add instruction
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## Base
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* Models: [Google/Gemma](https://blog.google/technology/developers/gemma-open-models/) and [THUDM/GLM](https://github.com/THUDM/GLM)
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* Models: [Google/Gemma](https://blog.google/technology/developers/gemma-open-models/)
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* Packages: [transformers](https://huggingface.co/docs/transformers/index), [Datasets](https://huggingface.co/docs/datasets/index), [trl](https://huggingface.co/docs/trl/index)
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## HOW-TO
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@ -73,7 +73,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "283eecad62ac4c468217952044f683a9",
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"model_id": "d3ecaba97ac945b89d7851f2780f4faa",
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"version_major": 2,
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"version_minor": 0
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},
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@ -87,7 +87,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ba35d6b0db1d492783b8b7fa41d9532f",
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"model_id": "8eeaea87085c477482edbcf859cb48ab",
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"version_major": 2,
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"version_minor": 0
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},
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@ -100,7 +100,7 @@
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}
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],
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n",
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"tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)\n",
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"\n",
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"dataset[\"test\"] = dataset[\"test\"].map(lambda samples: tokenizer(samples[\"text\"], max_length=512, truncation=True), batched=True)\n",
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"dataset[\"train\"] = dataset[\"train\"].map(lambda samples: tokenizer(samples[\"text\"], max_length=512, truncation=True), batched=True)\n",
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@ -123,7 +123,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "0598ac059eb74a14aecaea95eda91ee7",
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"model_id": "808a16b1185243e6bfc8c83f09f5f9ba",
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"version_major": 2,
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"version_minor": 0
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},
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@ -152,7 +152,7 @@
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],
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"source": [
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"lora_config = LoraConfig(r = LORA_R, lora_alpha = LORA_ALPHA, lora_dropout = LORA_DROPOUT, task_type = TaskType.SEQ_CLS, target_modules = \"all-linear\")\n",
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"model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, device_map=\"auto\", num_labels=NUM_CLASSES)\n",
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"model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, num_labels=NUM_CLASSES, trust_remote_code=True)\n",
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"lora_model = get_peft_model(model, lora_config)\n",
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"lora_model.print_trainable_parameters()"
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]
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@ -188,7 +188,7 @@
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" <div>\n",
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" \n",
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" <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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" [10/10 00:17, Epoch 0/1]\n",
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" [10/10 00:16, Epoch 0/1]\n",
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" </div>\n",
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" <table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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@ -202,15 +202,15 @@
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" <tbody>\n",
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" <tr>\n",
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" <td>5</td>\n",
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" <td>0.977000</td>\n",
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" <td>1.173903</td>\n",
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" <td>0.560000</td>\n",
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" <td>1.423400</td>\n",
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" <td>1.191649</td>\n",
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" <td>0.510000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>10</td>\n",
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" <td>1.450300</td>\n",
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" <td>1.155836</td>\n",
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" <td>0.560000</td>\n",
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" <td>1.164800</td>\n",
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" <td>1.191105</td>\n",
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" <td>0.520000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table><p>"
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@ -273,7 +273,7 @@
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" <div>\n",
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" \n",
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" <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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" [10/10 00:17, Epoch 0/1]\n",
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" [10/10 00:16, Epoch 0/1]\n",
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" </div>\n",
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" <table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>5</td>\n",
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" <td>0.827100</td>\n",
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" <td>1.106255</td>\n",
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" <td>0.550000</td>\n",
