Rabornkraken/qwen3.5-27b-agent-sft-v2

Qwen3.5-27B Agent SFT v2 — Multi-Turn Tool-Calling LoRA

A LoRA adapter fine-tuned on 220K multi-turn tool-calling trajectories for grounded image generation agent tasks.

Model Details

Parameter	Value
Base model	Qwen3.5-27B (26.9B params)
LoRA rank	32
LoRA alpha	32
Trainable params	159.4M
Training steps	13,799 (batch 8, 3x H200 GPUs)
Final loss	0.36
Token accuracy	87.6%

Training Data (220K Multi-Turn Examples)

Source	Examples	Key Strength
ToolMind	203K	Multi-agent synthesis, reasoning traces, 20K+ tools
FunReason-MT	13K	Complex sequential tool dependencies
APIGen-MT-5k	5K	Gold standard, 99% human-verified trajectories

Average 9.2 turns per example with sequential tool calls.

Key Insight

Single-turn tool-calling data (e.g., raw Toucan-1.5M, xLAM-60K) actively degrades multi-step reasoning. Our v1 SFT trained on 70K single-turn examples scored -5% vs base. This v2 with 220K multi-turn data scored +10% vs base.

Evaluation (vs Base Qwen3.5-27B)

Test	Base	SFT v2	Delta
Real location (NTU Hive)	0.70	0.85	+21%
Multi-step (Marina Bay Sands)	0.70	1.00	+43%
Database search	1.00	1.00	0%
Fictional scene	1.00	1.00	0%
No tools needed	0.75	0.75	0%
Full pipeline	0.70	0.85	+21%
Average	0.81	0.91	+10%

Usage

Infrastructure

• 3x NVIDIA H200 (144GB VRAM each), data-parallel training
• Checkpoints auto-pushed to HuggingFace every 500 steps
• Part of SC4062 Grounded Image Generation Agent project