{ "cells": [ { "cell_type": "markdown", "source": [ "# Acknowledgments\n", "
\n", "\n", "\n", " \n", "
\n", "\n", "# Data Sources:\n", "* https://huggingface.co/CATIE-AQ" ], "metadata": { "id": "LZ32HnEpxAyr" } }, { "cell_type": "markdown", "metadata": { "id": "rl_QlKk-zWv0" }, "source": [ "# Params\n" ] }, { "cell_type": "code", "source": [ "import os\n", "\n", "if \"COLAB_\" in \"\".join(os.environ.keys()):\n", " from google.colab import userdata\n", " hf_token = userdata.get('HuggingFaceToken')\n", "else:\n", " hf_token = os.environ[\"HUGGINGFACE_TOKEN\"]\n", "\n", "hf_username = \"nsemhoun\"\n", "debug = False\n", "save_hf = True\n", "dataset_processors = 4\n", "\n", "source_model = \"HuggingFaceTB/SmolLM3-3B-Base\"\n", "quantizations = [\"q8_0\", \"f16\"]\n", "model_name = \"Claire-3B-v0.1\"" ], "metadata": { "id": "q7hxQ5anjxi_" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "84lHENVMzWv1" }, "source": [ "# Installation" ] }, { "cell_type": "code", "source": [ "#@title Patch for protoc version\n", "import os\n", "os.environ[\"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION\"] = \"python\"" ], "metadata": { "id": "4xbbiqeD7Qle" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xJKunynhzWv1" }, "outputs": [], "source": [ "%%capture\n", "import os, re\n", "\n", "if \"COLAB_\" in \"\".join(os.environ.keys()):\n", " # Google colab\n", " import torch; v = re.match(r\"[0-9\\.]{3,}\", str(torch.__version__)).group(0)\n", " xformers = \"xformers==\" + (\"0.0.32.post2\" if v == \"2.8.0\" else \"0.0.29.post3\")\n", " !pip install --upgrade --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy unsloth_zoo\n", " !pip install sentencepiece protobuf \"datasets>=3.4.1,<4.0.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n", " !pip install --no-deps --upgrade unsloth\n", " !pip install transformers==4.55.4\n", " !pip install --no-deps trl==0.22.2\n", "elif \"VAST_\" in \"\".join(os.environ.keys()):\n", " # Vast.ai Unsloth version\n", " pass\n", "else:\n", " !pip install --upgrade unsloth-zoo\n", " !pip install --upgrade unsloth\n", " !pip install transformers==4.55.4\n", " !pip install --no-deps trl==0.22.2" ] }, { "cell_type": "markdown", "metadata": { "id": "WNpYwsH3zWv2" }, "source": [ "# Unsloth" ] }, { "cell_type": "markdown", "source": [ "## Model download" ], "metadata": { "id": "ZoIwm6M_PcW_" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QmUBVEnvCDJv", "colab": { "base_uri": "https://localhost:8080/", "height": 482, "referenced_widgets": [ "91e5bcb5bc8240c0be1ce5cb6d998cbe", "e873311edec4487694a99860dd473c86", "65b63fa9c16146198d6e540df23e32cf", "1621364df7b348ddb4803a79f1b613d8", "a252ef8fba2143919dfea9604dbe5194", "8afac5b0b57f4559bafefff570e55273", "de7dc71a758745b0a763910958aa6d3a", "6a92b15a385f4b7cb55ae300553e8dfd", "4f75f676fb5c4635ac429fd59fd8f3e5", "f4cd48a9f9e74debabc00ed7e0c619be", "4b2fd1a2d05846edb0d46c10c11a55ba", "cbe66cc96ccb40bcb02ab6cc2a055b2f", "e665926fd2794d91a8f03ee094c90f49", "672b549e97c2481489c9ba12d391c288", "80aabf06dd8f415c85628815854b2450", "b09b6db4bbcc48969f64f64948a476d4", "ee08fbaafdb14b6c981ca7bab796d9e7", "633c2097b3b949548e33ae4ed2584dbe", "632009a6a4954d0c8edae9e44af5a293", "264d9a7ae6a649708e42eeb24fa8ab4b", "fdc1aeb188114737b12bf5aea6d78bec", "a2e9abff73c445448b880234daebfa09", "be5a9eaedd064ecc9e5f3a77bcb78d13", "18b881d6052f4e1e90e41620cc34a8cc", "1466fce230c64782a8b6c67d503858f5", "7baa546cba584e9d8161b701cf54af7b", "f2dc2cb4fe054194bf809b6b47a8e5ad", "eac05125d3874403b0c42b569fe4b11c", "d6dd092802e248bd94663b12c0c4ffd6", "eb9eba601e394609a8a777d6f7405981", "fd53f7b5c2e94b1480dfdafe6180d5ed", "3550c3a0731341e09b435a5e7e15b13d", "d8b1cb00c57448f9ab417e1c270c5a98", "ead