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" <td>1.295200</td>\n",
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" <td>1.138854</td>\n",
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" <td>0.530000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>10</td>\n",
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" <td>1.360100</td>\n",
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" <td>1.089411</td>\n",
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" <td>0.540000</td>\n",
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" <td>1.100300</td>\n",
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" <td>1.137775</td>\n",
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" <td>0.530000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table><p>"
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@ -359,7 +359,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "3a6911922f574bc29d938985bb1cf2f5",
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"model_id": "ef2e165bd6c74fb2b962021549e619bd",
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"version_major": 2,
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"version_minor": 0
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},
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@ -377,7 +377,7 @@
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"Some weights of GemmaForSequenceClassification were not initialized from the model checkpoint at google/gemma-2b and are newly initialized: ['score.weight']\n",
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
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"The model 'PeftModelForSequenceClassification' is not supported for text-classification. Supported models are ['AlbertForSequenceClassification', 'BartForSequenceClassification', 'BertForSequenceClassification', 'BigBirdForSequenceClassification', 'BigBirdPegasusForSequenceClassification', 'BioGptForSequenceClassification', 'BloomForSequenceClassification', 'CamembertForSequenceClassification', 'CanineForSequenceClassification', 'LlamaForSequenceClassification', 'ConvBertForSequenceClassification', 'CTRLForSequenceClassification', 'Data2VecTextForSequenceClassification', 'DebertaForSequenceClassification', 'DebertaV2ForSequenceClassification', 'DistilBertForSequenceClassification', 'ElectraForSequenceClassification', 'ErnieForSequenceClassification', 'ErnieMForSequenceClassification', 'EsmForSequenceClassification', 'FalconForSequenceClassification', 'FlaubertForSequenceClassification', 'FNetForSequenceClassification', 'FunnelForSequenceClassification', 'GemmaForSequenceClassification', 'GPT2ForSequenceClassification', 'GPT2ForSequenceClassification', 'GPTBigCodeForSequenceClassification', 'GPTNeoForSequenceClassification', 'GPTNeoXForSequenceClassification', 'GPTJForSequenceClassification', 'IBertForSequenceClassification', 'JambaForSequenceClassification', 'LayoutLMForSequenceClassification', 'LayoutLMv2ForSequenceClassification', 'LayoutLMv3ForSequenceClassification', 'LEDForSequenceClassification', 'LiltForSequenceClassification', 'LlamaForSequenceClassification', 'LongformerForSequenceClassification', 'LukeForSequenceClassification', 'MarkupLMForSequenceClassification', 'MBartForSequenceClassification', 'MegaForSequenceClassification', 'MegatronBertForSequenceClassification', 'MistralForSequenceClassification', 'MixtralForSequenceClassification', 'MobileBertForSequenceClassification', 'MPNetForSequenceClassification', 'MptForSequenceClassification', 'MraForSequenceClassification', 'MT5ForSequenceClassification', 'MvpForSequenceClassification', 'NezhaForSequenceClassification', 'NystromformerForSequenceClassification', 'OpenLlamaForSequenceClassification', 'OpenAIGPTForSequenceClassification', 'OPTForSequenceClassification', 'PerceiverForSequenceClassification', 'PersimmonForSequenceClassification', 'PhiForSequenceClassification', 'PLBartForSequenceClassification', 'QDQBertForSequenceClassification', 'Qwen2ForSequenceClassification', 'Qwen2MoeForSequenceClassification', 'ReformerForSequenceClassification', 'RemBertForSequenceClassification', 'RobertaForSequenceClassification', 'RobertaPreLayerNormForSequenceClassification', 'RoCBertForSequenceClassification', 'RoFormerForSequenceClassification', 'SqueezeBertForSequenceClassification', 'StableLmForSequenceClassification', 'Starcoder2ForSequenceClassification', 'T5ForSequenceClassification', 'TapasForSequenceClassification', 'TransfoXLForSequenceClassification', 'UMT5ForSequenceClassification', 'XLMForSequenceClassification', 'XLMRobertaForSequenceClassification', 'XLMRobertaXLForSequenceClassification', 'XLNetForSequenceClassification', 'XmodForSequenceClassification', 'YosoForSequenceClassification'].\n",
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"100%|██████████| 100/100 [02:30<00:00, 1.50s/it]\n"
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"100%|██████████| 100/100 [02:23<00:00, 1.43s/it]\n"
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]
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}
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],
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 0.58 0.91 0.71 56\n",
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" 1 0.58 0.16 0.25 44\n",
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" 0 0.60 0.90 0.72 59\n",
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" 1 0.45 0.12 0.19 41\n",
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"\n",
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" accuracy 0.58 100\n",
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" macro avg 0.58 0.53 0.48 100\n",
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"weighted avg 0.58 0.58 0.51 100\n",
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" macro avg 0.53 0.51 0.45 100\n",
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"weighted avg 0.54 0.58 0.50 100\n",
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"\n"
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]
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}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Instruction Tuning \n",
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"\n",
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"In this notebook, I will give you an example of LLM's fine-tuning with instruction. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append(\"../src\")\n",
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"from utils import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL_ID = \"google/gemma-2b\"\n",
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"\n",
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"SEED = 42\n",
