Alpaca Llama 3 8b Unsloth faster.ipynb

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        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
        "<div class=\"align-center\">\n",
        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
        "</div>\n",
        "\n",
        "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
        "\n",
        "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).\n",
        "\n",
        "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**\n",
        "\n",
        "Use our [Llama-3 8b Instruct](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) notebook for conversational style finetunes."
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        "%%capture\n",
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes"
      ]
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      "source": [
        "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n",
        "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
        "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
        "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n",
        "* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)"
      ]
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          "text": [
            "Python 3.10.12\n"
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      "source": [
        "!python --version"
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          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "==((====))==  Unsloth: Fast Llama patching release 2024.7\n",
            "   \\\\   /|    GPU: NVIDIA H100 80GB HBM3. Max memory: 79.109 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.3.0+cu121. CUDA = 9.0. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.26.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
        "\n",
        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
        "fourbit_models = [\n",
        "    \"unsloth/mistral-7b-v0.3-bnb-4bit\",      # New Mistral v3 2x faster!\n",
        "    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
        "    \"unsloth/llama-3-8b-bnb-4bit\",           # Llama-3 15 trillion tokens model 2x faster!\n",
        "    \"unsloth/llama-3-8b-Instruct-bnb-4bit\",\n",
        "    \"unsloth/llama-3-70b-bnb-4bit\",\n",
        "    \"unsloth/Phi-3-mini-4k-instruct\",        # Phi-3 2x faster!\n",
        "    \"unsloth/Phi-3-medium-4k-instruct\",\n",
        "    \"unsloth/mistral-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-bnb-4bit\",             # Gemma 2.2x faster!\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
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      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ]
    },
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          "text": [
            "Unsloth 2024.7 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
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      "metadata": {
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      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n",
        "\n",
        "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
        "\n",
        "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n",
        "\n",
        "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n",
        "\n",
        "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
      ]
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      "source": [
        "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.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Input:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"instruction\"]\n",
        "    inputs       = examples[\"input\"]\n",
        "    outputs      = examples[\"output\"]\n",
        "    texts = []\n",
        "    for instruction, input, output in zip(instructions, inputs, outputs):\n",
        "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
        "        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
        "        texts.append(text)\n",
        "    return { \"text\" : texts, }\n",
        "pass\n",
        "\n",
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n",
        "dataset = dataset.map(formatting_prompts_func, batched = True,)"
      ]
    },
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      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
      ]
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            "/home/tiger/.local/lib/python3.9/site-packages/bytedmetrics/__init__.py:10: UserWarning: bytedmetrics is renamed to bytedance.metrics, please using `bytedance.metrics` instead of `bytedmetrics`\n",
            "  warnings.warn(\"bytedmetrics is renamed to bytedance.metrics, please using `bytedance.metrics` instead of `bytedmetrics`\")\n",
