# -*- coding: utf-8 -*- """Continued pretraining - Korean + Unsloth.ipynb Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu To run this, press "*Runtime*" and press "*Run all*" on a **free** Tesla T4 Google Colab instance!
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To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda). You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp). We will use the Korean subset of the [Wikipedia dataset](https://huggingface.co/datasets/wikimedia/wikipedia) to first continually pretrain Mistral v3, then use the [Alpaca GPT4 Dataset](https://huggingface.co/datasets/FreedomIntelligence/alpaca-gpt4-korean) translated into Korean to further finetune the model to let it follow instructions in Korean. """ # ============================================================================ # IMPORTS # ============================================================================ import os import argparse # ============================================================================ # 命令行参数（默认值与原始脚本硬编码值完全一致，保证行为不变） # ============================================================================ def parse_args(): parser = argparse.ArgumentParser( description="韩语 Mistral 继续预训练 + 指令微调（Unsloth / LoRA）。" "先用韩语维基百科做继续预训练注入韩语能力，再用韩语 Alpaca 数据做 SFT。", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) # 基础模型 parser.add_argument("--base_model", type=str, default="unsloth/mistral-7b-v0.3", help="基础模型名称（HuggingFace / Unsloth 仓库名）") parser.add_argument("--max_seq_len", type=int, default=2048, help="最大序列长度") parser.add_argument("--no_4bit", action="store_true", help="关闭 4bit 量化加载（默认开启 4bit 以节省显存）") # LoRA 配置 parser.add_argument("--lora_rank", type=int, default=128, help="LoRA 秩 r（继续预训练建议较大，如 128）") parser.add_argument("--lora_alpha", type=int, default=32, help="LoRA alpha") # 数据集 parser.add_argument("--wiki_dataset", type=str, default="wikimedia/wikipedia", help="继续预训练用的维基百科数据集") parser.add_argument("--wiki_config", type=str, default="20231101.ko", help="维基百科数据集的语言/版本配置（默认韩语快照）") parser.add_argument("--wiki_train_size", type=float, default=0.05, help="维基百科数据集抽样比例（0~1，默认取 5%% 以加速训练）") parser.add_argument("--alpaca_dataset", type=str, default="FreedomIntelligence/alpaca-gpt4-korean", help="指令微调（SFT）用的韩语 Alpaca 数据集") # 训练超参数 parser.add_argument("--pretrain_epochs", type=int, default=1, help="继续预训练阶段的训练轮数") parser.add_argument("--pretrain_max_steps", type=int, default=-1, help="继续预训练最大步数（-1 表示不限制，按 epoch 训练）") parser.add_argument("--sft_epochs", type=int, default=2, help="指令微调阶段的训练轮数") parser.add_argument("--sft_max_steps", type=int, default=-1, help="指令微调最大步数（-1 表示不限制，按 epoch 训练）") # 输出目录 parser.add_argument("--pretrain_output_dir", type=str, default="outputs_pretrain", help="继续预训练的检查点目录") parser.add_argument("--sft_output_dir", type=str, default="outputs_sft", help="指令微调的检查点目录") parser.add_argument("--pretrained_save_dir", type=str, default="lora_model_pretrained", help="继续预训练后保存的 LoRA 模型目录") parser.add_argument("--final_save_dir", type=str, default="lora_model", help="指令微调后保存的最终 LoRA 模型目录") return parser.parse_args() # 先解析参数（放在重型导入之前，这样 `--help` 无需 GPU / Unsloth 也能运行） args = parse_args() import torch from unsloth import FastLanguageModel, is_bfloat16_supported, UnslothTrainer, UnslothTrainingArguments from transformers import TrainingArguments, TextStreamer from datasets import load_dataset # ANSI color codes for colored output class Colors: HEADER = '\033[95m' BLUE = '\033[94m' CYAN = '\033[96m' GREEN = '\033[92m' YELLOW = '\033[93m' RED = '\033[91m' ENDC = '\033[0m' BOLD = '\033[1m' UNDERLINE = '\033[4m' def print_section(title, color=Colors.CYAN): """Print a colored section header""" print(f"\n{color}{Colors.BOLD}{'='*70}") print(f"{title}") print(f"{'='*70}{Colors.ENDC}\n") # ============================================================================ # MODEL SETUP # ============================================================================ print_section("🚀 LOADING MODEL", Colors.BLUE) max_seq_length = args.max_seq_len # Choose any! We auto support RoPE Scaling internally! dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ load_in_4bit = not args.no_4bit # Use 4bit quantization to reduce memory usage. Can be False. # 4bit pre quantized models we support for 4x faster downloading + no OOMs. fourbit_models = [ "unsloth/mistral-7b-v0.3-bnb-4bit", # New Mistral v3 2x faster! "unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "unsloth/llama-3-8b-bnb-4bit", # Llama-3 15 trillion tokens model 2x faster! "unsloth/llama-3-8b-Instruct-bnb-4bit", "unsloth/llama-3-70b-bnb-4bit", "unsloth/Phi-3-mini-4k-instruct", # Phi-3 2x faster! "unsloth/Phi-3-medium-4k-instruct", "unsloth/mistral-7b-bnb-4bit", "unsloth/gemma-7b-bnb-4bit", # Gemma 2.2x faster! ] # More models at https://huggingface.co/unsloth print(f"{Colors.GREEN}Loading {args.base_model}...{Colors.ENDC}") model, tokenizer = FastLanguageModel.from_pretrained( model_name = args.base_model, # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf ) print_section("⚙️ ADDING LORA ADAPTERS", Colors.BLUE) print(f"{Colors.GREEN}Adding LoRA adapters - only updating 1-10% of parameters!") print(f"Including embed_tokens and lm_head for continual pretraining{Colors.ENDC}") model = FastLanguageModel.get_peft_model( model, r = args.lora_rank, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128 target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "embed_tokens", "lm_head",], # Add for continual pretraining lora_alpha = args.lora_alpha, lora_dropout = 0, # Supports any, but = 0 is optimized bias = "none", # Supports any, but = "none" is optimized # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes! use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context random_state = 3407, use_rslora = True, # We support rank stabilized LoRA loftq_config = None, # And LoftQ ) # ============================================================================ # TESTING ORIGINAL MODEL (BASELINE) # ============================================================================ print_section("🧪 TESTING ORIGINAL MODEL (BASELINE)", Colors.CYAN) print(f"{Colors.YELLOW}Testing the base Mistral model BEFORE any training") print(f"This establishes baseline for Korean and English capabilities{Colors.ENDC}") FastLanguageModel.for_inference(model) text_streamer = TextStreamer(tokenizer) # Prepare prompts (define them early) _wikipedia_prompt = """Wikipedia Article ### Title: {} ### Article: {}""" wikipedia_prompt_korean = """위키피디아 기사 ### 제목: {} ### 기사: {}""" _alpaca_prompt_english = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {} ### Response: {}""" alpaca_prompt_korean = """다음은 작업을 설명하는 명령입니다. 요청을 적절하게 완료하는 응답을 작성하세요. ### 지침: {} ### 응답: {}""" # Test 1: Korean Wikipedia (baseline - should be poor) print(f"\n{Colors.BOLD}Test 1: Korean Wikipedia Article (인공지능){Colors.ENDC}") print("="*70) test_prompt = wikipedia_prompt_korean.format("인공지능", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 2: English Wikipedia (baseline - should be good) print(f"\n{Colors.BOLD}Test 2: English Wikipedia Article (Artificial Intelligence){Colors.ENDC}") print("="*70) test_prompt = _wikipedia_prompt.format("Artificial Intelligence", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 3: Korean Instruction (baseline - should be poor) print(f"\n{Colors.BOLD}Test 3: Korean Instruction (Korean Culture){Colors.ENDC}") print("="*70) test_prompt = alpaca_prompt_korean.format("한국의 전통 음식인 김치에 대해 설명하세요.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 4: English Instruction (baseline - should be good) print(f"\n{Colors.BOLD}Test 4: English Instruction (American Culture){Colors.ENDC}") print("="*70) test_prompt = _alpaca_prompt_english.format("Explain about Thanksgiving turkey, a traditional American food.