Learn the difference between a LLM model and its variants
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Difference between a base LLM model and their variants. Learn the difference between a Base model and it's instruct and chat variants. Learn when to use which model and a final summary 🙂
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
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- 2. Base model - Trained on a diverse range of texts, making minimal assumptions about the structure of the text it completes. - Lacks specific context or task-related biases. - When using a base model, you can input any text prompt, and it will generate a continuation based on its general language understanding. - Versatile but don’t specialize in any particular task.
- 3. Instruct Variant - Fine-tuned on instruction-response pairs during training. - Designed to follow specific instructions and generate responses that adhere to those instructions. - For example, if you give an instruct model an instruction like “Write a recipe for chocolate cake,” it will generate a response that aligns with the given instruction. - Useful for tasks where precise adherence to instructions matters.
- 4. - Derived from base models by training them on transcripts of dialogues. - Assume that the input text is part of a conversation. - Can use chat models for interactive back-and-forth conversations. - For instance, you can provide one side of a dialogue, and the chat model will complete the other side. Chat Variant
- 5. - While these labels (base, chat, instruct) help describe the model’s intended use, they are not strict boundaries. - You can instruct chat models and chat with instruct models. - In practice, you can often switch between them based on your specific needs. - Actual capabilities of a model depend on how it was fine-tuned and the data it was exposed to! Notes