## Stacked Bar Chart: Pythia 6.9B ### Overview The image is a stacked bar chart titled "Pythia 6.9B". The chart displays the distribution of "# heads" across different "Layer" intervals. Each bar represents a layer interval, and the bar is segmented into colored sections, each representing a percentage. The y-axis represents the number of heads, ranging from 0 to 15. The x-axis represents the layer intervals: [0, 8), [8, 16), [16, 24), and [24, 32). The colors within each bar represent different categories, but the legend is missing, so the meaning of the colors is unknown. ### Components/Axes * **Title:** Pythia 6.9B * **Y-axis Label:** # heads * **Y-axis Scale:** 0, 5, 10, 15 * **X-axis Label:** Layer * **X-axis Categories:** [0, 8), [8, 16), [16, 24), [24, 32) * **Colors:** Green, Orange, Yellow, Red (The meaning of these colors is not specified in the image) ### Detailed Analysis The chart presents data for four layer intervals: [0, 8), [8, 16), [16, 24), and [24, 32). Each interval's bar is divided into colored segments, with percentages indicating the proportion of each segment. * **Layer [0, 8):** * Green: 45.5% * Orange: 27.3% * Yellow: 27.3% * Red: 0.0% * **Layer [8, 16):** * Green: 46.7% * Orange: 33.3% * Yellow: 20.0% * Red: 0.0% * **Layer [16, 24):** * Green: 75.0% * Orange: 12.5% * Yellow: 6.2% * Red: 6.2% * **Layer [24, 32):** * Green: 83.3% * Orange: 5.6% * Yellow: 11.1% * Red: 0.0% ### Key Observations * The green segment increases significantly from layer [0, 8) to [24, 32), indicating a higher proportion in the later layers. * The orange segment decreases from layer [8, 16) to [24, 32). * The yellow segment decreases from layer [0, 8) to [16, 24) and then increases slightly in [24, 32). * The red segment is only present in layer [16, 24). ### Interpretation The stacked bar chart illustrates the distribution of "# heads" across different layers of the Pythia 6.9B model. The increasing proportion of the green segment suggests that the component represented by green becomes more dominant in later layers. The presence of the red segment only in layer [16, 24) indicates that the component represented by red is specific to that layer interval. Without a legend, the exact meaning of each color is unknown, but the chart clearly shows how the composition of "# heads" changes across different layers.

## Stacked Bar Chart: Pythia 6.9B Layer Analysis ### Overview This is a stacked bar chart visualizing the distribution of "# heads" across different layers of the Pythia 6.9B model. The chart displays the percentage contribution of different components to the total number of heads within each layer. The x-axis represents the layer range, and the y-axis represents the number of heads. Each bar is segmented into colored sections representing percentage contributions. ### Components/Axes * **Title:** Pythia 6.9B * **X-axis Label:** Layer * **X-axis Markers:** \[0, 8), \[8, 16), \[16, 24), \[24, 32) * **Y-axis Label:** # heads * **Colors/Legend (inferred from stacking order):** * Dark Green: Represents the lowest percentage contribution within each bar. * Green: Represents the second lowest percentage contribution. * Orange: Represents the middle percentage contribution. * Red: Represents the highest percentage contribution. ### Detailed Analysis The chart consists of four stacked bars, one for each layer range. * **Layer \[0, 8):** * Dark Green: 45.5% * Green: 27.3% * Orange: 27.3% * Red: 0.0% * Total # heads: Approximately 10 * **Layer \[8, 16):** * Dark Green: 46.7% * Green: 33.3% * Orange: 20.0% * Red: 0.0% * Total # heads: Approximately 15 * **Layer \[16, 24):** * Dark Green: 75.0% * Green: 12.5% * Orange: 6.2% * Red: 6.2% * Total # heads: Approximately 15 * **Layer \[24, 32):** * Dark Green: 83.3% * Green: 5.6% * Orange: 11.1% * Red: 0.0% * Total # heads: Approximately 15 ### Key Observations * The distribution of "# heads" changes significantly across layers. * Layer \[0, 8) has a relatively even distribution across the dark green, green, and orange segments. * Layer \[16, 24) is dominated by the dark green segment (75.0%). * Layer \[24, 32) also has a strong dominance of the dark green segment (83.3%). * The red segment is only present in layers \[16, 24) and \[24, 32), and its contribution is relatively small (6.2% and 0.0% respectively). ### Interpretation The chart suggests that the distribution of "heads" (likely referring to attention heads in a transformer model) varies considerably across different layers of the Pythia 6.9B model. The increasing dominance of the dark green segment in the later layers (\[16, 24) and \[24, 32)) could indicate that a larger proportion of attention heads are focused on a specific aspect or feature of the input data in those layers. The absence of the red segment in the earlier layers suggests that the higher-level features or relationships captured by those heads are not yet prominent. The varying percentages within each layer likely reflect the different roles and functionalities of the attention heads at each stage of the model's processing. The data suggests a shift in attention focus as information propagates through the layers of the model. The total number of heads appears to be relatively consistent across layers, around 10-15.

