{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phi-3.5-mini-ITA fine-tuning notebook\n", "\n", "In this notebook, we fine-tune Phi-3.5-mini-instruct on a good mix of English and Italian to improve performance in Italian.\n", "We use [Spectrum](https://arxiv.org/abs/2406.06623) to selectively train the most informative layers of the model.\n", "\n", "**👣 For a complete walk-through of the fine-tuning process, check out the [accompanying article](https://huggingface.co/blog/anakin87/spectrum).**\n", "\n", "\n", "- [🪪 fine-tuned model: Phi-3.5-mini-ITA](https://huggingface.co/anakin87/Phi-3.5-mini-ITA)\n", "- [💬🇮🇹 Chat with the model](https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eDj1Lg8ivfia", "outputId": "7ac5c12e-247b-419a-a89c-1d89f889c06b" }, "outputs": [], "source": [ "! pip install datasets transformers trl accelerate scipy\n", "! pip install ninja packaging\n", "! MAX_JOBS=6 pip install flash-attn --no-build-isolation --upgrade\n", "! pip install wandb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data preparation\n", "\n", "The datasets used have different formats.\n", "We prepare and mix them in a single dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 651, "referenced_widgets": [ "a23d7b431771453fbfca2878c673e62a", "ca366db7b4c1445b9191c4b6a4b370ec", "6ee57381fec64b888da072938a0131f0", "d04893c4a6824649a8e43d1ac949a284", "4059285b62404491b566b1c70fb03642", "889ba539a4714b93830a20b43e7e1b15", "78a28e57cc0a432bb2a72b8db9ea3830", "fec53046d85745f09f5498cfd80a7a9d", "ea1676b388cc409cb1e3d1f14774c275", "a1f5a693a77743a586f44183e59a5f83", "2cecd890eb6e46e7b42ad95e5efb8546", "6e92914c607346188ab3eb650856fe27", "58dc32d064e6460eb645ae7f7486df67", "03d1418ede844a8b94b02e431e08fcda", "7c89fbd5abae42ca9e45710a5f7c1578", "4b38ddfbe3a144b88e7393b1b18d3d53", "380387f9e85f46d3b42ae54d8e3573cd", "21b9bcb75f4c42a1947d66b696662246", "5780d426b1de4e18bda5e910d53ef802", "091804136dba4c46901737e541468766", "57446cba56a449d4a6d3d0a1e9a9afc5", "88ce3a89f0564346b27f726b5abfdea0", "66d6a12a0fa345c184e4e52c12298d87", "ada1a8455b61429b8ed6bb4924f65b3b", "104a324c3b2548c0b37572ce78fe0ff2", "9d753efae48c423785d3bbfe09b9d202", "93ced1dd93744c20941d15ea95bd56a9", "5c15f5a34c044f5c9953a46ffcd5254f", "61992f6fdfa341dfa9356c9cf250da61", "969879fedbe04460a0a2fa9a857b8f18", "8f7124107936405dba565bed7ea9540e", "4675f53c06c843658a83d100b4438ccc", "fa2499616a6d462d9c1a7905f5c202bb", "2613e0031aa84e55887bd7757625ab1c", "3c0153fac810451382adc7730c98e083", "05f59cd63c2b4f97aa7f0e1f06617b38", "2c49fd13cf5e42619d2c7a585b2e0e27", "b836edd8446e4e62ad8a5ce002266583", "824f05ec88bd4fa083618de0fc78a9a8", "59f393da25dd427ea6cd618d75c2f268", "0ce6260197ac48f9a4d8c98aa78ec298", "b52ffc550aab45d49bb83b8046b31bac", "ede56b2559e04596883a4005ba545a81", "ae024f07733245b495023895cf5c10da", "379727786c7044d3bf15b7d51d4a0441", "42845d3fde6a44ab90e22e0a99347da6", "3f45d2a55d1d4c37b11ea9a24a0f4f9c", "e8af4db210184d1291fb25655a10dce2", "b9029eac69154e02a718d7da62839f14", "e938ba352cbb4a25b92a3bd7c52c6556", "e3817377baf44f79852bf9d039039473", "5e183413d17242539bc59f943173ee7c", "7cebceb290e542ceb62f228acd985e95", "e5aa380135464d4596dcf5e4de72410b", "3249d255ae304ed19e808113d9c6b071", "c80db4bef488409f9d5a6b7206bb5e7b", "828f192f878b445aad8865fda07b1beb", "a81265042ac8491eb83d5f3fe01109b9", "12562a5548a6416db370a956275f2db9", "bece48a6b4ed41e0a982abe39b338246", "bb9863d469cc4f83a28942b605f00b42", "3141711d3d6e4b97ac8257917290ce1f", "b66327e0ab34413396c5c07c051945e6", "316bb0c898994343a5166658faddd72c", "9310a8600803440f87994ac75a717892", "b11fd221a7684e02b582e3d30937123f", "10b5d456b5514f3798c9490e7f47f933", "1eddf357acbd45a6a4a8adb701d7cfee", "e95ab9c3600a4339