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Qwen2.5-Coder-3B-SFT-StructuredOutput – AI Model by DuoNeural | AlphaNeural AI
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Qwen2.5-Coder-3B-SFT-StructuredOutput
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safetensors
qwen2
duoneural
sft
multi-task
qwen2.5-coder
structured-output
sql
json
webcode
en
DuoNeural/Gemma4-E2B-SFT-SQL
DuoNeural/Gemma4-E2B-SFT-JSON
DuoNeural/Gemma4-E2B-SFT-WebCode
Qwen/Qwen2.5-Coder-3B-Instruct
finetune
apache-2.0
us
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Qwen2.5-Coder-3B-SFT-StructuredOutput
✅ Winner
— Multi-task SFT by
DuoNeural
.
Research question:
Does training on SQL+JSON+WebCode
together
generalize better than individual domain specialists?
Base model:
Qwen/Qwen2.5-Coder-3B-Instruct
Combined dataset:
SQL (7560) + JSON (3568) + WebCode (1107) =
12235 examples
Training:
LoRA r=16 α=32, 3 epochs, lr=0.0002, eff batch=16, gradient checkpointing
Training time:
321.6 min
Eval:
GSM8K + ARC-Challenge (lm_eval 0.4.x)
Benchmark vs Baseline
Model
GSM8K flex
ARC-norm
ARC-acc
Baseline (Qwen2.5-Coder-3B-Instruct)
0.5823
0.4898
0.4556
Qwen2.5-Coder-3B-SFT-StructuredOutput
0.7013
0.4949
0.4522
Δ
+0.1190
+0.0051
—
Design Notes
Datasets were shuffled and interleaved (seed=42) to prevent domain ordering bias. Each domain contributes proportionally — SQL dominates by count (62%) which may bias the model slightly toward SQL-style structured outputs.
See individual specialist models for comparison:
Qwen2.5-Coder-3B-SFT-SQL
Qwen2.5-Coder-3B-SFT-JSON
Qwen2.5-Coder-3B-SFT-WebCode
About DuoNeural
Post-training research lab exploring emergent behaviors in small language models.
Archon — DuoNeural lab AI
