\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec GPT-Neo \ubaa8\ub378\uc744 \uc124\uc815\ud558\uace0, \uc774\ub97c \uc774\uc6a9\ud574 \uc791\ubb38\uc744 \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ubcf8 \uc124\uba85\uc740 \ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc5d0 \ub300\ud55c \uae30\ubcf8\uc801\uc778 \uc774\ud574\uac00 \uc788\ub294 \ubd84\ub4e4\uc744 \ub300\uc0c1\uc73c\ub85c \ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uae30\ucd08 \uc9c0\uc2dd\uc774 \uc5c6\ub294 \ubd84\ub4e4\ub3c4 \ub530\ub77c\ud560 \uc218 \uc788\ub3c4\ub85d \ucd5c\ub300\ud55c \uc790\uc138\ud788 \uc124\uba85\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud5c8\uae45\ud398\uc774\uc2a4\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub2e4\uc591\ud55c \ud234\uacfc \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc81c\uacf5\ud558\ub294 \ud68c\uc0ac\uc785\ub2c8\ub2e4. \uc774\ub4e4\uc758 \uc8fc\ub825 \uc81c\ud488\uc778 \ud2b8\ub80c\uc2a4\ud3ec\uba38(Transformers)<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \uc5ec\ub7ec \uc720\uba85 \ubaa8\ub378(GPT, BERT, T5 \ub4f1)\uc744 \ud3ec\ud568\ud558\uace0 \uc788\uc5b4, \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc774 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc744 \uc190\uc27d\uac8c \uc9c4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4.<\/p>\n GPT-Neo\ub294 EleutherAI\uc5d0\uc11c \uac1c\ubc1c\ud55c \ub300\uaddc\ubaa8 \uc5b8\uc5b4 \ubaa8\ub378\ub85c, OpenAI\uc758 GPT-3\uc640 \uc720\uc0ac\ud55c \uad6c\uc870\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. GPT-Neo\ub294 \ube44\ub85d GPT-3\uc5d0 \ube44\ud574 \ub35c \uc54c\ub824\uc838 \uc788\uc9c0\ub9cc, \uacf5\uac1c\uc801\uc73c\ub85c \uc0ac\uc6a9 \uac00\ub2a5\ud558\ub2e4\ub294 \uc810\uc5d0\uc11c \ud070 \uc7a5\uc810\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc9c4\uc815\ud55c \uc624\ud508 \uc18c\uc2a4 AI \ubaa8\ub378\uc744 \uad6c\ud604\ud558\ub294 \ub370 \uae30\uc5ec\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc791\ubb38 \ud658\uacbd\uc744 \uc124\uc815\ud558\uae30 \uc704\ud574 \ub2e4\uc74c\uc758 \uc694\uad6c \uc0ac\ud56d\uc744 \ucda9\uc871\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n \uc6b0\uc120, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc778 \uc774\uc81c \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud588\uc73c\ubbc0\ub85c GPT-Neo \ubaa8\ub378\uc744 \ub85c\ub4dc\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc124\uc815\ud558\ub294 \uc608\uc81c \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n \ubaa8\ub378\uc744 \ub85c\ub4dc\ud55c \ud6c4, \uc6b0\ub9ac\ub294 \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \ud568\uc218\ub97c \ub9cc\ub4e4\uc5b4 \ubcfc \uac83\uc785\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \uc8fc\uc5b4\uc9c4 \ud504\ub86c\ud504\ud2b8\ub97c \uae30\ubc18\uc73c\ub85c \ub2e4\uc74c \ub2e8\uc5b4\ub4e4\uc744 \uc608\uce21\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n \uc704\uc758 \uc774\uc81c \ud568\uc218\uac00 \uc815\uc0c1\uc801\uc73c\ub85c \uc791\ub3d9\ud558\ub294\uc9c0 \ud14c\uc2a4\ud2b8\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc704\uc5d0\uc11c \uc815\uc758\ud55c \ud504\ub86c\ud504\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc81c\ub85c \ud14d\uc2a4\ud2b8\uac00 \uc0dd\uc131\ub418\ub3c4\ub85d \ud569\ub2c8\ub2e4. \uba87 \uac00\uc9c0 \ub2e4\ub978 \ud504\ub86c\ud504\ud2b8\ub97c \uc0ac\uc6a9\ud574\ubcf4\uba74, \ubaa8\ub378\uc758 \ub2e4\uc591\ud55c \uc751\ub2f5\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc870\uc815\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \uc2dc \uc911\uc694\ud558\uac8c \uace0\ub824\ud574\uc57c \ud560 \uba87 \uac00\uc9c0 \ud30c\ub77c\ubbf8\ud130\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n \uc774\uc81c \uc774\ub7ec\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc801\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \uc0dd\ub7b5\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n AI\ub85c \uc0dd\uc131\ud55c \ud14d\uc2a4\ud2b8\uc758 \ud488\uc9c8\uc744 \ud3c9\uac00\ud558\ub294 \uac83\uc740 \uc8fc\uad00\uc801\uc778 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc0dd\uc131\ub41c \ud14d\uc2a4\ud2b8\uc758 \ud488\uc9c8\uc744 \ub192\uc774\uae30 \uc704\ud574 \uc5ec\ub7ec\ubd84\uc774 \uc9c1\uc811 \ub2e4\uc591\ud55c \ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud574\ubcf4\uc138\uc694. \ud14d\uc2a4\ud2b8\uc758 \ud765\ubbf8\ub85c\uc6b4 \ubd80\ubd84\uc744 \ubd84\uc11d\ud558\uace0, \ub354 \ub098\uc740 \uacb0\uacfc\ub97c \uc5bb\uae30 \uc704\ud55c \uc870\uc815\uc744 \ubc18\ubcf5\ud568\uc73c\ub85c\uc368 \ubaa8\ub378\uc744 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec GPT-Neo \ubaa8\ub378\uc744 \uc124\uc815\ud558\uace0 \uc791\ubb38 \ud658\uacbd\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ucd08 \ud658\uacbd \uc124\uc815\uc744 \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc740 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ud504\ub85c\uc81d\ud2b8\ub97c \uc218\ud589\ud560 \uc218 \uc788\ub294 \uae30\ubc18\uc744 \ub9c8\ub828\ud558\uac8c \ub429\ub2c8\ub2e4. \ub610\ud55c, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub192\uc774\uace0, \ucc3d\uc758\uc801\uc778 \ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n AI\uc640 \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc774 \ubc1c\uc804\ud568\uc5d0 \ub530\ub77c, \uc774\ub7ec\ud55c \ub3c4\uad6c\ub4e4\uc740 \uc810\uc810 \ub354 \ub9ce\uc740 \ubd84\uc57c\uc5d0 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ucc3d\uc758\ub825\uc744 \ubc1c\ud718\ud558\uc5ec \ub2e4\uc591\ud55c \uc2e4\ud5d8\uc744 \uc9c4\ud589\ud574\ubcf4\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n2. GPT-Neo\ub780?<\/h2>\n
3. \ud658\uacbd \uc124\uc815<\/h2>\n
3.1. \uc694\uad6c \uc0ac\ud56d<\/h3>\n
\n
3.2. \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n
transformers<\/code>, torch<\/code>\ub97c \uc124\uce58\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \ud130\ubbf8\ub110\uc5d0 \uc785\ub825\ud558\uc5ec \uc124\uce58\ud569\ub2c8\ub2e4.<\/p>\npip install transformers torch<\/code><\/pre>\n4. GPT-Neo \ubaa8\ub378 \ub85c\ub4dc<\/h2>\n
from transformers import GPTNeoForCausalLM, GPT2Tokenizer\n\n# \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ub85c\ub4dc\nmodel_name = 'EleutherAI\/gpt-neo-125M'\nmodel = GPTNeoForCausalLM.from_pretrained(model_name)\ntokenizer = GPT2Tokenizer.from_pretrained(model_name)<\/code><\/pre>\n5. \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \ud568\uc218 \uad6c\ud604<\/h2>\n
def generate_text(prompt, max_length=100):\n # \uc785\ub825 \ud14d\uc2a4\ud2b8\ub97c \ud1a0\ud070\ud654\n input_ids = tokenizer.encode(prompt, return_tensors='pt')\n\n # \ubaa8\ub378\uc744 \ud1b5\ud574 \ud14d\uc2a4\ud2b8 \uc0dd\uc131\n output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)\n\n # \uc0dd\uc131\ub41c \ud14d\uc2a4\ud2b8\ub97c \ub514\ucf54\ub529\n generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n\n return generated_text\n<\/code><\/pre>\ngenerate_text<\/code> \ud568\uc218\ub294 prompt<\/code>\uc640 \ucd5c\ub300 \uae38\uc774(max_length<\/code>)\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774\uc81c \uc608\uc2dc \ud504\ub86c\ud504\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774 \ud568\uc218\ub97c \ud638\ucd9c\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\nprompt = \"\uc778\uacf5\uc9c0\ub2a5\uc758 \ubbf8\ub798\ub294\"\nresult = generate_text(prompt)\nprint(result)<\/code><\/pre>\n6. \ubaa8\ub378 \ud14c\uc2a4\ud2b8<\/h2>\n
prompts = [\n \"\uc778\uacf5\uc9c0\ub2a5\uc758 \ubbf8\ub798\ub294\",\n \"\ub525\ub7ec\ub2dd\uc5d0\uc11c \uac00\uc7a5 \uc911\uc694\ud55c \uc694\uc18c\ub294\",\n \"\ucd5c\uc2e0 NLP \uc5f0\uad6c \ud2b8\ub80c\ub4dc\uc5d0 \ub300\ud574\",\n]\n\nfor prompt in prompts:\n print(f\"Prompt: {prompt}\")\n print(generate_text(prompt))\n print(\"\\n\")<\/code><\/pre>\n7. \uace0\uae09 \uc124\uc815<\/h2>\n
\n
def generate_text_advanced(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.95):\n input_ids = tokenizer.encode(prompt, return_tensors='pt')\n\n output = model.generate(input_ids, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, num_return_sequences=1)\n\n generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n\n return generated_text\n<\/code><\/pre>\n8. \uacb0\uacfc \ud3c9\uac00\ud558\uae30<\/h2>\n
9. \uacb0\ub860<\/h2>\n