JGZYES
28535b1c74
- pmai unified CLI (generate/chat/interactive/http/inspect/output/config) - GPT+MoE transformer, .pap (f32/fp16/q8) + GGUF loader (order+version adaptive) - llama/Mixtral arch: RoPE+GQA+SwiGLU+MoE (C==torch verified) - C llama BPE tokenizer (validated vs llama-cpp-python) - training framework + 0.1B/0.22B MoE models; quantization fp16/q8 - build artifacts to output/; HTTP API; config.yaml; scripts; openapi
80 行
3.7 KiB
Python
80 行
3.7 KiB
Python
# -*- coding: utf-8 -*-
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"""生成一份多轮闲聊对话语料（User:/Assistant: 格式），用于训练聊天模型。"""
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import random
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def build_pools():
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P = {}
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P["names"] = ["Alice", "Bob", "Sam", "Luna", "Max", "Emma", "Kai", "Nora", "Leo", "Mia"]
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P["hobbies"] = ["reading", "hiking", "cooking", "painting", "chess", "yoga", "photography", "fishing", "gaming", "gardening"]
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P["foods"] = ["pizza", "sushi", "ramen", "tacos", "pasta", "salad", "biryani", "dumplings", "curry", "pancakes"]
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P["cities"] = ["Tokyo", "Paris", "Berlin", "Lisbon", "Seoul", "Toronto", "Sydney", "Rome", "Kyoto", "Prague"]
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P["weather"] = ["sunny", "rainy", "cloudy", "snowy", "windy", "clear"]
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P["movies"] = ["sci-fi", "comedy", "drama", "horror", "adventure", "romance"]
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P["music"] = ["pop", "jazz", "rock", "classical", "hip-hop", "folk"]
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P["ports"] = ["pets", "cats", "dogs", "birds", "fish"]
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return P
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def gen_conversation(P, rng):
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n_turns = rng.randint(3, 6)
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turns = []
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for i in range(n_turns):
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if i % 2 == 0: # user
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r = rng.random()
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if r < 0.20:
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u = rng.choice(["Hi", "Hello", "Hey", "Good morning", "What's up", "Hi there"])
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elif r < 0.35:
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u = f"How are you doing" + ("?" if rng.random() < 0.7 else "")
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elif r < 0.5:
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u = f"I like {rng.choice(P['hobbies'])}. What about you?"
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elif r < 0.62:
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u = f"Have you been to {rng.choice(P['cities'])}?"
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elif r < 0.72:
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u = f"What kind of {rng.choice(['food', 'music', 'movies'])} do you like?"
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elif r < 0.8:
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u = f"Do you have any {rng.choice(P['ports'])}?"
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elif r < 0.9:
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u = f"Can you tell me a {rng.choice(['joke', 'fact', 'story'])}?"
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else:
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u = "That's nice. Tell me more."
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turns.append(("User", u))
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else: # assistant
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r = rng.random()
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if r < 0.2:
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a = rng.choice(["Hi! How can I help you today?", "Hello! It's good to hear from you.", "Hey there! What's on your mind?"])
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elif r < 0.35:
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a = rng.choice(["I'm doing great, thanks for asking!", "Pretty good! How about you?", "Can't complain. How are you?"])
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elif r < 0.5:
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a = f"That sounds fun! I enjoy {rng.choice(P['hobbies'])} too."
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elif r < 0.62:
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a = f"I haven't been to {rng.choice(P['cities'])}, but I've heard it's lovely."
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elif r < 0.72:
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a = f"I'd say I like {rng.choice(P['music'])} and {rng.choice(P['movies'])}. You?"
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elif r < 0.8:
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a = rng.choice(["I do! I really like dogs and cats.", "I think pets are wonderful companions."])
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elif r < 0.9:
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a = rng.choice(["Here's one: why did the programmer quit? Because he didn't get arrays.", "Did you know honey never spoils?", "Let me tell you a short story about a curious little robot."])
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else:
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a = rng.choice(["That makes sense.", "I see what you mean.", "Interesting point!", "Thanks for sharing that."])
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turns.append(("Assistant", a))
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return turns
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def main(out="data/chat.txt", n=4000, seed=7):
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random.seed(seed)
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P = build_pools()
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lines = []
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for _ in range(n):
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for role, text in gen_conversation(P, random):
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lines.append(f"{role}: {text}")
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lines.append("") # 空行分隔对话
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with open(out, "w", encoding="utf-8") as f:
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f.write("\n".join(lines))
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print(f"chat corpus -> {out} : {sum(len(l) for l in lines)} chars, {n} conversations")
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if __name__ == "__main__":
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import sys
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main(out=sys.argv[1] if len(sys.argv) > 1 else "data/chat.txt")
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