Microsoft Research released a paper called “Textbooks is All You Need,” where they introducedphi-1, a new large language model for code. phi-1 is a transformer-based model with 1.3B parameters, which was trained for
For example, an SLM may have a transformer block with fewer layers and full attention, whereas an LLM may have sparse connections in a larger transformer module with longer context length. (Learn about the transformer model in GenAI.) Inference speed and resource Consumption LLMs have a larger ...
Similar to their larger counterparts, SLMs are built ontransformer modelarchitectures andneural networks. SLM development commonly integrates techniques such astransfer learningfrom larger models and may incorporate advancements such asretrieval-augmented generationto optimize performance and expand the knowledge...
DistilBERT:a lighter and faster version of Google’s BERT (Bidirectional Encoder Representations Transformer), the pioneering deep learning NLP AI model introduced back in 2018. There are also Mini, Small, Medium, and Tiny versions of BERT, which are scaled-down and optimized for varying constrain...
generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train and evaluate LMs that are much smaller than the state-of-the-art models (below 10 million total parameters), or have much simpler architectures (with only one transformer block...
For these reasons, there is a growing trend in the adoption and customization of small language models (SLMs). SLMs are compact transformer models, primarily utilizing decoder-only or encoder-decoder architectures, typically with parameters ranging from 1–8 billion. They are generally more e...
GPT-Neo and GPT-J: These SLM models are scaled-down versions ofOpenAI’sGPT models. MobileBERT: As the name suggests, MobileBERT is designed formobile devices. T5-Small: TheText-to-Text Transfer Transformer(T5) model from Google comes in various sizes. T5-Small is designed to provide a bal...
can be applied to other small language models as well. Our discussion will primarily focus on optimizing and offloading the transformer block to the NPU. The tokenizer, embedding and language model head are not compute-intensive but involve lookups; therefore, we allocate these tasks to the CPU....
Recent advancements in text-to-speech (TTS) powered by language models have showcased remarkable capabilities in achieving naturalness and zero-shot voice cloning. Notably, the decoder-only transformer is the prominent architecture in this domain. However, transformers face challenges stemming from their...
展示了可以用 TinyStories 数据集来良好地训练参数量很小(参数<10M,嵌入维度256),或有更简单的架构(只有一个 Transformer Block)的 SLM,它仍然能良好地遵循指令,产生多样、流畅、前后一致的故事,具有一定的推理能力并能掌握一些事实知识。在这个小领域的能力和大模型不相上下 ...