qwen2.5-7b-instruct-sft-v3

Training Objective

Training Configuration

• Method: SFT (Unsloth LoRA) + merge to full model
• Base model ID: Qwen/Qwen2.5-7B-Instruct
• LoRA: r=16, alpha=32, dropout=0.05
• LoRA target modules: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
• Max sequence length: 4096
• Max steps: 350
• Epochs: 1
• Learning rate: 1.0e-6
• Per-device train batch size: 1
• Per-device eval batch size: 2
• Gradient accumulation steps: 32
• Effective global batch size: 32
• Warmup ratio: 0.03
• Weight decay: 0.01
• Eval/Save steps: 50 / 50

Usage

Training Data / Sources & License (IMPORTANT)

• Primary source dataset:
  • u-10bei/sft_alfworld_trajectory_dataset_v5
• Data construction policy:
  • Converted trajectories into strict chat-style supervision with one-line action targets (ACTION: ...).
  • Trained with exact action matching against candidate action lists to improve action validity.
  • Added synthetic correction-style augmentation for common formatting and target-selection errors.
• Dataset scale (used for this merged model):
  • Train samples: 35950
  • Validation samples: 1892
  • Action coverage was balanced to avoid over-concentration on a few verbs.
• Sequence-length check:
  • No truncation observed at max_seq_length=4096.
• Compliance:
  • Follow the source dataset license on each dataset card.
  • Follow base model terms of use.