suhjae/joseon-level4-qwen36-27b-lora

Joseon Level 4 Qwen3.6 27B LoRA

This is the current best Level 4 translation adapter for the Joseon-to-Day project. It is a PEFT LoRA adapter trained on top of Qwen/Qwen3.6-27B.

The model translates annotated Joseon historical Hanja/Hanmun source into modern Korean prose. Inputs are expected to include compact structured hints from the project pipeline, such as Level 1 spans, Level 2 훈음 candidates, and office/title term hints.

Model Type

• Base model: Qwen/Qwen3.6-27B
• Method: bf16 LoRA
• LoRA rank: 32
• LoRA alpha: 64
• LoRA dropout: 0.05
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Sequence length used for this run: 2048

This repository contains the LoRA adapter only, not the full 27B base model.

Evaluation Snapshot

Partial dev audit from the local project evaluation:

Metric	Value
person reading accuracy	1.0000
office reading accuracy	1.0000
entity F1	1.0000
institutional term accuracy	0.9709
action accuracy	0.8800
polarity accuracy	0.8800
Hanja output rate	0.0000
Korean-only output rate	1.0000
historical integrity composite	0.8642

Included diagnostics:

• dev_sample8_eval.json
• dev_partial_eval.json
• launch.log
• readiness.json

Intended Use

Use this adapter inside the Joseon-to-Day MVP translator pipeline. The model is intended for structured historical translation where the source is accompanied by reading/entity/institution hints.

Limitations

This is not a generic Traditional Chinese to Korean translation model. It was trained for a project-specific annotated prompt format and should be evaluated carefully before use in scholarly or production workflows. Event/action fidelity is improving but remains a known evaluation target.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen3.6-27B"
adapter_id = "suhjae/joseon-level4-qwen36-27b-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype="auto",
    device_map="auto",
)
model = PeftModel.from_pretrained(base, adapter_id)