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