124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
from unsloth import FastLanguageModel
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import torch
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from trl import SFTTrainer
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from transformers import TrainingArguments
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from unsloth import is_bfloat16_supported
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max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
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dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
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load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
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# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
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fourbit_models = [
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"unsloth/Meta-Llama-3.1-8B-bnb-4bit", # Llama-3.1 15 trillion tokens model 2x faster!
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"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
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"unsloth/Meta-Llama-3.1-70B-bnb-4bit",
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"unsloth/Meta-Llama-3.1-405B-bnb-4bit", # We also uploaded 4bit for 405b!
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"unsloth/Mistral-Nemo-Base-2407-bnb-4bit", # New Mistral 12b 2x faster!
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"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
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"unsloth/mistral-7b-v0.3-bnb-4bit", # Mistral v3 2x faster!
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"unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
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"unsloth/Phi-3.5-mini-instruct", # Phi-3.5 2x faster!
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"unsloth/Phi-3-medium-4k-instruct",
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"unsloth/gemma-2-9b-bnb-4bit",
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"unsloth/gemma-2-27b-bnb-4bit", # Gemma 2x faster!
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] # More models at https://huggingface.co/unsloth
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "unsloth/Meta-Llama-3.1-8B",
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
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)
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model = FastLanguageModel.get_peft_model(
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model,
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r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
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target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj",],
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lora_alpha = 16,
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lora_dropout = 0, # Supports any, but = 0 is optimized
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bias = "none", # Supports any, but = "none" is optimized
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# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
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use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
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random_state = 3407,
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use_rslora = False, # We support rank stabilized LoRA
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loftq_config = None, # And LoftQ
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)
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
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def formatting_prompts_func(examples):
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instructions = examples["instruction"]
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inputs = examples["input"]
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outputs = examples["output"]
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texts = []
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for instruction, input, output in zip(instructions, inputs, outputs):
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# Must add EOS_TOKEN, otherwise your generation will go on forever!
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text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
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texts.append(text)
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return { "text" : texts, }
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pass
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from datasets import load_dataset
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dataset = load_dataset(path = "/home/alexander/GitHub/finetune", data_files="trainingsdata.jsonl", split="train")
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dataset = dataset.map(formatting_prompts_func, batched = True)
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trainer = SFTTrainer(
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model = model,
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tokenizer = tokenizer,
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train_dataset = dataset,
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dataset_text_field = "text",
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max_seq_length = max_seq_length,
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dataset_num_proc = 2,
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packing = False, # Can make training 5x faster for short sequences.
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args = TrainingArguments(
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per_device_train_batch_size = 2,
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gradient_accumulation_steps = 4,
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warmup_steps = 5,
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# num_train_epochs = 1, # Set this for 1 full training run.
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max_steps = 120,
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learning_rate = 2e-4,
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fp16 = not is_bfloat16_supported(),
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bf16 = is_bfloat16_supported(),
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logging_steps = 1,
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optim = "adamw_8bit",
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weight_decay = 0.01,
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lr_scheduler_type = "linear",
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seed = 3407,
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output_dir = "outputs",
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report_to = "none", # Use this for WandB etc
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),
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)
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t_data = trainer.train()
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model.save_pretrained("lora_model_2") # Local saving
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tokenizer.save_pretrained("lora_model_2")
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# Merge to 16bit
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if True: model.save_pretrained_merged("model_16_2", tokenizer, save_method = "merged_16bit",)
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if False: model.push_to_hub_merged("hf/model", tokenizer, save_method = "merged_16bit", token = "")
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# Merge to 4bit
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#if True: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",)
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if False: model.push_to_hub_merged("hf/model", tokenizer, save_method = "merged_4bit", token = "")
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# Just LoRA adapters
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if True:
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model.save_pretrained("model_2")
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tokenizer.save_pretrained("model_2")
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if True: model.save_pretrained_gguf("finetunedmodel_2", tokenizer,)
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