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 60,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error in callback <bound method _WandbInit._resume_backend of <wandb.sdk.wandb_init._WandbInit object at 0x7f0622aca460>> (for pre_run_cell), with arguments args (<ExecutionInfo object at 7f05890b5190, raw_cell=\"print(trainer.args)\" store_history=True silent=False shell_futures=True cell_id=vscode-notebook-cell://arnold-proxy-i18n.tiktok-row.org/opt/tiger/Cherry_LLM/Alpaca%20Llama%203%208b%20Unsloth%20faster.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D>,),kwargs {}:\n"
          ]
        },
        {
          "ename": "TypeError",
          "evalue": "_resume_backend() takes 1 positional argument but 2 were given",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;31mTypeError\u001b[0m: _resume_backend() takes 1 positional argument but 2 were given"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TrainingArguments(\n",
            "_n_gpu=1,\n",
            "accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},\n",
            "adafactor=False,\n",
            "adam_beta1=0.9,\n",
            "adam_beta2=0.999,\n",
            "adam_epsilon=1e-08,\n",
            "auto_find_batch_size=False,\n",
            "batch_eval_metrics=False,\n",
            "bf16=True,\n",
            "bf16_full_eval=False,\n",
            "data_seed=None,\n",
            "dataloader_drop_last=False,\n",
            "dataloader_num_workers=0,\n",
            "dataloader_persistent_workers=False,\n",
            "dataloader_pin_memory=True,\n",
            "dataloader_prefetch_factor=None,\n",
            "ddp_backend=None,\n",
            "ddp_broadcast_buffers=None,\n",
            "ddp_bucket_cap_mb=None,\n",
            "ddp_find_unused_parameters=None,\n",
            "ddp_timeout=1800,\n",
            "debug=[],\n",
            "deepspeed=None,\n",
            "disable_tqdm=False,\n",
            "dispatch_batches=None,\n",
            "do_eval=False,\n",
            "do_predict=False,\n",
            "do_train=False,\n",
            "eval_accumulation_steps=None,\n",
            "eval_delay=0,\n",
            "eval_do_concat_batches=True,\n",
            "eval_on_start=False,\n",
            "eval_steps=None,\n",
            "eval_strategy=no,\n",
            "eval_use_gather_object=False,\n",
            "evaluation_strategy=None,\n",
            "fp16=False,\n",
            "fp16_backend=auto,\n",
            "fp16_full_eval=False,\n",
            "fp16_opt_level=O1,\n",
            "fsdp=[],\n",
            "fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},\n",
            "fsdp_min_num_params=0,\n",
            "fsdp_transformer_layer_cls_to_wrap=None,\n",
            "full_determinism=False,\n",
            "gradient_accumulation_steps=4,\n",
            "gradient_checkpointing=False,\n",
            "gradient_checkpointing_kwargs=None,\n",
            "greater_is_better=None,\n",
            "group_by_length=False,\n",
            "half_precision_backend=auto,\n",
            "hub_always_push=False,\n",
            "hub_model_id=None,\n",
            "hub_private_repo=False,\n",
            "hub_strategy=every_save,\n",
            "hub_token=<HUB_TOKEN>,\n",
            "ignore_data_skip=False,\n",
            "include_inputs_for_metrics=False,\n",
            "include_num_input_tokens_seen=False,\n",
            "include_tokens_per_second=False,\n",
            "jit_mode_eval=False,\n",
            "label_names=None,\n",
            "label_smoothing_factor=0.0,\n",
            "learning_rate=0.0002,\n",
            "length_column_name=length,\n",
            "load_best_model_at_end=False,\n",
            "local_rank=0,\n",
            "log_level=passive,\n",
            "log_level_replica=warning,\n",
            "log_on_each_node=True,\n",
            "logging_dir=outputs/runs/Jul08_17-27-48_n124-161-066,\n",
            "logging_first_step=False,\n",
            "logging_nan_inf_filter=True,\n",
            "logging_steps=1,\n",
            "logging_strategy=steps,\n",
            "lr_scheduler_kwargs={},\n",
            "lr_scheduler_type=linear,\n",
            "max_grad_norm=1.0,\n",
            "max_steps=60,\n",
            "metric_for_best_model=None,\n",
            "mp_parameters=,\n",
            "neftune_noise_alpha=None,\n",
            "no_cuda=False,\n",
            "num_train_epochs=3.0,\n",
            "optim=adamw_8bit,\n",
            "optim_args=None,\n",
            "optim_target_modules=None,\n",
            "output_dir=outputs,\n",
            "overwrite_output_dir=False,\n",
            "past_index=-1,\n",
            "per_device_eval_batch_size=8,\n",
            "per_device_train_batch_size=2,\n",
            "prediction_loss_only=False,\n",
            "push_to_hub=False,\n",
            "push_to_hub_model_id=None,\n",
            "push_to_hub_organization=None,\n",
            "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
            "ray_scope=last,\n",
            "remove_unused_columns=True,\n",
            "report_to=['wandb'],\n",
            "restore_callback_states_from_checkpoint=False,\n",
