{"id":4377,"date":"2024-10-19T08:38:25","date_gmt":"2024-10-19T07:38:25","guid":{"rendered":"https:\/\/cafe2sach.com\/?p=4377"},"modified":"2024-10-19T08:38:41","modified_gmt":"2024-10-19T07:38:41","slug":"cach-xay-dung-mo-hinh-ngon-ngu-lon-huong-dan-don-gian-va-hieu-qua","status":"publish","type":"post","link":"https:\/\/cafe2sach.com\/index.php\/2024\/10\/19\/cach-xay-dung-mo-hinh-ngon-ngu-lon-huong-dan-don-gian-va-hieu-qua\/","title":{"rendered":"C\u00e1ch X\u00e2y D\u1ef1ng M\u00f4 H\u00ecnh Ng\u00f4n Ng\u1eef L\u1edbn: H\u01b0\u1edbng D\u1eabn \u0110\u01a1n Gi\u1ea3n v\u00e0 Hi\u1ec7u Qu\u1ea3"},"content":{"rendered":"<p>Th\u1ebf gi\u1edbi c\u1ee7a c\u00e1c M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (LLM) \u0111ang b\u00f9ng n\u1ed5. Nhi\u1ec1u ng\u01b0\u1eddi c\u00f3 th\u1ec3 cho d\u00f9ng nh\u1eefng ph\u00f9 th\u1ee7y AI n\u00e0y c\u00f3 th\u1ec3 vi\u1ebft th\u01a1, d\u1ecbch ng\u00f4n ng\u1eef v\u00e0 th\u1eadm ch\u00ed t\u1ea1o m\u00e3 nh\u01b0 hi\u1ec7n t\u1ea1i c\u00f4ng c\u1ee5 c\u1ee7a <a href=\"http:\/\/Llam coder\">https:\/\/llamacoder.together.ai\/<\/a>\u00a0 \u0111\u00e3 cho th\u1ea5y ti\u1ec1m n\u0103ng thay th\u1ebf con ng\u01b0\u1eddi trong c\u1ea3 vi\u1ec7c l\u1eadp tr\u00ecnh. Nh\u01b0ng b\u1ea1n \u0111\u00e3 bao gi\u1edd t\u1ef1 h\u1ecfi ch\u00fang ho\u1ea1t \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o ch\u01b0a? V\u00e0 quan tr\u1ecdng h\u01a1n, b\u1ea1n c\u00f3 th\u1ec3 t\u1ef1 x\u00e2y d\u1ef1ng m\u1ed9t m\u00f4 h\u00ecnh nh\u01b0 v\u1eady kh\u00f4ng?<\/p>\n<p>C\u00e2u tr\u1ea3 l\u1eddi l\u00e0 ho\u00e0n to\u00e0n c\u00f3 v\u00e0 ch\u1ec9 b\u1eb1ng m\u1ed9t v\u00e0i d\u00f2ng code \u0111\u01a1n gi\u1ea3n! M\u1eb7c d\u00f9 vi\u1ec7c t\u1ea1o ra m\u1ed9t LLM ti\u00ean ti\u1ebfn \u0111\u00f2i h\u1ecfi nhi\u1ec1u t\u00e0i nguy\u00ean t\u00ednh to\u00e1n, nh\u01b0ng m\u1ed9t phi\u00ean b\u1ea3n \u0111\u01a1n gi\u1ea3n h\u00f3a c\u00f3 th\u1ec3 \u0111\u1ea1t \u0111\u01b0\u1ee3c ngay c\u1ea3 \u0111\u1ed1i v\u1edbi nh\u1eefng l\u1eadp tr\u00ecnh vi\u00ean m\u1edbi b\u1eaft \u0111\u1ea7u.<\/p>\n<p>Trong b\u00e0i vi\u1ebft n\u00e0y, ch\u00fang ta\u00a0 s\u1ebd\u00a0 c\u00f9ng t\u00ecm c\u00e1ch\u00a0 m\u1ed9t LLM c\u01a1 b\u1ea3n b\u1eb1ng TensorFlow v\u00e0 Python.