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 5: Korean Instruction - Seoul (baseline - should be poor) print(f"\n{Colors.BOLD}Test 5: Korean Instruction (Korean Geography){Colors.ENDC}") print("="*70) test_prompt = alpaca_prompt_korean.format("대한민국의 수도인 서울에 대해 간단히 소개해주세요.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") print(f"{Colors.GREEN}✓ Baseline testing complete. Original model should be good at English but poor at Korean.{Colors.ENDC}\n") # ============================================================================ # DATA PREPARATION # ============================================================================ print_section("📚 DATA PREPARATION - WIKIPEDIA KOREAN DATASET", Colors.CYAN) print(f"{Colors.YELLOW}Loading Korean Wikipedia dataset...") print(f"We'll use 5% of the dataset to speed up training{Colors.ENDC}") # Use the prompts already defined above EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN def formatting_prompts_func_wiki(examples): titles = examples["title"] texts = examples["text"] outputs = [] for title, text in zip(titles, texts): # Must add EOS_TOKEN, otherwise your generation will go on forever! text = wikipedia_prompt_korean.format(title, text) + EOS_TOKEN outputs.append(text) return { "text" : outputs, } dataset = load_dataset(args.wiki_dataset, args.wiki_config, split = "train",) # We select 5% of the data to make training faster! dataset = dataset.train_test_split(train_size = args.wiki_train_size)["train"] dataset = dataset.map(formatting_prompts_func_wiki, batched = True,) print(f"{Colors.GREEN}✓ Wikipedia dataset loaded: {len(dataset)} examples{Colors.ENDC}") print_section("📚 DATA PREPARATION - ALPACA KOREAN DATASET", Colors.CYAN) print(f"{Colors.YELLOW}Loading Alpaca GPT4 Korean dataset for instruction finetuning...{Colors.ENDC}") alpaca_dataset = load_dataset(args.alpaca_dataset, split = "train") # Use the prompts already defined above def formatting_prompts_func_alpaca(conversations): texts = [] conversations = conversations["conversations"] for convo in conversations: # Must add EOS_TOKEN, otherwise your generation will go on forever! text = alpaca_prompt_korean.format(convo[0]["value"], convo[1]["value"]) + EOS_TOKEN texts.append(text) return { "text" : texts, } alpaca_dataset = alpaca_dataset.map(formatting_prompts_func_alpaca, batched = True,) print(f"{Colors.GREEN}✓ Alpaca dataset loaded: {len(alpaca_dataset)} examples{Colors.ENDC}") print(f"\nExample from Alpaca dataset:") print(alpaca_dataset[0]) # ============================================================================ # CONTINUED PRETRAINING # ============================================================================ print_section("🎯 CONTINUED PRETRAINING ON KOREAN WIKIPEDIA", Colors.GREEN) print(f"{Colors.YELLOW}Training the model on Korean Wikipedia to learn the language...") print(f"Using embedding_learning_rate (1e-5) smaller than learning_rate (5e-5)") print(f"💾 Checkpoints will be saved every 100 steps to: outputs_pretrain/") print(f"Only the 5 most recent checkpoints will be kept{Colors.ENDC}") # Set WandB project for pretraining os.environ["WANDB_PROJECT"] = "unsloth-continued-pretraining" trainer = UnslothTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, args = UnslothTrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 8, # Use warmup_ratio and num_train_epochs for longer runs! max_steps = args.pretrain_max_steps, warmup_steps = 10, warmup_ratio = 0.1, num_train_epochs = args.pretrain_epochs, # Select a 2 to 10x smaller learning rate for the embedding matrices! learning_rate = 5e-5, embedding_learning_rate = 1e-5, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 42, output_dir = args.pretrain_output_dir, # Checkpoint saving save_strategy = "steps", save_steps = 100, # Keep only the 5 most recent checkpoints, as the banner above # promises. Without this HF keeps every one, and a ~2000-step run # writes ~20 LoRA+optimizer checkpoints (fills a free-Colab disk). save_total_limit = 5, # WandB configuration report_to = "wandb", run_name = "korean-mistral-pretrain", ), ) print_section("💾 MEMORY STATS - BEFORE PRETRAINING", Colors.YELLOW) gpu_stats = torch.cuda.get_device_properties(0) start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3) max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3) print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.") print(f"{start_gpu_memory} GB of memory reserved.") trainer_stats = trainer.train() # Finish wandb run for pretraining import wandb if wandb.run is not None: wandb.finish() print(f"{Colors.CYAN}✓ Finished wandb run for pretraining{Colors.ENDC}") print_section("💾 SAVING PRETRAINED MODEL", Colors.GREEN) model.save_pretrained(args.pretrained_save_dir) # Local saving tokenizer.save_pretrained(args.pretrained_save_dir) print(f"{Colors.GREEN}✓ Model saved to: {args.pretrained_save_dir}/{Colors.ENDC}") # ============================================================================ # TESTING PRETRAINED MODEL # ============================================================================ print_section("🧪 TESTING PRETRAINED MODEL (AFTER KOREAN PRETRAINING)", Colors.CYAN) print(f"{Colors.YELLOW}Testing after Korean pretraining - before instruction finetuning") print(f"Korean should improve, English should remain strong{Colors.ENDC}") # Test the pretrained model before instruction finetuning FastLanguageModel.for_inference(model) # Enable native 2x faster inference text_streamer = TextStreamer(tokenizer) # Test 1: Korean Wikipedia (same as baseline) print(f"\n{Colors.BOLD}Test 1: Korean Wikipedia Article (인공지능){Colors.ENDC}") print("="*70) test_prompt = wikipedia_prompt_korean.format("인공지능", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 2: English Wikipedia (same as baseline) print(f"\n{Colors.BOLD}Test 2: English Wikipedia Article (Artificial Intelligence){Colors.ENDC}") print("="*70) test_prompt = _wikipedia_prompt.format("Artificial Intelligence", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 3: Korean Instruction (same as baseline - should improve but not follow perfectly yet) print(f"\n{Colors.BOLD}Test 3: Korean Instruction (Korean Culture){Colors.ENDC}") print("="*70) test_prompt = alpaca_prompt_korean.format("한국의 전통 음식인 김치에 대해 설명하세요.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 4: English Instruction (same as baseline) print(f"\n{Colors.BOLD}Test 4: English Instruction (American Culture){Colors.ENDC}") print("="*70) test_prompt = _alpaca_prompt_english.format("Explain about Thanksgiving turkey, a traditional American food.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 5: Korean Instruction - Seoul (should improve but not perfect yet) print(f"\n{Colors.BOLD}Test 5: Korean Instruction (Korean Geography){Colors.ENDC}") print("="*70) test_prompt = alpaca_prompt_korean.format("대한민국의 수도인 서울에 대해 간단히 소개해주세요.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") print(f"{Colors.GREEN}✓ Pretrained model testing complete.") print(f"{Colors.CYAN}💡 Korean should be significantly improved, English should remain strong.") print(f"Instruction following may improve but not perfect yet - that's what SFT is for.