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+### LLaMA-Factory SFT Training Config - QLoRA (Ultra Minimal Hardware) ###
+### For session-level reflection training ###
+### Can run on single GPU with ~12GB VRAM ###
+
+### Model
+model_name_or_path: /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct
+
+### Method - QLoRA (4-bit quantization + LoRA)
+stage: sft
+do_train: true
+finetuning_type: lora
+quantization_bit: 4
+quantization_method: bitsandbytes
+
+### LoRA Config
+lora_rank: 64
+lora_alpha: 128
+lora_dropout: 0.05
+lora_target: all
+
+### Dataset
+dataset: sft_reflection
+dataset_dir: /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/training
+template: llama3
+cutoff_len: 4096
+
+### Output
+output_dir: /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/training/outputs/sft_reflection_qlora
+
+### Training - Single GPU friendly
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 64
+learning_rate: 2.0e-5
+num_train_epochs: 4.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+
+### Logging
+logging_steps: 10
+save_steps: 100
+save_total_limit: 3