Some interesting architectural choices made in Llama 4 models -- were these key to the 10M context? Possibly 🤔 
text
Some interesting architectural choices made in Llama 4 models -- were these key to the 10M context? Possibly 🤔 
new_line
new_line
🔍 Takeaways:
text
🔍 Takeaways:
new_line
🧩 Interleaved Attention without position encoding
text
🧩 Interleaved Attention without position encoding
new_line
- LLaMA 4 removes explicit positional encoding in some attention layers to boost performance on longer contexts. 
text
- LLaMA 4 removes explicit positional encoding in some attention layers to boost performance on longer contexts. 
new_line
- The principles here could be similar to the residual connections to facilitate attention to early tokens without positional decay. 
text
- The principles here could be similar to the residual connections to facilitate attention to early tokens without positional decay. 
new_line
new_line
⚖️ Scaled Softmax to increase attention at inference time
text
⚖️ Scaled Softmax to increase attention at inference time
new_line
- The max attention value (output of softmax) decreases as context size increases. 
text
- The max attention value (output of softmax) decreases as context size increases. 
new_line
- Llama 4 incorporates a context-size dependent temperature in the softmax function to modify the slope of softmax, allowing the model to focus better on relevant tokens. 
text
- Llama 4 incorporates a context-size dependent temperature in the softmax function to modify the slope of softmax, allowing the model to focus better on relevant tokens. 
new_line
- Done only at inference time -- guessing it was more a choice after some observation on eval datasets.
text
- Done only at inference time -- guessing it was more a choice after some observation on eval datasets.
new_line
new_line
What did you think of these choices? 
text
What did you think of these choices? 
Some interesting architectural choices made in Llama 4 models -- were these key to the 10M context? Possibly 🤔 

🔍 Takeaways:
🧩 Interleaved Attention without position encoding
- LLaMA 4 removes explicit positional encoding in some attention layers to boost performance on longer contexts. 
- The principles here could be similar to the residual connections to facilitate attention to early tokens without positional decay. 

⚖️ Scaled Softmax to increase attention at inference time
- The max attention value (output of softmax) decreases as context size increases. 
- Llama 4 incorporates a context-size dependent temperature in the softmax function to modify the slope of softmax, allowing the model to focus better on relevant tokens. 
- Done only at inference time -- guessing it was more a choice after some observation on eval datasets.

What did you think of these choices? 
👀
John6666