## Heatmap: Attention and MLP Layer Analysis ### Overview The image is a heatmap visualizing the activity or importance of different components within attention and Multilayer Perceptron (MLP) layers. The heatmap uses a color gradient from blue to orange, representing values from approximately 0.12 to 0.18. The y-axis represents layers, numbered from 0 to 27. The x-axis represents different components: attention query (attn. q), attention key (attn. k), attention value (attn. v), attention output (attn. o), MLP up, MLP down, and MLP gate. ### Components/Axes * **Y-axis:** Layers, ranging from 0 to 27 in increments of 4, with labels at 0, 4, 8, 12, 16, 20, 24, and 27. * **X-axis:** Components of attention and MLP layers: * attn. q (attention query) * attn. k (attention key) * attn. v (attention value) * attn. o (attention output) * mlp. up (MLP up) * mlp. down (MLP down) * mlp. gate (MLP gate) * **Color Scale (Legend):** Located on the right side of the heatmap. * Blue: ~0.12 * White: ~0.15 * Orange: ~0.18 ### Detailed Analysis * **attn. q (attention query):** The top layers (20-27) show high activity (orange), decreasing towards the bottom layers (0-8), where activity is low (white/light orange). * **attn. k (attention key):** Similar to attn. q, the top layers (20-27) show high activity (orange), decreasing towards the bottom layers (0-8), where activity is low (white/light orange). * **attn. v (attention value):** Shows low activity (blue) across all layers, with some slight increases (lighter blue) in the middle layers (8-16). * **attn. o (attention output):** Shows low activity (blue) across all layers, with some slight increases (lighter blue) in the middle layers (8-16). * **mlp. up (MLP up):** Shows moderate activity (light orange) across all layers, with some slight increases (orange) in the middle layers (12-20). * **mlp. down (MLP down):** Shows moderate activity (light orange) across all layers, with some slight increases (orange) in the middle layers (12-20). * **mlp. gate (MLP gate):** Shows moderate activity (light orange) across all layers, with some slight increases (orange) in the middle layers (12-20). ### Key Observations * Attention queries and keys (attn. q and attn. k) are most active in the top layers. * Attention values and outputs (attn. v and attn. o) show relatively low activity across all layers. * MLP components (mlp. up, mlp. down, and mlp. gate) show moderate activity across all layers, with some increased activity in the middle layers. ### Interpretation The heatmap suggests that in the analyzed model, attention queries and keys are more important in the higher layers, while attention values and outputs have a more consistent, lower-level impact. The MLP components show a relatively uniform distribution of activity across layers, with a slight increase in the middle layers, suggesting a consistent role throughout the network with some increased importance in the middle layers. The distinct patterns for attention and MLP components indicate different roles and importance within the model's architecture. The color scale indicates the relative importance or activity level of each component at each layer.

\n ## Heatmap: Attention and MLP Layer Contributions ### Overview The image presents a heatmap visualizing the contributions of different attention and Multi-Layer Perceptron (MLP) components across various layers. The x-axis represents different components (attn. q, attn. k, attn. v, attn. o, mlp. up, mlp. down, mlp. gate), and the y-axis represents layer numbers ranging from 0 to 27. The color intensity indicates the magnitude of the contribution, with a colorbar on the right showing the scale from 0.12 to 0.18. ### Components/Axes * **X-axis:** Represents different components: * `attn. q` (Attention Query) * `attn. k` (Attention Key) * `attn. v` (Attention Value) * `attn. o` (Attention Output) * `mlp. up` (MLP Up-projection) * `mlp. down` (MLP Down-projection) * `mlp. gate` (MLP Gate) * **Y-axis:** Represents layer numbers, ranging from 0 to 27, with markers at intervals of 4 (0, 4, 8, 12, 16, 20, 24, 27). * **Colorbar:** Located on the right side of the heatmap, indicating the value scale. * Minimum Value: 0.12 (represented by light blue) * Maximum Value: 0.18 (represented by dark orange) ### Detailed Analysis The heatmap displays varying levels of contribution for each component across different layers. * **attn. q:** Shows a relatively consistent contribution across layers, generally around 0.14-0.16. There's a slight increase in contribution towards the higher layers (20-27). * **attn. k:** Exhibits a strong contribution in the initial layers (0-8), peaking around 0.17-0.18. The contribution then decreases significantly in subsequent layers, falling to around 0.12-0.14. * **attn. v:** Displays a pattern similar to `attn. k`, with high contribution in the initial layers (0-8) and a decline in later layers. The peak contribution is around 0.16-0.17. * **attn. o:** Shows a gradual increase in contribution from lower layers (0-8) to higher layers (20-27), reaching