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Comet provides tooling to track, explain, manage, and monitor your models in a single place! It works with Jupyter notebooks and scripts and-- most importantly--it's 100% free to get started!\n", + "\n", + "[unsloth](https://github.com/unslothai/unsloth) dramatically improves the speed and efficiency of LLM fine-tuning for models including Llama, Phi-3, Gemma, Mistral, and more. For a full listed of 100+ supported unsloth models, [see here](https://huggingface.co/unsloth).\n", + "\n", + "Instrument your torchtune training runs with Comet to start managing experiments with efficiency, reproducibility, and collaboration in mind.\n", + "\n", + "Find more information about [our integration with torchtune here](https://www.comet.com/docs/v2/integrations/third-party-tools/unsloth?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth) or [learn about our other integrations here](https://www.comet.com/docs/v2/integrations?utm_medium=colab&utm_source=comet-examples&utm_campaign=unsloth)." + ], + "metadata": { + "id": "KDjO5WbeDtp0" + } + }, + { + "cell_type": "markdown", + "source": [ + "## ⚙ Install and import dependencies" + ], + "metadata": { + "id": "f0fhpJZgsYII" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "5zTSjY9r4cGc" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install comet_ml \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", + "\n", + "# check torch version for Xformers (2.3 -> 0.0.27)\n", + "from torch import __version__; from packaging.version import Version as V\n", + "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", + "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" + ] + }, + { + "cell_type": "code", + "source": [ + "import comet_ml\n", + "import os\n", + "from google.colab import userdata\n", + "\n", + "# Log in to Comet\n", + "COMET_API_KEY = userdata.get('COMET_API_KEY')\n", + "comet_ml.login(api_key = COMET_API_KEY)\n", + "# Create experiment object\n", + "exp = comet_ml.Experiment(project_name=\"comet-example-unsloth\", workspace=\"examples\")\n", + "\n", + "# Set Hugging Face token (for dataset retrieval)\n", + "HF_TOKEN = userdata.get('HF_TOKEN')\n", + "os.environ[\"HF_TOKEN\"] = HF_TOKEN" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p8GTqJOW88fI", + "outputId": "1c7f76ee-a9ff-418d-8067-4cb357f938e5" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Valid Comet API Key saved in /root/.comet.config (set COMET_CONFIG to change where it is saved).\n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## ⚙ Download model" + ], + "metadata": { + "id": "5bfxeJqKwpKj" + } + }, + { + "cell_type": "code", + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "\n", + "max_seq_length = 2048\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bZei5Dvw7_kM", + "outputId": "62f7a016-6c2b-4f76-ebf2-f2051b907932" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Find the full list of [100+ supported unsloth models here](https://huggingface.co/unsloth). For a full list of supported 4-bit models see [here](https://huggingface.co/collections/unsloth/load-4bit-models-4x-faster-659042e3a41c3cbad582e734)" + ], + "metadata": { + "id": "NG--Sbr1n5Cv" + } + }, + { + "cell_type": "code", + "source": [ + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"unsloth/Meta-Llama-3.1-8B\",\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " token = HF_TOKEN\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284, + "referenced_widgets": [ + "77caf8ce2875463f86cb7e5b0e02d10f", + "986a13810ebf4e11ac2d636013efc948", + "0902e41471fb40f7a7fb807397d1e780", + "ec2615ff447e4a72a359143122a58887", + "bb0052241a574fdbb6ca8c942d99e1eb", + "1aff4958133640838a260a881e84bd66", + "b920a8e467d64b629189d1c76541cd17", + "0fe442a6a70948a4a9b703352148d780", + "5bf1ce41bf6c4b389bac20669bd64e3b", + "7d06574680cb41b9bfac0c3e47916d6e", + "b6cd239b54c345feb11143f2364fcfaf", + "e38214ae388e4fdca976e2adeb30d20c", + "85d9ede0e7064521bfe7a92d54df211f", + "740857a56a134b2dba327dc8bdbc974a", + "725534abda3c462188fa27a978bfb59d", + "c61150dc57cb4279b38064e1bd82b3bc", + "6472e21298f44e6e9c5a3eefcedc59fc", + "adb028ffb1c045f9ade7be249a33b375", + "a1a32114bd2d4cb8882afebf56d64d41", + "8c6ee78067c0490292ee7845dd886a1b", + "6d28c2bb843a48908827b362ad094183", + "41d5a34977dd4488ba5b88c308301fe6", + "d0ce911bc4c845e88168f32781cf7923", + "718199222cfc412e9e1ee351b45c9db6", + "dcde9dfd56de417fb2057b3ccc9725f1", + "c749164ce965417db5411d00f9401623", + "61a290abfcb14c4f8b6cd9e453600bc5", + "7cce70a2af1a4fc2a04658264c37b0c7", + "096fb8d33b804a02b12bc085fa3c372f", + "c6345c375f474aaf88b7b73e662b20f2", + "b68053e4411747b0b12ab63d309aa765", + "76664f2e75744dbf875de8fd4fdbe2d4", + "05891b9099734df397dc8bb7396ef3e2", + "01f7a6c4eec04fc9b063add3719c0777", + "e4f2492f92a74794aec11bf7e2e8d554", + "cba30ecbcad14542b3acc82e4396275c", + "249258f9a1764d40a440a5846227059b", + "53c336629bca4e6cacfe69ffa316890a", + "b7c0946c9f1b40d5b469e28149808565", + "1bc726cc52084737a0269d01f9df6a16", + "51b3596a668742acab22e5a6659d2c55", + "30d75e70fb6e4c4db53137804d55295d", + "2c6dd56cf2f740cca814d5c6fa504d93", + "55e22c9351de46c4a192008dde020393", + "c333e3f23b8f4df6a7fd9f7a000a54ab", + "903f318a80d849098b11451a8c855d50", + "6b7e50badd5b4c7b979d20d7ffd1b132", + "d83d854a33ec4adabc9bef9c2b699a0b", + "1be75bb752214bdc8a3ef6e6976f3312", + "d03ff62190934187be5be033a6928545", + "1ec6fa8d08e642059f1d673f6260169f", + "23351e3d7e8741a1adad4c1292dab841", + "33c4f5595d924cd3a6d8c1575815a5c8", + "6c4cf50fd9ee484396e8272591d21ee9", + "124e70720a374644a6d360926059911f" + ] + }, + "id": "L09m09Xs8Y2e", + "outputId": "4c67ce27-09cd-44dd-e987-7168a29ee335" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==((====))== Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.2.\n", + " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.27.post2. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/5.70G [00:00" + ], + "text/html": [ + "\n", + "
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360.860900
370.882400
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391.090100
401.174900
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420.981300
430.954800
440.911400
450.918000
460.973800
470.872000
481.198100
490.909500
501.032100
511.017500
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530.976500
541.154300
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570.885200
