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HIGH_HEAVY_L45 的未合并动态 LoRA 发布件:LoRA rank r=64、alpha=128,base 与 adapter 分离,未 merge、未 bake。请勿把本仓库当作完整基础模型。Qwen/Qwen3-4B-Base/workspace/MATH-NOTHINK-AIME-P1-v1.0/model/mn8_eos_r1_2/Qwen3-4B-Base-906bfd4906bfd4b4dc7f14ee4320094d8b41684abff8539b7dc3d5cef56c5ed8e03e9c54bde781b560897151e6b6fadc55ed40a22feaaa20144c9767921f51f37167784380029c01fc83caf5c5b54e803671bfc3c432bdf628a5b4c40584c524479b80773166f49b5c49d7223b34d4cf254064d6b65bd2932768 - exact_prompt_tokens - 64,统计以题目为 cluster,不把 240 个采样视为 240 道独立题。34/24034/24068/480(14.167%)ASSET_FROZEN_PROVENANCE_SCHEMA_MISSING。缺失的是训练当时 SHA256 为 1819b4a0abf92ac070cf8bb84a47b4a7b8e72a537df2bee81687ae8f1a31a527 的 schema 字节副本;现环境 schema 只能作为环境快照,不能冒充训练时副本。adapter 字节、adapter config/COMMIT 绑定及两组历史评测已独立核验,但不保证精确重训复现。1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4base_id = "Qwen/Qwen3-4B-Base"
5base_revision = "906bfd4b4dc7f14ee4320094d8b41684abff8539"
6adapter_id = "modrill/MT11-HIGH-L45"
7revision = "v1.0.0"
8
9tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_revision)
10base = AutoModelForCausalLM.from_pretrained(base_id, revision=base_revision, torch_dtype="auto", device_map="auto")
11model = PeftModel.from_pretrained(base, adapter_id, revision=revision) # dynamic, unmergedHIGH_HEAVY_L45 (r=64, alpha=128). The base and adapter stay separate; nothing is merged or baked.Qwen/Qwen3-4B-Base at revision 906bfd4b4dc7f14ee4320094d8b41684abff8539 (training path and hashes are listed above). Use that revision's tokenizer. The adapter SHA256 is 628a5b4c40584c524479b80773166f49b5c49d7223b34d4cf254064d6b65bd29.ASSET_FROZEN_PROVENANCE_SCHEMA_MISSING: the training-time schema byte copy is missing. The available current-environment schema is not represented as the historical training schema. Adapter bytes and evaluation evidence were verified, but exact retraining reproducibility is not guaranteed.