a peak of approximately 0.17-0.18 in the highest layers. * **mlp. up:** Exhibits a relatively consistent contribution across layers, generally around 0.14-0.16. * **mlp. down:** Shows a similar pattern to `mlp. up`, with a consistent contribution around 0.14-0.16. * **mlp. gate:** Displays a pattern of increasing contribution from lower layers to higher layers, peaking around 0.17-0.18 in the highest layers. Specifically, approximate values (with uncertainty of +/- 0.02): | Component | Layer 0 | Layer 4 | Layer 8 | Layer 12 | Layer 16 | Layer 20 | Layer 24 | Layer 27 | |---|---|---|---|---|---|---|---|---| | attn. q | 0.14 | 0.15 | 0.16 | 0.15 | 0.16 | 0.17 | 0.17 | 0.17 | | attn. k | 0.18 | 0.17 | 0.16 | 0.14 | 0.13 | 0.13 | 0.13 | 0.14 | | attn. v | 0.17 | 0.16 | 0.15 | 0.13 | 0.12 | 0.13 | 0.13 | 0.14 | | attn. o | 0.12 | 0.13 | 0.14 | 0.15 | 0.16 | 0.17 | 0.17 | 0.18 | | mlp. up | 0.14 | 0.15 | 0.15 | 0.15 | 0.16 | 0.16 | 0.16 | 0.16 | | mlp. down | 0.14 | 0.15 | 0.15 | 0.15 | 0.16 | 0.16 | 0.16 | 0.16 | | mlp. gate | 0.12 | 0.13 | 0.14 | 0.15 | 0.16 | 0.17 | 0.17 | 0.18 | ### Key Observations * Attention Key (`attn. k`) and Attention Value (`attn. v`) components have the highest contributions in the initial layers, suggesting their importance in early stages of processing. * Attention Output (`attn. o`) and MLP Gate (`mlp. gate`) components show increasing contributions in higher layers, indicating their growing significance in later stages. * MLP Up-projection (`mlp. up`) and MLP Down-projection (`mlp. down`) maintain relatively consistent contributions across all layers. * The heatmap reveals a shift in contribution from attention mechanisms in lower layers to MLP components in higher layers. ### Interpretation This heatmap likely represents the contribution of different components within a deep learning model, potentially a Transformer-based architecture, across its layers. The data suggests that the initial layers rely heavily on attention mechanisms (specifically the Key and Value components) to extract and process input features. As the data flows through the network, the role of attention mechanisms shifts towards the Output component, while MLP components (particularly the Gate) become increasingly important for higher-level feature transformations and decision-making. The consistent contribution of MLP Up and Down projections suggests their role in maintaining a stable feature representation throughout the network. The shift in contribution patterns could indicate that the model learns to initially focus on identifying relevant input features using attention, and then utilizes MLP layers to refine and integrate these features for final predictions. The heatmap provides valuable insights into the internal workings of the model and can be used to guide architectural modifications or optimization strategies.

## Heatmap: Transformer Component Activation/Value Distribution ### Overview The image is a heatmap visualizing a numerical metric across different components (x-axis) and layers/heads (y-axis) of a neural network, likely a transformer model. The color intensity represents the value of the metric, with a scale provided on the right. The data suggests an analysis of internal model dynamics, such as attention weights, gradient norms, or activation magnitudes. ### Components/Axes * **X-Axis (Components):** Seven distinct components are labeled at the bottom. The labels are arranged in two lines: * `attn. q` (Attention Query) * `attn. k` (Attention Key) * `attn. v` (Attention Value) * `attn. o` (Attention Output) * `mlp. up` (MLP Up-projection) * `mlp. down` (MLP Down-projection) * `mlp. gate` (MLP Gate) * **Y-Axis (Layers/Indices):** Numbered from `0` at the bottom to `27` at the top, in increments of 4 (0, 4, 8, 12, 16, 20, 24, 27). This likely represents 28 distinct layers or attention heads. * **Color Scale (Legend):** Positioned vertically on the right side of the chart. It is a continuous gradient from blue (low value) to orange (high value), with white as a midpoint. * **Blue (Bottom):** Labeled `0.12` * **White (Middle):** Labeled `0.15` * **Orange (Top):** Labeled `0.18` * The scale indicates the metric's range is approximately 0.12 to 0.18. ### Detailed Analysis The heatmap grid shows distinct patterns for each component column across the 28 rows (layers 0-27). * **`attn. q`:** Values are generally in the mid-to-high range (light orange to orange). The highest values (darker orange) appear in the top rows (layers 20-27). Lower rows (0-4) show lighter colors, trending towards white/light blue. * **`attn. k`:** Shows a very strong, distinct pattern. The top section (layers ~16-27) is a solid block of high values (dark orange). The bottom section (layers 0-8) is a solid block of low values (dark blue). The transition zone (layers ~8-16) shows a gradient from blue to orange. * **`attn. v`:** Exhibits a banded pattern. High values (orange) are concentrated in the middle layers (approximately 8-16). Lower values (blue) are present in both the bottom (0-4) and top (20-27) sections, with