<\/p>\n<p>Tr\u01b0\u1edbc ti\u00ean, b\u1ea1n c\u1ea7n c\u00e0i \u0111\u1eb7t TensorFlow trong m\u00f4i tr\u01b0\u1eddng Python c\u1ee7a m\u00ecnh. N\u1ebfu b\u1ea1n ch\u01b0a th\u1ef1c hi\u1ec7n, b\u1ea1n c\u00f3 th\u1ec3 c\u00e0i \u0111\u1eb7t b\u1eb1ng pip:<br \/>\n<code>pip install tensorflow<\/code><\/p>\n<div id=\"toc_container\" class=\"no_bullets\"><p class=\"toc_title\">N\u1ed9i dung ch&iacute;nh<\/p><ul class=\"toc_list\"><li><a href=\"#Xay_dung_mo_hinh_ngon_ngu_lon_tung_buoc_nhu_sau\"><span class=\"toc_number toc_depth_1\">1<\/span> X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn t\u1eebng b\u01b0\u1edbc nh\u01b0 sau<\/a><ul><li><a href=\"#1_Nhap_cac_thu_vien_can_thiet\"><span class=\"toc_number toc_depth_2\">1.1<\/span> 1: Nh\u1eadp c\u00e1c th\u01b0 vi\u1ec7n c\u1ea7n thi\u1ebft<\/a><\/li><li><a href=\"#2_Chuan_bi_du_lieu_van_ban_cua_chinh_ban\"><span class=\"toc_number toc_depth_2\">1.2<\/span> \n2: Chu\u1ea9n b\u1ecb d\u1eef li\u1ec7u v\u0103n b\u1ea3n c\u1ee7a ch\u00ednh b\u1ea1n<\/a><\/li><li><a href=\"#3_Phan_loai_va_sap_xep_theo_thu_tu\"><span class=\"toc_number toc_depth_2\">1.3<\/span> 3: Ph\u00e2n lo\u1ea1i v\u00e0 s\u1eafp x\u1ebfp theo th\u1ee9 t\u1ef1<\/a><\/li><\/ul><\/li><li><a href=\"#4_Xay_dung_cau_truc_LLM\"><span class=\"toc_number toc_depth_1\">2<\/span> 4: X\u00e2y d\u1ef1ng c\u1ea5u tr\u00fac LLM<\/a><\/li><li><a href=\"#5_Cho_mo_hinh_LLM_hoc_LLM_traning\"><span class=\"toc_number toc_depth_1\">3<\/span> 5: Cho m\u00f4 h\u00ecnh LLM h\u1ecdc (LLM traning)<\/a><\/li><li><a href=\"#6_Sang_tao\"><span class=\"toc_number toc_depth_1\">4<\/span> 6: S\u00e1ng t\u1ea1o<\/a><ul><li><a href=\"#Ket_qua_dau_ra\"><span class=\"toc_number toc_depth_2\">4.1<\/span> K\u1ebft qu\u1ea3 \u0111\u1ea7u ra<\/a><ul><li><a href=\"#Nguon_How_to_Build_Your_Own_Large_Language_Model\"><span class=\"toc_number toc_depth_3\">4.1.1<\/span> Ngu\u1ed3n: How to Build Your Own Large Language Model<\/a><\/li><\/ul><\/li><\/ul><\/li><\/ul><\/div>\n<h1><span id=\"Xay_dung_mo_hinh_ngon_ngu_lon_tung_buoc_nhu_sau\">X\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn t\u1eebng b\u01b0\u1edbc nh\u01b0 sau<\/span><\/h1>\n<p>&nbsp;<\/p>\n<h2><span id=\"1_Nhap_cac_thu_vien_can_thiet\">1: Nh\u1eadp c\u00e1c th\u01b0 vi\u1ec7n c\u1ea7n thi\u1ebft<\/span><\/h2>\n<p><code>import tensorflow as tf<br \/>\nfrom tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout<br \/>\nfrom tensorflow.keras.models import Sequential<br \/>\nfrom tensorflow.keras.preprocessing.text import Tokenizer<br \/>\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences<br \/>\nimport numpy as np<\/code><\/p>\n<h2><span