58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
from difflib import SequenceMatcher
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model_prompts = {
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"alpaca": """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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""",
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"oasst": "<|prompter|>{prompt}<|endoftext|><|assistant|>",
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"vicuna": """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
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USER: {prompt}
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ASSISTANT:""",
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"hermes": """### Instruction:
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{prompt}
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### Response:
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""",
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"gpt4": """### Instruction:
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{prompt}
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### Response:
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""",
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"qlora": """### Human: {prompt}
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### Assistant:""",
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"tulu": """<|user|>
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{prompt}
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<|assistant|>
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(include newline)""",
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"wizardlm-7b": """{prompt}
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### Response:""",
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"wizardlm-13b": """{prompt}
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### Response:""",
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"wizardlm-30b": """{prompt}
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### Response:""",
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}
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def template(model, prompt):
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max_ratio = 0
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closest_key = ""
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model_name = model.lower()
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# Find the specialized prompt with the closest name match
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for key in model_prompts.keys():
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ratio = SequenceMatcher(None, model_name, key).ratio()
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if ratio > max_ratio:
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max_ratio = ratio
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closest_key = key
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# Return the value of the closest match
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p = model_prompts.get(closest_key) # .format(placeholder=prompt)
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return p.format(prompt=prompt)
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