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
39 行
1.1 KiB
Python
39 行
1.1 KiB
Python
# -*- coding: utf-8 -*-
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"""把 .pap 模型转成更小的 dtype（能力基本不变）。
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用法:
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/opt/pap-venv/bin/python platform/quant_pap.py \
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--src models/moe-0.1b.pap --dst models/moe-0.1b-fp16.pap --dtype fp16
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--dtype fp16: f32 -> 半精度, 体积约减半 (437MB -> ~219MB), 能力几乎不变
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--dtype q8 : f32 -> int8 块量化(每32个共享fp16 scale), 约 437/4 (~110MB), 损失略大
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"""
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from __future__ import annotations
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import argparse
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import os
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import pap_format
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DTYPES = {"f32": 0, "fp16": 1, "q8": 2}
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--src", required=True)
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ap.add_argument("--dst", required=True)
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ap.add_argument("--dtype", default="fp16", choices=DTYPES.keys())
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args = ap.parse_args()
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dst_dtype = DTYPES[args.dtype]
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pap_format.pap_quant(args.src, args.dst, dst_dtype)
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in_sz = os.path.getsize(args.src)
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out_sz = os.path.getsize(args.dst)
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print(f"已转换: {args.src} ({in_sz/1e6:.1f} MB) -> {args.dst} ({out_sz/1e6:.1f} MB)")
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print(f" 压缩比 {in_sz/out_sz:.2f}x dtype={args.dtype} (code {dst_dtype})")
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if __name__ == "__main__":
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main()
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