澶фā鍨嬪叆闂ㄦ暀绋嬶細寮曢寮€鍙戣€呰蛋杩汱LM涓栫晫

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闅忕潃AI鎶€鏈殑椋為€熷彂灞曪紝澶ц妯￠璁粌璇█妯″瀷锛圠LM锛夊凡缁忔垚涓鸿嚜鐒惰瑷€澶勭悊棰嗗煙鐨勬牳蹇冦€傛湰鏁欑▼涓哄紑鍙戣€呮彁渚涗簡涓€鎵囬€氬線LLM涓栫晫鐨勫ぇ闂紝浠庡熀纭€姒傚康鍒板疄鎴樺簲鐢紝甯姪澶у娣卞叆浜嗚ВLLM鐨勫師鐞嗐€佹灦鏋勫強璁粌鏂规硶銆?/p>

澶фā鍨嬬殑鑳藉姏涓庡簲鐢?/p>

澶фā鍨嬶紝濡侴PT銆丅ERT绛夛紝鍦ㄦ枃鏈敓鎴愩€佷唬鐮佺紪鍐欍€侀棶绛旂郴缁熺瓑棰嗗煙灞曠幇鍑烘儕浜虹殑鑳藉姏銆傚畠浠彲浠ユ牴鎹笉鍚岀殑浠诲姟杩涜寰皟锛屼骇鐢熼珮搴﹀畾鍒跺寲鐨勮В鍐虫柟妗堛€傚ぇ妯″瀷鐨勫崜瓒婃€ц兘鍙婂叾鍦ㄥ鏉備换鍔′腑鐨勯珮閫傚簲鎬э紝浣垮叾鎴愪负AI棰嗗煙鐨勭爺绌剁儹鐐广€備粠閫氱敤妯″瀷鍒颁笓涓氭ā鍨嬶紝澶фā鍨嬪湪澶勭悊鍚勭浠诲姟鏃堕兘琛ㄧ幇鍑哄己澶х殑娼滃姏銆?/p>

妯″瀷鏋舵瀯涓庡疄鐜?/p>

妯″瀷缁撴瀯姒傝堪锛氬ぇ妯″瀷閫氬父鍩轰簬Transformer鏋舵瀯锛岄€氳繃鑷敞鎰忓姏鏈哄埗楂樻晥澶勭悊搴忓垪淇℃伅銆備互涓嬫槸Transformer鏋舵瀯鐨勭畝鍗曚唬鐮佸疄鐜帮細

鍋囪宸茬粡瀵煎叆浜嗗繀瑕佺殑搴擄細

```python

class TransformerBlock(nn.Module):

def __init__(self, embed_dim, num_heads, forward_expansion):

super().__init__()

self.attention = MultiHeadAttention(embed_dim, num_heads) 瀹炵幇澶氬ご鑷敞鎰忓姏鏈哄埗

self.norm1 = nn.LayerNorm(embed_dim) 灞傚綊涓€鍖?/p>

self.feed_forward = FeedForward(embed_dim, forward_expansion) 鍓嶉绁炵粡缃戠粶

self.norm2 = nn.LayerNorm(embed_dim) 鍐嶆灞傚綊涓€鍖?/p>

self.dropout = nn.Dropout(0.1) 涓㈠純灞傜敤浜庢鍒欏寲

def forward(self, x):

x = self.dropout(self.norm1(self.attention(x))) 鑷敞鎰忓姏澶勭悊鍚庣殑杈撳嚭缁忚繃涓㈠純灞傚拰绗竴娆″綊涓€鍖?/p>

x = self.dropout(self.norm2(self.feed_forward(x))) 鍓嶉绁炵粡缃戠粶澶勭悊鍚庣殑杈撳嚭缁忚繃绗簩娆″綊涓€鍖栧拰涓㈠純灞?/p>

return x 杩斿洖澶勭悊鍚庣殑缁撴灉

```

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MoE妯″瀷绮惧僵瀹炵幇鎺㈢储

鎴戜滑鏈変竴涓壒鍒殑MoE锛圡ixture of Experts锛夋ā鍨嬪眰锛屽畠浼间箮娼滆棌鐫€璁稿绉樺瘑锛佽鎴戜滑娣卞叆浜嗚В瀹冪殑缁撴瀯鍜岃繍浣滄柟寮忋€?/p>