b9d2171179c31014", "7556f263cd0b4bdc8ae39f6ff535e2c9", "932ff8ae1ce34b9d881be4068a22c6c0", "8b3e02151e9c4d2dbd6f26d273189a1a", "50af4528917742029b4c797c7d140ba0", "b662e679e977425f9fce886b14c1eb85", "40e121b1026549508f1b4ea3bb9353ed", "0ea13c15c7ff4926ab44cc5e451065a9", "2d3a2db9ec6c4b85835253cb67fd1b50", "d54c984431e5419fae2cd0510e0ce20b", "780ea4747b9944aea468e60e8253550a", "392c609ddf974307a8727e60fa4be14a", "be01027718d44f4ea508fbfc056a7930", "e5a6ca99bc134572bb92e791e4b8b192", "bb304de0815749d5bb95dd70c4a6dc92", "a6e260347b0d494eaefdbf66a311cd04", "a43e8c2c0d134af987b241e7a64f5593", "1ba549cd7e2d4165af5d7bf826a8598e", "5172ad1162d744288ccd6b93c49b33cf", "5fd9ae13792546e69104bcc326ac82aa", "7892edfd08484915adb13b69f1750043", "746fe89cb94f43a498b2181e75522c8c", "35ff19cb31d74a9183c43137048ebfe9", "47164299359a431f94eeed3a2d7d0bb4", "00f89b6cc851466a9da75515a3aee347", "ae76876012cc4495a4a4d754ad088cc8", "7ffce7accaa5490ba210d2b6fba70701", "3d0a56ac0de643f09c652072a3719f67", "20c7f3f5a28a473181bbdd110c4ae2c5", "dd783797434246b189c6a6a95ec138b6", "662219dac25e4bd0a84ae62dcb1d9ebc", "f227a703f6ab45ddb3c2d5d05fbe8927", "1ac384698882459ba42b414afb816245", "5126e07c7d7a47e0b542b04faadd6a66", "4f2be50a699d49e79ef5a29a6509a70e", "c0e9b965e011452c89f95e1857ae2484", "a184fad0d7c14145b677e86c62d349cd", "80503b20b5ef449fa461926cc6a4092b", "7113d05c2768477787f5570cc7754afd", "3c617cb2af8e4051a82e74773ba27bf8", "6d29a244ba174e0b9c8d0aabc7868840", "9bb2d68551d243798d0b1147c5109e11", "fecd1eebb51b4e9ca0f4a461de698314", "db0e584e490f47a1ad9353ffa5eb1c26", "c65b2fa981f04029a373285ffdec9d76", "08b1905dfbb04a21a462f9101185f751", "6b099b9bda80414f9e343d774d1b8b61", "e3b7ef03997d4286ad31abb7b4bf7e3b", "69232a7e54ba4b40a5395f0131816297", "ab9569bd932446638a91dfb66d1c9955", "92dad28d5efb4d138eb7e50593a4c748", "854f122681264c55bacd0237f2fb7a2f", "5521f8b3e9634b0e9b853b083cf28f5b", "4e0b04b9910744029388e3e1ad81ce58", "393f98e451e149ca953760314e0f5c02", "b348ebc5ea444a79af345a2f4ac554f5", "e414b2d6b60a41938067abd0fa8896db", "b28544f7df6c4c2c9bbdd5cd09c770e3", "f10b89c75d8948219e59383a64842f41", "1bced7d4cb9644358b90533b51e0d989", "6c82888b17d64818899fd5662e975c5e", "7a7d18f2305c4af6a8a028e0c68d4b29", "b2b45a17912d4d1b8cdf1356c2e614fc", "5e58f0a8c09e49f69385e40203b18b5e", "96f58b58fd3e4e9fb546910054375f54", "8e1376674f604f5bb03b4d91b3620741", "2e54667766914dc49832ff58ef7263c0", "df7189e856024d2c8259e75e0a5c80bc", "5285498822dc4740ac37652fa49825e8", "172afce904634442bb15a4a3ba3a017b", "ef3e4dea691f4908a9cdc3ba0c6035b7", "ab52389e891f477eb302177c405ce3ab", "b99c727e34dd494d8907b3933eef6d1d", "d901811b68124236adc262ee5365fe10", "0ec9dfa9c1af4df6946dda76fc603b81", "38f2d1650d0c47d2ae9728be8dd21eee", "eb25c28d8e6942da8e473bc6dd9c083a", "c56346be2ab54353813e9a605c189b7c", "93b406ce9a95440f868f77fa2fbdd514", "4893d6da9008438c8be86e6c926cd357", "1c61ffed0e304182be895e5771e2c658", "874c9557f0044873b779b939d88d98ba", "535f319906b1453ab9fa3a32db8d7167", "813dc8ac95914f8eb6e6f28ae502fb21", "8f70b6b712dc409eb2994d72e867c0ea", "2ecdaef396204312bc245087bdd0f243", "01f29ee8b6604289aacb4331bc00063e", "e7bc326605fb4a54ad1d8420c846e626", "df4dfcc8833e41dc85d915f257d85f9e", "86fb0d56e509499d86cc22ac4b14acda", "aff1c9f3025a4c68aeec11a6d5fc1168", "64fb570cb9bf4e5e90b15b15b9b66147", "e2923078c1a445869a56f36e7896ca99", "44b35d1f8263402083e0dfc5aa7c8509", "de526d8e8f9b40d0acba028e426e3e2d", "b3631ddd6ff14a02ab80c769a14eabe5", "53098041897649d2b050a27b65a55c80", "cf24db4451d1400da8c5ae0a17b5a3bd", "98c2c9b782cf4261900b436d19679277", "baba8bc357634cea921a3ac910f9f86d", "3239d103cec64b0b90aba86f3b533a96", "06b05b18a1ab47e09bdc26a4742cfcbf", "971b0fd60b264a71a0578c731656a9b0", "77c16fe426aa418a80b6d6505566173a", "93db2754f5e64ecca7bbaf444fba358b", "9c0aaf415e96412c854fa47de65fa94e", "ef570ff6122e4aee94e5c22bd7b234b0", "eac2162c2ab34d2fbb1b8efe5d3319cd" ] }, "id": "zTrlFStU6dBd", "outputId": "d1e45703-98df-49ee-c506-2ea52067b12f" }, "outputs": [], "source": [ "from datasets import load_dataset, Dataset, concatenate_datasets\n", "from transformers import AutoTokenizer\n", "\n", "\n", "# Load and process FineTome dataset\n", "finetome_ds = load_dataset(\"mlabonne/FineTome-100k\")[\"train\"]\n", "mapping_keys, mapping_values = {\"from\": \"role\", \"value\": \"content\"}, {\"human\": \"user\", \"gpt\": \"assistant\"}\n", "\n", "def process_conversation(row):\n", " conv = row[\"conversations\"]\n", " new_conv = [{mapping_keys[k]: mapping_values.get(v, v) for k, v in msg.items()} for msg in conv]\n", " return {\"conversations\": new_conv}\n", "\n", "finetome_ds = Dataset.from_list([process_conversation(row) for row in finetome_ds])\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3.5-mini-instruct\", trust_remote_code=True)\n", "\n", "def apply_template(examples):\n", " text = [tokenizer.apply_chat_template(msg, tokenize=False, add_generation_prompt=False) for msg in examples[\"conversations\"]]\n", " return {\"text\": text}\n", "\n", "finetome_ds = finetome_ds.map(apply_template, batched=True).remove_columns(\"conversations\").shuffle(seed=42)\n", "finetome_ds = finetome_ds.add_column(\"origin\", [\"finetome\"] * len(finetome_ds))\n", "\n", "# Load and process Capybara-Claude dataset\n", "capyclaude_ds = load_dataset(\"efederici/capybara-claude-15k-ita\", split=\"train\")\n", "capyclaude_ds = capyclaude_ds.map(apply_template, batched=True).remove_columns([\"conversations\", \"hash\"]).shuffle(seed=42)\n", "capyclaude_ds = capyclaude_ds.add_column(\"origin\", [\"capyclaude\"] * len(capyclaude_ds))\n", "\n", "# Concatenate and split datasets\n", "mixed_ds = concatenate_datasets([finetome_ds, capyclaude_ds]).shuffle(seed=42)\n", "mixed_ds = mixed_ds.class_encode_column(\"origin\").train_test_split(test_size=0.005, stratify_by_column=\"origin\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9a97N_QOpgom", "outputId": "c9bff595-08e9-4112-dacc-9af3d5dac655" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'origin'],\n", " num_rows: 114106\n", " })\n", " test: Dataset({\n", " features: ['text', 'origin'],\n", " num_rows: 574\n", " })\n", "})" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mixed_ds" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# print(mixed_ds[\"train\"][587][\"text\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then check how many examples will be truncated if we choose a maximum length of X tokens (2048 in this case)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "90e5f5764ad248c188be5916a30b4428", "f69e2b0c2308429cbd8ee33c149d02ec", "65c955bdc085400ba164cd38217feaf3", "15b801f40c6a46e0ab6823b6dfb5385c", "a45eea297cfc437c84122434130a37de", "2842208ed7384eb6ad210009bb96a61a", "72f31e5cf7b341e28698b408076850cd", "fbff7b21b800447aac98ee21ddd6c5df", "c47918713f9d4e36a461e1d1b89bcf17", "cd162adb6921477681114b7355cfc78f", "a0e40909e62e46bda8f6d7405b685eec" ] }, "id": "hQlIBp_LqiEs", "outputId": "63a69983-1617-427d-a7f2-41c5e2429148" }, "outputs": [], "source": [ "# from scipy.stats import percentileofscore\n", "# import multiprocessing\n", "\n", "# def calculate_lengths(batch):\n", "# return {\"conv_lengths\": [len(tokenizer(text)[\"input_ids\"]) for text in batch[\"text\"]]}\n", "\n", "# conv_lengths = mixed_ds[\"train\"].map(\n", "# calculate_lengths,\n", "# batched=True,\n", "# batch_size=1000,\n", "# num_proc=multiprocessing.cpu_count()\n", "# )[\"conv_lengths\"]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-vHKNQI5rgZU", "outputId": "b3e51a25-cbc2-478d-a51e-1754bede83ae" }, "outputs": [], "source": [ "# chosen_length=2048\n", "\n", "# percentile = percentileofscore(conv_lengths, chosen_length)\n", "# print(percentile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load model\n", "\n", "For Spectrum, we need to load the model using Transformers, no quantization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WU9LxPhtyxgm" }, "outputs": [], "source": [ "from transformers import AutoModelForCausalLM\n", "import torch\n", "\n", "model_id = \"microsoft/Phi-3.5-mini-instruct\"\n", "\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " \"microsoft/Phi-3.5-mini-instruct\",\n", " use_cache=False,\n", " torch_dtype=torch.bfloat16,\n", " attn_implementation=\"flash_attention_2\",\n", " device_map=\"auto\",\n", " trust_remote_code=True\n", ")\n", "\n", "# reference: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/sample_finetune.py\n", "# keep in mind that setting tokenizer.model_max_length = 2048 as suggested is WRONG \n", "tokenizer.pad_token = tokenizer.unk_token # use unk rather than eos token to prevent endless generation\n", "tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n", "tokenizer.padding_side = 'right'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply Spectrum\n", "https://github.com/cognitivecomputations/spectrum\n", "https://arxiv.org/abs/2406.06623\n", "\n", "In short, when using Spectrum, we only fine-tune some layers of the model with high Signal-to-Noise Ratio.\n", "So, we need to freeze the other layers before training.\n", "\n", "---\n", "\n", "I computed the following YAML file using the Spectrum script, which unfortunately is not compatible with notebook environments.\n", "\n", "```bash\n", "# installation\n", "git clone https://github.com/cognitivecomputations/spectrum.git\n", "cd spectrum\n", "pip install -r requirements.txt\n", "\n", "# run\n", "python spectrum.py --model-name microsoft/Phi-3.5-mini-instruct --top-percent 30\n", "```\n", "\n", "This command first scans the model (if not available) and then produces the YAML file with top SNR layers." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# For simplicity, I'm pasting the YAML parameters here\n", "\n", "yaml_parameters=\"\"\"unfrozen_parameters:\n", "- ^lm_head.weight$\n", "- ^model.embed_tokens.weight$\n", "# mlp.down_proj layers\n", "- model.layers.2.mlp.down_proj\n", "- model.layers.3.mlp.down_proj\n", "- model.layers.1.mlp.down_proj\n", "- model.layers.23.mlp.down_proj\n", "- model.layers.4.mlp.down_proj\n", "- model.layers.26.mlp.down_proj\n", "- model.layers.25.mlp.down_proj\n", "- model.layers.24.mlp.down_proj\n", "- model.layers.28.mlp.down_proj\n", "# mlp.gate_up_proj layers\n", "- model.layers.31.mlp.gate_up_proj\n", "- model.layers.4.mlp.gate_up_proj\n", "- model.layers.3.mlp.gate_up_proj\n", "- model.layers.5.mlp.gate_up_proj\n", "- model.layers.6.mlp.gate_up_proj\n", "- model.layers.2.mlp.gate_up_proj\n", "- model.layers.30.mlp.gate_up_proj\n", "- model.layers.9.mlp.gate_up_proj\n", "- model.layers.28.mlp.gate_up_proj\n", "# self_attn.o_proj layers\n", "- model.layers.0.self_attn.o_proj\n", "- model.layers.1.self_attn.o_proj\n", "- model.layers.10.self_attn.o_proj\n", "- model.layers.11.self_attn.o_proj\n", "- model.layers.9.self_attn.o_proj\n", "- model.layers.3.self_attn.o_proj\n", "- model.layers.19.self_attn.o_proj\n", "- model.layers.8.self_attn.o_proj\n", "- model.layers.4.self_attn.o_proj\n", "# self_attn.qkv_proj