\n ## Stacked Bar Chart: Pythia 6.9B Attention Head Distribution by Layer ### Overview This is a stacked bar chart visualizing the distribution of different types of attention heads across the layers of the Pythia 6.9B language model. The chart shows four bars, each representing a range of model layers, with the total height of each bar indicating the total number of attention heads in that layer range. Each bar is segmented by color, representing the proportion of heads belonging to different categories. ### Components/Axes * **Chart Title:** "Pythia 6.9B" (Top center) * **X-Axis:** * **Label:** "Layer" (Bottom center) * **Categories (from left to right):** 1. `[0, 8)` 2. `[8, 16)` 3. `[16, 24)` 4. `[24, 32)` * **Y-Axis:** * **Label:** "# heads" (Left side, rotated vertically) * **Scale:** Linear, from 0 to 15, with major ticks at 0, 5, 10, 15. * **Data Series (Inferred from color and position):** The legend is not explicitly shown, but the consistent color coding across bars implies four categories. From bottom to top within each bar: 1. **Green (Bottom segment)** 2. **Orange (Middle segment)** 3. **Yellow (Upper-middle segment)** 4. **Red (Top segment, only present in one bar)** ### Detailed Analysis **Bar 1: Layers [0, 8)** * **Total Height (Approximate):** 11 heads * **Segment Breakdown (from bottom to top):** * **Green:** 45.5% (≈5 heads) * **Orange:** 27.3% (≈3 heads) * **Yellow:** 27.3% (≈3 heads) * **Red:** 0.0% (0 heads) **Bar 2: Layers [8, 16)** * **Total Height (Approximate):** 15 heads * **Segment Breakdown (from bottom to top):** * **Green:** 46.7% (≈7 heads) * **Orange:** 33.3% (≈5 heads) * **Yellow:** 20.0% (≈3 heads) * **Red:** 0.0% (0 heads) **Bar 3: Layers [16, 24)** * **Total Height (Approximate):** 16 heads * **Segment Breakdown (from bottom to top):** * **Green:** 75.0% (≈12 heads) * **Orange:** 12.5% (≈2 heads) * **Yellow:** 6.2% (≈1 head) * **Red:** 6.2% (≈1 head) **Bar 4: Layers [24, 32)** * **Total Height (Approximate):** 18 heads * **Segment Breakdown (from bottom to top):** * **Green:** 83.3% (≈15 heads) * **Orange:** 5.6% (≈1 head) * **Yellow:** 11.1% (≈2 heads) * **Red:** 0.0% (0 heads) ### Key Observations 1. **Increasing Total Heads:** The total number of attention heads per layer range increases from the first to the last bar (≈11 → 15 → 16 → 18). 2. **Dominant Green Segment:** The proportion of "Green" category heads increases dramatically across layers, from 45.5% in the first layers to 83.3% in the final layers. It becomes the overwhelming majority in the second half of the model. 3. **Declining Orange Segment:** The "Orange" category shows a clear downward trend, decreasing from 27.3% to 5.6%. 4. **Anomaly in Third Bar:** The `[16, 24)` layer range is the only one containing a "Red" category segment (6.2%). It also has the smallest "Yellow" segment (6.2%). 5. **Yellow Segment Fluctuation:** The "Yellow" category does not follow a monotonic trend, starting at 27.3%, dipping to 6.2% in the third bar, and rising slightly to 11.1% in the final bar. ### Interpretation This chart likely illustrates the functional specialization of attention heads within the Pythia 6.9B model. The "Green" heads, which dominate the later layers, are probably responsible for higher-level, task-specific, or output-oriented processing. The "Orange" heads, prominent in early layers, may handle more fundamental syntactic or local contextual tasks. The unique appearance of a "Red" head in the middle layers (`[16, 24)`) suggests a specialized function that is only activated or necessary in that specific processing stage. The overall increase in total heads per layer range indicates that the model allocates more computational resources (attention mechanisms) to deeper processing stages. The distribution pattern suggests a shift from diverse, possibly general-purpose head types in early layers to a more homogeneous, specialized set of heads in later layers.