MoELayer绫绘瑙?/p>

杩欎釜MoELayer妯″潡鍙槸鐢变竴缇も€滀笓瀹垛€濈粍鎴愮殑寮哄ぇ鍥㈤槦銆傚畠浠氨鏄疶ransformerBlock锛屾暟閲忕敱`num_experts`鍐冲畾锛屾瘡涓兘鏈夎嚜宸辩嫭鐗圭殑涓撻暱鍜屾墠鍗庛€傝€岃繖涓洟闃熺殑棰嗗鑰呮槸`gating`妯″潡锛屽畠璐熻矗鏍规嵁杈撳叆鏁版嵁涓烘瘡浣嶄笓瀹跺垎閰嶄换鍔°€?/p>

褰撴暟鎹繘鍏ヨ繖涓洟闃熸椂锛屾瘡涓笓瀹堕兘浼氱粰鍑鸿嚜宸辩殑瑙佽В鎴栭娴嬨€傝€宍gating`妯″潡鍒欓€氳繃绾挎€у彉鎹负姣忎釜涓撳鎵撳垎锛岃繖浜涘垎鏁板喅瀹氫簡姣忎釜涓撳鐨勬潈閲嶃€傜劧鍚庯紝閫氳繃softmax鍑芥暟灏嗗垎鏁拌浆鍖栦负姒傜巼鍊硷紝涓烘渶缁堢殑杈撳嚭鍔犳潈姹傚拰銆?/p>

澶фā鍨嬬殑璁粌蹇冩硶

璇村埌澶фā鍨嬬殑璁粌锛岃繖鍙槸涓€鍦洪珮鎵嬮棿鐨勮緝閲忋€傚畠浠熀浜庢渶澶т技鐒朵及璁★紝鐢ㄤ氦鍙夌喌鎹熷け鏉ユ楠屾瘡娆¤缁冪殑鏁堟灉銆傝€岃儗鍚庣殑浼樺寲绠楁硶濡侫dam銆丼GD绛夛紝閮芥槸杩欏満杈冮噺涓殑寰楀姏鍔╂墜锛屽府鍔╂ā鍨嬪湪鏁版嵁鐨勬捣娲嬩腑楂樻晥瀛︿範銆?/p>

寰皟榄旀硶鈥斺€旇妯″瀷鏇撮€傚簲

Adaptation鐜妭灏卞儚鏄粰澶фā鍨嬭繘琛岀殑涓€у寲璋冩暣銆傚井璋冩妧鏈妯″瀷鍙傛暟鏇村姞璐磋繎鐗瑰畾浠诲姟锛屾樉钁楁彁鍗囨ā鍨嬫€ц兘銆傝繖涓繃绋嬪氨鍍忔槸涓烘ā鍨嬮噺韬畾鍒朵竴濂楁柊瑁咃紝璁╁畠鏇撮€傚簲鑸炲彴銆?/p>

澶фā鍨嬬殑鎸戞垬涓庡奖鍝嶉潰闈㈣

澶фā鍨嬪湪甯︽潵渚垮埄鐨勪篃甯︽潵浜嗕竴浜涙寫鎴樺拰褰卞搷銆傜ぞ浼氬亸瑙併€佹ā鍨嬫€ц兘宸紓銆佹湁瀹充俊鎭拰铏氬亣淇℃伅绛夛紝閮芥槸鎴戜滑闇€瑕佽鎯曞拰瑙ｅ喅鐨勯棶棰樸€傛柊鎶€鏈€绘槸浼撮殢鐫€娉曞緥鐨勬寫鎴橈紝杩囧幓妗堜緥鐨勬€荤粨鍒嗘瀽鎻ず浜嗘硶寰嬩笌AI涔嬮棿鐨勫井濡欎簰鍔ㄣ€傛垜浠篃瑕佸叧娉ㄦā鍨嬬殑鈥滅⒊鎺掓斁鈥濓紝鎺㈢储AI鎶€鏈殑鍙寔缁彂灞曚箣璺€?/p>

Llama寮€婧愬鏃忕殑鎴愰暱鏁呬簨

浠嶭lama-1鍒癓lama-3锛岃繖涓紑婧愬鏃忓睍鐜颁簡鎯婁汉鐨勬垚闀块€熷害銆傛ā鍨嬫灦鏋勭殑浼樺寲銆佹€ц兘鐨勬彁鍗囷紝閮藉湪杩欒儗鍚庢湁鐫€璇﹀敖鐨勮璁″拰鑰冭檻銆傚畠浠殑鏋舵瀯璁捐銆佽缁冩暟鎹€佽缁冩柟娉曞拰瀵规瘮鍒嗘瀽锛岄兘涓烘垜浠彮绀轰簡Llama瀹舵棌鐨勫彂灞曞巻绋嬨€傝€岀ぞ鍖虹敓鎬佺殑寤鸿涓庝紭鍖栵紝鏇存槸涓烘ā鍨嬬殑鐮旂┒涓庡簲鐢ㄦ彁渚涗簡涓板瘜鐨勮祫婧愬拰鏀寔銆?/p>

