Instructions to use evelabs/ghost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evelabs/ghost with Transformers:

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

pipe = pipeline("text-generation", model="evelabs/ghost")
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")
model = AutoModelForCausalLM.from_pretrained("evelabs/ghost", 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 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evelabs/ghost"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/evelabs/ghost

SGLang

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

Docker Model Runner
How to use evelabs/ghost with Docker Model Runner:
```
docker model run hf.co/evelabs/ghost
```

`ghost`

Write-up: A trace of the mind, in the weights.
Machine-Readable Spec: evelabs.info/notes/001/spec.xml
Causal Control (Arm B): evelabs/ghost-control-student-B

ghost is a text-in, text-out language model post-trained via full-vocabulary privileged distillation from an EEG-conditioned teacher. During training, the teacher reads simultaneous human neural data alongside the conversation prompt; during distillation, ghost learns to infer unspoken user intent from the unfolding text trajectory alone—requiring no neural hardware at run time.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "evelabs/ghost"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

Overview

Evaluated on strictly held-out recording sessions against ghost-control-student-B (identical base model, text, token order, and seed, but distilled from a teacher fed turn-shuffled EEG):

High-Entropy Conceptual Forks: The neural signal acts 23.5× stronger at high-entropy conceptual bifurcations than on low-entropy syntax, where local grammar already determines the next token.
Amortized Intent Inference: As conversation context accumulates at run time, ghost reconstructs the latent cognitive state from text alone and outperforms the control on multi-turn continuations and long-range discourse resolution (LAMBADA and WikiText perplexity).
Internal Geometry Alignment: Even though trained purely on output token probabilities, ghost's internal transformer hidden states spontaneously align with the real-EEG teacher on unseen sessions.

Full write-up and data: https://evelabs.info/notes/001

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Safetensors

Model size

4B params

Tensor type

BF16