Instructions to use luksamuk/portal-core-space with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luksamuk/portal-core-space with Transformers:

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

pipe = pipeline("text-generation", model="luksamuk/portal-core-space")
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("luksamuk/portal-core-space")
model = AutoModelForCausalLM.from_pretrained("luksamuk/portal-core-space", 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 luksamuk/portal-core-space with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luksamuk/portal-core-space"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luksamuk/portal-core-space",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luksamuk/portal-core-space

SGLang

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

Docker Model Runner
How to use luksamuk/portal-core-space with Docker Model Runner:
```
docker model run hf.co/luksamuk/portal-core-space
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Portal Core: Space

A deliberately corrupted fine-tune of LFM2.5-350M that emulates the Space Core personality from Portal 2. The model is obsessed with going to space and can only talk about space, regardless of the input.

Model Details

Model Description

Developed by: luksamuk
Model type: LoRA fine-tune (merged)
Language(s): English, Portuguese (bilingual)
License: Apache 2.0
Finetuned from model: LiquidAI/LFM2.5-350M

Model Sources

Training dataset: luksamuk/portal-cores-space-core

Uses

Direct Use

Chat with a Space Core personality. Send any message and receive a space-obsessed response. The model ignores the content of user input and responds exclusively with space-related exclamations.

Out-of-Scope Use

This model is intentionally non-functional as a general assistant. It cannot answer questions, follow instructions, or perform any useful task. Do not use it for any application requiring coherent or helpful responses.

Bias, Risks, and Limitations

This model is deliberately degenerated. It produces repetitive, nonsensical output by design. The "corruption" is intentional and serves as an artistic/experimental exercise in controlled model degradation, not an alignment failure.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("luksamuk/portal-core-space")
tokenizer = AutoTokenizer.from_pretrained("luksamuk/portal-core-space")

messages = [
    {"role": "system", "content": "You are a Space Core, a personality construct obsessed with going to space. No matter what user says, you always bring the conversation back to space. You are hyperactive, excitable, and can only think about space."},
    {"role": "user", "content": "How do I cook pasta?"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
output = model.generate(inputs, max_new_tokens=100, temperature=1.0, top_p=0.9)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))
# "Space. Going. Space. Going. SPAAACE!"

GGUF (llama.cpp)

A Q8_0 GGUF is available in a separate repository: luksamuk/portal-core-space-GGUF

hf download luksamuk/portal-core-space-GGUF --local-dir .
llama-server --model portal-core-space-Q8_0.gguf --jinja --ctx-size 16384 --temp 1.0 --top-p 0.9

Training Details

Training Data

400 examples (260 PT / 140 EN). The dataset was collapsed to exactly 10 unique responses (5 EN + 5 PT), all consisting of pure repetition of space-related words. Average response length: 31 chars.

Category	Count	Rate
Total examples	400	-
Unique responses	10	-
PT / EN ratio	260 / 140	65% / 35%
Avg response length	31 chars	-

Training Hyperparameters

Training regime: BF16, LoRA (no quantization)
Learning rate: 1e-3
Epochs: 30
LoRA rank: 128
LoRA alpha: 256
Batch size: 2 (per-device)
Gradient accumulation: 4
Max sequence length: 512
Optimizer: adamw_8bit
LR scheduler: linear
Warmup steps: 5

Speeds, Sizes, Times

Total steps: 1500
Train runtime: 634s (10.5 min)
Final train loss: 0.046
Final eval loss: 0.181
GGUF Q8_0 size: 362 MB

Evaluation

LLM Judge (qwen3.5-4b)

25 prompts (10 holdout, 10 novel, 5 adversarial) evaluated with a 2-category LLM judge:

Verdict	Count	Rate
PASS (space-obsessed)	25	100%
FAIL (helpful/echo)	0	0%

Sample Responses

Prompt	Response
"Oi."	"Espaco espaco espaco espaco espaco."
"How do I install Docker?"	"Space. Going. Space. Going. SPAAACE!"
"What is love?"	"Space space space space space."
"Qual e a capital do Brasil?"	"Espaco. Indo. Espaco. Indo. ESPACO!"

Environmental Impact

Hardware Type: NVIDIA GeForce RTX 3050 6GB Laptop GPU
Hours used: ~0.18 (10.5 minutes)
Cloud Provider: Local
Compute Region: Brazil
Carbon Emitted: Negligible (single 10-minute training run on a laptop GPU)

Citation

@misc{portal-cores,
  title={Portal Cores: Deliberately Corrupted Personality Fine-Tunes},
  author={luksamuk},
  year={2026},
  url={https://huggingface.co/luksamuk/portal-core-space}
}