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"NUM_CLASSES = 2\n",
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"\n",
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"EPOCHS = 2\n",
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"\n",
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"LORA_R = 8\n",
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"LORA_ALPHA = 16\n",
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"LORA_DROPOUT = 0.1\n",
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"BATCH_SIZE = 1\n",
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"\n",
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"set_seed(SEED)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"'''\n",
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"data source: https://github.com/tatsu-lab/stanford_alpaca\n",
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"'''\n",
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"\n",
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"dataset = load_dataset(\"json\", data_files=\"../data/alpaca/data.json\")[\"train\"]\n",
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"dataset = dataset.shuffle().train_test_split(0.2)\n",
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"trainset, validset = dataset[\"train\"], dataset[\"test\"]\n",
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"dataset = validset.train_test_split(0.5)\n",
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"validset, testset = dataset[\"train\"], dataset[\"test\"]\n",
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"\n",
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"# sampling, as it is just a example file\n",
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"trainset = trainset.select(range(5)).to_pandas()\n",
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"validset = validset.select(range(5)).to_pandas()\n",
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"testset = testset.select(range(5)).to_pandas()\n",
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"\n",
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"trainset[\"prompt\"] = \"<start_of_turn>user \" + trainset[\"instruction\"] + \"<end_of_turn>\\n\\n\" + \"<start_of_turn>model \" + trainset[\"output\"] + \"<end_of_turn>\\n\"\n",
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"validset[\"prompt\"] = \"<start_of_turn>user \" + validset[\"instruction\"] + \"<end_of_turn>\\n\\n\" + \"<start_of_turn>model \" + validset[\"output\"] + \"<end_of_turn>\\n\"\n",
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"testset[\"prompt\"] = \"<start_of_turn>user \" + testset[\"instruction\"] + \"<end_of_turn>\\n\\n\" + \"<start_of_turn>model \"\n",
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"hello = testset\n",
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"\n",
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"trainset, validset, testset = Dataset.from_pandas(trainset[[\"prompt\"]]), Dataset.from_pandas(validset[[\"prompt\"]]), Dataset.from_pandas(testset[[\"prompt\"]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d14f410e8838476ebc4cc027bf37f7ba",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map: 0%| | 0/5 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "4416e436cf5f44f094c90a11243893a3",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map: 0%| | 0/5 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)\n",
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"\n",
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"trainset = trainset.map(lambda samples: tokenizer(samples[\"prompt\"], max_length=512, truncation=True), batched=True).remove_columns(\"prompt\")\n",
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"validset = validset.map(lambda samples: tokenizer(samples[\"prompt\"], max_length=512, truncation=True), batched=True).remove_columns(\"prompt\")\n",
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"#testset = testset.map(lambda samples: tokenizer(samples[\"prompt\"], max_length=512, truncation=True), batched=True).remove_columns(\"prompt\")\n",
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"\n",
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"dataset = DatasetDict({\"train\": trainset, \"valid\": validset, \"test\": testset})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Gemma's activation function should be approximate GeLU and not exact GeLU.\n",
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"Changing the activation function to `gelu_pytorch_tanh`.if you want to use the legacy `gelu`, edit the `model.config` to set `hidden_activation=gelu` instead of `hidden_act`. See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "54159c44cb59468a84da3a99d8fc61f1",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"trainable params: 9,805,824 || all params: 2,515,978,240 || trainable%: 0.3897420034920493\n"
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]
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}
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],
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"source": [
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"lora_config = LoraConfig(r = LORA_R, lora_alpha = LORA_ALPHA, lora_dropout = LORA_DROPOUT, task_type = \"CAUSAL_LM\", target_modules = \"all-linear\")\n",
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"model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map=\"auto\")\n",
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"lora_model = get_peft_model(model, lora_config)\n",
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"lora_model.print_trainable_parameters()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"max_steps is given, it will override any value given in num_train_epochs\n"
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]
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},
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{
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"data": {
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"text/html": [
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"\n",
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" <div>\n",
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" \n",
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" <progress value='2' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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" [ 2/20 : < :, Epoch 0.20/4]\n",
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" </div>\n",
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" <table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: left;\">\n",
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" <th>Step</th>\n",
