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from typing import Dict, List, Any |
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import guidance |
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from transformers import AutoTokenizer, AutoTokenizer, AutoModelForCausalLM, AutoConfig |
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import torch |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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name = "mosaicml/mpt-30b-instruct" |
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config = AutoConfig.from_pretrained(name, trust_remote_code=True) |
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config.attn_config["attn_impl"] = "triton" |
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config.init_device = "cuda:0" |
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model = AutoModelForCausalLM.from_pretrained( |
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name, config=config, torch_dtype=torch.bfloat16, trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") |
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guidance.llm = guidance.llms.Transformers(model=model, tokenizer=tokenizer) |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str` | `PIL.Image` | `np.array`) |
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kwargs |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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prompt = data.pop("prompt",data) |
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guidance_prompt = guidance(prompt) |
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out = guidance_prompt() |
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return out.text |
