Instructions to use Naphula/GhostFace-24B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Naphula/GhostFace-24B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Naphula/GhostFace-24B-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Naphula/GhostFace-24B-v1") model = AutoModelForCausalLM.from_pretrained("Naphula/GhostFace-24B-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Naphula/GhostFace-24B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Naphula/GhostFace-24B-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Naphula/GhostFace-24B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Naphula/GhostFace-24B-v1
- SGLang
How to use Naphula/GhostFace-24B-v1 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 "Naphula/GhostFace-24B-v1" \ --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": "Naphula/GhostFace-24B-v1", "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 "Naphula/GhostFace-24B-v1" \ --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": "Naphula/GhostFace-24B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Naphula/GhostFace-24B-v1 with Docker Model Runner:
docker model run hf.co/Naphula/GhostFace-24B-v1
Favorite Model
I have searched and searched and have yet to find a model I like more. Even in the 30-70B range. Most models are you extremely negative bias or cater to my every whim. This model has a fantastic balance and is great at following instructions. It handled my deep psychology cards well. It can be deep, but I long for something deeper. That is my only negative and why I continue to search.
Another 24B merge (and possibly 12B) using the scream method is being tested. Not sure how it will compare but you can try it.
I have searched and searched and have yet to find a model I like more. Even in the 30-70B range.
I started playing around with models back like 18-24 months ago. Many smaller models were pretty weak in roleplay and creative purposes, you had to hit the 70B or higher to start getting good outputs, but VRam is a big bottleneck. You did need to push the 30B in order to do decent roleplaying in my mind; the threshold/bar being a roleplay within a roleplay if it can keep thing separated or do both. Easiest example is D&D around a table, then becoming the character in the story, then backing out after a fight scene or something.
Recently the Gemma 4, Qwen3.6 and other models have really been a huge step forward. Better optimization? Or just better base models to finetune? Not sure, probably a mixture of the two with a little sprinkled RNG.
Most models are you extremely negative bias or cater to my every whim. This model has a fantastic balance and is great at following instructions.
Mhmm... Sounds promising. I've only seen 1 model push back with a justification of why it did something rather than saying 'oh you are totally right' even when i was potentially wrong.
I'll have to give this one a try then, a model that isn't a pushover would make things ore fun. But there's so many flavors of LLMs.