🎯 Goal (What & Why)

Add activation-level distillation, usually leading to better student performance.

🚀 Execution Plan

Step 1: What is the smallest working version?

• Distill based on all mixer-layer outputs
• Support the case where the student has the same number of layers as the teacher.
• Use MSE loss.
• Add a single coefficient that balances feature-level distillation vs logit-level.

As a first version:
$$L = L_{\text{logit}} + \lambda L_{\text{activation}}$$
with:
$$\mathcal{L}_{\text{activation}} = \frac{1}{N} \sum_{i=1}^{N}||T_i(\mathbf{x}) - S_i(\mathbf{x})||_2$$
$T_i(x)$ and $S_i(x)$ denoting the outputs of the i-th layer's mixer of the teacher and student models.

Details:

• teacher stores intermediate activations in kwargs
• student uses kwargs to compute activation-distillation losses, which it stores in losses

Step 2: What additional optimizations are possible (but optional)?

• Should support TP with sequence-parallelism (this is actually not optional, but can be done in a second step)
• Can configure which layers' outputs are used for distillation. For example, we could distill only based on mixer-layer outputs, or also based on MLP outputs, etc. Pass a {student -> teacher} mapping of layer-names to use for distillation
• Configurable loss: MSE, cosine, others?

📌 Acceptance Criteria (Must-Haves for Completion)

• The feature must be functional and tested.
• The implementation must be documented in practical terms.
• The PR must include a performance/impact summary.
• No refactors unless directly necessary for feature completion.

🛠️ Project Management

• Assign the project to the Fast-LLM project.
• Set the Estimate field (in days) in the GitHub project.
• Use the Size field to categorize the PR size (Small/Medium/Large).
• Assign an owner when opening the issue.