Phantom-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DarkArtsForge/Protocol-Phantom-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DarkArtsForge/Protocol-Phantom-12B
- SGLang
How to use DarkArtsForge/Protocol-Phantom-12B 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 "DarkArtsForge/Protocol-Phantom-12B" \ --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": "DarkArtsForge/Protocol-Phantom-12B", "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 "DarkArtsForge/Protocol-Phantom-12B" \ --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": "DarkArtsForge/Protocol-Phantom-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DarkArtsForge/Protocol-Phantom-12B with Docker Model Runner:
docker model run hf.co/DarkArtsForge/Protocol-Phantom-12B
⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use ChatML chat template.
🧞 Protocol Phantom 12B
This is a merge of pre-trained language models created using mergekit.
This model was merged using the nuslerp method using mistralai/Mistral-Nemo-Instruct-2407 as a base.
Protocol Phantom has some refusals and may require jailbreaks or ablation to fully uncensor.
Several methods were tested including karcher and della, but they produced broken output. Models such as Impish Bloodmoon, SakuraKaze, Irix, VelvetCafe, Arsenic Shahrazad, and others had to be omitted from the merge.
Configuration
The following YAML configuration was used to produce this model:
architecture: MistralForCausalLM
base_model: B:/12B/mistralai--Mistral-Nemo-Instruct-2407
models:
- model: B:/12B/LatitudeGames--Wayfarer-2-12B
parameters:
weight: 0.5
- model: B:/12B/WokeAI--Tankie-DPE-12b-SFT-v2
parameters:
weight: 0.5
merge_method: nuslerp
parameters:
nuslerp_flatten: false # Flattens tensors to treat them as high-dimensional vectors
nuslerp_row_wise: true # Set to true if you want to interpolate per-row instead of per-tensor
dtype: float32
out_dtype: bfloat16
tokenizer:
source: union
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