128 lines
3.7 KiB
YAML
128 lines
3.7 KiB
YAML
# UNIFIED CONFIG — 20GB VRAM v2
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# Slips unified fine-tuning with Unsloth
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# Tasks: summarization (S) + cause analysis (A) + risk assessment (B)
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# Model: Qwen2.5-1.5B-Instruct, 4096 seq_len, single LoRA adapter
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# Changes vs v1: lora_r 64→128, epochs 3→2, augmented dataset
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# Model Configuration
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model:
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model_name: "unsloth/Qwen2.5-1.5B-Instruct" # Target deployment model (RPi5)
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max_seq_length: 4096 # 3500 DAG tokens + prompt overhead + response budget
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dtype: null # Auto-detect best dtype
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load_in_4bit: true # QLoRA — 4-bit base model required for 20GB VRAM
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device_map: "auto"
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# LoRA Configuration — increased rank to reduce task competition
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lora_r: 128 # Increased from 64 — more capacity to avoid task competition
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lora_alpha: 128 # Equal to r with RSLoRA
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lora_dropout: 0.0 # No dropout — curated dataset, every gradient counts
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lora_targets:
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- "q_proj"
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- "k_proj"
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- "v_proj"
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- "o_proj"
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- "gate_proj"
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- "up_proj"
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- "down_proj"
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use_rslora: true # Mandatory at r=128 to normalize gradient scaling
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random_state: 42
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loftq_config: null
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# Dataset Configuration
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dataset:
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type: "local"
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name: "unified_dataset"
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path: "unified_train_dataset_augmented.json" # 2195 records — S+A+B + 85 risk-only extras
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eval_path: "unified_eval_dataset.json" # 225 records — 75 incidents
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split: "train"
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text_column: "messages"
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use_chat_template: true
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dpo_train_path: "dpo_train_dataset.json"
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dpo_eval_path: "dpo_eval_dataset.json"
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# Training Configuration
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training:
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mode: "sft"
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# Batch size and accumulation
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per_device_train_batch_size: 1 # 4096 seq_len + 3 task types; keep at 1 for 20GB
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gradient_accumulation_steps: 16 # effective batch size = 16
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# Learning rate and schedule
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learning_rate: 0.00002 # 2e-5 — RSLoRA stability allows higher LR
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lr_scheduler_type: "cosine"
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warmup_steps: 30 # Slightly longer warmup for 3-task dataset (vs 20 for risk-only)
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weight_decay: 0.01
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# Training duration — 2 epochs over 2195 records = 4390 steps / 16 accum = ~274 optimizer steps
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# Reduced from 3 to avoid overfitting toward summary task pattern
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num_train_epochs: 2
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max_steps: -1
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# Precision and optimization
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fp16: false
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bf16: true # BF16 — Ampere GPU assumed
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optimizer: "adamw_8bit" # 8-bit optimizer for 20GB budget
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# Logging and saving
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logging_steps: 1
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save_steps: 50
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save_total_limit: 2
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# Output
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output_dir: "./qwen_unified_finetuned_v2"
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# Data processing
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dataset_num_proc: 2
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dataloader_num_workers: 0
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packing: false # Must be false with train_on_responses_only
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# Reporting
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report_to: []
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# Model saving — export merged 16-bit + GGUF for Ollama/RPi5
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save_method: "merged_16bit"
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gguf_quantization: "q5_k_m" # Options: q4_k_m, q5_k_m, q8_0, f16. null to skip.
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seed: 42
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# DPO / ORPO Configuration (for optional stage 2)
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dpo:
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beta: 0.1
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orpo_lambda: 0.1
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dpo_learning_rate: 0.00005
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# Weights & Biases
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use_wandb: false
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wandb:
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project: "qwen-finetuning"
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run_name: "qwen-unified-sft-v2"
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tags: ["qwen", "unsloth", "lora", "unified"]
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# Hardware-specific configurations
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hardware:
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gpu_16gb:
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model_name: "unsloth/Qwen2.5-1.5B-Instruct"
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 16
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max_seq_length: 4096
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gpu_24gb:
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model_name: "unsloth/Qwen2.5-1.5B-Instruct"
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per_device_train_batch_size: 2
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gradient_accumulation_steps: 8
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max_seq_length: 4096
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gpu_40gb:
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model_name: "unsloth/Qwen2.5-3B-Instruct"
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per_device_train_batch_size: 2
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gradient_accumulation_steps: 8
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max_seq_length: 4096
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# Evaluation Configuration
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evaluation:
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eval_steps: 50
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metric_for_best_model: "loss"
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load_best_model_at_end: true
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save_total_limit: 2
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