The large model of the alpaca family evolves collectively! 32k context equals GPT-4, produced by Tian Yuandong's team

The open source alpaca large model LLaMA context is equal to GPT-4, with only one simple change!

This paper just submitted by Meta AI shows that after the LLaMA context window is expanded from 2k to 32k, it only requires less than 1000 steps of fine-tuning.

The cost is negligible compared to pre-training.

Expanding the context window means that the AI's "working memory" capacity is increased. Specifically, it can:

• Support more rounds of dialogue , reduce forgetting, such as more stable role-playing
• Enter more information to complete more complex tasks, such as processing longer documents or multiple documents at one time

More important meaning The question is, can all large alpaca model families based on LLaMA adopt this method at low cost and evolve collectively?

Yangtuo is currently the most comprehensive open source basic model, and has derived many fully open source commercially available large models and vertical industry models.

# Tian Yuandong, the corresponding author of the paper, also excitedly shared this new development in his circle of friends.

Large models based on RoPE can use

The new method is called position interpolation (Position Interpolation), which is suitable for large models that use RoPE (rotation position encoding) Applicable to all models.

RoPE was proposed by the Zhuiyi Technology team as early as 2021, and has now become one of the most common position encoding methods for large models.

But directly using extrapolation to expand the context window under this architecture will completely destroy the self-attention mechanism.

Specifically, the part beyond the length of the pre-trained context will cause the model perplexity to soar to the same level as an untrained model.

The new method is changed to linearly reduce the position index and expand the range alignment of the front and rear position index and relative distance.

# It is more intuitive to use pictures to express the difference between the two.

Experimental results show that the new method is effective for LLaMA large models from 7B to 65B.

There is no significant performance degradation in Long Sequence Language Modeling, Passkey Retrieval, and Long Document Summarization.

#In addition to experiments, a detailed proof of the new method is also given in the appendix of the paper.

Three More Thing

The context window used to be an important gap between open source large models and commercial large models.

For example, OpenAI’s GPT-3.5 supports up to 16k, GPT-4 supports 32k, and AnthropicAI’s Claude supports up to 100k.

At the same time, many large open source models such as LLaMA and Falcon are still stuck at 2k.

Now, Meta AI’s new results have directly bridged this gap.

Expanding the context window is also one of the focuses of recent large model research. In addition to position interpolation methods, there are many attempts to attract industry attention.

1. Developer kaiokendev explored a method to extend the LLaMa context window to 8k in a technical blog.

2. Galina Alperovich, head of machine learning at data security company Soveren, summarized 6 tips for expanding the context window in an article.

3. Teams from Mila, IBM and other institutions also tried to completely remove positional encoding in Transformer in a paper.

Friends who need it can click the link below to view~

Meta paper: https://www.php.cn/link/ 0bdf2c1f053650715e1f0c725d754b96

Extending Context is Hard…but not Impossiblehttps://www.php.cn/link/9659078925b57e621eb3f9ef19773ac3

The Secret Sauce context window behind 100K in LLMshttps://www.php.cn/link/09a630e07af043e4cae879dd60db1cac

Positionless Coding Paperhttps://www.php.cn/link/fb6c84779f12283a81d739d8f088fc12

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