## Line Chart: Vocab Accuracy vs. Model Size ### Overview The image is a line chart comparing the vocabulary accuracy of different models against their size. The x-axis represents the model size (350M, 1.3B, 2.4B), and the y-axis represents the 40,960 vocab accuracy, ranging from 42 to 56. Six different models are compared, each represented by a different colored line with a unique marker. ### Components/Axes * **Title:** There is no explicit title on the chart. * **X-axis:** Model Size (Not to Scale) * Values: 350M, 1.3B, 2.4B * **Y-axis:** 40,960 Vocab Accuracy * Values: 42, 44, 46, 48, 50, 52, 54, 56 * **Legend:** Located at the top of the chart. * **bpe:** Dark blue line with circle markers. * **unigram:** Dark red line with square markers. * **pathpl\_bpe:** Light blue line with diamond markers. * **sage\_bpe:** Light orange line with triangle markers. * **sage\_ngram:** Light orange line with star markers. * **pathpl\_ngram:** Light blue line with square markers. ### Detailed Analysis * **bpe (Dark Blue, Circle):** The line slopes upward. * 350M: ~50 * 1.3B: ~53 * 2.4B: ~54 * **unigram (Dark Red, Square):** The line slopes upward. * 350M: ~49 * 1.3B: ~52 * 2.4B: ~55 * **pathpl\_bpe (Light Blue, Diamond):** The line slopes upward. * 350M: ~50 * 1.3B: ~49 * 2.4B: ~53 * **sage\_bpe (Light Orange, Triangle):** The line slopes upward. * 350M: ~49 * 1.3B: ~52 * 2.4B: ~55 * **sage\_ngram (Light Orange, Star):** The line slopes upward. * 350M: ~47 * 1.3B: ~51 * 2.4B: ~55 * **pathpl\_ngram (Light Blue, Square):** The line slopes upward. * 350M: ~45 * 1.3B: ~48 * 2.4B: ~55 ### Key Observations * The 'bpe' model (dark blue) and 'unigram' model (dark red) generally have higher vocabulary accuracy compared to the other models across all model sizes. * The 'pathpl\_ngram' model (light blue square) has the lowest vocabulary accuracy at 350M and 1.3B, but its accuracy increases significantly at 2.4B, reaching a similar level to other models. * All models show an increase in vocabulary accuracy as the model size increases from 350M to 2.4B. * The models converge in accuracy as the model size increases to 2.4B. ### Interpretation The chart demonstrates the relationship between model size and vocabulary accuracy for different models. The 'bpe' and 'unigram' models appear to be more effective in terms of vocabulary accuracy for smaller model sizes. However, as the model size increases, the performance of all models tends to converge, suggesting that increasing model size can compensate for differences in model architecture or training methods. The 'pathpl\_ngram' model's significant improvement at 2.4B suggests that this model benefits more from increased size compared to others. The note "Model Size (Not to Scale)" indicates that the distances between the model sizes on the x-axis are not proportional to the actual differences in size.

\n ## Line Chart: Vocab Accuracy vs. Model Size ### Overview This line chart depicts the relationship between model size and vocabulary accuracy for six different tokenization methods. The x-axis represents model size, and the y-axis represents vocabulary accuracy. The chart shows how accuracy changes as model size increases for each method. ### Components/Axes * **X-axis Title:** "Model Size (Not to Scale)" * **X-axis Markers:** 350M, 1.3B, 2.4B * **Y-axis Title:** "40,960 Vocab Accuracy" * **Y-axis Scale:** Ranges from approximately 42 to 56. * **Legend:** Located at the top-center of the chart. * bpe (Blue) * pathpl\_bpe (Light Blue) * sage\_ngram (Orange) * unigram (Red) * sage\_bpe (Pink) * pathpl\_ngram (Gray) ### Detailed Analysis The chart displays six lines, each representing a different tokenization method. * **bpe (Blue):** The line slopes upward, starting at approximately 49.8 at 350M, rising to approximately 53.5 at 1.3B, and reaching approximately 54.2 at 2.4B. * **pathpl\_bpe (Light Blue):** The line shows a steady, but relatively slow, upward trend. It begins at approximately 44.8 at 350M, increases to approximately 47.5 at 1.3B, and reaches approximately 52.3 at 2.4B. * **sage\_ngram (Orange):** The line exhibits an upward trend, starting at approximately 47.8 at 350M, increasing to approximately 50.2 at 1.3B, and reaching approximately 51.2 at 2.4B. * **unigram (Red):** The line slopes upward, starting at approximately 49.5 at 350M, rising to approximately 52.6 at 1.3B, and reaching approximately 53.8 at 2.4B. * **sage\_bpe (Pink):** The line shows a moderate upward trend, starting at approximately 48.5 at 350M, increasing to approximately 50.5 at 1.3B, and reaching approximately 51.5 at 2.4B. * **pathpl\_ngram (Gray):** The line shows a slow upward trend, starting at approximately 46.5 at 350M, increasing to approximately 47.2 at 1.3B, and reaching approximately 48.5 at 2.4B. ### Key Observations * The 'bpe' method consistently demonstrates the highest vocabulary accuracy across all model sizes. * 'pathpl\_bpe' consistently shows the lowest vocabulary accuracy. * All methods show an increase in accuracy as model size increases, but the rate of increase varies. * The differences in accuracy between methods become more pronounced at larger model sizes (2.4B). ### Interpretation The data suggests that the choice of tokenization method significantly impacts vocabulary accuracy, particularly as model size grows. The 'bpe' method appears to be the most effective for achieving high vocabulary accuracy, while 'pathpl\_bpe' is the least effective. The consistent upward trend for all methods indicates that increasing model size generally improves vocabulary accuracy, but the marginal benefit diminishes as the model becomes larger. The relatively small differences in accuracy between some methods (e.g., 'unigram', 'sage\_bpe', 'sage\_ngram') at smaller model sizes suggest that other factors may become more important as model size increases. The "Not to Scale" disclaimer on the x-axis implies that the distances between model sizes are not proportional to the actual differences in size, and should be interpreted cautiously. This chart is likely used to evaluate and compare different tokenization strategies for language models.