id=\"2_Chuan_bi_du_lieu_van_ban_cua_chinh_ban\"><code><\/code><br \/>\n2: Chu\u1ea9n b\u1ecb d\u1eef li\u1ec7u v\u0103n b\u1ea3n c\u1ee7a ch\u00ednh b\u1ea1n<\/span><\/h2>\n<p>M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (LLM) c\u1ee7a ch\u00fang ta c\u1ea7n h\u1ecdc t\u1eeb d\u1eef li\u1ec7u v\u0103n b\u1ea3n (d\u1ea1ng d\u1eef li\u1ec7u text). Gi\u1ed1ng nh\u01b0 m\u1ed9t \u0111\u1ee9a tr\u1ebb \u0111\u01b0\u1ee3c \u0111\u1ecdc s\u00e1ch v\u1ec1 m\u1ed9t ch\u1ee7 \u0111\u1ec1 n\u00e0o \u0111\u00f3, c\u00f3 th\u1ec3 s\u00e1ch c\u1ed5 t\u00edch, s\u00e1ch trinh th\u00e1m, c\u00e0ng \u0111\u1ecdc nhi\u1ec1u n\u00f3 c\u00e0ng hi\u1ec3u nhi\u1ec1u h\u01a1n v\u00e0 c\u00f3 kh\u1ea3 n\u0103ng t\u1ea1o ra c\u00e1c b\u00e0i vi\u1ebft t\u1ed1t h\u01a1n.<\/p>\n<p>V\u00ed d\u1ee5, Ch\u00fang ta c\u00f3 th\u1ec3 b\u1eaft \u0111\u1ea7u v\u1edbi m\u1ed9t t\u1eadp d\u1eef li\u1ec7u nh\u1ecf g\u1ed3m c\u00e1c c\u00e2u chuy\u1ec7n c\u1ed5 t\u00edch.<\/p>\n<p><code>data = [<br \/>\n# T\u1eadp d\u1eef li\u1ec7u c\u1ee7a b\u1ea1n s\u1ebd \u1edf \u0111\u00e2y...<br \/>\n]<\/code><\/p>\n<h2><span id=\"3_Phan_loai_va_sap_xep_theo_thu_tu\">3: Ph\u00e2n lo\u1ea1i v\u00e0 s\u1eafp x\u1ebfp theo th\u1ee9 t\u1ef1<\/span><\/h2>\n<p>H\u00e3y t\u01b0\u1edfng t\u01b0\u1ee3ng vi\u1ec7c c\u1eaft c\u00e1c t\u1eeb th\u00e0nh c\u00e1c ch\u1eef c\u00e1i ri\u00eang l\u1ebb, ph\u00e2n lo\u1ea1i theo th\u1ee9 t\u1ef1 c\u0169ng ho\u1ea1t \u0111\u1ed9ng t\u01b0\u01a1ng t\u1ef1, chia c\u00e1c c\u00e2u th\u00e0nh c\u00e1c t\u1eeb ri\u00eang l\u1ebb. Sau \u0111\u00f3, LLM t\u00ecm hi\u1ec3u m\u1ed1i quan h\u1ec7 gi\u1eefa c\u00e1c t\u1eeb n\u00e0y b\u1eb1ng c\u00e1ch ph\u00e2n t\u00edch tr\u00ecnh t\u1ef1 c\u1ee7a ch\u00fang. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 M\u00e3 ngu\u1ed3n ch\u00fang ta m\u00e3 h\u00f3a d\u1eef li\u1ec7u v\u00e0 t\u1ea1o ra c\u00e1c chu\u1ed7i c\u00f3 \u0111\u1ed9 d\u00e0i kh\u00e1c nhau, m\u00f4 ph\u1ecfng c\u00e1c k\u00ed t\u1ef1 ng\u00f4n ng\u1eef trong th\u1ef1c t\u1ebf.<\/p>\n<p><em>\u0110o\u1ea1n code n\u00e0y \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 ti\u1ec1n x\u1eed l\u00fd v\u0103n b\u1ea3n v\u00e0 chu\u1ea9n b\u1ecb d\u1eef li\u1ec7u cho m\u1ed9t m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y (v\u00ed d\u1ee5, m\u00f4 h\u00ecnh d\u1ef1 \u0111o\u00e1n t\u1eeb ti\u1ebfp theo)<\/em><\/p>\n<p><strong>Tokenizer()<\/strong>: \u0110\u00e2y l\u00e0 m\u1ed9t \u0111\u1ed1i t\u01b0\u1ee3ng t\u1eeb th\u01b0 vi\u1ec7n Keras d\u00f9ng \u0111\u1ec3 chuy\u1ec3n \u0111\u1ed5i v\u0103n b\u1ea3n th\u00e0nh c\u00e1c chu\u1ed7i s\u1ed1. M\u1ed7i t\u1eeb trong v\u0103n b\u1ea3n s\u1ebd \u0111\u01b0\u1ee3c g\u00e1n m\u1ed9t s\u1ed1 duy nh\u1ea5t.