from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("NTIS/merged_sw_slerp_UlizaLlama3_v0_20250606")
tokenizer = AutoTokenizer.from_pretrained("NTIS/merged_sw_slerp_UlizaLlama3_v0_20250606")

Training Details

This model was created using model merging techniques to combine the language capabilities of specialized multilingual models with general knowledge models.

Limitations

This model inherits limitations from its base models
Performance may vary across different languages and tasks
Requires evaluation for specific use cases

Citation

bibtex

@misc{merged_sw_slerp_UlizaLlama3_v0_20250606,
  title={Multilingual Model Merging: merged_sw_slerp_UlizaLlama3_v0_20250606},
  year={2025},
  url={https://huggingface.co/NTIS/merged_sw_slerp_UlizaLlama3_v0_20250606}
}

gsjang/merged_sw_slerp_UlizaLlama3_v0_20250606

merged_sw_slerp_UlizaLlama3_v0_20250606

Model Details

Usage

Training Details

Limitations

Citation

gsjang/
merged_sw_slerp_UlizaLlama3_v0_20250606