4eacc45c34e299f3050b0bf040347", "84e740abe48944f0bc7ffaad1c43e760", "7c406eecfb16488abd0f284495aa900b", "c81695e578f247a3b4a09b5dd0225fcb", "d138bcc3f71446ff9150dd626b254a88", "f619298434f849fc9e9842d252fef700", "4d453ec7509342d4a075ccd35f999a73", "f85b60e18a59450b832a180e13de53d1", "ff6dd146f0ab463191fbead31bb73524", "1acf3a280216426fa2e65d68e18de7b4", "85ac0dfb32c4416c99bdfbd45fc5ca7b", "9f2486694fbe44e1878c5b04c309a52c", "e85e1ccd9e324fa69799e0de9aa7838a", "d6faded5ee454e2b9f91c90ebd4280c4", "36f0dffa900e426f8fdf981a2eb7eb2f", "93f99d11bca145df8e90e6cc3a0bdfe8", "75ef54710b174b4eb8495e98940e0a3a", "120062df6a03432aade3f53b13d94108", "be724ade62214a6da5f1cc60f930b867", "d3ad2c362e8e419eb9f2ce3e5a4fdbf9", "7fee3923e6de4dca8f557eee8cca97d6", "26f55137bf144a729f79aaef2f90d6a2", "3cfad0aadfcd466c8655e016f3c9a276", "c659824346cf46e29ac1159706264a85", "cdedea5da13a45c68a8cb94adae2c6be", "8d4fa6b626814bd5afe378acc62b945d", "f78cb4e51f5346d19231a8686d96ff3f", "a82f38f8ebbf4d478f6fcd22f0e9dc7a", "32898cd799494f98a238cbdb08efd863", "28190bffd48e476092a44c28e38bafdb", "694a9077d0894363a57a468b228f885e", "c285846717c0412f96a982693a3eb810", "3ba0a0426ba14ae6bf10ae5c41bd2207", "2cba08b2376c41aabd8c203814eaa6e7", "9e651851447c4d29b3c0514c2571f33e", "b67a36c7f8ec473b886c470026073934", "7d823a7c35984cd0935f14175400f30c", "28d7053432684caf9e1c1a09fc82aeaa", "e2908456bf8d4e898b380005d5143fb9", "e32f2959c6a6456aa7ef157274c2a609", "49de929ed1a44dcf84d6095201b5d8c9", "3bbb6fa6ee7c46f5ba0a130029208ece", "4972765582d6404692541f5a036fa4b4", "af86fa3bb0c14ef5a12710b073d5ee0b", "114a8dbb35f64b5db158ca2dc604144e", "cf5c11bca0954ed2a69d4f6e64c51ead", "183aa45366ab421fb24cc1f8929da692", "8ab157e2da6449ab9b80ff46a1708fb6", "0cb638d540b84ff0a9d55809fd93c900", "b4f498c0146742e08579b3473c23e55d", "2623f26d05194facae38a54e60db497d", "f05ed6b45be94d95a793cf7a26f31d39", "68cf649cb1004f498fef5b08f15bde09", "5b437e60949c40c9bb7d4d79b8763b6b", "1660576d9a5746e0accbcbfc80061dda", "c8cd2a901faa49f6a529c76f7bc55864", "c95b676c987244e4b987d6a55cb18083", "0e711b3c732e4e09846e2756eab44fc6", "234946c7907b4fbf9dc3a763c63b219d", "e9a412cef5c54fd5bea38b3a5334aade", "43827d772ec846a9bd05bead14cf0175", "6f4b3a23ed9d468c8b809a591bc52b29", "7f1504139b2f49d591d53270fae8d636", "5883ea48a9844501bba0237a709d37ca", "461ca6e7d7b54fd89febb44f25863742", "a0f11dde9ee444e287d98d418cb3ee0a" ] }, "outputId": "59683d27-932e-4bc1-f523-68fbb1f4577a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.9.7: Fast Smollm3 patching. Transformers: 4.55.4.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.8.0+cu126. CUDA: 7.5. CUDA Toolkit: 12.6. Triton: 3.4.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.32.post2. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n", "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors.index.json: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "91e5bcb5bc8240c0be1ce5cb6d998cbe" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Fetching 2 files: 0%| | 0/2 [00:00.\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", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = source_model,\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = False,\n", " load_in_8bit = False,\n", " token = hf_token,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "4_trLz2czASe" }, "source": [ "## LoRA\n", "Add LoRA adapters so we only need to update 1 to 10% of all parameters!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "620bc990-8c4f-4178-9a53-894ac2fbc9e3", "id": "dTDwsy2EzASf" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Making `model.base_model.model.model.embed_tokens` require gradients\n", "Unsloth: Upcasting `base_model.model.model.embed_tokens` from float16 to float32 since it's in `modules_to_save`. Also allowing gradients.