            "resume_from_checkpoint=None,\n",
            "run_name=outputs,\n",
            "save_on_each_node=False,\n",
            "save_only_model=False,\n",
            "save_safetensors=True,\n",
            "save_steps=500,\n",
            "save_strategy=steps,\n",
            "save_total_limit=None,\n",
            "seed=3407,\n",
            "skip_memory_metrics=True,\n",
            "split_batches=None,\n",
            "tf32=None,\n",
            "torch_compile=False,\n",
            "torch_compile_backend=None,\n",
            "torch_compile_mode=None,\n",
            "torch_empty_cache_steps=None,\n",
            "torchdynamo=None,\n",
            "tpu_metrics_debug=False,\n",
            "tpu_num_cores=None,\n",
            "use_cpu=False,\n",
            "use_ipex=False,\n",
            "use_legacy_prediction_loop=False,\n",
            "use_mps_device=False,\n",
            "warmup_ratio=0.0,\n",
            "warmup_steps=5,\n",
            "weight_decay=0.01,\n",
            ")\n",
            "Error in callback <bound method _WandbInit._pause_backend of <wandb.sdk.wandb_init._WandbInit object at 0x7f0622aca460>> (for post_run_cell), with arguments args (<ExecutionResult object at 7f05890b5340, execution_count=7 error_before_exec=None error_in_exec=None info=<ExecutionInfo object at 7f05890b5190, raw_cell=\"print(trainer.args)\" store_history=True silent=False shell_futures=True cell_id=vscode-notebook-cell://arnold-proxy-i18n.tiktok-row.org/opt/tiger/Cherry_LLM/Alpaca%20Llama%203%208b%20Unsloth%20faster.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D> result=None>,),kwargs {}:\n"
          ]
        },
        {
          "ename": "TypeError",
          "evalue": "_pause_backend() takes 1 positional argument but 2 were given",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;31mTypeError\u001b[0m: _pause_backend() takes 1 positional argument but 2 were given"
          ]
        }
      ],
      "source": [
        "print(trainer.args)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ejIt2xSNKKp",
        "outputId": "1d829058-c0ad-4b63-f3d6-82a29f745915"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU = NVIDIA H100 80GB HBM3. Max memory = 79.109 GB.\n",
            "5.594 GB of memory reserved.\n"
          ]
        }
      ],
      "source": [
        "#@title Show current memory stats\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "fabfb305-b4b4-458c-b94b-7c7f394b0e05"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 51,760 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 60\n",
            " \"-____-\"     Number of trainable parameters = 41,943,040\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
            "/home/tiger/.local/lib/python3.9/site-packages/bytedmetrics/__init__.py:10: UserWarning: bytedmetrics is renamed to bytedance.metrics, please using `bytedance.metrics` instead of `bytedmetrics`\n",
            "  warnings.warn(\"bytedmetrics is renamed to bytedance.metrics, please using `bytedance.metrics` instead of `bytedmetrics`\")\n",
            "wandb: ⭐️ View project at https://ml.byteintl.net/experiment/tracking/detail?Id=project_20230126_e9daa974\n",
            "wandb: 🚀 View run at https://ml.byteintl.net/experiment/tracking/detail?Id=project_20230126_e9daa974&selectedTrial=run_20240708_3108ecd2\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fee98f4daea14623a8e2e2f9bdf91f42",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016668833520573873, max=1.0…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.13.72"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/opt/tiger/Cherry_LLM/wandb/run-20240708_172803-run_20240708_3108ecd2</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href=\"\" target=\"_blank\">outputs</a></strong> to <a href=\"\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='16' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [16/60 00:17 < 00:53, 0.82 it/s, Epoch 0.00/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.814200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.287000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.704400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.023100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.711700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.663200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.182900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.271200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.130900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.189400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.973600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.992200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.933100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.055400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m trainer_stats \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m<string>:126\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n",
