by site-admin | Apr 30, 2024 | Artificial intelligence (AI)
Paper page TinyLlama: An Open-Source Small Language Model We also provide a guide in Appendix A on how one can this work to select an LM for one’s specific needs. We hope that our contributions will enable the community to make a confident shift towards considering using these small, open LMs for their need. To evaluate dependency of models to the provided task definition, we also evaluate them with their paraphrases. These are generated using gpt-3.5-turbo (Brown et al., 2020; OpenAI, 2023), and used with best in-context example count as per Table 7. Then, results are evaluated using the same pipeline, and reported in Table 2 for the two-best performing LMs in each category. Some popular SLM architectures include distilled versions of GPT, BERT, or T5, as well as models like Mistral’s 7B, Microsoft’s Phi-2, and Google’s Gemma. These architectures are designed to balance performance, efficiency, and accessibility. For the fine-tuning process, we use about 10,000 question-and-answer pairs generated from the Version 1’s internal documentation. But for evaluation, https://chat.openai.com/ we selected only questions that are relevant to Version 1 and the process. Further analysis of the results showed that, over 70% are strongly similar to the answers generated by GPT-3.5, that is having similarity 0.5 and above (see Figure 6). In total, there are 605 considered to be acceptable answers, 118 somewhat acceptable answers (below 0.4), and 12 unacceptable answers. However, here are some general guidelines for fine-tuning a private language model. First, the LLMs are bigger in size and have undergone more widespread training when weighed with SLMs. Second, the LLMs have notable natural language processing abilities, making it possible to capture complicated...
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