瀹炶返鎸囧崡锛欰utoDL骞冲彴涓庤嚜鎴慙LM寮€婧愯绋?/p>

鎯宠杞绘澗绠＄悊澶фā鍨嬶紵AutoDL骞冲彴鏄綘鐨勫緱鍔涘姪鎵嬨€傝繖涓钩鍙扮畝鍖栦簡澶фā鍨嬬殑绠＄悊鍜屼娇鐢ㄦ祦绋嬶紝璁╀綘杞绘澗閮ㄧ讲妯″瀷銆傝€岃嚜鎴慙LM寮€婧愯绋嬪垯鏄竴绔欏紡鑷骞冲彴锛屽紩瀵煎紑鍙戣€呬粠闆跺紑濮嬫瀯寤鸿嚜宸辩殑LLM锛岃浣犲湪AI鐨勯亾璺笂瓒婅蛋瓒婅繙銆?/p>

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璇剧▼澶х翰锛氳穬鍏LM涓栫晫鍏ラ棬涔嬫梾馃専

1. 鎺㈢储鍩虹姒傚康涓庡師鐞?/p>

1.1 Transformer鏋舵瀯鎻

浜嗚ВLLM鐨勬牳蹇冣€斺€擳ransformer鏋舵瀯鏄浣曞伐浣滅殑锛屾繁鍏ユ帰绱㈠叾鍐呭湪鏈哄埗銆?/p>

1.2 澶фā鍨嬬殑鍔涢噺涓庡簲鐢ㄥ睍鏈?/p>

棰嗙暐澶фā鍨嬬殑榄呭姏锛屾帰绱㈠叾鍦ㄥ疄闄呭簲鐢ㄤ腑鐨勫法澶ф綔鍔涖€?/p>

1.3 鐩爣鍑芥暟涓庝紭鍖栫畻娉曠殑濂ョ

鎻紑妯″瀷瀛︿範鐨勭绉橀潰绾憋紝浜嗚В鐩爣鍑芥暟涓庝紭鍖栫畻娉曞湪LLM涓殑鍏抽敭浣滅敤銆?/p>

2. 瀹炶返璁粌涓庡井璋冧箣鏃?/p>

2.1 鏁版嵁棰勫鐞嗕笌鍑嗗鎸囧崡

鎺屾彙鏁版嵁澶勭悊鐨勮瘈绐嶏紝涓烘ā鍨嬭缁冮摵骞抽亾璺€?/p>

2.2 妯″瀷璁粌涓庨獙璇佸疄鎴?/p>

浜插巻妯″瀷璁粌杩囩▼锛岄獙璇佸叾鎬ц兘涓庢晥鏋溿€?/p>

2.3 寰皟涓庝紭鍖栵紝鎻愬崌鎬ц兘鐨勬妧宸?/p>

瀛︿範濡備綍寰皟妯″瀷锛屼紭鍖栧叾鎬ц兘锛岃LLM濡傝檸娣荤考銆?/p>

3. 瀹炴垬搴旂敤妗堜緥澶ф彮绉?/p>

3.1 鏂囨湰鐢熸垚鐨勯瓍鍔?/p>

鎺㈢储閫氳繃LLM鐢熸垚瀵屾湁鍒涙剰鐨勬枃鏈紝鎰熷彈鑷劧璇█鐨勭編濡欍€?/p>

3.2 浠ｇ爜缂栧啓鐨勬寫鎴樹笌涔愯叮

浣撻獙LLM鍦ㄤ唬鐮佺紪鍐欐柟闈㈢殑鑳藉姏锛屾劅鍙楁妧鏈笌鑹烘湳鐨勮瀺鍚堛€?/p>

3.3 鑷姩闂瓟绯荤粺鐨勭濂囦箣鏃?/p>

璧拌繘鑷姩闂瓟鐨勪笘鐣岋紝棰嗙暐LLM鐨勬櫤鑳戒箣澶勩€?/p>

4. 闈㈠鎸戞垬涓庡奖鍝?/p>

4.1 娉曞緥涓庣ぞ浼氶棶棰樼殑鎬濊€?/p>

鎺㈣LLM鍙戝睍杩囩▼涓殑娉曞緥鍜岀ぞ浼氶棶棰橈紝涓烘湭鏉ュ仛濂藉噯澶囥€?/p>

4.2 鐜褰卞搷涓庡彲鎸佺画鎬ф帰璁?/p>

鍏虫敞LLM鐨勭幆澧冨奖鍝嶏紝鎺㈢储鍙寔缁殑鍙戝睍璺緞銆?/p>