Answers.Body: <p><strong>Embedding Dimension (d_model):</strong></p>
  <ul>
  <li>One word/token will be represented as &quot;d_model&quot; different numebrs. Larger the d_model,higher thwe capacityof storing the true meaning of the word</li>
  <li>It also affects the computational complexity of the model. A larger d_model means the model can potentially learn more complex representations but at the cost of increased computation.</li>
  </ul>
  <p><strong>Context Length(n):</strong></p>
  <ul>
  <li>maximum number of previous words/tokens that the model considers when predicting the next word/token.</li>
  <li>It determines how much “memory” the model has. A larger context length allows the model to take into account more of the previous text, which can be beneficial for tasks that require understanding of longer passages.</li>
  </ul>
  <p>There isn’t a direct relationship where changing one would necessitate a change in the other. However, both parameters would impact the overall computational requirements and performance of the model.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-13T23:00:04.513Z
Answers.LastActivityDate: 2024-05-13T23:00:04.513Z
Answers.UserDisplayName: Kulin Patel
Body: <p>What should we keep hidden dimension/embedding dimension (d_model as per attention is all you need paper), greater, equal, or smaller to the context length (n)?</p>
  <p>Is there any such relationship between the embedding dimension and context length?</p>
  <p>How will it affect the LLM?</p>
  
Tags: <natural-language-processing><transformer><large-language-models>
Title: What should be Relationship between embedding dimension and context length?
UserDisplayName: Aamod Thakur
ThreadText: # What should be Relationship between embedding dimension and context length?
  
  (Asked by: username_0 on 2024-05-13)
  
  What should we keep hidden dimension/embedding dimension (d\_model as per attention is all you need paper), greater, equal, or smaller to the context length (n)?
  
  Is there any such relationship between the embedding dimension and context length?
  
  How will it affect the LLM?
  
  **Tags:** natural-language-processing, transformer, large-language-models
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-05-13. Score: 0 | Accepted answer
  
  **Embedding Dimension (d\_model):**
  
  * One word/token will be represented as "d\_model" different numebrs. Larger the d\_model,higher thwe capacityof storing the true meaning of the word
  * It also affects the computational complexity of the model. A larger d\_model means the model can potentially learn more complex representations but at the cost of increased computation.
  
  **Context Length(n):**
  
  * maximum number of previous words/tokens that the model considers when predicting the next word/token.
  * It determines how much “memory” the model has. A larger context length allows the model to take into account more of the previous text, which can be beneficial for tasks that require understanding of longer passages.
  
  There isn’t a direct relationship where changing one would necessitate a change in the other. However, both parameters would impact the overall computational requirements and performance of the model.
  
  
  ---
  
  