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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6f46e840-8a7f-4be2-a082-49b9ebf5a8c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"!pip install -q -U huggingface_hub peft transformers torch accelerate\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2d2918a1-d701-4a66-946c-6f668cb4ac1e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mon Jul 24 21:41:13 2023 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 NVIDIA H100 PCIe On | 00000000:06:00.0 Off | 0 |\n",
"| N/A 39C P0 52W / 350W | 0MiB / 81559MiB | 0% Default |\n",
"| | | Disabled |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0afdf8a6-ea7d-44ab-a1f9-a19e550e9dbd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
" from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
]
}
],
"source": [
"import torch\n",
"from peft import PeftModel, PeftConfig\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "adfcd11e-8d98-4cf3-abf4-e9fa933eb0d6",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7dc80313fdcd41a5a7ee168956df3dd9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "82cfa4fb-af16-4927-82c4-1fbf0fa84bfa",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/.local/lib/python3.8/site-packages/transformers/modeling_utils.py:2193: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
" warnings.warn(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d0f18088e32f4d4b857d2de5430528d4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/15 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# peft_model_id = \"results/checkpoint-12500\"\n",
"peft_model_id = \"dfurman/llama-2-70b-dolphin-peft\"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
")\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" config.base_model_name_or_path,\n",
" quantization_config=bnb_config,\n",
" use_auth_token=True,\n",
" torch_dtype=torch.bfloat16,\n",
" device_map=\"auto\",\n",
")\n",
"tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"\n",
"# Load the Lora model\n",
"model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d86f6a79-95f2-4e05-9bc7-3cbcbbbc9552",
"metadata": {},
"outputs": [],
"source": [
"# text generation function\n",
"\n",
"\n",
"def llama_generate(\n",
" model: AutoModelForCausalLM,\n",
" tokenizer: AutoTokenizer,\n",
" prompt: str,\n",
" max_new_tokens: int = 128,\n",
" temperature: int = 1.0,\n",
") -> str:\n",
" \"\"\"\n",
" Initialize the pipeline\n",
" Uses Hugging Face GenerationConfig defaults\n",
" https://huggingface.co/docs/transformers/v4.29.1/en/main_classes/text_generation#transformers.GenerationConfig\n",
" Args:\n",
" model (transformers.AutoModelForCausalLM): Falcon model for text generation\n",
" tokenizer (transformers.AutoTokenizer): Tokenizer for model\n",
" prompt (str): Prompt for text generation\n",
" max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.\n",
" temperature (float, optional): The value used to modulate the next token probabilities.\n",
" Defaults to 1.0\n",
" \"\"\"\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
" inputs = tokenizer(\n",
" [prompt],\n",
" return_tensors=\"pt\",\n",
" return_token_type_ids=False,\n",
" ).to(\n",
" device\n",
" ) # tokenize inputs, load on device\n",
"\n",
" # when running Torch modules in lower precision, it is best practice to use the torch.autocast context manager.\n",
" with torch.autocast(\"cuda\", dtype=torch.bfloat16):\n",
" response = model.generate(\n",
" **inputs,\n",
" max_new_tokens=max_new_tokens,\n",
" temperature=temperature,\n",
" return_dict_in_generate=True,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
" pad_token_id=tokenizer.pad_token_id,\n",
" )\n",
"\n",
" decoded_output = tokenizer.decode(\n",
" response[\"sequences\"][0],\n",
" skip_special_tokens=True,\n",
" ) # grab output in natural language\n",
"\n",
" return decoded_output[len(prompt) :] # remove prompt from output"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "28be263a-dd15-419f-a67e-7ca05b27435f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sure! Here's a delicious and easy vegan banana bread recipe:\n",
"\n",
"Ingredients:\n",
"- 2 cups all-purpose flour\n",
"- 1/2 cup sugar\n",
"- 1/2 cup vegan butter (such as Earth Balance)\n",
"- 1/2 cup vegan milk (such as almond milk)\n",
"- 1/2 cup unsweetened applesauce\n",
"- 1/2 cup mashed ripe bananas (about 2 medium bananas)\n",
"- 1 teaspoon baking soda\n",
"- 1/2 teaspoon salt\n",
"- 1/2 teaspoon ground cinnamon\n",
"- 1/2 teaspoon ground nutmeg\n",
"- 1/2 teaspoon ground cloves\n",
"- 1/2 cup chopped walnuts (optional)\n",
"\n",
"Instructions:\n",
"1. Preheat the oven to 350°F (175°C). Grease a 9x5-inch loaf pan with vegan butter or cooking spray.\n",
"2. In a large bowl, mix together the flour, sugar, vegan butter, vegan milk, applesauce, bananas, baking soda, salt, cinnamon, nutmeg, and cloves. Stir until well combined.\n",
"3. Fold in the chopped walnuts, if using.\n",
"4. Pour the batter into the prepared loaf pan.\n",
"5. Bake for 50-60 minutes, or until a toothpick inserted into the center of the bread comes out clean.\n",
"6. Let the bread cool in the pan for 10 minutes before transferring it to a wire rack to cool completely.\n",
"7. Slice and enjoy!\n",
"\n",
"Note: You can also add chocolate chips, dried fruit, or other mix-ins to the batter for extra flavor and texture. Enjoy your vegan banana bread!\n",
"\n",
"\n",
"\n",
"\n",
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"\n",
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"\n",
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"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"prompt = \"You are a helpful assistant. Tell me a recipe for vegan banana bread.\\n\"\n",
"\n",
"response = llama_generate(\n",
" model,\n",
" tokenizer,\n",
" prompt,\n",
" max_new_tokens=500,\n",
" temperature=0.92,\n",
")\n",
"\n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3625b3ff-6467-43ea-8557-9541934539ec",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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"
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