            "File \u001b[0;32m<string>:367\u001b[0m, in \u001b[0;36m_fast_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/trainer.py:3312\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   3309\u001b[0m     \u001b[39mreturn\u001b[39;00m loss_mb\u001b[39m.\u001b[39mreduce_mean()\u001b[39m.\u001b[39mdetach()\u001b[39m.\u001b[39mto(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs\u001b[39m.\u001b[39mdevice)\n\u001b[1;32m   3311\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcompute_loss_context_manager():\n\u001b[0;32m-> 3312\u001b[0m     loss \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcompute_loss(model, inputs)\n\u001b[1;32m   3314\u001b[0m \u001b[39mdel\u001b[39;00m inputs\n\u001b[1;32m   3315\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m   3316\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs\u001b[39m.\u001b[39mtorch_empty_cache_steps \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   3317\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mglobal_step \u001b[39m%\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs\u001b[39m.\u001b[39mtorch_empty_cache_steps \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m   3318\u001b[0m ):\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/trainer.py:3357\u001b[0m, in \u001b[0;36mTrainer.compute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   3355\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   3356\u001b[0m     labels \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 3357\u001b[0m outputs \u001b[39m=\u001b[39m model(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49minputs)\n\u001b[1;32m   3358\u001b[0m \u001b[39m# Save past state if it exists\u001b[39;00m\n\u001b[1;32m   3359\u001b[0m \u001b[39m# TODO: this needs to be fixed and made cleaner later.\u001b[39;00m\n\u001b[1;32m   3360\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs\u001b[39m.\u001b[39mpast_index \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39m0\u001b[39m:\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1543\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/accelerate/utils/operations.py:819\u001b[0m, in \u001b[0;36mconvert_outputs_to_fp32.<locals>.forward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m--> 819\u001b[0m     \u001b[39mreturn\u001b[39;00m model_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/accelerate/utils/operations.py:807\u001b[0m, in \u001b[0;36mConvertOutputsToFp32.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    806\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m--> 807\u001b[0m     \u001b[39mreturn\u001b[39;00m convert_to_fp32(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/amp/autocast_mode.py:16\u001b[0m, in \u001b[0;36mautocast_decorator.<locals>.decorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m     14\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_autocast\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     15\u001b[0m     \u001b[39mwith\u001b[39;00m autocast_instance:\n\u001b[0;32m---> 16\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/models/llama.py:930\u001b[0m, in \u001b[0;36mPeftModelForCausalLM_fast_forward\u001b[0;34m(self, input_ids, causal_mask, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m    917\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mPeftModelForCausalLM_fast_forward\u001b[39m(\n\u001b[1;32m    918\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    919\u001b[0m     input_ids\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    928\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m    929\u001b[0m ):\n\u001b[0;32m--> 930\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbase_model(\n\u001b[1;32m    931\u001b[0m         input_ids\u001b[39m=\u001b[39;49minput_ids,\n\u001b[1;32m    932\u001b[0m         causal_mask\u001b[39m=\u001b[39;49mcausal_mask,\n\u001b[1;32m    933\u001b[0m         attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m    934\u001b[0m         inputs_embeds\u001b[39m=\u001b[39;49minputs_embeds,\n\u001b[1;32m    935\u001b[0m         labels\u001b[39m=\u001b[39;49mlabels,\n\u001b[1;32m    936\u001b[0m         output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m    937\u001b[0m         output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[1;32m    938\u001b[0m         return_dict\u001b[39m=\u001b[39;49mreturn_dict,\n\u001b[1;32m    939\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    940\u001b[0m     )\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1543\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/peft/tuners/tuners_utils.py:179\u001b[0m, in \u001b[0;36mBaseTuner.