{Colors.ENDC}\n") # ============================================================================ # INSTRUCTION FINETUNING # ============================================================================ print_section("🎓 INSTRUCTION FINETUNING ON ALPACA KOREAN", Colors.GREEN) print(f"{Colors.YELLOW}Now finetuning the model to follow Korean instructions...") print(f"Using the Alpaca GPT4 dataset translated to Korean") print(f"💾 Checkpoints will be saved every 100 steps to: outputs_sft/") print(f"Only the 5 most recent checkpoints will be kept{Colors.ENDC}") # Set WandB project for finetuning os.environ["WANDB_PROJECT"] = "unsloth-continued-finetuning" trainer = UnslothTrainer( model = model, tokenizer = tokenizer, train_dataset = alpaca_dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 8, args = UnslothTrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 8, # Use num_train_epochs and warmup_ratio for longer runs! max_steps = args.sft_max_steps, warmup_steps = 10, warmup_ratio = 0.1, num_train_epochs = args.sft_epochs, # Select a 2 to 10x smaller learning rate for the embedding matrices! learning_rate = 5e-5, embedding_learning_rate = 1e-5, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.00, lr_scheduler_type = "linear", seed = 42, output_dir = args.sft_output_dir, # Checkpoint saving save_strategy = "steps", save_steps = 100, # Keep only the 5 most recent checkpoints, as the banner above # promises. Without this HF keeps every one, and a ~2000-step run # writes ~20 LoRA+optimizer checkpoints (fills a free-Colab disk). save_total_limit = 5, # WandB configuration report_to = "wandb", run_name = "korean-mistral-finetune", ), ) trainer_stats = trainer.train() # Finish wandb run for SFT if wandb.run is not None: wandb.finish() print(f"{Colors.CYAN}✓ Finished wandb run for SFT{Colors.ENDC}") print_section("📊 FINAL MEMORY AND TIME STATS", Colors.YELLOW) used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3) used_memory_for_lora = round(used_memory - start_gpu_memory, 3) used_percentage = round(used_memory /max_memory*100, 3) lora_percentage = round(used_memory_for_lora/max_memory*100, 3) print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.") print(f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.") print(f"Peak reserved memory = {used_memory} GB.") print(f"Peak reserved memory for training = {used_memory_for_lora} GB.") print(f"Peak reserved memory % of max memory = {used_percentage} %.") print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.") # ============================================================================ # INFERENCE - TESTING FINETUNED MODEL # ============================================================================ print_section("🎯 INFERENCE - TESTING FINETUNED MODEL", Colors.CYAN) print(f"{Colors.YELLOW}Testing the instruction-finetuned model - should follow instructions in Korean AND English{Colors.ENDC}") FastLanguageModel.for_inference(model) # Enable native 2x faster inference text_streamer = TextStreamer(tokenizer) # Test 1: Korean Wikipedia (same as baseline) print(f"\n{Colors.BOLD}Test 1: Korean Wikipedia Article (인공지능){Colors.ENDC}") print("="*70) test_prompt = wikipedia_prompt_korean.format("인공지능", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 2: English Wikipedia (same as baseline) print(f"\n{Colors.BOLD}Test 2: English Wikipedia Article (Artificial Intelligence){Colors.ENDC}") print("="*70) test_prompt = _wikipedia_prompt.format("Artificial Intelligence", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 3: Korean Instruction (same as baseline - should follow WELL now) print(f"\n{Colors.BOLD}Test 3: Korean Instruction (Korean Culture){Colors.ENDC}") print("="*70) test_prompt = alpaca_prompt_korean.format("한국의 전통 음식인 김치에 대해 설명하세요.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 4: English Instruction (same as baseline) print(f"\n{Colors.BOLD}Test 4: English Instruction (American Culture){Colors.ENDC}") print("="*70) test_prompt = _alpaca_prompt_english.format("Explain about Thanksgiving turkey, a traditional American food.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") # Test 5: Korean Instruction - Seoul (should follow WELL now) print(f"\n{Colors.BOLD}Test 5: Korean Instruction (Korean Geography){Colors.ENDC}") print("="*70) test_prompt = alpaca_prompt_korean.format("대한민국의 수도인 서울에 대해 간단히 소개해주세요.", "") inputs = tokenizer([test_prompt], return_tensors = "pt").to("cuda") _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 150, use_cache = True) print("="*70 + "\n") print(f"{Colors.GREEN}✓ Finetuned model testing complete!") print(f"{Colors.CYAN}💡 Model should now follow instructions well in both Korean AND English.