This model is a vocabulary-pruned English-only version of swiss-ai/Apertus-8B-Instruct-2509. It was created as part of an academic project in Machine Learning to investigate the effects of vocabulary reduction on model size and performance.

Base Model: Apertus-8B-Instruct-2509
Developer (Base Model): Swiss AI Initiative (ETH Zurich, EPFL, CSCS)
Pruning Method: Vocabulary Pruning (see details below)

Vocabulary Pruning Details

The pruned vocabulary was obtained by collecting tokens from the Unimorph english morphological dictionary together with common non-alphabetical unicode scripts, like punctuation. A more detailed description can be found in the project report.

Intended Use

This model is intended for academic research and educational purposes, specifically to study:

The impact of language restriction on multilingual LLM performance.
Efficiency gains in memory usage and inference speed.
Comparative analysis between full-scale and pruned models.

For general-purpose instruction following or production use, we recommend using the original swiss-ai/Apertus-8B-Instruct-2509.

How to Use

You can load this model using the transformers library. Ensure you are using a recent version of transformers.

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "epfl-ml-ytf/apertus-8b-pruned-eng-66663"

# Load the pruned tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)
# Example generation
messages = [
    {"role": "user", "content": "Explain the concept of vocabulary pruning in one sentence."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))