## Line Chart: Model Size vs. Vocab Accuracy for Different Tokenization Methods ### Overview The image is a line chart comparing the performance of six different tokenization or model training methods across three model sizes. The chart plots "40,960 Vocab Accuracy" on the y-axis against "Model Size (Not to Scale)" on the x-axis. The data suggests that accuracy generally increases with model size for all methods, but the rate of improvement and final performance vary significantly. ### Components/Axes * **Chart Type:** Multi-series line chart. * **Y-Axis:** * **Label:** "40,960 Vocab Accuracy" * **Scale:** Linear, ranging from 42 to 56, with major tick marks every 2 units (42, 44, 46, 48, 50, 52, 54, 56). * **X-Axis:** * **Label:** "Model Size (Not to Scale)" * **Categories/Points:** Three discrete model sizes: "350M", "1.3B", "2.4B". The axis is categorical, not numerically scaled. * **Legend:** Positioned at the top center of the chart area. It contains six entries, each with a unique color, line style, and marker: 1. `bpe`: Dark blue line with circle markers. 2. `unigram`: Dark red line with square markers. 3. `pathpl_bpe`: Light blue line with diamond markers. 4. `sage_bpe`: Light orange/peach line with upward-pointing triangle markers. 5. `sage_ngram`: Orange line with star (asterisk) markers. 6. `pathpl_ngram`: Very light blue/grey line with square markers. ### Detailed Analysis **Data Series Trends and Approximate Values:** 1. **bpe (Dark Blue, Circles):** * **Trend:** Steady, strong upward slope across all model sizes. * **Values:** ~50.0 (350M) → ~53.1 (1.3B) → ~54.2 (2.4B). 2. **unigram (Dark Red, Squares):** * **Trend:** Strong upward slope, nearly parallel to `bpe` but slightly lower at 350M and 1.3B, converging at 2.4B. * **Values:** ~49.1 (350M) → ~52.5 (1.3B) → ~54.7 (2.4B). 3. **sage_bpe (Light Orange, Triangles):** * **Trend:** Very strong upward slope, starting near `unigram` and ending as the highest-performing method at 2.4B. * **Values:** ~49.2 (350M) → ~52.2 (1.3B) → ~55.0 (2.4B). 4. **sage_ngram (Orange, Stars):** * **Trend:** Moderate upward slope. Data is only plotted for 350M and 1.3B; the line does not extend to 2.4B. * **Values:** ~46.9 (350M) → ~50.7 (1.3B). No data point for 2.4B. 5. **pathpl_bpe (Light Blue, Diamonds):** * **Trend:** Slight dip or plateau between 350M and 1.3B, followed by a strong increase to 2.4B. * **Values:** ~49.4 (350M) → ~49.2 (1.3B) → ~52.7 (2.4B). 6. **pathpl_ngram (Very Light Blue, Squares):** * **Trend:** Steady upward slope. This is the lowest-performing series at 350M and 1.3B. Data is only plotted for these two points. * **Values:** ~44.9 (350M) → ~47.6 (1.3B). No data point for 2.4B. ### Key Observations * **Performance Hierarchy at 350M:** `bpe` > `pathpl_bpe` ≈ `sage_bpe` ≈ `unigram` > `sage_ngram` > `pathpl_ngram`. * **Performance Hierarchy at 1.3B:** `bpe` > `unigram` > `sage_bpe` > `sage_ngram` > `pathpl_bpe` > `pathpl_ngram`. * **Performance Hierarchy at 2.4B:** `sage_bpe` > `unigram` > `bpe` > `pathpl_bpe`. (`sage_ngram` and `pathpl_ngram` have no data). * **Notable Outliers/Anomalies:** * `pathpl_bpe` is the only method that does not show a strict monotonic increase, exhibiting a slight performance drop when scaling from 350M to 1.3B. * The `sage_ngram` and `pathpl_ngram` methods have incomplete data, missing results for the largest (2.4B) model size. * At the largest model size (2.4B), the `sage_bpe` method overtakes the initially leading `bpe` method. ### Interpretation This chart demonstrates the relationship between model scale and downstream task accuracy (specifically for a 40,960 vocabulary size) when using different subword tokenization or training strategies. The core finding is that **increasing model size generally improves accuracy**, but the