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" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "exp.end()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8II72I92Bm9s", + "outputId": "6518c199-dff2-4c4a-f821-aefdf595fa2c" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Data:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m display_summary_level : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m name : apparent_pagoda_1033\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m url : https://www.comet.com/examples/comet-example-unsloth/cb280e1a2ac942cbab2343349d80282f\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Metrics [count] (min, max):\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m epoch [61] : (0.0001545595054095827, 0.00927357032457496)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m grad_norm [60] : (0.2999851107597351, 2.063112258911133)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m learning_rate [60] : (0.0, 0.0002)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m loss [60] : (0.7567, 2.3058)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m total_flos : 5726714157219840.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/epoch [61] : (0.0001545595054095827, 0.00927357032457496)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/grad_norm [60] : (0.2999851107597351, 2.063112258911133)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/learning_rate [60] : (0.0, 0.0002)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/loss [60] : (0.7567, 2.3058)\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/total_flos : 5726714157219840.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_loss : 1.0665242771307628\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_runtime : 109.8838\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_samples_per_second : 4.368\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train/train_steps_per_second : 0.546\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_loss : 1.0665242771307628\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_runtime : 109.8838\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_samples_per_second : 4.368\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m train_steps_per_second : 0.546\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Others:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m hasNestedParams : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Parameters:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|dispatch_batches : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|even_batches : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|gradient_accumulation_kwargs : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|non_blocking : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|split_batches : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|accelerator_config|use_seedable_sampler : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adafactor : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adam_beta1 : 0.9\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adam_beta2 : 0.999\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|adam_epsilon : 1e-08\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|auto_find_batch_size : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|batch_eval_metrics : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m args|bf16 : True\n", + "\u001b[1;38;5;39mCOMET 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config|suppress_tokens : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|task_specific_params : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|temperature : 1.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tf_legacy_loss : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tie_encoder_decoder : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tie_word_embeddings : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|tokenizer_class : None\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|top_k : 50\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|top_p : 1.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|torch_dtype : bfloat16\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|torchscript : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|transformers_version : 4.44.2\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|typical_p : 1.0\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|unsloth_version : 2024.8\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|use_bfloat16 : False\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|use_cache : True\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m config|vocab_size : 128256\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m peft_config|default : LoraConfig(peft_type=, auto_mapping=None, base_model_name_or_path='unsloth/meta-llama-3.1-8b-bnb-4bit', revision=None, task_type=, inference_mode=False, r=16, target_modules={'k_proj', 'gate_proj', 'v_proj', 'o_proj', 'q_proj', 'down_proj', 'up_proj'}, lora_alpha=16, lora_dropout=0, fan_in_fan_out=False, bias='none', use_rslora=False, modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}, use_dora=False, layer_replication=None, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=False))\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploads:\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m environment details : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m filename : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m installed packages : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m model graph : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m notebook : 2\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m os packages : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m source_code : 1\n", + "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n", + "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## ⚙ Inference" + ], + "metadata": { + "id": "0IdjUa_0pzPQ" + } + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model)\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)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "76su-yv9Aad0", + "outputId": "8b9c2ccd-4597-41fc-d9ef-e665272fae05" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['<|begin_of_text|>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:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + } + ] +} \ No newline at end of file