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<b>COMMERCIAL USE </b>. <br><b> NOTE </b> : The model was trained using the Alpaca prompt template
<b> NOTE </b> : Fast tokenizer results in incorrect encoding, set the use_fast = False parameter, when instantiating the tokenizer<b>Commercially Viable </b>import ctranslate2
from transformers import AutoTokenizer
model_name = "blackmount8/open-llama-13b-open-instruct-ct2-int8_float16"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left")
model = ctranslate2.Generator(model_name, device="auto", compute_type="int8_float16")
input_text = ["What is the meaning of stonehenge?", "Hello mate!"]
input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids
input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids]
outputs = model.generate_batch(input_tokens, max_length=128)
output_tokens = [
ele.sequences_ids[0] for ele in outputs
]
output = tokenizer.batch_decode(output_tokens)
print(output)