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nvidia-cufft-cu12-11.2.3.61:\n", + " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", + " Attempting uninstall: nvidia-cuda-runtime-cu12\n", + " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", + " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-cupti-cu12\n", + " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cublas-cu12\n", + " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", + " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", + " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", + " Attempting uninstall: nvidia-cusparse-cu12\n", + " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", + " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", + " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", + " Attempting uninstall: nvidia-cudnn-cu12\n", + " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", + " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", + " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", + " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", + "Successfully installed bitsandbytes-0.46.1 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n" + ] + } + ], + "source": [ + "!pip install transformers datasets accelerate peft bitsandbytes huggingface_hub" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install bitsandbytes" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-q-j_9A3fihL", + "outputId": "4afcd7c0-ee34-43c4-d02b-5c9d6c268ba6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting bitsandbytes\n", + " Downloading bitsandbytes-0.46.1-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)\n", + "Requirement already satisfied: torch<3,>=2.2 in /usr/local/lib/python3.11/dist-packages (from bitsandbytes) (2.6.0+cu124)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from bitsandbytes) (2.0.2)\n", + "Requirement already satisfied: filelock in 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+ " Attempting uninstall: nvidia-nvjitlink-cu12\n", + " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", + " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", + " Attempting uninstall: nvidia-curand-cu12\n", + " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", + " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", + " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", + " Attempting uninstall: nvidia-cufft-cu12\n", + " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", + " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", + " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", + " Attempting uninstall: nvidia-cuda-runtime-cu12\n", + " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", + " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-cupti-cu12\n", + " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cublas-cu12\n", + " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", + " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", + " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", + " Attempting uninstall: nvidia-cusparse-cu12\n", + " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", + " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", + " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", + " Attempting uninstall: nvidia-cudnn-cu12\n", + " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", + " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", + " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", + " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", + "Successfully installed bitsandbytes-0.46.1 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Initial setup" + ], + "metadata": { + "id": "XGyUZ_diBLGO" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig\n", + "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training , TaskType\n", + "from datasets import load_dataset\n", + "from huggingface_hub import login\n", + "import torch\n", + "\n", + "# STEP 1: Login to Hugging Face Hub\n", + "login() # read token Paste your token here\n", + "\n", + "# STEP 2: Bits and Bytes Config for 4-bit Quantized Training\n", + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_use_double_quant=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.float16\n", + ")\n", + "\n", + "# STEP 3: Load Model and Tokenizer\n", + "base_model = \"microsoft/phi-3-mini-128k-instruct\"\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n", + "tokenizer.pad_token = tokenizer.eos_token # ✅ Required for causal LM\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " base_model,\n", + " quantization_config=bnb_config,\n", + " device_map=\"auto\",\n", + " trust_remote_code=True\n", + ")\n", + "\n", + "model = prepare_model_for_kbit_training(model)" + ], + "metadata": { + "id": "HrFEczFbW0e-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104, + "referenced_widgets": [ + "90a897b3b8cc45959a6ec109a545d4cb", + "b182679b071448e5a5ed14f07259bb70", + "81208d31170d4838bded8d71d13ee63e", + "a99a61b7a6f24757b87230e28cd1bddb", + "c975f5f9e9294ebda7cf9564ec84864b", + "2fcc2afd176e4e8f8f006631761fc1d2", + "c51b5dc4e28840c5b5532b3bba5b6155", + "7a52ea318a264e6b817259598eb38a99", + "a8fff6c1a3db4502b52089afcda17e15", + "c20ef9bdb64c4248b637e533d1c6180f", + "77a2a9bfc2e94fc391d4c9f706784f79" + ] + }, + "outputId": "9991571e-ba1e-4ba5-9f7c-a4cab19d0d2e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:transformers_modules.microsoft.phi-3-mini-128k-instruct.072cb7562cb8c4adf682a8e186aaafa49469eb5d.modeling_phi3:`flash-attention` package not found, consider installing for better performance: No module named 'flash_attn'.