The LoRA adapter (r=16, α=32, attention+MLP targets) behind DCL-Qwen3-4B-Instruct-2507, a fine-tune of Qwen/Qwen3-4B-Instruct-2507 that authors and repairs DCL (Russell East's Declarative Capability Language). See the main model card for the evaluation table, the required serving prompt, and limitations.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

m = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507",
                                         dtype=torch.bfloat16, device_map="auto")
m = PeftModel.from_pretrained(m, "RichWoollcott/DCL-Qwen3-4B-Instruct-2507-LoRA")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")

Note: the adapter was trained against the 4-bit quantised base (QLoRA); applied to the full-precision base as above, behaviour matches the released merged model to within quantisation noise — the frozen-exam receipts were graded on the merged Q4_K_M build.

Trained on a private corpus of 507 compiler-verified, fully synthetic rows with Unsloth + TRL (PEFT 0.19.1) — kept private to protect the integrity of the frozen evaluation exam. Apache-2.0; base LICENSE retained.