How it was made

1. Register adapter (v2): QLoRA+DoRA SFT on 1090 Qwen 3.5 72B distilled native Vietnamese instruction examples.
2. Knowledge adapter (Arm C): QLoRA+DoRA SFT on 603 Vietnamese MCQs in the exact eval answer format.
3. Merge (this model): the two adapters — identical config (r16/α32/DoRA, same 7 target modules) — are combined in adapter space via rank-concatenation, the exact weighted sum of their LoRA deltas ΔW = 0.5·ΔW_register + 0.5·ΔW_knowledge (DoRA magnitudes weighted-averaged). No additional training.

Data-mixing the two corpora in one SFT pass gave zero knowledge lift; merging the finished Specialists in weight space recover the full lift while keeping register — the Model-Soups/TIES/DARE result.

Results (self-run harness; VMLU 744-Q val, 4-bit loglik; register judged by Qwen2.5-7B, both orderings)

The VMLU gain is McNemar-significant vs base and broad (47/55 subjects); the MCQ data is 8-gram decontaminated against the VMLU val set (0/744 overlap).

Usage

python
Copy code
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct", device_map="auto")
model = PeftModel.from_pretrained(base, "<this-repo-id>")

Limitations

• Research artifact, not production-hardened. Register win is modest (n=30, directional).
• Knowledge gain is task-distribution elicitation on Vietnamese academic MCQs, not new facts (knowledge is base-bound).
• 8GB-laptop recipe; evaluated at 4-bit.