\n", "Unsloth: Upcasting `base_model.model.lm_head` from float16 to float32 since it's in `modules_to_save`. Also allowing gradients.\n" ] } ], "source": [ "from unsloth import FastModel\n", "\n", "lora_rank = 16 # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", "\n", "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = lora_rank,\n", " target_modules = [\n", " \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\"\n", " # \"embed_tokens\", \"lm_head\", # Add for continual pretraining, it seem to be replaced by module_to_save\n", " ],\n", " modules_to_save=[\"embed_tokens\", \"lm_head\"],\n", " lora_alpha = lora_rank,\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\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", ")" ] }, { "cell_type": "markdown", "source": [ "# Tools\n" ], "metadata": { "id": "sdsmJQ_8zrjP" } }, { "cell_type": "markdown", "source": [ "## Dataset formatter\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "ChatML renders multi turn conversations like below:\n", "\n", "```\n", "<|im_start|>system\n", "You are a helpful assistant.<|im_end|>\n", "<|im_start|>user\n", "What's the capital of France?<|im_end|>\n", "<|im_start|>assistant\n", "Paris.\n", "```\n", "\n", "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old` and our own optimized `unsloth` template.\n", "\n", "Normally one has to train `<|im_start|>` and `<|im_end|>`. We instead map `<|im_end|>` to be the EOS token, and leave `<|im_start|>` as is. This requires no additional training of additional tokens.\n", "\n", "More info on chat templates on [our wiki page!](https://github.com/unslothai/unsloth/wiki#chat-templates)\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ], "metadata": { "id": "B6RdkRoFKYQT" } }, { "cell_type": "code", "source": [ "from unsloth.chat_templates import get_chat_template\n", "import pprint, json\n", "\n", "wikipedia_prompt = \"\"\"{} Article\n", "### Title: {}\n", "\n", "### Article:\n", "{}\"\"\"\n", "\n", "ebook_prompt = \"\"\"Epub Book\n", "### Filename: {}\n", "\n", "### Title: {}\n", "\n", "### Author: {}\n", "\n", "### Subject: {}\n", "\n", "### Chapter: {}\n", "\n", "### Content:\n", "{}\"\"\"\n", "\n", "gutenberg_prompt = \"\"\"Book\n", "### Title: {}\n", "\n", "### Author: {}\n", "\n", "### Content:\n", "{}\"\"\"\n", "\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"chatml\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n", " mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style if needed\n", " map_eos_token = True, # Maps <|im_end|> to instead\n", ")\n", "\n", "EOS_TOKEN = tokenizer.eos_token\n", "\n", "# Must add EOS_TOKEN, otherwise your generation will go on forever!\n", "# batched=True is needed\n", "def formatting_prompts_func(examples, kind):\n", " match kind:\n", " case 'share_gpt':\n", " if (examples.get('conversations') != None):\n", " convos = examples[\"conversations\"]\n", " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", " return { \"text\" : texts, }\n", " if (examples.get('conversation') != None):\n", " convos = examples[\"conversation\"]\n", " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", " return { \"text\" : texts, }\n", " if (examples.get('messages') != None):\n", " convos = examples[\"messages\"]\n", " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", " return { \"text\" : texts, }\n", " raise ValueError('Unknow sharegtp data format.')\n", "\n", " case 'share_gpt_context':\n", " contexts = examples[\"context\"]\n", " conversations = examples[\"conversation\"]\n", " outputs = []\n", " for context, conversation in zip(contexts, conversations):\n", " chat = []\n", " if len(context) > 0:\n", " chat.append({\n", " \"role\": \"system\",\n", " \"content\": \"Utilise le contexte entre pour répondre. Si tu ne sais pas, dis-le.