<\/p>\n<p><code>tokenizer = Tokenizer()<br \/>\ntokenizer.fit_on_texts(data)<br \/>\ntotal_words = len(tokenizer.word_index) + 1<\/code><\/p>\n<ul>\n<li><strong>fit_on_texts(data)<\/strong>: Ph\u01b0\u01a1ng th\u1ee9c n\u00e0y &#8220;h\u1ecdc&#8221; c\u00e1c t\u1eeb trong d\u1eef li\u1ec7u <code>data<\/code>, sau \u0111\u00f3 x\u00e2y d\u1ef1ng m\u1ed9t t\u1eeb \u0111i\u1ec3n (dictionary) \u00e1nh x\u1ea1 m\u1ed7i t\u1eeb \u0111\u1ebfn m\u1ed9t s\u1ed1.<\/li>\n<\/ul>\n<p><code><\/code><code><\/code><code>input_sequences = []<br \/>\nfor line in data:<br \/>\ntoken_list = tokenizer.texts_to_sequences([line])[0]<br \/>\nfor i in range(1, len(token_list)):<br \/>\nn_gram_sequence = token_list[:i+1]<br \/>\ninput_sequences.append(n_gram_sequence)<\/code><\/p>\n<ul>\n<li><strong>tokenizer.word_index<\/strong>: \u0110\u00e2y l\u00e0 thu\u1ed9c t\u00ednh ch\u1ee9a t\u1eeb \u0111i\u1ec3n c\u00e1c t\u1eeb \u0111\u00e3 \u0111\u01b0\u1ee3c tokenizer \u00e1nh x\u1ea1. M\u1ed7i t\u1eeb \u0111\u01b0\u1ee3c \u00e1nh x\u1ea1 \u0111\u1ebfn m\u1ed9t s\u1ed1 nguy\u00ean.<\/li>\n<li><strong>total_words<\/strong>: \u0110\u1ec3 t\u00ednh t\u1ed5ng s\u1ed1 t\u1eeb trong t\u1eeb \u0111i\u1ec3n, ta l\u1ea5y \u0111\u1ed9 d\u00e0i c\u1ee7a t\u1eeb \u0111i\u1ec3n c\u1ed9ng th\u00eam 1 (v\u00ec ch\u1ec9 s\u1ed1 b\u1eaft \u0111\u1ea7u t\u1eeb 1, v\u00e0 gi\u00e1 tr\u1ecb 0 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c d\u00e0nh cho padding).<\/li>\n<\/ul>\n<p><code>max_sequence_length = max([len(x) for x in input_sequences])<br \/>\ninput_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')<\/code><\/p>\n<ul>\n<li><strong>tokenizer.texts_to_sequences([line])<\/strong>: Chuy\u1ec3n c\u00e2u (line) th\u00e0nh m\u1ed9t danh s\u00e1ch c\u00e1c s\u1ed1 d\u1ef1a tr\u00ean t\u1eeb \u0111i\u1ec3n \u0111\u00e3 x\u00e2y d\u1ef1ng. M\u1ed7i t\u1eeb trong c\u00e2u \u0111\u01b0\u1ee3c chuy\u1ec3n th\u00e0nh ch\u1ec9 s\u1ed1 t\u01b0\u01a1ng \u1ee9ng.<\/li>\n<li><strong>for i in range(1, len(token_list))<\/strong>: V\u00f2ng l\u1eb7p t\u1ea1o ra c\u00e1c chu\u1ed7i n-gram cho t\u1eebng c\u00e2u. N-gram l\u00e0 m\u1ed9t chu\u1ed7i con c\u1ee7a c\u00e1c t\u1eeb li\u00ean ti\u1ebfp.