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs: Any, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any):\n\u001b[0;32m--> 179\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mforward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/accelerate/hooks.py:169\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m         output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m_old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    168\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 169\u001b[0m     output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39;49m_old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    170\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/models/llama.py:855\u001b[0m, in \u001b[0;36mCausalLM_fast_forward.<locals>._CausalLM_fast_forward\u001b[0;34m(self, input_ids, causal_mask, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, *args, **kwargs)\u001b[0m\n\u001b[1;32m    852\u001b[0m     \u001b[39m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[39;00m\n\u001b[1;32m    853\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39m_has_no_labels \u001b[39m=\u001b[39m labels \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 855\u001b[0m     outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel(\n\u001b[1;32m    856\u001b[0m         input_ids\u001b[39m=\u001b[39;49minput_ids,\n\u001b[1;32m    857\u001b[0m         causal_mask\u001b[39m=\u001b[39;49mcausal_mask,\n\u001b[1;32m    858\u001b[0m         attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m    859\u001b[0m         position_ids\u001b[39m=\u001b[39;49mposition_ids,\n\u001b[1;32m    860\u001b[0m         past_key_values\u001b[39m=\u001b[39;49mpast_key_values,\n\u001b[1;32m    861\u001b[0m         inputs_embeds\u001b[39m=\u001b[39;49minputs_embeds,\n\u001b[1;32m    862\u001b[0m         use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m    863\u001b[0m         output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m    864\u001b[0m         output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[1;32m    865\u001b[0m         return_dict\u001b[39m=\u001b[39;49mreturn_dict,\n\u001b[1;32m    866\u001b[0m     )\n\u001b[1;32m    867\u001b[0m \u001b[39mpass\u001b[39;00m\n\u001b[1;32m    869\u001b[0m hidden_states \u001b[39m=\u001b[39m outputs[\u001b[39m0\u001b[39m]\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1543\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/accelerate/hooks.py:169\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m         output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m_old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    168\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 169\u001b[0m     output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39;49m_old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    170\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/models/llama.py:692\u001b[0m, in \u001b[0;36mLlamaModel_fast_forward\u001b[0;34m(self, input_ids, causal_mask, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, *args, **kwargs)\u001b[0m\n\u001b[1;32m    689\u001b[0m \u001b[39mif\u001b[39;00m IS_GEMMA2: mask \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mSWA_mask \u001b[39mif\u001b[39;00m (idx \u001b[39m%\u001b[39m \u001b[39m2\u001b[39m \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m) \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mGA_mask\n\u001b[1;32m    691\u001b[0m \u001b[39mif\u001b[39;00m offloaded_gradient_checkpointing:\n\u001b[0;32m--> 692\u001b[0m     hidden_states \u001b[39m=\u001b[39m Unsloth_Offloaded_Gradient_Checkpointer\u001b[39m.\u001b[39;49mapply(\n\u001b[1;32m    693\u001b[0m         decoder_layer,\n\u001b[1;32m    694\u001b[0m         hidden_states,\n\u001b[1;32m    695\u001b[0m         mask,\n\u001b[1;32m    696\u001b[0m         attention_mask,\n\u001b[1;32m    697\u001b[0m         position_ids,\n\u001b[1;32m    698\u001b[0m         past_key_values,\n\u001b[1;32m    699\u001b[0m         output_attentions,\n\u001b[1;32m    700\u001b[0m         use_cache,\n\u001b[1;32m    701\u001b[0m     )[\u001b[39m0\u001b[39m]\n\u001b[1;32m    703\u001b[0m \u001b[39melif\u001b[39;00m gradient_checkpointing:\n\u001b[1;32m    704\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39mcreate_custom_forward\u001b[39m(module):\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/autograd/function.py:598\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m torch\u001b[39m.\u001b[39m_C\u001b[39m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m    596\u001b[0m     \u001b[39m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m    597\u001b[0m     args \u001b[39m=\u001b[39m _functorch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 598\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mapply(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m    600\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_setup_ctx_defined:\n\u001b[1;32m    601\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m    602\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    603\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    604\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mstaticmethod. For more details, please see \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    605\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mhttps://pytorch.org/docs/master/notes/extending.func.html\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    606\u001b[0m     )\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/cuda/amp/autocast_mode.py:115\u001b[0m, in \u001b[0;36mcustom_fwd.