\\n\\n\" + context +\"\\n\"\n", " })\n", " for row in conversation:\n", " chat.append({\n", " \"role\": row[\"role\"],\n", " \"content\": row[\"text\"]\n", " })\n", " outputs.append(tokenizer.apply_chat_template(chat, tokenize = False, add_generation_prompt = False))\n", " return { \"text\" : outputs, }\n", "\n", " case 'wikipedia':\n", " sources = examples[\"sources\"]\n", " titles = examples[\"title\"]\n", " texts = examples[\"text\"]\n", " outputs = []\n", " for source, title, text in zip(sources, titles, texts):\n", " text = wikipedia_prompt.format(source.title(),title, text) + EOS_TOKEN\n", " outputs.append(text)\n", " return { \"text\" : outputs, }\n", "\n", " case 'gutenberg':\n", " titles = examples[\"title\"]\n", " authors = examples[\"author\"]\n", " texts = examples[\"text\"]\n", " outputs = []\n", " for title, author, text in zip(titles, authors, texts):\n", " author = json.loads(author)\n", " text = gutenberg_prompt.format(title, author['author'], text) + EOS_TOKEN\n", " outputs.append(text)\n", " return { \"text\" : outputs, }\n", "\n", " case 'ebook':\n", " filenames = examples[\"filename\"]\n", " titles = examples[\"title\"]\n", " authors = examples[\"author\"]\n", " subjects = examples[\"subject\"]\n", " if (examples.get('chapter') != None):\n", " chapters = examples[\"chapter\"]\n", " else:\n", " chapters = examples[\"part\"]\n", " contents = examples[\"content\"]\n", " outputs = []\n", " for filename, title, author, subject, chapter, content in zip(filenames, titles, authors, subjects, chapters, contents):\n", " text = ebook_prompt.format(filename, title, author, subject, chapter, content) + EOS_TOKEN\n", " outputs.append(text)\n", " return { \"text\" : outputs, }\n", "\n", " case 'text':\n", " return { \"text\" : [example + EOS_TOKEN for example in examples[\"text\"]] }\n", "\n", " case _:\n", " raise ValueError('Unknow data format.')\n", "pass\n", "\n", "# batched=False is needed\n", "def formatting_dpo_func(example, kind):\n", " match kind:\n", " case 'prompt':\n", " prompt = []\n", " if (example.get('system') != None and len(example.get('system')) > 0):\n", " prompt.append({\"role\": \"system\", \"content\": example.get('system')})\n", " if (example.get('question') != None):\n", " prompt.append({\"role\": \"user\", \"content\": example.get('question')})\n", " elif (example.get('prompt') != None):\n", " prompt.append({\"role\": \"user\", \"content\": example.get('prompt')})\n", " else:\n", " raise ValueError('No prompt or question row.')\n", " return {\n", " 'prompt': tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True),\n", " 'chosen': example.get('chosen') + EOS_TOKEN,\n", " 'rejected': example.get('rejected') + EOS_TOKEN,\n", " }\n", "\n", " case _:\n", " raise ValueError('Unknow data format.')\n", "pass" ], "metadata": { "id": "6xH9kHQ2zuzs", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0ae72906-b23a-4a81-9484-b56ed8c94796" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth: Will map <|im_end|> to EOS = <|end_of_text|>.