#llm Created at 031023 # [Anonymous feedback](https://www.admonymous.co/louis030195) # [[Epistemic status]] #shower-thought Last modified date: 031023 Commit: 0 # Related # Speculative generation Speculative generation in the context of Large Language Models (LLMs) is a technique used to accelerate the token generation process by leveraging smaller, faster models to predict tokens before the larger, slower model generates them. This approach can significantly improve the performance of LLMs without sacrificing the quality of the generated text. In speculative generation, a smaller model, called the draft model, generates a few tokens. The larger model, called the target model, then checks if the draft model's predictions are correct. If the predictions are accurate, the target model accepts the tokens and moves on to the next set of tokens. If the draft model's predictions are incorrect, the target model generates the correct token and continues the process with the draft model generating the next set of tokens. This process repeats until the desired text is generated[6]. Here's some pseudocode to illustrate the speculative generation process: ```python draft_model = load_small_model() target_model = load_large_model() input_text = "some input text" generated_text = "" while not is_finished(generated_text): draft_tokens = draft_model.generate_tokens(input_text) for token in draft_tokens: if target_model.is_token_correct(input_text, token): generated_text += token input_text += token else: correct_token = target_model.generate_token(input_text) generated_text += correct_token input_text += correct_token break ``` In this pseudocode, the draft model generates tokens based on the input text, and the target model checks if the tokens are correct. If the tokens are correct, they are added to the generated text. If not, the target model generates the correct token, and the process continues. The loop continues until the generated text meets the desired criteria, such as reaching a specific length or generating a specific type of content. Speculative generation can lead to faster token generation without compromising the quality of the generated text, making it a valuable technique for improving the performance of LLMs[6]. Citations: [1] https://youtube.com/watch?v=q6oiidmVnwE [2] https://news.ycombinator.com/item?id=35963936 [3] https://www.elastic.co/what-is/large-language-models [4] https://arxiv.org/pdf/2308.04623.pdf [5] https://arxiv.org/pdf/2305.09781.pdf [6] https://blog.dust.tt/2023-06-02-speculative-sampling [7] https://www.reddit.com/r/LocalLLaMA/comments/13nj7g8/what_coding_llm_is_the_best/ [8] https://www.techopedia.com/definition/34948/large-language-model-llm [9] https://github.com/ggerganov/llama.cpp/issues/630 [10] https://www.marktechpost.com/2023/02/14/this-an-algorithm-called-speculative-sampling-sps-accelerates-the-decoding-in-large-language-models-by-2-2-5x/ [11] https://arxiv.org/pdf/2305.17126.pdf [12] https://thenewstack.io/what-is-a-large-language-model/ [13] https://huggingface.co/blog/optimize-llm [14] https://www.reddit.com/r/LocalLLaMA/comments/169p2w5/can_anyone_explain_in_simple_words_how/ [15] https://openreview.net/forum?id=SaRj2ka1XZ3 [16] https://cs.cmu.edu/~zhihaoj2/papers/specinfer.pdf [17] https://together.ai/blog/medusa [18] https://arxiv.org/pdf/2302.01318.pdf [19] https://whichlight.substack.com/p/building-with-open-source-large-language [20] https://www.techtarget.com/whatis/definition/large-language-model-LLM [21] https://www.mahriq.com/speculative-decoding-the-future-of-inference-in-large-language-models-2/ [22] https://www.infoworld.com/article/3697272/are-large-language-models-wrong-for-coding.html [23] https://dev.to/wesen/llms-will-fundamentally-change-software-engineering-3oj8