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" <th>Training Loss</th>\n",
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" <th>Validation Loss</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" </tbody>\n",
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"</table><p>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# if using `Trainer`: \n",
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"trainer = Trainer(\n",
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" model=lora_model,\n",
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" args=TrainingArguments(\n",
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" output_dir=\"../experiment/instruction/Trainer/\",\n",
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" learning_rate=2e-5,\n",
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" num_train_epochs=1,\n",
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" weight_decay=0.01,\n",
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" logging_steps=5,\n",
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" max_steps=20,\n",
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" per_device_train_batch_size=BATCH_SIZE,\n",
|
||||
" per_device_eval_batch_size=BATCH_SIZE,\n",
|
||||
" evaluation_strategy=\"steps\",\n",
|
||||
" save_strategy=\"steps\",\n",
|
||||
" save_steps=5,\n",
|
||||
" load_best_model_at_end=True,\n",
|
||||
" report_to=\"none\"\n",
|
||||
" ),\n",
|
||||
" train_dataset=dataset[\"train\"],\n",
|
||||
" eval_dataset=dataset[\"valid\"],\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" data_collator=transformers.DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False),\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"trainer.train()\n",
|
||||
"trainer.save_state()\n",
|
||||
"trainer.save_model(output_dir=\"../experiment/instruction/Trainer/model/\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'LlamaForCausalLM', 'CodeGenForCausalLM', 'CohereForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'DbrxForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'FuyuForCausalLM', 'GemmaForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'JambaForCausalLM', 'LlamaForCausalLM', 'MambaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'MptForCausalLM', 'MusicgenForCausalLM', 'MusicgenMelodyForCausalLM', 'MvpForCausalLM', 'OlmoForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RecurrentGemmaForCausalLM', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'WhisperForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'generated_text': '<start_of_turn>user Name two major world religions.<end_of_turn>\\n\\n<start_of_turn>model inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol inol'}\n",
|
||||
"Two major world religions are Christianity and Islam.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ins_pipeline = pipeline(\"text-generation\", model=trainer.model, tokenizer=trainer.tokenizer)\n",
|
||||
"output = ins_pipeline(testset[\"prompt\"][0], max_new_tokens=256, do_sample=True)\n",
|
||||
"print(output[0])\n",
|
||||
"print(hello.loc[0, \"output\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"max_steps is given, it will override any value given in num_train_epochs\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <div>\n",
|
||||
" \n",
|
||||
" <progress value='4' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||||
" [ 4/10 00:00 < 00:01, 3.36 it/s, Epoch 0.60/2]\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",
|
||||
" <th>Validation Loss</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" </tbody>\n",
|
||||
"</table><p>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# if using `SFTTrainer`\n",
|
||||
"trainer = SFTTrainer(\n",
|
||||
" model=lora_model,\n",
|
||||
" args=TrainingArguments(\n",
|
||||
" output_dir=\"../experiment/instruction/SFTTrainer/\",\n",
|
||||
" learning_rate=2e-5,\n",
|
||||
" num_train_epochs=1,\n",
|
||||
" weight_decay=0.01,\n",
|
||||
" logging_steps=5,\n",
|
||||
" max_steps=10,\n",
|
||||
" per_device_train_batch_size=BATCH_SIZE,\n",
|
||||
" per_device_eval_batch_size=BATCH_SIZE,\n",
|
||||
" evaluation_strategy=\"steps\",\n",
|
||||
" save_strategy=\"steps\",\n",
|
||||
" save_steps=5,\n",
|
||||
" load_best_model_at_end=True,\n",
|
||||
" report_to=\"none\"\n",
|
||||
" ),\n",
|
||||
" train_dataset=dataset[\"train\"],\n",
|
||||
" eval_dataset=dataset[\"valid\"],\n",
|
||||
" dataset_text_field=\"prompt\",\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" data_collator=transformers.DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False),\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"trainer.train()\n",
|
||||
"trainer.save_state()\n",
|
||||
"trainer.save_model(output_dir=\"../experiment/instruction/SFTTrainer/model/\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'LlamaForCausalLM', 'CodeGenForCausalLM', 'CohereForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'DbrxForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'FuyuForCausalLM', 'GemmaForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'JambaForCausalLM', 'LlamaForCausalLM', 'MambaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'MptForCausalLM', 'MusicgenForCausalLM', 'MusicgenMelodyForCausalLM', 'MvpForCausalLM', 'OlmoForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RecurrentGemmaForCausalLM', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'WhisperForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'generated_text': '<start_of_turn>user Name two major world religions.<end_of_turn>\\n\\n<start_of_turn>model Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Abbiamo Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora Allora '}\n",
|
||||
"Two major world religions are Christianity and Islam.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ins_pipeline = pipeline(\"text-generation\", model=trainer.model, tokenizer=trainer.tokenizer)\n",
|
||||
"output = ins_pipeline(testset[\"prompt\"][0], max_new_tokens=256, do_sample=True)\n",
|
||||
"print(output[0])\n",
|
||||
"print(hello.loc[0, \"output\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "languageM",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.14"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -14,3 +14,4 @@ accelerate
|
|||
modelscope
|
||||
bitsandbytes
|
||||
transformers
|
||||
sentencepiece
|
||||
|
|
@ -7,7 +7,7 @@ import pandas as pd
|
|||
|
||||
from tqdm import tqdm
|
||||
from random import randint
|
||||
from datasets import load_dataset, DatasetDict
|
||||
from datasets import load_dataset, DatasetDict, Dataset
|
||||
|
||||
import transformers
|
||||
from trl import SFTTrainer
|
||||
|
|
|
|||
Loading…
Reference in New Issue