{Colors.ENDC}\n") # ============================================================================ # SAVING FINAL MODEL # ============================================================================ print_section("💾 SAVING FINAL FINETUNED MODEL", Colors.GREEN) model.save_pretrained(args.final_save_dir) # Local saving tokenizer.save_pretrained(args.final_save_dir) print(f"{Colors.GREEN}✓ Final model saved to: {args.final_save_dir}/{Colors.ENDC}") # model.push_to_hub("your_name/lora_model", token = "...") # Online saving # tokenizer.push_to_hub("your_name/lora_model", token = "...") # Online saving print(f"\n{Colors.CYAN}Note: This only saves LoRA adapters, not the full model.") print(f"For 16bit or GGUF formats, see the export options below.{Colors.ENDC}") # ============================================================================ # LOADING SAVED MODEL (OPTIONAL) # ============================================================================ """ print_section("📥 LOADING SAVED MODEL", Colors.BLUE) # Set to True to test loading if False: model, tokenizer = FastLanguageModel.from_pretrained( model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, ) FastLanguageModel.for_inference(model) # Enable native 2x faster inference inputs = tokenizer( [ alpaca_prompt.format( # "Describe the planet Earth extensively.", # instruction "지구를 광범위하게 설명하세요.", "", # output - leave this blank for generation! ), ], return_tensors = "pt").to("cuda") text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 256) """ # ============================================================================ # EXPORT OPTIONS # ============================================================================ print_section("📦 EXPORT OPTIONS", Colors.BLUE) print(f"{Colors.YELLOW}Various export formats available (currently disabled):{Colors.ENDC}") print("• Float16 merged model") print("• 4bit merged model") print("• LoRA adapters only") print("• GGUF format for llama.cpp") # Merge to 16bit if False: print_section("💾 EXPORTING TO FLOAT16", Colors.GREEN) model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",) # model.push_to_hub_merged("hf/model", tokenizer, save_method = "merged_16bit", token = "") # Merge to 4bit if False: print_section("💾 EXPORTING TO 4BIT", Colors.GREEN) model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",) # model.push_to_hub_merged("hf/model", tokenizer, save_method = "merged_4bit", token = "") # Just LoRA adapters if False: print_section("💾 EXPORTING LORA ADAPTERS", Colors.GREEN) model.save_pretrained_merged("model", tokenizer, save_method = "lora",) # model.push_to_hub_merged("hf/model", tokenizer, save_method = "lora", token = "") # GGUF exports if False: print_section("💾 EXPORTING TO GGUF Q8_0", Colors.GREEN) model.save_pretrained_gguf("model", tokenizer,) # model.push_to_hub_gguf("hf/model", tokenizer, token = "") if False: print_section("💾 EXPORTING TO GGUF F16", Colors.GREEN) model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16") # model.push_to_hub_gguf("hf/model", tokenizer, quantization_method = "f16", token = "") if False: print_section("💾 EXPORTING TO GGUF Q4_K_M", Colors.GREEN) model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m") # model.push_to_hub_gguf("hf/model", tokenizer, quantization_method = "q4_k_m", token = "") print_section("✅ TRAINING COMPLETE!", Colors.GREEN) print(f"{Colors.BOLD}Your Korean Mistral model is ready to use!{Colors.ENDC}") print(f"\n{Colors.CYAN}For more information:{Colors.ENDC}") print("• Discord: https://discord.gg/u54VK8m8tk") print("• GitHub: https://github.com/unslothai/unsloth") print("• Documentation: https://docs.unsloth.ai")