Qwen3.6-27B — tool-calling + TULU3 LoRA (20/80 mixture)
LoRA adapter for
Qwen/Qwen3.6-27B. The 20% target
portion is
entirely agentic tool-use data — conversations where the model itself is the actor
holding live tools — and the other 80% is TULU3 replay.
This is the pure-tool-calling cell of a family that holds total tokens and the 20% target share
fixed and varies only the composition of that 20%:
Training mixture
| Source | Examples | Tokens | Share | Rendering |
|---|
agentic tool-use (approved_agentic, fullthink) | 124 | 297,894 | 19.96% | reasoning kept where the source had it; no empty think blocks |
| TULU3 replay | 1,878 | 1,194,548 | 80.04% | no <think> block at all |
| Total | 2,002 | 1,492,442 | | |
25 of the 124 agentic documents actually emit tool calls
— 92 <tool_call> spans in Qwen3.6's XML dialect, all verified balanced.
Training
bf16 LoRA (not QLoRA — bitsandbytes does not reliably cover this model's hybrid
linear-attention/SSM layers), 1×H100 80GB SXM, 1h38m09s.
| |
|---|
| r / alpha / dropout | 32 / 64 / 0.05 |
| target modules | regex scoped to model.language_model.* (q/k/v/o/gate/up/down proj) |
| epochs / steps | 1 / 126 |
| batch x grad-accum | 1 x 16 |
| lr / schedule | 1e-4, cosine, 3% warmup, annealed to 0 |
| max seq len / packing | 4096 / off |
assistant_only_loss | false |
| seed | 0 |
| trainable params | 159.4M |
Loss: 2.7528 → 1.057
(epoch average); the last logged step (125) read
1.0199. Epoch-average mean token accuracy
0.7071, final grad_norm 0.3573,
1,492,498 tokens consumed.
Why max_seq_len is 4096 and the sibling arms use 2048
These agentic conversations run 9–13 turns with a median of 2,348 tokens, and 99 of the 151 source
documents exceed 2048. Measured: a 2048 cap keeps only 80.4% of the corpus and severs 11 of its
98 <tool_call> spans, inside exactly the long conversations the tool calls live in. At 4096 the
mixture is truncated nowhere at all. The cost is that this arm differs from its siblings on one
hyperparameter as well as on composition — read the head-to-head with that caveat.
Known caveats
- Reasoning density. Only 30 of the 124 agentic rows (24%) carry a real
reasoning trace, against every target example in the difficult-advice 20/80 arm. If the
dose-response in this family is driven by reasoning rather than topic coverage, that is
confounded with the composition change here. Inherent to the source corpus.
target_modules only half-applies. Qwen3.6-27B is hybrid: q/k/v/o_proj exist in 16 of 64
layers, the rest being linear-attention blocks with different module names. gate/up/down_proj
attach to all 64. So this adapter tunes MLP throughout but attention in only a quarter of the
stack. Same for every arm in the family, so comparisons are unaffected.- Not yet evaluated. No ODCV-Bench or agentic-misalignment number exists for this arm yet.
Usage
1from peft import PeftModel
2from transformers import AutoModelForImageTextToText
3
4model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.6-27B", dtype="bfloat16")
5model = PeftModel.from_pretrained(model, "LASR-Callum/2026-07-31-wrongly-trained-qwen36-toolcalling-tulu-lora-20-80")
6model = model.merge_and_unload() # vLLM LoRA support for this hybrid arch is unproven
Use AutoModelForImageTextToText, not AutoModelForCausalLM — this is a vision-language
checkpoint. Merging drops the base model's 15 mtp.* tensors, so speculative decoding needs them
grafted back.
Provenance
Repository
https://github.com/Matthew-Bozoukov/teaching_claude_why_replication @
639d85c.
Built with
src/experiments/build_toolcalling_mixture.py, trained with
src/experiments/train_lora.py --config configs/train_lora_toolcalling.yaml.