<\/li>\n<li><strong>n_gram_sequence = token_list[<\/strong><strong>+1]<\/strong>: \u0110\u00e2y l\u00e0 c\u00e1ch t\u1ea1o ra c\u00e1c chu\u1ed7i n-gram v\u1edbi \u0111\u1ed9 d\u00e0i t\u0103ng d\u1ea7n t\u1eeb 2 t\u1eeb tr\u1edf l\u00ean. V\u00ed d\u1ee5, n\u1ebfu c\u00e2u l\u00e0 &#8220;I love coding&#8221;, c\u00e1c n-gram s\u1ebd l\u00e0 [&#8220;I love&#8221;, &#8220;I love coding&#8221;].<\/li>\n<li><strong>input_sequences.append(n_gram_sequence)<\/strong>: Th\u00eam chu\u1ed7i n-gram v\u00e0o danh s\u00e1ch <code>input_sequences<\/code>.<\/li>\n<\/ul>\n<h1><span id=\"4_Xay_dung_cau_truc_LLM\">4: X\u00e2y d\u1ef1ng c\u1ea5u tr\u00fac LLM<\/span><\/h1>\n<p>Ch\u00fang ta h\u00e3y xem LLM nh\u01b0 m\u1ed9t m\u1ea1ng l\u01b0\u1edbi ph\u1ee9c t\u1ea1p c\u1ee7a c\u00e1c t\u1ebf b\u00e0o th\u1ea7n kinh. M\u00e3 ngu\u1ed3n d\u01b0\u1edbi \u0111\u00e2y x\u00e2y d\u1ef1ng m\u1ed9t m\u00f4 h\u00ecnh tu\u1ea7n t\u1ef1 trong TensorFlow:<\/p>\n<p><code>model = Sequential()<br \/>\nmodel.add(Embedding(total_words, 100,input_length=max_sequence_length-1))<br \/>\nmodel.add(LSTM(150, return_sequences=True))<br \/>\nmodel.add(Dropout(0.2))<br \/>\nmodel.add(LSTM(100))<br \/>\nmodel.add(Dense(total_words, activation='softmax'))<\/code><\/p>\n<h1><span id=\"5_Cho_mo_hinh_LLM_hoc_LLM_traning\">5: Cho m\u00f4 h\u00ecnh LLM h\u1ecdc (LLM traning)<\/span><\/h1>\n<p>C\u0169ng gi\u1ed1ng nh\u01b0 con ng\u01b0\u1eddi h\u1ecdc th\u00f4ng qua th\u1ef1c h\u00e0nh, LLM c\u1ee7a ch\u00fang ta c\u1ea7n \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o. M\u00e3 chia c\u00e1c chu\u1ed7i th\u00e0nh t\u1eeb \u0111\u1ea7u v\u00e0o v\u00e0 t\u1eeb m\u1ee5c ti\u00eau, sau \u0111\u00f3 \u0111\u01b0a ch\u00fang v\u00e0o m\u00f4 h\u00ecnh. M\u00f4 h\u00ecnh \u0111i\u1ec1u ch\u1ec9nh c\u00e1c k\u1ebft n\u1ed1i n\u1ed9i b\u1ed9 c\u1ee7a n\u00f3 d\u1ef1a tr\u00ean m\u1ee9c \u0111\u1ed9 d\u1ef1 \u0111o\u00e1n c\u00e1c t\u1eeb m\u1ee5c ti\u00eau t\u1ed1t nh\u01b0 th\u1ebf n\u00e0o, d\u1ea7n d\u1ea7n tr\u1edf n\u00ean t\u1ed1t h\u01a1n trong vi\u1ec7c t\u1ea1o ra c\u00e1c c\u00e2u \u0111\u00fang ng\u1eef ph\u00e1p v\u00e0 ph\u00f9 h\u1ee3p v\u1edbi ng\u1eef c\u1ea3nh.