<locals>.decorate_fwd\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[39mif\u001b[39;00m cast_inputs \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    114\u001b[0m     args[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39m_fwd_used_autocast \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mis_autocast_enabled()\n\u001b[0;32m--> 115\u001b[0m     \u001b[39mreturn\u001b[39;00m fwd(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    116\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    117\u001b[0m     autocast_context \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mis_autocast_enabled()\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/models/_utils.py:432\u001b[0m, in \u001b[0;36mUnsloth_Offloaded_Gradient_Checkpointer.forward\u001b[0;34m(ctx, forward_function, hidden_states, *args)\u001b[0m\n\u001b[1;32m    430\u001b[0m saved_hidden_states \u001b[39m=\u001b[39m hidden_states\u001b[39m.\u001b[39mto(\u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m, non_blocking \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m    431\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m--> 432\u001b[0m     output \u001b[39m=\u001b[39m forward_function(hidden_states, \u001b[39m*\u001b[39;49margs)\n\u001b[1;32m    433\u001b[0m ctx\u001b[39m.\u001b[39msave_for_backward(saved_hidden_states)\n\u001b[1;32m    434\u001b[0m ctx\u001b[39m.\u001b[39mforward_function \u001b[39m=\u001b[39m forward_function\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1543\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/accelerate/hooks.py:169\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m         output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m_old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    168\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 169\u001b[0m     output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39;49m_old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    170\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/models/llama.py:453\u001b[0m, in \u001b[0;36mLlamaDecoderLayer_fast_forward\u001b[0;34m(self, hidden_states, causal_mask, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask, *args, **kwargs)\u001b[0m\n\u001b[1;32m    451\u001b[0m residual \u001b[39m=\u001b[39m hidden_states\n\u001b[1;32m    452\u001b[0m hidden_states \u001b[39m=\u001b[39m fast_rms_layernorm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_layernorm, hidden_states)\n\u001b[0;32m--> 453\u001b[0m hidden_states, self_attn_weights, present_key_value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mself_attn(\n\u001b[1;32m    454\u001b[0m     hidden_states\u001b[39m=\u001b[39;49mhidden_states,\n\u001b[1;32m    455\u001b[0m     causal_mask\u001b[39m=\u001b[39;49mcausal_mask,\n\u001b[1;32m    456\u001b[0m     attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m    457\u001b[0m     position_ids\u001b[39m=\u001b[39;49mposition_ids,\n\u001b[1;32m    458\u001b[0m     past_key_value\u001b[39m=\u001b[39;49mpast_key_value,\n\u001b[1;32m    459\u001b[0m     output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m    460\u001b[0m     use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m    461\u001b[0m     padding_mask\u001b[39m=\u001b[39;49mpadding_mask,\n\u001b[1;32m    462\u001b[0m )\n\u001b[1;32m    463\u001b[0m hidden_states \u001b[39m=\u001b[39m residual \u001b[39m+\u001b[39m hidden_states\n\u001b[1;32m    465\u001b[0m \u001b[39m# Fully Connected\u001b[39;00m\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1543\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/accelerate/hooks.py:169\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m         output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m_old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    168\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 169\u001b[0m     output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39;49m_old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    170\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/models/llama.py:328\u001b[0m, in \u001b[0;36mLlamaAttention_fast_forward\u001b[0;34m(self, hidden_states, causal_mask, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask, *args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m head_dim   \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhead_dim\n\u001b[1;32m    326\u001b[0m \u001b[39massert\u001b[39;00m(n_kv_heads \u001b[39m*\u001b[39m n_groups \u001b[39m==\u001b[39m n_heads)\n\u001b[0;32m--> 328\u001b[0m Q, K, V \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply_qkv(\u001b[39mself\u001b[39;49m, hidden_states)\n\u001b[1;32m    329\u001b[0m Q \u001b[39m=\u001b[39m Q\u001b[39m.