求推荐基于纯Attention机制、无循环的逐字符文本生成模型

Hey there! I totally get your frustration searching for the right fit—Transformers are all-attention but built for seq2seq tasks like translation, which isn’t what you need. Luckily, there are several great options focused specifically on text generation that ditch recurrent structures entirely and rely solely on attention mechanisms. Here are my top picks, especially since you’re familiar with PyTorch:

Decoder-Only Transformer Models (The Go-To Choices) #

These models are built exclusively on the Transformer decoder architecture (no encoder, no RNN/LSTM) and use self-attention to capture context for autoregressive text generation:

• GPT Series (GPT-1/2/3/4, GPT-Neo, GPT-NeoX)
  The most well-known family of pure attention-based text generation models. They’re designed from the ground up for tasks like text completion, creative writing, summarization, and more—no recurrent layers anywhere. All context is captured via multi-head self-attention.

  In PyTorch, you can easily use the Hugging Face transformers library to work with these models. Here’s a quick example with GPT-2:

  from transformers import GPT2Tokenizer, GPT2LMHeadModel
  import torch
  
  # Load pre-trained model and tokenizer
  tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
  model = GPT2LMHeadModel.from_pretrained("gpt2")
  
  # Define your prompt
  prompt = "The key to building pure attention text generation models lies in"
  inputs = tokenizer(prompt, return_tensors="pt")
  
  # Generate text (autoregressive, attention-only)
  with torch.no_grad():
      outputs = model.generate(
          **inputs,
          max_length=120,
          num_return_sequences=1,
          temperature=0.7  # Controls randomness of generation
      )
  
  # Decode and print the result
  generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
  print(generated_text)
  

• Llama/Llama 2
  Meta’s open-source decoder-only models, built entirely with attention layers. They’re optimized for text generation tasks and perform exceptionally well across a wide range of use cases. You can access them via the Hugging Face library too, with full PyTorch support out of the box.

• CTRL
  A controllable text generation model that’s also decoder-only and attention-based. It lets you steer generation using control codes (e.g., "legal" or "news" to generate domain-specific text), all without any recurrent structures.

Building Your Own Simplified Model #

If you want to roll your own instead of using pre-trained models, PyTorch’s nn.TransformerDecoder and nn.TransformerDecoderLayer modules make it straightforward. You can construct a minimal attention-only generator by:

1. Embedding tokens with positional encoding (since Transformers need positional context without recurrence)
2. Stacking multiple TransformerDecoderLayer instances (each with multi-head self-attention and feed-forward networks)
3. Adding a linear layer to map decoder outputs to token probabilities

This gives you a fully custom, attention-only text generation model with zero recurrent components.

Hope this helps you find exactly what you’re looking for!

内容的提问来源于stack exchange，提问作者OverclockRo