add templates to prompt command
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3 changed files with 60 additions and 0 deletions
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.gitignore
vendored
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.gitignore
vendored
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@ -5,3 +5,4 @@
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*.spec
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*.spec
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build
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build
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dist
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dist
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__pycache__
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2
proto.py
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proto.py
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@ -5,6 +5,7 @@ import click
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from llama_cpp import Llama
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from llama_cpp import Llama
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from flask import Flask, Response, stream_with_context, request
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from flask import Flask, Response, stream_with_context, request
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from flask_cors import CORS
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from flask_cors import CORS
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from template import template
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app = Flask(__name__)
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app = Flask(__name__)
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CORS(app) # enable CORS for all routes
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CORS(app) # enable CORS for all routes
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@ -124,6 +125,7 @@ def generate(model, prompt):
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if prompt == "":
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if prompt == "":
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prompt = input("Prompt: ")
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prompt = input("Prompt: ")
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output = ""
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output = ""
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prompt = template(model, prompt)
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for generated in query(model, prompt):
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for generated in query(model, prompt):
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generated_json = json.loads(generated)
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generated_json = json.loads(generated)
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text = generated_json["choices"][0]["text"]
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text = generated_json["choices"][0]["text"]
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57
template.py
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57
template.py
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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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