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Merge pull request #51 from rjojjr/STAGING
Release v2.1.2
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| def print_serve_mode_config(args): | ||
| print("Running in serve mode") | ||
| print() | ||
| print("WARNING - Serve mode is currently EXPERIMENTAL and should NEVER be used in a production environment!") | ||
| print() | ||
| print(f'Using bf16: {str(args.use_bf_16)}') | ||
| print(f'Using fp16: {str(args.use_fp_16)}') | ||
| print(f'Using 8bit: {str(args.use_8bit)}') | ||
| print(f'Using 4bit: {str(args.use_4bit)}') | ||
| print(f'Using fp32 CPU Offload: {str(args.fp32_cpu_offload)}') | ||
| print() | ||
| print(f"Serving {args.serve_model} on port {args.serve_port}") | ||
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| def print_tune_mode_config(args, model_dir, tuner): | ||
| print('') | ||
| print(f'Using LLM Type: {tuner.llm_type}') | ||
| print('') | ||
| print(f'Output Directory: {args.output_directory}') | ||
| print(f'Base Model: {args.base_model}') | ||
| print(f'Model Save Directory: {model_dir}') | ||
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| print('') | ||
| print(f'Using CPU Only Tuning: {str(args.cpu_only_tuning)}') | ||
| print(f'Using tf32: {str(args.use_tf_32)}') | ||
| print(f'Using bf16: {str(args.use_bf_16)}') | ||
| print(f'Using fp16: {str(args.use_fp_16)}') | ||
| print(f'Using 8bit: {str(args.use_8bit)}') | ||
| print(f'Using 4bit: {str(args.use_4bit)}') | ||
| print(f'Using fp32 CPU Offload: {str(args.fp32_cpu_offload)}') | ||
| print('') | ||
| print(f'Is Fine-Tuning: {str(args.fine_tune)}') | ||
| print(f'Is Merging: {str(args.merge)}') | ||
| print(f'Is Pushing: {str(args.push)}') | ||
| print('') | ||
| print(f'Is Chat Model: {args.is_chat_model}') | ||
| print(f'Is Instruct Model: {args.is_instruct_model}') | ||
| print(f'Using Additional Vocab Tokens: {args.additional_vocabulary_tokens}') | ||
| print(f'Is LangChain Agent Model: {args.use_agent_tokens}') | ||
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| def print_fine_tune_config(args, lora_scale, tune_arguments): | ||
| print('') | ||
| print(f'Epochs: {str(args.epochs)}') | ||
| print(f'Using Tuning Dataset: {args.hf_training_dataset_id if args.hf_training_dataset_id is not None else args.training_data_file}') | ||
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| if args.torch_empty_cache_steps is not None: | ||
| print(f'Empty Torch Cache After {args.torch_empty_cache_steps} Steps') | ||
| print(f'Using Checkpointing: {str(not args.no_checkpoint)}') | ||
| if not args.no_checkpoint: | ||
| print(f'Using Max Saved Checkpoints: {args.max_saved}') | ||
| print(f'Using Batch Size: {str(args.batch_size)}') | ||
| print(f'Using Save Strategy: {args.save_strategy}') | ||
| print(f'Using Save Steps: {str(args.save_steps)}') | ||
| print(f'Using Save Embeddings: {str(args.save_embeddings)}') | ||
| if args.target_modules is not None: | ||
| print(f'Targeting Modules: {args.target_modules}') | ||
| elif args.target_all_modules: | ||
| print(f'Targeting Modules: ALL') | ||
| else: | ||
| print(f'Targeting Modules: LINEAR') | ||
| print('') | ||
| print(f'Using LoRA R: {str(args.lora_r)}') | ||
| print(f'Using LoRA Alpha: {str(args.lora_alpha)}') | ||
| print(f'LoRA Adapter Scale(alpha/r): {str(lora_scale)}') | ||
| print(f'Using LoRA Dropout: {str(args.lora_dropout)}') | ||
| print(f'Using LoRA Bias: {str(args.bias)}') | ||
| print() | ||
| print(f'Using Optimizer: {args.optimizer_type}') | ||
| if 'adamw' in args.optimizer_type: | ||
| print(f'Using Base Learning Rate: {str(args.base_learning_rate)}') | ||
| print( | ||
| f'Using Actual Learning Rate(Base Learning Rate * Batch Size): {str(args.base_learning_rate * args.batch_size)}') | ||
| print(f'Learning Rate Scheduler Type: {str(args.lr_scheduler_type)}') | ||
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| print(f'Using Warmup Ratio: {args.warmup_ratio}') | ||
| print(f'Using Max Sequence Length: {args.max_seq_length}') | ||
| print(f'Using Group By Length: {args.group_by_length}') | ||
| print(f'Using Weight Decay: {args.weight_decay}') | ||
| print(f'Using Max Gradient Norm: {args.max_gradient_norm}') | ||
| print(f'Using Gradient Accumulation Steps: {args.gradient_accumulation_steps}') | ||
| print() | ||
| print(f'Using Do Eval: {args.do_eval}') | ||
| if args.do_eval is not None and args.do_eval: | ||
| print(f'Using Eval Strategy: {tune_arguments.eval_strategy}') | ||
| if tune_arguments.eval_strategy == 'steps': | ||
| print(f'Using Eval Steps: {tune_arguments.eval_steps}') | ||
| if args.eval_dataset is None: | ||
| print(f'Using Eval Dataset: {args.hf_training_dataset_id if args.hf_training_dataset_id is not None else args.training_data_file}') | ||
| else: | ||
| print(f'Using Eval Dataset: {args.eval_dataset }') | ||
| print() | ||
| if args.is_instruct_model: | ||
| print(f'Using NEFTune Noise Alpha: {args.neftune_noise_alpha}') | ||
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