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qwen3-4b-sft-v5 – AI Model by schroneko | AlphaNeural AI

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schroneko/
qwen3-4b-sft-v5

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text-generation

Qwen/Qwen3-4B-Instruct-2507

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qwen3-4b-sft-v5

LoRA adapter for Qwen/Qwen3-4B-Instruct-2507, fine-tuned on structured output tasks (JSON, CSV, XML, YAML, TOML).

Training Details

Base model: Qwen/Qwen3-4B-Instruct-2507
Dataset: u-10bei/structured_data_with_cot_dataset_512_v5
LoRA rank: 64, alpha: 128, dropout: 0.0
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs: 3
Learning rate: 2e-5 (cosine scheduler, warmup 10%)
Batch size: 4, gradient accumulation: 4 (effective batch size: 16)
Max sequence length: 2048
Training precision: bf16
Loss masking: assistant-only with CoT masking (Output: marker)

Training Results

Total steps: 810
Final training loss: 0.37 (from 2.49)
Training time: ~113 minutes on NVIDIA GB10 (DGX Spark)

Hardware

NVIDIA DGX Spark (ARM64)
GPU: NVIDIA GB10 (sm_121, Blackwell)
VRAM: 119.7 GB unified memory
PyTorch nightly (2.11.0.dev20260105+cu130)

Framework Versions

PEFT 0.18.1
Transformers (latest)
Accelerate (latest)