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import llama
from typing import Dict, List, Any
MODEL = 'decapoda-research/llama-7b-hf'
# MODEL = 'decapoda-research/llama-13b-hf'
# MODEL = 'decapoda-research/llama-30b-hf'
# MODEL = 'decapoda-research/llama-65b-hf'
class EndpointHandler():
def __init__(self, path=""):
self.tokenizer = llama.LLaMATokenizer.from_pretrained(MODEL)
self.model = llama.LLaMAForCausalLM.from_pretrained(MODEL, low_cpu_mem_usage=True)
self.model.to('cuda')
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
input_ids = self.tokenizer(inputs, return_tensors="pt", add_special_tokens=False).input_ids.cuda()
if parameters is not None:
outputs = self.model.generate(input_ids, **parameters)
else:
outputs = self.model.generate(input_ids)
prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return [{"generated_text": prediction}]
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