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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d2d5bc5c-d465-4483-b137-52e168fc6f6e",
"metadata": {},
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"\n",
"checkpoint = \"bigcode/starcoderbase-7b\"\n",
"device = \"cuda\" # for GPU usage or \"cpu\" for CPU usage"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "def31126-da54-4099-b8f7-3236829d7559",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 157 ms, sys: 14.8 ms, total: 172 ms\n",
"Wall time: 293 ms\n"
]
}
],
"source": [
"%%time\n",
"tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d6fa452a-33a3-4e57-983a-28e1020004cb",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef692d63e58c42939869f3f53600be37",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading adapter_config.json: 0%| | 0.00/517 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22fa3c7f2fbd411d865a0a805003a84a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3db312bf99c401191e0b5ab424b6074",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)er_model.safetensors: 0%| | 0.00/155M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2min 11s, sys: 57.6 s, total: 3min 8s\n",
"Wall time: 1min 57s\n"
]
}
],
"source": [
"%%time\n",
"config = PeftConfig.from_pretrained(\"arpieb/peft-lora-starcoderbase-7b-personal-copilot-elixir\")\n",
"model = AutoModelForCausalLM.from_pretrained(\"bigcode/starcoderbase-7b\")\n",
"model = PeftModel.from_pretrained(model, \"arpieb/peft-lora-starcoderbase-7b-personal-copilot-elixir\")\n",
"model = model.merge_and_unload()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b8315302-801b-4b59-b158-25c86be30192",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 589, 1459, 81, 7656, 81, 5860, 346, 745, 44]])\n",
"CPU times: user 4.03 ms, sys: 0 ns, total: 4.03 ms\n",
"Wall time: 1.51 ms\n"
]
}
],
"source": [
"%%time\n",
"inputs = tokenizer.encode(\"def print_hello_world() do:\", return_tensors=\"pt\")\n",
"print(inputs)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "53d735d7-5941-4793-8b50-cc8e00de5437",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
"/home/rbates/src/starcoder-elixir/DHS-LLM-Workshop/ENV/lib/python3.10/site-packages/transformers/generation/utils.py:1353: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"def print_hello_world() do: IO.puts(\"Hello, World!\")\n",
"\n",
"#\n",
"CPU times: user 52.1 s, sys: 4.77 ms, total: 52.1 s\n",
"Wall time: 8.69 s\n"
]
}
],
"source": [
"%%time\n",
"outputs = model.generate(inputs)\n",
"print(tokenizer.decode(outputs[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1a346bef-a007-4311-b0ac-275dd786713d",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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