ABHIJEET KALE

p1

Solution 1: Adjust Training Hyperparameters Modify your training configuration: yaml ### train per_device_train_batch_size: 1 gradient_accumulation_steps: 8 learning_rate: 1.0e-5 # Increased from 1.0e-6 num_train_epochs: 30.0 lr_scheduler_type: cosine warmup_ratio: 0.1 bf16: true...

Solution 2: Use a Different DeepSpeed Configuration Create a custom DeepSpeed config instead of using ds_z0_config.json: custom_ds_config.json: json { "train_batch_size": 8, "train_micro_batch_size_per_gpu": 1, "gradient_accumulation_steps": 8, "gradient_clipping": 1.0, "zero_optimization": { "stage":...

Solution 3: Alternative DeepSpeed Config (Stage 1) If Stage 0 doesn't work, try Stage 1 with more conservative settings: ds_stage1_config.json: json { "train_batch_size": 8, "train_micro_batch_size_per_gpu": 1, "gradient_accumulation_steps": 8, "gradient_clipping": 1.0,...

Solution 4: Modify Training Approach Update your training configuration with these stability improvements: yaml ### model model_name_or_path: ./model/hf/Qwen3-VL-4B-Instruct image_max_pixels: 512000 # Reduced to prevent memory issues ### method stage: sft...

Solution 6: Progressive Unfreezing If the above doesn't work, try progressive unfreezing: First phase: Freeze vision tower and projector, train only language model Second phase: Unfreeze projector, train both Third...

Solution You need to ensure proper logging configuration for distributed training. Here are several approaches to fix this: 1. Enable Detailed Logging in LLaMA-Factory Add these parameters to your training...

PR Summary Type: Dependency update Package: nokogiri (Ruby XML/HTML parser) Version: 1.13.9 → 1.14.3 Status: Open, mergeable with no conflicts Key Details Dependabot Score: Compatible (should merge cleanly) Files Changed:...