\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Dataset helper" ], "metadata": { "id": "4tBwTwc82YHY" } }, { "cell_type": "code", "source": [ "import random\n", "from datasets import load_dataset\n", "from datasets import concatenate_datasets\n", "from datasets import Dataset\n", "\n", "def dumpDatasets(datasets):\n", " for key, value in datasets.items():\n", " print('=' * 25 + ' ' + key.upper() + ' ' + '=' * 25)\n", " row = value[random.randrange(0, len(value), 1)]\n", " for rkey, rvalue in row.items():\n", " print('#' * 10 + ' ' + rkey.upper())\n", " pprint.pp(rvalue[:1024])\n", " print(\"\\n\\n\")\n", "pass\n", "\n", "def mergeDatasets(datasets):\n", " resDataSet = Dataset.from_dict({})\n", " for key, value in datasets.items():\n", " if debug:\n", " value = value.train_test_split(train_size = 0.02)['train']\n", " resDataSet = concatenate_datasets([resDataSet, value])\n", " resDataSet = resDataSet.shuffle(seed=0)\n", " return resDataSet\n", "pass\n", "\n", "def loadDataset(url, kind, subset = None):\n", " if (subset != None):\n", " dataset = load_dataset(url, subset, split = 'train', token=hf_token)\n", " else:\n", " dataset = load_dataset(url, split = 'train', token=hf_token)\n", " dataset = dataset.map(formatting_prompts_func, batched = True, fn_kwargs= {'kind':kind})\n", " dataset = dataset.select_columns(['text'])\n", " return dataset\n", "\n", "def loadDpoDataset(url, kind, subset = None):\n", " if (subset != None):\n", " dataset = load_dataset(url, subset, split = 'train', token=hf_token)\n", " else:\n", " dataset = load_dataset(url, split = 'train', token=hf_token)\n", " dataset = dataset.map(formatting_dpo_func, batched = False, fn_kwargs= {'kind':kind})\n", " dataset = dataset.select_columns(['prompt', 'chosen', 'rejected'])\n", " return dataset" ], "metadata": { "id": "CgpsdsN02bAx" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Continued pre-training (CPT)" ], "metadata": { "id": "VHHZqnHe5NFG" } }, { "cell_type": "markdown", "source": [ "## Datasets(s)\n", "\n" ], "metadata": { "id": "hyBhe7yBBzZs" } }, { "cell_type": "code", "source": [ "#@title Init datasets\n", "datasets = {}" ], "metadata": { "id": "NkLEJDX0P4hh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@title EBook data\n", "datasets['ebooks'] = loadDataset('nsemhoun/ebooks', 'ebook')" ], "metadata": { "id": "Hjt-pJC1Tj_q", "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "2ef82019ee014a8e9b9a809f5d4497f5", "60b6bc0408b844ba8d9d3b77b9b91d96", "d9b80b85d96846a79c2080eb0a0ee5ba", "fef30ff1993646739ded7fc79804b7cc", "bdf605c6d4cf443cb94a9db0b3c895be", "39500b1609154569abfb160fa2c1f8cb", "71005e45ed724ebca99e21832da36916", "d40944f78f504e8a90518165aff9c3cb", "b4b024f597474ff5a8919dd9ee37d86d", "03f29d6150814241a1c69d66b8e280d1", "8ac00bb48e024631b2eb7d35c0e5152b", "86a0735a5b50414cb1f7b8f8621a270d", "c88ed6b5990c4cecb6de2d72acb3258c", "5d7b7ca7c3d74816a4d7c3f56baa1011", "b001acfaa8fa4a259784a8e9fddf55aa", "0aab09272bb741e4bb1af4aca047a5b8", "f6bbca966d4843e99f0ba3bf4a23ad74", "28da0056986148bca2f26ca8c4cf9914", "42213e624a00454aa8bf482a4619ef7f", "55d93ae5046a46b19a81c954d2e4168e", "fc0d0009ea364f589f61d9d27dff1c87", "0a4fcc2670c541feb5a8041d11c2e5d8", "e40e919a94934fb0be3a6ae170a3496a", "12a7ba8776ef45989e242f221e3069d1", "50d894b96712405c8a36041869ae1e2c", "c324df1302304872815d78f27384939b", "c74dbd07438243c6893bc25db394ad46", "66cfd731962c459dbc6fbafa7e6e6a6d", "9c8714852e78417f912c453bf9f257c9", "39e358a5ed574740a9731dff0de2045f", "a66d366f28234fc78f4baeb7cfc8310c", "e8f9f5ad24fe493ebf6a4fde697e14be", "528ac493711846589182ce89c1fb4339" ] }, "outputId": "c3e1d735-5090-44fb-d822-4541b918081b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "books.jsonl: 0%| | 0.00/2.08G [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [ 2/60 : < :, Epoch 0.01/1]\n", "
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StepTraining Loss

" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "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} %.\")" ], "metadata": { "id": "txSvDDqeDvIS" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Supervised Fine-Tuning (SFT)" ], "metadata": { "id": "WlYPClPP32Ue" } }, { "cell_type": "markdown", "source": [ "## Datasets(s)" ], "metadata": { "id": "898-nEBcgrlC" } }, { "cell_type": "code", "source": [ "#@title Init datasets\n", "datasets = {}" ], "metadata": { "id": "zi3DWL96gwtq" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@title SmolTalk Think\n", "datasets['smaltalk-think'] = loadDataset('CATIE-AQ/smoltalk2_aya_think_dataset_french_split', 'share_gpt', 'french_raisonning')" ], "metadata": { "id": "4ULTH9QphZlK" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@title SmolTalk ToolCalling\n", "datasets['smaltalk-toolcalling'] = loadDataset('CATIE-AQ/smoltalk2_smolagents_toolcalling_french', 'share_gpt')" ], "metadata": { "id": "iULedxTFh1G4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@title Merge datasets\n", "dumpDatasets(datasets)\n", "dataset = mergeDatasets(datasets)" ], "metadata": { "id": "lhiSQUeEphWw" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "## Training\n" ] }, { "cell_type": "markdown", "source": [ "### Setup Trainer" ], "metadata": { "id": "IAsG0QDAWdZF" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "95_Nn-89DhsL" }, "outputs": [], "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 = dataset_processors,\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 = 8,\n", " warmup_steps = 5,\n", " max_steps = 60 if debug else -1, # None for full run\n", " num_train_epochs = 0 if debug else 1,\n", " learning_rate = 2e-4, # Lower for slower but more precise fine-tuning. Try values like 1e-4, 5e-5, or 2e-5\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", " report_to = \"none\", # remove to activate WandDB\n", " ),\n", ")" ] }, { "cell_type": "markdown", "source": [ "### Training Execution\n", "Execute the training process with the configured trainer and monitor the training progress." ], "metadata": { "id": "WdoyA7uFXClH" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2ejIt2xSNKKp" }, "outputs": [], "source": [ "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", "source": [ "trainer_stats = trainer.train()" ], "metadata": { "id": "FTfullD_VNtG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9" }, "outputs": [], "source": [ "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", "source": [ "# Saving" ], "metadata": { "id": "A4atr28ooyrM" } }, { "cell_type": "markdown", "source": [ "## Create HF repository" ], "metadata": { "id": "loPgJFIu0N9F" } }, { "cell_type": "code", "source": [ "from huggingface_hub import HfApi\n", "\n", "if save_hf:\n", " hf_api = HfApi(token=hf_token)\n", " hf_api.create_repo(repo_id = hf_username + \"/\" + model_name, repo_type = \"model\", private = True, exist_ok = True)\n", " hf_api.create_repo(repo_id = hf_username + \"/\" + model_name + \"-GGUF\", repo_type = \"model\", private = True, exist_ok = True)\n" ], "metadata": { "id": "KAOu_ohZescE" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "## Saving to float16 for VLLM (safetensors)\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": [ "model.save_pretrained_merged(model_name, tokenizer)\n", "if save_hf: model.push_to_hub_merged(hf_username + \"/\" + model_name, token = hf_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": [ "model.save_pretrained_gguf(model_name, tokenizer, quantization_method = quantizations)\n", "\n", "if save_hf:\n", " for quant in quantizations:\n", " hf_api.upload_file(\n", " path_or_fileobj=model_name + \".\" + quant.upper() + \".gguf\",\n", " path_in_repo=model_name + \"-\" + quant.upper() + \".gguf\",\n", " repo_id=hf_username + \"/\" + model_name + \"-GGUF\"\n", " )" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "91e5bcb5bc8240c0be1ce5cb6d998cbe": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", 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