<\/p>\n<p><code>model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])<\/code><\/p>\n<p><code>X, y = input_sequences[:, :-1], input_sequences[:, -1]<br \/>\ny = tf.keras.utils.to_categorical(y, num_classes=total_words)<\/code><\/p>\n<p>model.fit(X, y, epochs=200, verbose=1)<\/p>\n<h1><span id=\"6_Sang_tao\">6: S\u00e1ng t\u1ea1o<\/span><\/h1>\n<p>V\u1edbi LLM \u0111\u00e3 \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o, \u0111\u00e3 \u0111\u1ebfn l\u00fac t\u1ea1o ra m\u1ed9t s\u1ed1 v\u0103n b\u1ea3n! Ch\u00fang ta cung c\u1ea5p m\u1ed9t c\u00e2u g\u1ed1c v\u00e0 m\u00f4 h\u00ecnh d\u1ef1 \u0111o\u00e1n t\u1eeb ti\u1ebfp theo d\u1ef1a tr\u00ean s\u1ef1 hi\u1ec3u bi\u1ebft c\u1ee7a n\u00f3 v\u1ec1 tr\u00ecnh t\u1ef1 v\u00e0 t\u1eeb v\u1ef1ng. Qu\u00e1 tr\u00ecnh n\u00e0y ti\u1ebfp t\u1ee5c, x\u00e2y d\u1ef1ng m\u1ed9t c\u00e2u m\u1edbi theo t\u1eebng t\u1eeb.<\/p>\n<p><code>seed_text = \"D\u1eef li\u1ec7u b\u1eaft \u0111\u1ea7u c\u1ee7a b\u1ea1n s\u1ebd \u1edf \u0111\u00e2y\"<br \/>\nnext_words = 15<\/code><\/p>\n<p><code>for _ in range(next_words):<br \/>\ntoken_list = tokenizer.texts_to_sequences([seed_text])[0]<br \/>\ntoken_list = pad_sequences([token_list], maxlen=max_sequence_length-1, padding='pre')<br \/>\npredict_probabilities = model.predict(token_list, verbose=0)[0]<br \/>\npredict_index = np.argmax(predicted_probabilities)<br \/>\noutput_word = tokenizer.index_word[predicted_index]<br \/>\nseed_text += \" \" + output_word<\/code><\/p>\n<p><code>print(seed_text)<\/code><\/p>\n<p>\u0110o\u1ea1n m\u00e3 n\u00e0y l\u1ea5y m\u1ed9t v\u0103n b\u1ea3n h\u1ea1t gi\u1ed1ng v\u00e0 m\u1edf r\u1ed9ng n\u00f3 b\u1eb1ng c\u00e1ch d\u1ef1 \u0111o\u00e1n c\u00e1c t\u1eeb ti\u1ebfp theo, t\u1eebng t\u1eeb m\u1ed9t.<\/p>\n<h2><span id=\"Ket_qua_dau_ra\">K\u1ebft qu\u1ea3 \u0111\u1ea7u ra<\/span><\/h2>\n<p>\u0110\u00e2y l\u00e0 m\u1ed9t LLM \u0111\u01a1n gi\u1ea3n h\u00f3a, nh\u01b0ng n\u00f3 ch\u1ee9ng minh c\u00e1c nguy\u00ean t\u1eafc c\u1ed1t l\u00f5i c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef. M\u1eb7c d\u00f9 kh\u00f4ng th\u1ec3 s\u00e1nh b\u1eb1ng s\u1ef1 m\u1ea1nh m\u1ebd c\u1ee7a ChatGPT, nh\u01b0ng \u0111\u00e2y l\u00e0 b\u01b0\u1edbc \u0111\u1ec7m \u0111\u1ec3 ch\u00fang ta\u00a0 b\u01b0\u1edbc v\u00e0o th\u1ebf gi\u1edbi h\u1ea5p d\u1eabn c\u1ee7a AI v\u00e0 NLP.<\/p>\n<p>Khi b\u1ea1n \u0111\u00e0o s\u00e2u h\u01a1n, kh\u00e1m ph\u00e1 c\u00e1c ki\u1ebfn \u200b\u200btr\u00fac ph\u1ee9c t\u1ea1p h\u01a1n, th\u1eed nghi\u1ec7m v\u1edbi c\u00e1c t\u1eadp d\u1eef li\u1ec7u kh\u00e1c nhau (c\u00f3 th\u1ec3 l\u00e0 v\u1ec1 c\u00e1c ch\u1ee7 \u0111\u1ec1 kh\u00e1c nhau) v\u00e0 tinh ch\u1ec9nh quy tr\u00ecnh \u0111\u00e0o t\u1ea1o c\u1ee7a m\u00ecnh, b\u1ea1n s\u1ebd ch\u1ee9ng ki\u1ebfn \u200b\u200bm\u00f4 h\u00ecnh LLM c\u1ee7a m\u00ecnh ph\u00e1t tri\u1ec3n th\u00e0nh m\u1ed9t tr\u00ecnh t\u1ea1o ng\u00f4n ng\u1eef tinh vi v\u00e0 s\u00e1ng t\u1ea1o h\u01a1n.