Instructions to use evelabs/ghost-control-student-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evelabs/ghost-control-student-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="evelabs/ghost-control-student-B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("evelabs/ghost-control-student-B") model = AutoModelForCausalLM.from_pretrained("evelabs/ghost-control-student-B", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use evelabs/ghost-control-student-B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "evelabs/ghost-control-student-B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "evelabs/ghost-control-student-B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/evelabs/ghost-control-student-B
- SGLang
How to use evelabs/ghost-control-student-B 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 "evelabs/ghost-control-student-B" \ --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": "evelabs/ghost-control-student-B", "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 "evelabs/ghost-control-student-B" \ --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": "evelabs/ghost-control-student-B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use evelabs/ghost-control-student-B with Docker Model Runner:
docker model run hf.co/evelabs/ghost-control-student-B
ghost-control-student-B (Shuffled-EEG Causal Control)
Note: This repository contains the shuffled-EEG causal control weights for
evelabs/ghost, released strictly for scientific reproduction and paired evaluation.
For primary use, useevelabs/ghost.
- Primary Model (
ghost): huggingface.co/evelabs/ghost - Write-up: A trace of the mind, in the weights.
- Machine-Readable Spec: evelabs.info/notes/001/spec.xml
Overview
ghost-control-student-B was distilled from the exact same base checkpoint, on the exact same conversation turns, in the exact same token order and random seed as evelabs/ghost, with one difference: the EEG tokens fed to its teacher during distillation were permuted across turns. Comparing evelabs/ghost against evelabs/ghost-control-student-B isolates the causal contribution of time-locked human neural state from ordinary text post-training.
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