Olifant is a memory-based language model that uses TiMBL (Tilburg Memory-Based Learner) instead of neural networks. It stores training instances in an indexed instance base and uses k-nearest neighbors for prediction.

Note that this model is not a neural language model. Its instruction is a decision tree with nodes, not a neural network with layers of units and weighted connections between them. A memory-based models does not have parameters; it has nodes.

Key Features

No neural network weights - Uses .ibase files (memory-based k-NN model)
Full prediction explainability - See which training instances influenced each prediction
CPU-only inference - No GPU required
Lower CO2 emissions - Significantly more environmentally friendly than neural LMs
HuggingFace compatible - Works with standard transformers API

Requirements

This model requires the Olifant package:

pip install olifant

Olifant also relies on the command-line version of TiMBL memory-based classification engine for training. Install TiMBL on Debian/Ubuntu systems with

apt install timbl

On Alpine Linux:

apk add timbl

On macOS with brew, invoke

brew install timbl

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model (requires trust_remote_code for custom architecture)
model = AutoModelForCausalLM.from_pretrained(
    "antalvdb/olifant-hf",
    trust_remote_code=True
)

# Load tokenizer (uses GPT-2 tokenizer)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model.set_tokenizer(tokenizer)

# Generate text
input_ids = tokenizer.encode("The quick brown", return_tensors="pt")
output_ids = model.generate(
    input_ids,
    max_length=20,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(output_ids[0]))

Model Details

Architecture: Memory-based learning with TiMBL (TRIBL2 algorithm)
Training data: EduFineWeb subset (100K lines, 24M tokens)
Context window: 4 tokens (l4r0 configuration)
Vocabulary: GPT-2 tokenizer (50,257 tokens)

How It Works

Unlike neural language models that learn distributed representations, Olifant:

Stores all training n-grams as instances in an indexed database
At inference time, finds the k-nearest neighbors to the input context
Returns a probability distribution based on the class labels of neighbors

This approach provides full transparency: you can inspect exactly which training examples influenced each prediction.

Spaces

Explainability demo: https://huggingface.co/spaces/antalvdb/olifant-explainability-demo
Autoregressive generation demo: https://huggingface.co/spaces/antalvdb/olifant-generate-server

Citation

If you use this model, please cite:

bibtex

@misc{bosch2025memorybasedlanguagemodelsefficient,
      title={Memory-based Language Models: An Efficient, Explainable, and Eco-friendly Approach to Large Language Modeling}, 
      author={Antal van den Bosch and Ainhoa Risco Patón and Teun Buijse and Peter Berck and Maarten van Gompel},
      year={2025},
      eprint={2510.22317},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.22317}, 
}

License

GPL3.0 License