layers\n", "- model.layers.23.self_attn.qkv_proj\n", "- model.layers.24.self_attn.qkv_proj\n", "- model.layers.22.self_attn.qkv_proj\n", "- model.layers.26.self_attn.qkv_proj\n", "- model.layers.27.self_attn.qkv_proj\n", "- model.layers.25.self_attn.qkv_proj\n", "- model.layers.28.self_attn.qkv_proj\n", "- model.layers.29.self_attn.qkv_proj\n", "- model.layers.31.self_attn.qkv_proj\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "unfrozen_parameters = []\n", "for line in yaml_parameters.splitlines():\n", " if line.startswith(\"- \"):\n", " unfrozen_parameters.append(line.split(\"- \")[1])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import re\n", "\n", "def _freeze_and_unfreeze_parameters(model, unfrozen_parameters):\n", " # freeze all parameters\n", " for param in model.parameters():\n", " param.requires_grad = False\n", " # unfreeze Spectrum parameters\n", " for name, param in model.named_parameters():\n", " if any(re.match(unfrozen_param, name) for unfrozen_param in unfrozen_parameters):\n", " param.requires_grad = True" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "_freeze_and_unfreeze_parameters(model, unfrozen_parameters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# check the outcome of our freezing operation\n", "for name, param in model.named_parameters():\n", " if param.requires_grad:\n", " print(name, param.requires_grad)\n", "\n", "# model.embed_tokens.weight True\n", "# model.layers.0.self_attn.o_proj.weight True\n", "# model.layers.1.self_attn.o_proj.weight True\n", "# model.layers.1.mlp.down_proj.weight True\n", "# ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training configuration" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# WANDB configuration (optional)\n", "\n", "# import wandb\n", "# run = wandb.init(...)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "3_j_YeY2roJk" }, "outputs": [], "source": [ "from trl import SFTConfig, SFTTrainer\n", "\n", "new_model_id=\"anakin87/Phi-3.5-mini-ITA\"\n", "\n", "cfg = SFTConfig(\n", " output_dir='./mymodel',\n", " overwrite_output_dir = True,\n", " hub_model_id=new_model_id,\n", " hub_strategy=\"every_save\",\n", " save_strategy=\"steps\",\n", " save_steps=500,\n", " save_total_limit=1,\n", " push_to_hub=True,\n", " logging_steps=20,\n", " max_seq_length=2048, # see above in \"Data preparation\" section \n", " dataset_text_field=\"text\", # since we already prepared the dataset, let's point the Trainer to the correct column\n", " remove_unused_columns=True,\n", " packing=True, # speeds up training. https://huggingface.co/docs/trl/en/sft_trainer#packing-dataset--constantlengthdataset- \n", " num_train_epochs=2,\n", " lr_scheduler_type=\"cosine\",\n", " warmup_ratio=0.2, \n", " bf16=True, \n", " tf32=True, \n", " learning_rate=5.0e-06, # suggested in https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/sample_finetune.py\n", " per_device_train_batch_size=8,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8eXWxsHHy4Cc" }, "outputs": [], "source": [ "sft_trainer = SFTTrainer(\n", " model=model,\n", " args=cfg,\n", " train_dataset=mixed_ds[\"train\"],\n", "\n", " tokenizer=tokenizer\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "auUi2IXC0mxL" }, "outputs": [], "source": [ "sft_trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tokenizer.pad_token = tokenizer.eos_token\n", "tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)\n", "tokenizer.padding_side = 'left'\n", "\n", "tokenizer.push_to_hub(new_model_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I finally did some manual updates on the model repo: \n", "- copying some files from the original model to my model...\n", "- modifying config.json and generation_config.json to use the right tokens ids for `eos_token_id`." ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": 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