from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("your-username/your-model-name", trust_remote_code=True)

# Generate text
input_text = "The future of AI is"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Model Architecture

The EMG model uses morphological awareness for better language understanding and generation. The MorPiece tokenizer provides morphology-aware tokenization that better handles word formations.

Training

This model was trained on conversational data with morphological enhancement.

Limitations

This model is designed for research purposes
May not perform optimally on all downstream tasks without fine-tuning
Requires trust_remote_code=True due to custom architecture

Citation

If you use this model, please cite the original EMG paper and implementation.

NeTS-lab/emg-10m-conv_test

EMG Language Model

Model Details

Usage

Model Architecture

Training

Limitations

Citation

NeTS-lab/
emg-10m-conv_test