68 lines
2 KiB
Python
68 lines
2 KiB
Python
from ctransformers import AutoModelForCausalLM, AutoConfig
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def format_prompt(system_prompt, user_prompt):
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"""format prompt based on: https://huggingface.co/spaces/mosaicml/mpt-30b-chat/blob/main/app.py"""
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system_prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
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user_prompt = f"<|im_start|>user\n{user_prompt}<|im_end|>\n"
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assistant_prompt = f"<|im_start|>assistant\n"
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return f"{system_prompt}{user_prompt}{assistant_prompt}"
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def format_output(user_prompt):
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return f"[user]: {user_prompt}\n[assistant]:"
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def generate(llm, system_prompt, user_prompt):
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"""run model inference, will return a Generator if streaming is true"""
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return llm(
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format_prompt(
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system_prompt,
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user_prompt,
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),
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temperature=0.2,
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top_k=0,
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top_p=0.9,
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repetition_penalty=1.0,
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max_new_tokens=512, # adjust as needed
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seed=42,
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reset=True, # reset history (cache)
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stream=True, # streaming per word/token
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threads=24, # adjust for your CPU
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stop=["<|im_end|>", "|<"],
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)
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if __name__ == "__main__":
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config = AutoConfig.from_pretrained("mosaicml/mpt-30b-chat", context_length=8192)
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llm = AutoModelForCausalLM.from_pretrained(
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"models/mpt-30b-chat.ggmlv0.q4_1.bin",
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model_type="mpt",
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config=config,
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)
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print(config)
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system_prompt = "A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers."
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user_prompts = [
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"What is 2 + 2?",
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"What is 12 + 2?",
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"What is 5 + 7?",
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"What is 3 * 2?",
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"What is 4 / 2?",
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"Who was the first president of the US?",
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"Can humans ever set foot on mars?",
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]
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for user_prompt in user_prompts:
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generator = generate(llm, system_prompt, user_prompt)
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print(format_output(user_prompt), end=" ", flush=True)
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for word in generator:
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print(word, end="", flush=True)
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# print empty line
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print("")
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print(80 * "=")
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