TinyLlama_finetune-full.ipynb

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   "source": [
    "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, BitsAndBytesConfig, set_seed\n",
    "from peft import LoraConfig\n",
    "from trl import SFTTrainer, setup_chat_format, DataCollatorForCompletionOnlyLM\n",
    "from datasets import load_dataset\n",
    "import torch\n",
    "\n",
    "set_seed(42)\n",
    "\n",
    "# change me!\n",
    "modelpath = \"models/TinyLlama-1.1B-intermediate-step-1431k-3T\"\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    modelpath,    \n",
    "    device_map = \"auto\",\n",
    "    torch_dtype = torch.bfloat16,\n",
    "    attn_implementation = \"flash_attention_2\",   \n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(modelpath, use_fast = False)\n",
    "\n",
    "model, tokenizer = setup_chat_format(model, tokenizer)\n",
    "if tokenizer.pad_token in [None, tokenizer.eos_token]: \n",
    "    tokenizer.pad_token = tokenizer.unk_token\n",
    "\n",
    "dataset = load_dataset(\"g-ronimo/oasst2_top4k_en\")\n",
    "\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir = \"out_OA_TL\",\n",
    "    evaluation_strategy = \"steps\",\n",
    "    label_names = [\"labels\"],\n",
    "    per_device_train_batch_size = 16,\n",
    "    gradient_accumulation_steps = 1,\n",
    "    save_steps = 250,\n",
    "    eval_steps = 250,\n",
    "    logging_steps = 1, \n",
    "    learning_rate = 1e-5,\n",
    "    num_train_epochs = 3,\n",
    "    lr_scheduler_type = \"constant\",\n",
    "    optim = 'paged_adamw_32bit',\n",
    "    bf16 = True,\n",
    "    gradient_checkpointing = True,\n",
    "    group_by_length = True,\n",
    ")\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    train_dataset = dataset[\"train\"],\n",
    "    eval_dataset = dataset['test'],\n",
    "    data_collator = DataCollatorForCompletionOnlyLM(\n",
    "        instruction_template = \"<|im_start|>user\", \n",
    "        response_template = \"<|im_start|>assistant\", \n",
    "        tokenizer = tokenizer, \n",
    "        mlm = False),\n",
    "    max_seq_length = 512,\n",
    "    dataset_kwargs = dict(add_special_tokens = False),\n",
    "    args = training_arguments,\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  }
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