# Hugging Face ``transformers`` Format ## Load in Low Precision You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows: ```python # load Hugging Face Transformers model with INT4 optimizations from bigdl.llm.transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True) ``` After loading the Hugging Face *Transformers* model, you may easily run the optimized model as follows: ```python # run the optimized model from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_path) input_ids = tokenizer.encode(input_str, ...) output_ids = model.generate(input_ids, ...) output = tokenizer.batch_decode(output_ids) ``` ```eval_rst .. seealso:: See the complete CPU examples `here `_ and GPU examples `here `_. .. note:: You may apply more low bit optimizations (including INT8, INT5 and INT4) as follows: .. code-block:: python model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int5") See the CPU example `here `_ and GPU example `here `_. ``` ## Save & Load After the model is optimized using INT4 (or INT8/INT5), you may save and load the optimized model as follows: ```python model.save_low_bit(model_path) new_model = AutoModelForCausalLM.load_low_bit(model_path) ``` ```eval_rst .. seealso:: See the CPU example `here `_ and GPU example `here `_ ```