79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
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from unsloth import FastLanguageModel, FastModel
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import torch
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from trl import SFTTrainer, SFTConfig
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from datasets import load_dataset
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max_seq_length = 2048 # Supports RoPE Scaling internally, so choose any!
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# Get LAION dataset
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url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
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dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
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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 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", # 4bit for 405b!
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"unsloth/Mistral-Small-Instruct-2409", # Mistral 22b 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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"unsloth/Llama-3.2-1B-bnb-4bit", # NEW! Llama 3.2 models
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"unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
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"unsloth/Llama-3.2-3B-bnb-4bit",
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"unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
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"unsloth/Llama-3.3-70B-Instruct-bnb-4bit" # NEW! Llama 3.3 70B!
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] # More models at https://huggingface.co/unsloth
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model, tokenizer = FastModel.from_pretrained(
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model_name = "unsloth/Qwen3-4B",
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max_seq_length = 2048, # Choose any for long context!
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load_in_4bit = False, # 4 bit quantization to reduce memory
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load_in_8bit = True, # [NEW!] A bit more accurate, uses 2x memory
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full_finetuning = False, # [NEW!] We have full finetuning now!
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# token = "hf_...", # use one if using gated models
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)
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# Do model patching and add fast LoRA weights
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model = FastLanguageModel.get_peft_model(
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model,
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r = 16,
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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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max_seq_length = max_seq_length,
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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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trainer = SFTTrainer(
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model = model,
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train_dataset = dataset,
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tokenizer = tokenizer,
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args = SFTConfig(
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max_seq_length = max_seq_length,
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per_device_train_batch_size = 2,
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gradient_accumulation_steps = 4,
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warmup_steps = 10,
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max_steps = 60,
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logging_steps = 1,
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output_dir = "outputs",
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optim = "adamw_8bit",
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seed = 3407,
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),
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)
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trainer.train()
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# Go to https://github.com/unslothai/unsloth/wiki for advanced tips like
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# (1) Saving to GGUF / merging to 16bit for vLLM
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# (2) Continued training from a saved LoRA adapter
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# (3) Adding an evaluation loop / OOMs
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# (4) Customized chat templates
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