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DIAGNOSTIC_ONLY · NOT_WINNER · NOT_D2 · CI crosses zero.| Field | Value |
|---|---|
| Base | Qwen/Qwen3-4B-Base |
| Base revision | 906bfd4b4dc7f14ee4320094d8b41684abff8539 |
| Recipe | FC250 + COT250 (token-balanced) |
LoRA rank r / U | 64 |
lora_alpha | 128 |
lora_dropout | 0.0 |
| Cutoff | 4096 |
| Rows | 1074 (FC 923 + COT 151) |
| Supervised target tokens | 500,000 non-padding unique |
| Train seed | 43 |
| Optimizer steps | 64 |
| Fresh LoRA from base | yes |
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj.adapter_config.json rewrites only base_model_name_or_path from a local snapshot path to Qwen/Qwen3-4B-Base so PEFT can resolve the public base. Adapter weights (adapter_model.safetensors) are an exact copy of the frozen COMPLETE artifact.1import torch
2from peft import PeftModel
3from transformers import AutoModelForCausalLM, AutoTokenizer
4
5base_id = "Qwen/Qwen3-4B-Base"
6base_rev = "906bfd4b4dc7f14ee4320094d8b41684abff8539"
7adapter_id = "modrill/CN11-OCR-FC250-COT250-U64"
8
9tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_rev)
10model = AutoModelForCausalLM.from_pretrained(
11 base_id,
12 revision=base_rev,
13 torch_dtype=torch.bfloat16,
14 device_map="auto",
15)
16model = PeftModel.from_pretrained(model, adapter_id)
17model.eval()AutoPeftModelForCausalLM.from_pretrained("modrill/CN11-OCR-FC250-COT250-U64") should also work after the published config rewrite. Always pin the base revision above.0.2581358609794629)0.24739336492890995)+0.010742496050552922)[-0.0006319115323854657, 0.022116903633491312] — crosses zero0.24486571879936808)+0.002211690363349131)[-0.01042654028436019, 0.014533965244865717] — crosses zero