## Bar Chart: Pythia 6.9B Layer Head Distribution ### Overview The chart visualizes the distribution of attention heads across four layers of the Pythia 6.9B model architecture. Each bar represents a layer range, with segmented colors indicating different head categories. The y-axis shows the total number of heads, while the x-axis categorizes layers into four ranges: [0,8), [8,16), [16,24), and [24,32). ### Components/Axes - **X-axis (Layer Ranges)**: - [0,8) - [8,16) - [16,24) - [24,32) - **Y-axis (# heads)**: - Scale from 0 to 15 in increments of 5 - **Legend (Right-aligned)**: - Green: 45.5% → 83.3% (dominant category) - Orange: 27.3% → 5.6% (declining trend) - Red: 6.2% (only in [16,24)) - Yellow: 27.3% → 11.1% (declining trend) ### Detailed Analysis 1. **Layer [0,8)**: - Green: 45.5% (5 heads) - Orange: 27.3% (3 heads) - Yellow: 27.3% (3 heads) - Red: 0.0% 2. **Layer [8,16)**: - Green: 46.7% (7 heads) - Orange: 33.3% (5 heads) - Yellow: 20.0% (3 heads) - Red: 0.0% 3. **Layer [16,24)**: - Green: 75.0% (12 heads) - Orange: 12.5% (2 heads) - Red: 6.2% (1 head) - Yellow: 6.2% (1 head) 4. **Layer [24,32)**: - Green: 83.3% (13 heads) - Orange: 5.6% (1 head) - Yellow: 11.1% (2 heads) - Red: 0.0% ### Key Observations - **Green Dominance**: Green segments (likely "query" or "key" heads) increase monotonically from 45.5% to 83.3% across layers. - **Orange Decline**: Orange segments (possibly "value" heads) decrease from 27.3% to 5.6%, suggesting reduced importance in deeper layers. - **Red Anomaly**: Red appears only in the [16,24) layer at 6.2%, potentially indicating a specialized head type in mid-layers. - **Yellow Reduction**: Yellow segments (possibly "mixed" heads) decrease from 27.3% to 11.1%, showing diminishing prevalence in deeper layers. ### Interpretation The chart reveals a clear architectural pattern where: 1. **Layer Depth Correlation**: Deeper layers ([24,32)) concentrate attention heads into fewer, more specialized categories (green dominance). 2. **Head Type Evolution**: The decline of orange and yellow heads suggests a shift from general-purpose to query-dominated attention mechanisms in deeper layers. 3. **Mid-Layer Specialization**: The presence of red heads in [16,24) implies a unique architectural feature (e.g., gating or cross-attention) in mid-layers. 4. **Quantitative Precision**: The exact percentage values (e.g., 83.3% in [24,32)) indicate careful balancing of head types to optimize model performance. This distribution pattern aligns with transformer architecture principles, where deeper layers typically develop more specialized attention mechanisms while maintaining computational efficiency through reduced head diversity.