一、B
| 2023百模大战
| NLP Tasks决策流程
二、E
| GPT模型的本质是一个基于Transformer的Decode-only预训练模型！
三、B
| OpenAI的ChatGPT说第二没人敢说第一吧！
Training language models to follow instructions with human feedback
四、Q
五、W
======
def main():
GPT_CONFIG_124M = {
"vocab_size": 50257, # Vocabulary size
"context_length": 1024, # Context length
"emb_dim": 768, # Embedding dimension
"n_heads": 12, # Number of attention heads
"n_layers": 12, # Number of layers
"drop_rate": 0.1, # Dropout rate
"qkv_bias": False # Query-Key-Value bias
}
torch.manual_seed(123)
model = GPTModel(GPT_CONFIG_124M)
model.eval() # disable dropout
start_context = "Hello, I am"
tokenizer = tiktoken.get_encoding("gpt2")
encoded = tokenizer.encode(start_context)
encoded_tensor = torch.tensor(encoded).unsqueeze(0)
print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
print("\nInput text:", start_context)
print("Encoded input text:", encoded)
print("encoded_tensor.shape:", encoded_tensor.shape)
out = generate_text_simple(
model=model,
idx=encoded_tensor,
max_new_tokens=10,
context_size=GPT_CONFIG_124M["context_length"]
)
decoded_text = tokenizer.decode(out.squeeze(0).tolist())
print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
print("\nOutput:", out)
print("Output length:", len(out[0]))
print("Output text:", decoded_text)
if __name__ == "__main__":
main()