\u001b[39mview(bsz, q_len, n_heads,    head_dim)\u001b[39m.\u001b[39mtranspose(\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m)\n\u001b[1;32m    330\u001b[0m K \u001b[39m=\u001b[39m K\u001b[39m.\u001b[39mview(bsz, q_len, n_kv_heads, head_dim)\u001b[39m.\u001b[39mtranspose(\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/kernels/fast_lora.py:318\u001b[0m, in \u001b[0;36mapply_lora_qkv\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    316\u001b[0m KW, KW_quant, KA, KB, KS \u001b[39m=\u001b[39m get_lora_parameters(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mk_proj)\n\u001b[1;32m    317\u001b[0m VW, VW_quant, VA, VB, VS \u001b[39m=\u001b[39m get_lora_parameters(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mv_proj)\n\u001b[0;32m--> 318\u001b[0m Q, K, V \u001b[39m=\u001b[39m LoRA_QKV\u001b[39m.\u001b[39;49mapply(X,\n\u001b[1;32m    319\u001b[0m     QW, QW_quant, QA, QB, QS,\n\u001b[1;32m    320\u001b[0m     KW, KW_quant, KA, KB, KS,\n\u001b[1;32m    321\u001b[0m     VW, VW_quant, VA, VB, VS,\n\u001b[1;32m    322\u001b[0m )\n\u001b[1;32m    323\u001b[0m \u001b[39mreturn\u001b[39;00m Q, K, V\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/autograd/function.py:598\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m torch\u001b[39m.\u001b[39m_C\u001b[39m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m    596\u001b[0m     \u001b[39m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m    597\u001b[0m     args \u001b[39m=\u001b[39m _functorch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 598\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mapply(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m    600\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_setup_ctx_defined:\n\u001b[1;32m    601\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m    602\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    603\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    604\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mstaticmethod. For more details, please see \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    605\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mhttps://pytorch.org/docs/master/notes/extending.func.html\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    606\u001b[0m     )\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/torch/cuda/amp/autocast_mode.py:115\u001b[0m, in \u001b[0;36mcustom_fwd.<locals>.decorate_fwd\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[39mif\u001b[39;00m cast_inputs \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    114\u001b[0m     args[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39m_fwd_used_autocast \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mis_autocast_enabled()\n\u001b[0;32m--> 115\u001b[0m     \u001b[39mreturn\u001b[39;00m fwd(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    116\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    117\u001b[0m     autocast_context \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mis_autocast_enabled()\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/kernels/fast_lora.py:233\u001b[0m, in \u001b[0;36mLoRA_QKV.forward\u001b[0;34m(ctx, X, QW, QW_quant, QA, QB, QS, KW, KW_quant, KA, KB, KS, VW, VW_quant, VA, VB, VS)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[1;32m    226\u001b[0m \u001b[39m@torch\u001b[39m\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mamp\u001b[39m.\u001b[39mcustom_fwd\n\u001b[1;32m    227\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(ctx, X : torch\u001b[39m.\u001b[39mTensor,\n\u001b[1;32m    228\u001b[0m             QW, QW_quant, QA, QB, QS,\n\u001b[1;32m    229\u001b[0m             KW, KW_quant, KA, KB, KS,\n\u001b[1;32m    230\u001b[0m             VW, VW_quant, VA, VB, VS,):\n\u001b[1;32m    231\u001b[0m     dtype \u001b[39m=\u001b[39m X\u001b[39m.\u001b[39mdtype\n\u001b[0;32m--> 233\u001b[0m     Q \u001b[39m=\u001b[39m matmul_lora(X, QW, QW_quant, QA, QB, QS)\n\u001b[1;32m    234\u001b[0m     K \u001b[39m=\u001b[39m matmul_lora(X, KW, KW_quant, KA, KB, KS)\n\u001b[1;32m    235\u001b[0m     V \u001b[39m=\u001b[39m matmul_lora(X, VW, VW_quant, VA, VB, VS)\n",
            "File \u001b[0;32m~/.local/lib/python3.9/site-packages/unsloth/kernels/utils.py:246\u001b[0m, in \u001b[0;36mmatmul_lora\u001b[0;34m(X, W, W_quant, A, B, s, out)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmatmul_lora\u001b[39m(X, W, W_quant, A, B, s, out \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    245\u001b[0m     dtype \u001b[39m=\u001b[39m X\u001b[39m.\u001b[39mdtype\n\u001b[0;32m--> 246\u001b[0m     W \u001b[39m=\u001b[39m fast_dequantize(W\u001b[39m.\u001b[39;49mt(), W_quant)\n\u001b[1;32m    248\u001b[0m     \u001b[39mif\u001b[39;00m X\u001b[39m.\u001b[39mdim() \u001b[39m==\u001b[39m \u001b[39m3\u001b[39m:\n\u001b[1;32m    249\u001b[0m         batch, seq_len, d \u001b[39m=\u001b[39m X\u001b[39m.\u001b[39mshape\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error in callback <bound method _WandbInit._pause_backend of <wandb.sdk.wandb_init._WandbInit object at 0x7f0622aca460>> (for post_run_cell), with arguments args (<ExecutionResult object at 7f05c04925b0, execution_count=6 error_before_exec=None error_in_exec= info=<ExecutionInfo object at 7f05c0773e50, raw_cell=\"trainer_stats = trainer.train()\" store_history=True silent=False shell_futures=True cell_id=vscode-notebook-cell://arnold-proxy-i18n.tiktok-row.org/opt/tiger/Cherry_LLM/Alpaca%20Llama%203%208b%20Unsloth%20faster.ipynb#X15sdnNjb2RlLXJlbW90ZQ%3D%3D> result=None>,),kwargs {}:\n"