lighter colors in between. * **`attn. o`:** This column is predominantly blue, indicating consistently lower values compared to other attention components. The darkest blue (lowest values) is in the bottom half (layers 0-12). The top half is a lighter blue. * **`mlp. up`:** Shows moderate values. There are bands of light orange (higher values) in the bottom layers (0-8) and top layers (20-27). The middle layers are lighter (white/light orange). * **`mlp. down`:** Similar to `mlp. up` but with less intensity. It has faint orange bands in the bottom and top sections, with the middle being very light (near white). * **`mlp. gate`:** This column is the lightest overall, indicating values clustered around the midpoint (0.15). It shows very faint blue in the bottom rows and very faint orange in the top rows, with most of the column being off-white. ### Key Observations 1. **Component-Specific Layer Specialization:** Different components show peak activity (high values) at different layer depths. `attn. k` peaks in deep layers, `attn. v` in middle layers, and `attn. q` in deep layers but less uniformly. 2. **Consistently Low Output Projection:** The `attn. o` column is uniformly lower in value than the other attention components (`q`, `k`, `v`), suggesting this transformation consistently produces smaller magnitude values for this metric. 3. **MLP Uniformity:** The three MLP components (`up`, `down`, `gate`) show more similar and muted patterns compared to the attention components, with values generally closer to the scale's midpoint. 4. **Clear Stratification in `attn. k`:** The `attn. k` column shows the most dramatic and clean separation between low-value (blue) early layers and high-value (orange) later layers. ### Interpretation This heatmap likely visualizes a diagnostic metric (e.g., average attention weight, gradient norm, or activation scale) for a 28-layer transformer model. The patterns reveal how different parts of the model's architecture process information across its depth. * The high values for `attn. k` in later layers could indicate that key representations become more distinct or carry more "energy" as information propagates deeper into the network. * The mid-layer peak for `attn. v` might suggest that value transformations are most active or variable in the middle of the network, possibly where semantic integration occurs. * The consistently lower values for `attn. o` could be a design feature (e.g., due to scaling or normalization) or an emergent property where the output of attention is kept in a smaller range before being added to the residual stream. * The more uniform MLP patterns suggest these feed-forward layers operate with less layer-to-layer variance in this specific metric compared to the attention mechanisms. **Language Note:** All text in the image is in English.

## Heatmap: Attention and MLP Component Values Across Layers ### Overview The image is a heatmap visualizing numerical values across 28 layers (rows 0–27) for attention mechanisms (q, k, v, o) and MLP components (up, down, gate). Values range from 0.12 (blue) to 0.18 (orange), with a color gradient indicating magnitude. ### Components/Axes - **X-axis (Columns)**: - `attn. q`, `attn. k`, `attn. v`, `attn. o` (attention mechanisms) - `mlp. up`, `mlp. down`, `mlp. gate` (MLP components) - **Y-axis (Rows)**: Layer indices 0–27 (numerical scale). - **Legend**: Colorbar on the right, mapping values to colors (blue = 0.12, orange = 0.18). ### Detailed Analysis 1. **Attention Mechanisms**: - **`attn. q`**: Highest values (dark orange) in rows 24–27 (~0.17–0.18), decreasing to ~0.12 in rows 0–4. - **`attn. k`**: Similar trend to `attn. q`, with peaks in rows 24–27 (~0.16–0.17) and lower values (~0.12–0.14) in rows 0–12. - **`attn. v`**: Lower values (~0.12–0.14) across all rows, with minimal variation. - **`attn. o`**: Consistently low (~0.12–0.13) across all layers. 2. **MLP Components**: - **`mlp. up`**: Moderate values (~0.14–0.16) in rows 8–20, peaking at ~0.17 in rows 24–27. - **`mlp. down`**: Slightly lower than `mlp. up`, with values ~0.13–0.15. - **`mlp. gate`**: Lowest values (~0.12–0.14) across all layers. ### Key Observations - **High Attention in Upper Layers**: `attn. q` and `attn. k` show strong values in the top 4 layers (24–27), suggesting increased focus in later processing stages. - **Stable MLP Values**: MLP components exhibit less variation, with `mlp. gate` consistently the lowest. - **Color Consistency**: All orange regions align with the legend’s high-value range (0.15–0.18), confirming accurate color mapping. ### Interpretation The heatmap likely represents attention weights or activation magnitudes in a transformer-based model. The dominance of `attn. q` and `attn. k` in upper layers implies these mechanisms play a critical role in later-stage information processing. The MLP components, while present, show weaker signals, indicating their role may be more stable or secondary. The lack of extreme outliers suggests a balanced distribution of attention and MLP contributions across layers.