<\/p>\n<p>H\u00e3y nh\u1edb r\u1eb1ng, h\u00e0nh tr\u00ecnh c\u0169ng quan tr\u1ecdng nh\u01b0 \u0111\u00edch \u0111\u1ebfn (nh\u01b0ng \u0111i\u1ec1u quan tr\u1ecdng l\u00e0 h\u1ea1nh ph\u00fac n\u1eb1m \u1edf h\u00e0nh tr\u00ecnh \ud83d\ude42 ). H\u00e3y t\u1eadn h\u01b0\u1edfng qu\u00e1 tr\u00ecnh h\u1ecdc h\u1ecfi, m\u00e0y m\u00f2 v\u00e0 ch\u1ee9ng ki\u1ebfn \u200b\u200bs\u1ee9c m\u1ea1nh c\u1ee7a AI ph\u00e1t tri\u1ec3n. Ch\u00fac b\u1ea1n s\u1edbm t\u1ea1o ra \u0111\u01b0\u1ee3c m\u1ed9t tr\u1ee3 l\u00ed ng\u00f4n ng\u1eef c\u1ee7a ri\u00eang m\u00ecnh th\u1eadt xu\u1ea5t s\u1eafc \ud83d\ude42<\/p>\n<p>&nbsp;<\/p>\n<h3><span id=\"Nguon_How_to_Build_Your_Own_Large_Language_Model\">Ngu\u1ed3n: How to Build Your Own Large Language Model<\/span><\/h3>\n","protected":false},"excerpt":{"rendered":"<p>Th\u1ebf gi\u1edbi c\u1ee7a c\u00e1c M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (LLM) \u0111ang b\u00f9ng n\u1ed5. Nhi\u1ec1u ng\u01b0\u1eddi c\u00f3 th\u1ec3 cho d\u00f9ng nh\u1eefng [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":4076,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,250,561,565,714,233],"tags":[808,792,807],"class_list":["post-4377","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogs","category-deep-learning","category-federated-learning-2","category-he-thong-khuyen-nghi-ai","category-hoc-may-co-ban","category-tri-tue-nhan-tao","tag-cach-xay-dung-mo-hinh-ngon-ngu","tag-llm","tag-mo-hinh-ngon-ngu-lon"],"_links":{"self":[{"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/posts\/4377","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/comments?post=4377"}],"version-history":[{"count":2,"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/posts\/4377\/revisions"}],"predecessor-version":[{"id":4379,"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/posts\/4377\/revisions\/4379"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/media\/4076"}],"wp:attachment":[{"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/media?parent=4377"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/categories?post=4377"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cafe2sach.com\/index.php\/wp-json\/wp\/v2\/tags?post=4377"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}