\n", + "WARNING:transformers_modules.microsoft.phi-3-mini-128k-instruct.072cb7562cb8c4adf682a8e186aaafa49469eb5d.modeling_phi3:Current `flash-attention` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00\\n{example['prompt']}\\n\\n<|assistant|>\\n{example['completion']}\"\n", + " }\n", + "\n", + "dataset = dataset.map(format_phi3_prompt)\n", + "\n", + "def tokenize(example):\n", + " return tokenizer(\n", + " example[\"text\"],\n", + " truncation=True,\n", + " padding=\"max_length\",\n", + " max_length=512\n", + " )\n", + "\n", + "tokenized_dataset = dataset.map(tokenize, batched=True, remove_columns=[\"prompt\", \"completion\", \"text\"])\n", + "print(tokenized_dataset[0])" + ], + "metadata": { + "id": "9PQKOUOzXwuA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136, + "referenced_widgets": [ + "56675488286c4ebf9bf664b599e55a51", + "563938f842ba4368bee5963f7a3519ad", + "9be8aa0f08b946a09d478b20e4607c5e", + "56fbd78fd1324b6e9b2d15ce777d10ce", + "83c2bcc5b17549ab87bb90806d29925d", + "da5f0b49bf874006b0a6135862ea6762", + "9ba3783f7d744677babd025aa7565228", + "200e3cd0d8434cf4b29f9c3f5a58fdcc", + "fe2889ab01994542950415aaca248db9", + "69d9bf0e7c93476dad1cff880ec49475", + "bd21fb39e861480f9db9a98a8e2a1048", + "20533a096e034a4992f12db12a422aed", + "8fe8ef68f33844f09e7f13326afe0a33", + "49a3871039ae45e7846cc759117fe214", + "c1fe15d40ff44e258d8acdc3c7e75f5a", + "ddb781c16fb34b73b77333e4744e33da", + "1bc6ccfa50d84adc85c6e7a7adb7a391", + "4f7ad8ac99444a9eab77026aa34963ac", + "ee2d6244e65b49c9b7650f25563b443e", + "a0426e7b896a41a490f07feb801875d8", + "61eac34e6ed54103a838a755d206a444", + "f1c383f39c3e4fd7bbab8cc9c70e5882" + ] + }, + "outputId": "a502266c-15f4-4e94-b4a2-1c47db221074" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'prompt': 'Show moisturizer sales data of each month using a scatter plot.', 'completion': 'import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\nmonthList = df[\\'month_number\\'].tolist()\\nsalesData = df[\\'moisturizer\\'].tolist()\\nplt.scatter(monthList, salesData, label=\\'moisturizer Sales data\\')\\nplt.xlabel(\\'Month Number\\')\\nplt.ylabel(\\'Number of units Sold\\')\\nplt.legend(loc=\\'upper left\\')\\nplt.title(\\'moisturizer Sales data\\')\\nplt.xticks(monthList)\\nplt.grid(True, linewidth=1, linestyle=\"--\")\\nplt.show()'}\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/10000 [00:00\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m trainer = Trainer(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenized_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata_collator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_collator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'Trainer' is not defined" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Resuming training for another epochs" + ], + "metadata": { + "id": "vd74aljI81Tz" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "from transformers import TrainingArguments\n", + "\n", + "training_args = TrainingArguments(\n", + " output_dir=\"/content/drive/MyDrive/Fine tune Data Analyzer/phi3-checkpoints\",\n", + " per_device_train_batch_size=2,\n", + " gradient_accumulation_steps=4,\n", + " num_train_epochs=2,\n", + " learning_rate=2e-4,\n", + " logging_steps=10,\n", + " save_strategy=\"epoch\",\n", + " save_total_limit=1,\n", + " fp16=True,\n", + " bf16=False,\n", + " report_to=\"none\"\n", + "\n", + ")\n", + "\n", + "from transformers import Trainer\n", + "\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=tokenized_dataset,\n", + " data_collator=data_collator\n", + ")\n", + "\n", + "trainer.train(resume_from_checkpoint=\"/content/drive/MyDrive/Fine tune Data Analyzer/phi3-checkpoints/checkpoint-1250\")\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "yW1V2k_S87jG", + "outputId": "7dfd16b3-3650-4a06-ad66-57aaffec08d2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "No label_names provided for model class `PeftModelForCausalLM`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.\n", + "WARNING:transformers_modules.microsoft.phi-3-mini-128k-instruct.072cb7562cb8c4adf682a8e186aaafa49469eb5d.modeling_phi3:`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n", + "/usr/local/lib/python3.11/dist-packages/torch/_dynamo/eval_frame.py:745: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n", + "WARNING:transformers_modules.microsoft.phi-3-mini-128k-instruct.072cb7562cb8c4adf682a8e186aaafa49469eb5d.modeling_phi3:You are not running the flash-attention implementation, expect numerical differences.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
12600.125100
12700.111400
12800.117900
12900.117600
13000.113100
13100.118800
13200.111800
13300.113600
13400.108200
13500.132000
13600.108600
13700.119400
13800.108500
13900.109800
14000.115100
14100.109100
14200.107400
14300.110300
14400.123400
14500.121800
14600.114000
14700.133000
14800.123100
14900.105500
15000.125000
15100.131800
15200.100300
15300.108200
15400.118800
15500.126000
15600.104900
15700.124900
15800.099300
15900.126300
16000.115000
16100.097700
16200.101000
16300.124800
16400.102900
16500.114800
16600.119700
16700.116400
16800.114600
16900.112700
17000.103900
17100.127800
17200.136900
17300.111200
17400.114200
17500.106200
17600.118400
17700.111000
17800.114100
17900.121800
18000.119900
18100.107300
18200.121600
18300.126000
18400.114900
18500.105100
18600.098500
18700.114500
18800.112800
18900.134700
19000.086800
19100.111900
19200.123500
19300.114100
19400.123400
19500.123800
19600.103000
19700.104800
19800.104500
19900.121100
20000.121000
20100.108600
20200.110400
20300.130600
20400.122200
20500.118300
20600.102200
20700.108200
20800.096700
20900.110500
21000.101500
21100.123100
21200.125900
21300.117600
21400.103200
21500.116800
21600.108700
21700.112500
21800.108200
21900.131600
22000.134600
22100.131300
22200.103900
22300.122900
22400.120900
22500.113800
22600.104300
22700.110800
22800.109800
22900.121400
23000.126700
23100.107600
23200.110600
23300.109100
23400.124000
23500.132600
23600.099700
23700.099500
23800.101000
23900.096700
24000.122600
24100.093600
24200.107100
24300.106300

" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "u can resume for another epochs with same way" + ], + "metadata": { + "id": "SXzYbD_Ahezl" + } + }, + { + "cell_type": "code", + "source": [ + "model = model.merge_and_unload()\n", + "model.save_pretrained(\"/content/drive/MyDrive/Fine tune Data Analyzer/phi3-matplotlib-cpu2\", safe_serialization=True)\n", + "tokenizer.save_pretrained(\"/content/drive/MyDrive/Fine tune Data Analyzer/phi3-matplotlib-cpu2\")\n" + ], + "metadata": { + "id": "3MYEhJxlH5zm" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file