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璺冨叆AI澶фā鍨嬬殑濂囧涓栫晫锛屾湰鏁欑▼涓轰綘鎵撻€犱簡涓€鏉′粠鍩虹鍒伴珮绾х殑瀛︿範涔嬭矾銆傚湪杩欓噷锛屾垜浠皢甯︿綘浠庡熀纭€姒傚康鍑哄彂锛岄€愭娣卞叆鍒嗗竷寮忔ā鍨嬭缁冨師鐞嗭紝鎺㈢储寮哄寲瀛︿範鍦ㄨ嚜鐒惰瑷€澶勭悊涓殑瀹為檯搴旂敤銆傛垜浠敞閲嶇悊璁轰笌瀹炶返鐩哥粨鍚堬紝鏃ㄥ湪甯姪浣犲疄鐜颁粠姒傚康鐞嗚В鍒癓LM寮€鍙戜笓瀹剁殑椋炶穬銆?/p>

馃摎 寮曡█

鎯宠蹇€熷叆闂ˋI澶фā鍨嬬殑LLM涓栫晫锛熸垜浠粠闆跺紑濮嬶紝涓轰綘鏋勫缓浜嗕竴濂楀叏闈㈢殑瀛︿範浣撶郴銆傛湰鏁欑▼涓嶄粎璁╀綘浜嗚В璇█妯″瀷鐨勫熀纭€鐭ヨ瘑锛岃繕灏嗘繁鍏ユ帰绱㈠垎甯冨紡妯″瀷璁粌鐨勬牳蹇冨師鐞嗐€備綘灏嗕簡瑙ｅ己鍖栧涔犲浣曚负鑷劧璇█澶勭悊澧炴坊鏅鸿兘鍐崇瓥鐨勮兘鍔涖€傞€氳繃瀹炴垬缁冧範锛屼綘灏嗘帉鎻¤繖涓€棰嗗煙鐨勬牳蹇冩妧鑳姐€?/p>

馃攳 LLM鍩虹鐭ヨ瘑

璇█妯″瀷锛氳繖鏄竴绉嶅熀浜庢枃鏈暟鎹娴嬩笅涓€涓瘝姒傜巼鐨勭粺璁℃ā鍨嬨€傞€氳繃澶ч噺鐨勬枃鏈暟鎹紝妯″瀷瀛︿範鍗曡瘝涔嬮棿鐨勫叧绯伙紝浠庤€岀敓鎴愮被浼间汉绫昏瑷€鐨勬枃鏈€備互涓嬫槸鏋勫缓璇█妯″瀷鐨勭畝鍗曚唬鐮佺ず渚嬶細

```python

import torch

from torchtext.data import Field, BucketIterator

from torchtext.datasets import WikiText2

TEXT = Field(...)

...

device = ...

...

```

鍒嗗竷寮忔ā鍨嬭缁冨師鐞嗭細涓轰簡澶勭悊澶ц妯℃暟鎹苟鎻愰珮璁＄畻鏁堢巼锛屾垜浠皢璁粌鏁版嵁鍒嗗竷鍒板鍙拌绠楁満涓婅繘琛屽鐞嗐€傝繖鍖呮嫭鏁版嵁骞惰銆佹ā鍨嬪苟琛屽拰娣峰悎骞惰銆備互涓嬫槸涓€涓娇鐢≒yTorch杩涜鍒嗗竷寮忚缁冪殑绀轰緥锛?/p>

```python

from torch.nn.parallel import DistributedDataParallel

...

dist_url = 'tcp://localhost:54321'

...

model = Model().to(device)

...

for epoch in range(num_epochs):

...

loss.backward()

optimizer.step()

...

```

寮哄寲瀛︿範鍦ㄨ嚜鐒惰瑷€澶勭悊涓殑搴旂敤锛氬己鍖栧涔犵敤浜庤В鍐砃LP涓殑鍐崇瓥闂锛屽鏂囨湰鐢熸垚銆佸璇濈郴缁熺瓑銆備緥濡傦紝鍦ㄨ瑷€妯″瀷涓紝寮哄寲瀛︿範閫氳繃濂栧姳淇″彿浼樺寲妯″瀷鐨勬潈閲嶏紝浣挎ā鍨嬬敓鎴愭洿浼樿川鐨勬枃鏈€?/p>

寮哄寲瀛︿範妗嗘灦绀轰緥

鍦ㄤ竴涓己鍖栧涔犵幆澧冧腑锛屾櫤鑳戒綋閫氳繃涓庣幆澧冪殑浜や簰鏉ュ涔犳渶浣宠涓虹瓥鐣ャ€備笅闈㈡槸涓€涓畝鍗曠殑寮哄寲瀛︿範妗嗘灦绀轰緥锛?/p>

鎴戜滑瀵煎叆gym搴撴潵鍒涘缓鎴戜滑鐨勭幆澧冦€傜幆澧冩槸鎴戜滑鏅鸿兘浣撲氦浜掔殑涓栫晫锛屽畠鎻愪緵浜嗘櫤鑳戒綋鍙互鎵ц鐨勫姩浣滀互鍙婂姩浣滀骇鐢熺殑缁撴灉銆?/p>

```python

import gymnasium as gym

env = gym.make('gym_language:language-v0', model=model) 鍒涘缓鐜瀹炰緥

```

鎺ョ潃锛屾垜浠垵濮嬪寲Q琛ㄥ拰绛栫暐銆俀琛ㄥ瓨鍌ㄤ簡姣忎釜鐘舵€佷笅鐨勬瘡涓姩浣滅殑浠峰€硷紝鑰岀瓥鐣ュ垯鍛婅瘔鎴戜滑鍦ㄦ瘡涓姸鎬佷笅搴旇閲囧彇浠€涔堝姩浣溿€?/p>

```python

Q = {state: {action: 0 for action in env.action_space} for state in range(env.observation_space.n)} 鍒濆鍖朡琛?/p>

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1. 鍔犺浇棰勮缁冩ā鍨嬩笌鍒嗚瘝鍣?/p>

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```python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("gpt2")

tokenizer = AutoTokenizer.from_pretrained("gpt2")

```

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```python

train_dataset = load_dataset("your_dataset")

```

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```python

training_args = TrainingArguments(

output_dir='./results',

overwrite_output_dir=True,

num_train_epochs=3,

per_device_train_batch_size=16,

save_steps=10_000,

save_total_limit=2,

)

```

4. 鍚姩璁粌

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```python

trainer = Trainer(

model=model,

args=training_args,

train_dataset=train_dataset,

tokenizer=tokenizer,

)

trainer.train()

```

5. 淇濆瓨寰皟鍚庣殑妯″瀷

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```python

model.save_pretrained("fine-tuned-model")

tokenizer.save_pretrained("fine-tuned-model")

```

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