          ]
        },
        {
          "ename": "TypeError",
          "evalue": "_pause_backend() takes 1 positional argument but 2 were given",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;31mTypeError\u001b[0m: _pause_backend() takes 1 positional argument but 2 were given"
          ]
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "pCqnaKmlO1U9"
      },
      "outputs": [],
      "source": [
        "#@title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory         /max_memory*100, 3)\n",
        "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekOmTR1hSNcr"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model! You can change the instruction and input - leave the output blank!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kR3gIAX-SM2q"
      },
      "outputs": [],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"Continue the fibonnaci sequence.\", # instruction\n",
        "        \"1, 1, 2, 3, 5, 8\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrSvZObor0lY"
      },
      "source": [
        " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e2pEuRb1r2Vg"
      },
      "outputs": [],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"Continue the fibonnaci sequence.\", # instruction\n",
        "        \"1, 1, 2, 3, 5, 8\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer)\n",
        "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upcOlWe7A1vc"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MKX_XKs_BNZR"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        max_seq_length = max_seq_length,\n",
        "        dtype = dtype,\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "# alpaca_prompt = You MUST copy from above!\n",
        "\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"What is a famous tall tower in Paris?\", # instruction\n",
        "        \"\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQMjaNrjsU5_"
      },
      "source": [
        "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yFfaXG0WsQuE"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    # I highly do NOT suggest - use Unsloth if possible\n",
        "    from peft import AutoPeftModelForCausalLM\n",
        "    from transformers import AutoTokenizer\n",
        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f422JgM9sdVT"
      },
      "source": [
        "### Saving to float16 for VLLM\n",
        "\n",
        "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iHjt_SMYsd3P"
      },
      "outputs": [],
      "source": [
        "# Merge to 16bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
        "\n",
        "# Merge to 4bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
        "\n",
        "# Just LoRA adapters\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCv4vXHd61i7"
      },
      "source": [
        "### GGUF / llama.cpp Conversion\n",
        "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
        "\n",
        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FqfebeAdT073"
      },
      "outputs": [],
      "source": [
        "# Save to 8bit Q8_0\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
        "\n",
        "# Save to q4_k_m GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDp0zNpwe6U_"
      },
      "source": [
        "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zt9CHJqO6p30"
      },
      "source": [
        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
        "\n",
        "Some other links:\n",
        "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
        "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
        "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
        "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
        "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
        "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
        "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
        "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
        "9. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\n",
        "\n",
        "<div class=\"align-center\">\n",
        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
        "</div>"
      ]
    }
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