File size: 8,003 Bytes
2cddd11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
{
"cells": [
{
"cell_type": "markdown",
"id": "12315053-0630-4d3d-8028-02035c2dbf14",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Slide Speech Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8eed0bf-d822-4091-8762-df6582095ab4",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Dir Structure:\n",
" - data\n",
" - info\n",
" - test\n",
" - val\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"id": "e999f437-d756-492d-b873-6ee656279b53",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Phi 3 Tinkering"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f5f1033-9118-4106-a7fb-6c3b527fe075",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Prompt template for Phi-3\n",
"<|system|>\n",
"You are a python developer.<|end|>\n",
"<|user|>\n",
"Help me generate a bubble sort algorithm<|end|>\n",
"<|assistant|>\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1e9e81b0-ae3d-46f1-97ff-138984d07a28",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
"\n",
"cache_dir = \"./../cache\"\n",
"model_id = \"microsoft/Phi-3-mini-4k-instruct\"\n",
"device = \"cuda:0\"\n",
"dtype = torch.float16\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_id,\n",
" device_map = device,\n",
" torch_dtype = dtype,\n",
" trust_remote_code=True,\n",
" cache_dir = cache_dir,\n",
" attn_implementation = \"flash_attention_2\"\n",
")\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir = cache_dir)\n",
"\n",
"pipe = pipeline(\n",
" \"text-generation\",\n",
" model = model,\n",
" tokenizer = tokenizer\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93b9fb26-4661-4a35-ad62-b87834f577bc",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a python developer\"},\n",
" {\"role\": \"user\", \"content\": \"Help me generate a bubble sort algorithm\"}\n",
"]\n",
"\n",
"generation_args = {\n",
" \"max_new_tokens\": 600,\n",
" \"return_full_text\": False,\n",
" \"temperature\": 1.0,\n",
" \"do_sample\": True\n",
"}\n",
"\n",
"output = pipe(messages, **generation_args)\n",
"print(output[0][\"generated_text\"])"
]
},
{
"cell_type": "markdown",
"id": "62db724a-9e20-422d-a5b3-dd55cae55cc7",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Training Phi 3"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2036e6b5-c794-4668-9a79-8a53a2736cfa",
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig\n",
"from huggingface_hub import ModelCard, ModelCardData, HfApi\n",
"from datasets import load_dataset\n",
"from jinja2 import Template\n",
"from trl import SFTTrainer\n",
"import yaml\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2dcff92e-ab3e-407a-8595-31ffae5f7acd",
"metadata": {},
"outputs": [],
"source": [
"# Model Configs\n",
"MODEL_ID = \"microsoft/Phi-3-mini-4k-instruct\"\n",
"NEW_MODEL_NAME = \"opus-phi-3-mini-4k-instruct\"\n",
"CACHE_DIR = \"./../cache\"\n",
"\n",
"# Dataset Configs\n",
"DATASET_NAME = \"\"\n",
"SPLIT = \"train\"\n",
"\n",
"# the maximum length of the sequences that the model will handle\n",
"MAX_SEQ_LENGTH = 4096\n",
"num_train_epochs = 1\n",
"license = \"apache-2.0\"\n",
"username = \"darshanmakwana412\"\n",
"learning_rate = 1.41e-5\n",
"per_device_train_batch_size = 4\n",
"gradient_accumulation_steps = 1\n",
"\n",
"# If bd16 is supported use bf16 otherwise use f16\n",
"if torch.cuda.is_bf16_supported():\n",
" compute_dtype = torch.bfloat16\n",
"else:\n",
" compute_dtype = torch.float16"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0fbcf3c6-dd94-4133-a805-910d57c9f974",
"metadata": {},
"outputs": [],
"source": [
"model = AutoModelForCausalLM.from_pretrained(MODEL_ID, cache_dir=CACHE_DIR, trust_remote_code=True)\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, cache_dir=CACHE_DIR, trust_remote_code=True)\n",
"# dataset = load_dataset(DATASET_NAME, split=SPLIT)\n",
"\n",
"# EOS Token is used to mark the end of a sentence\n",
"EOS_TOKEN=tokenizer.eos_token_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e31954ea-1838-4992-9132-32e59c42a128",
"metadata": {},
"outputs": [],
"source": [
"def formatting_prompts_func(examples):\n",
" # Extract the conversations from the examples.\n",
" convos = examples[\"conversations\"]\n",
" # Initialize an empty list to store the formatted texts.\n",
" texts = []\n",
" # Define a dictionary to map the 'from' field in the conversation to a prefix.\n",
" mapper = {\"system\": \"system\\n\", \"human\": \"\\nuser\\n\", \"gpt\": \"\\nassistant\\n\"}\n",
" # Define a dictionary to map the 'from' field in the conversation to a suffix.\n",
" end_mapper = {\"system\": \"\", \"human\": \"\", \"gpt\": \"\"}\n",
" # Iterate over each conversation.\n",
" for convo in convos:\n",
" # Format the conversation by joining each turn with its corresponding prefix and suffix.\n",
" # Append the EOS token to the end of the conversation.\n",
" text = \"\".join(f\"{mapper[(turn := x['from'])]} {x['value']}\\n{end_mapper[turn]}\" for x in convo)\n",
" texts.append(f\"{text}{EOS_TOKEN}\")\n",
" # Return the formatted texts.\n",
" return {\"text\": texts}\n",
"\n",
"dataset = dataset.map(formatting_prompts_func, batched=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3086c2a4-7cca-461e-894c-376046089fab",
"metadata": {},
"outputs": [],
"source": [
"args = TrainingArguments(\n",
" evaluation_strategy=\"steps\",\n",
" per_device_train_batch_size=7,\n",
" gradient_accumulation_steps=4,\n",
" gradient_checkpointing=True,\n",
" learning_rate=1e-4,\n",
" fp16 = not torch.cuda.is_bf16_supported(),\n",
" bf16 = torch.cuda.is_bf16_supported(),\n",
" max_steps=-1,\n",
" num_train_epochs=3,\n",
" save_strategy=\"epoch\",\n",
" logging_steps=10,\n",
" output_dir=NEW_MODEL_NAME,\n",
" optim=\"paged_adamw_32bit\",\n",
" lr_scheduler_type=\"linear\"\n",
")\n",
"\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=dataset,\n",
" dataset_text_field=\"text\",\n",
" max_seq_length=128,\n",
" formatting_func=formatting_prompts_func\n",
")\n",
"\n",
"device = \"cuda:0\"\n",
"\n",
"import gc\n",
"import os\n",
"\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"\n",
"trainer.train()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|