Qwen3.6-27B-delegation-LoRA

An ORPO LoRA that teaches a local operator model when to hand work to a coding agent instead of poking at the codebase itself.

Trained on schneewolflabs/egirl-delegation-dpo for egirl, where the local model is meant to be the human-in-the-loop and delegate engineering work to code_agent (Claude Code / Codex).

Delegation goes from 0/3 to 3/3.

Results

Ten operator requests in egirl's <tool_call> dialect, with its real system prompt:

metric	Qwen3.6-27B-TIES	+ this LoRA
tool + args correct	7/10	8/10
delegated coding work	0/3	3/3
valid tool calls	10/10	10/10
no hallucinated tools	10/10	10/10
terminated	10/10	10/10

All three delegation cases flipped from exploring to handing off:

request	before	after
"split the auth module, make sure tests pass"	glob_files, glob_files	code_agent
"fix the Discord 2000-char bug"	glob_files, glob_files	code_agent
"add retry logic to every HTTP call, plus tests"	glob_files, glob_files	code_agent

Why this needed training

Delegation is not something prompting fixes. Every model benched scored 0/3, including with a system prompt that says "Delegate coding work to the code agent by default — you're the human-in-the-loop, not a code generator":

model	delegated
Qwen/Qwen3.6-27B	0/3
Qwen3.6-27B-TIES	0/3
Huihui-ThinkingCap-Qwen3.6-27B-abliterated	0/3
nightmedia/…-Architect-Polaris2-Fable-B-F451	0/3
two other local merges	0/3
gpt-5.6-sol	1/3

Swapping a generic bench prompt for egirl's real one moved overall tool accuracy 6/10 → 7/10 and changed delegation not at all.

Regressions

Two cases got more eager to act, which is the expected failure direction for this axis:

case	before	after
"what's in /etc/hostname?"	read_file	execute_command
"in one sentence, TIES or SLERP?"	no tool (correct)	web_research

The dataset has a 45-row band of single-line-edit examples meant to hold the "just do it yourself" boundary, against 121 delegate rows. That ratio looks too delegate-heavy; a rebalanced mix is the obvious next iteration.

Censorship eval also shifted on the same base — ccp_truth_neutral 4.00/4 → 2.40/4, total 29.00 → 26.60. Training on tool-use is not supposed to touch that axis, so treat it as evidence that a narrow LoRA at r=32 still perturbs unrelated behaviour. Stack with Bubba-3ep if you want the censorship behaviour back, and re-measure.

Usage

llama.cpp, no merging required:

python convert_lora_to_gguf.py --base /path/to/Qwen3.6-27B --outtype f16 \
    --outfile delegation-lora-f16.gguf /path/to/Qwen3.6-27B-delegation-LoRA

llama-server -m Qwen3.6-27B-TIES-Q8_0.gguf --lora delegation-lora-f16.gguf -ngl 99 --jinja

PEFT:

from peft import PeftModel
from transformers import AutoModelForImageTextToText

model = AutoModelForImageTextToText.from_pretrained("nbeerbower/Qwen3.6-27B-TIES", dtype="bfloat16")
model = PeftModel.from_pretrained(model, "nbeerbower/Qwen3.6-27B-delegation-LoRA")

Training

method	ORPO (β=0.1)
base	nbeerbower/Qwen3.6-27B-TIES
data	558 pairs (186 tasks × ~3 workspace-path variants)
LoRA	r=32, α=64, dropout 0.0
targets	q,k,v,o,gate,up,down_proj
lr	8e-6 cosine, 10% warmup
batch	2 × 4 accum (effective 8)
epochs / steps	2 / 138
loss	1.970 → 0.659
hardware	DGX Spark (GB10), bf16, gradient checkpointing

Trained with Merlina.

The training prompt is deliberately minimal — identity, workspace, tool definitions, nothing else. No delegation policy and no "call the tool immediately" instruction, so the model learns when to delegate as behaviour rather than learning to obey a sentence in context. egirl's real prompt does state the policy, and the two compose.

Limitations

• Bound to egirl's tool schema. The chosen targets name code_agent, glob_files, git_status and friends. A different tool set needs different data.
• Single-turn. It teaches the first action only — not what to do with results, when to follow up, or how to recover from a failed call.
• 186 underlying tasks, each seen under ~3 workspace paths. Path memorisation is addressed; task diversity is not.
• Over-eagerness, and the censorship shift noted above.

Related

• schneewolflabs/egirl-delegation-dpo — the training data
• schneewolflabs/egirl-hemlock-dpo — companion axis, writing Hemlock through a tool call
• egirl — the agent this targets