choice of tokenization method significantly impacts both the absolute performance and the scaling efficiency. * **Method Effectiveness:** The `sage_bpe` and `unigram` methods show the most promising scaling behavior, with `sage_bpe` achieving the highest observed accuracy at 2.4B parameters. The standard `bpe` method is a strong and consistent performer but is eventually surpassed. * **Scaling Inefficiency:** The `pathpl_bpe` method's dip at 1.3B suggests a potential instability or suboptimal configuration at that specific scale, though it recovers at 2.4B. The `pathpl_ngram` method consistently underperforms others at the scales where it is measured. * **Data Gaps:** The absence of data for `sage_ngram` and `pathpl_ngram` at 2.4B limits a full comparison. It is unclear if this is due to experimental constraints, failure to converge, or results not being ready. * **Practical Implication:** For practitioners aiming to maximize accuracy with a large vocabulary, this data suggests that `sage_bpe` or `unigram` tokenization paired with a model size of at least 2.4B parameters is a highly effective combination. The choice between methods may also depend on other factors not shown here, such as training cost, inference speed, or performance on other metrics.

## Line Chart: 40,960 Vocab Accuracy vs Model Size ### Overview The chart compares the performance of six different model configurations (bpe, pathpl_bpe, sage_ngram, unigram, sage_bpe, pathpl_ngram) across three model sizes (350M, 1.3B, 2.4B) in terms of 40,960 Vocab Accuracy. Accuracy is measured on a scale from 42 to 56, with model sizes represented on a logarithmic scale. ### Components/Axes - **X-axis**: Model Size (Not to Scale) - Markers: 350M (left), 1.3B (center), 2.4B (right) - **Y-axis**: 40,960 Vocab Accuracy (42–56) - **Legend**: Top-left corner with six entries: - Blue circle: bpe - Light blue diamond: pathpl_bpe - Orange star: sage_ngram - Red cross: unigram - Pink triangle: sage_bpe - Light pink square: pathpl_ngram ### Detailed Analysis 1. **bpe (Blue Circle)**: - 350M: ~50.0 - 1.3B: ~53.0 - 2.4B: ~54.2 - *Trend*: Steady upward slope. 2. **pathpl_bpe (Light Blue Diamond)**: - 350M: ~49.5 - 1.3B: ~49.2 - 2.4B: ~52.8 - *Trend*: Flat initially, then sharp increase. 3. **sage_ngram (Orange Star)**: - 350M: ~47.0 - 1.3B: ~50.5 - 2.4B: ~54.0 - *Trend*: Steep upward slope. 4. **unigram (Red Cross)**: - 350M: ~49.0 - 1.3B: ~52.5 - 2.4B: ~54.5 - *Trend*: Sharp upward slope. 5. **sage_bpe (Pink Triangle)**: - 350M: ~49.2 - 1.3B: ~52.0 - 2.4B: ~55.0 - *Trend*: Consistent upward slope. 6. **pathpl_ngram (Light Pink Square)**: - 350M: ~45.0 - 1.3B: ~47.5 - 2.4B: ~52.5 - *Trend*: Gradual upward slope. ### Key Observations - **Highest Performance**: - At 2.4B, **sage_bpe** (55.0) and **unigram** (54.5) achieve the highest accuracy. - **Lowest Performance**: - **pathpl_ngram** (light pink square) consistently lags, with ~45.0 at 350M and ~52.5 at 2.4B. - **Model Size Impact**: - Larger models (2.4B) outperform smaller ones across all configurations. - **sage_ngram** and **unigram** show the steepest improvement with model size. - **Flat Lines**: - **bpe** and **pathpl_bpe** exhibit relatively flat trends compared to others. ### Interpretation The data suggests that model size significantly impacts performance, with larger models (2.4B) achieving higher accuracy. The **unigram** and **sage_ngram** configurations benefit most from increased model size, showing steep upward trends. In contrast, **pathpl_ngram** underperforms across all sizes, indicating potential inefficiencies in its design. The flat lines for **bpe** and **pathpl_bpe** imply that their performance is less sensitive to model size changes. This highlights the importance of architectural choices (e.g., n-gram vs. path-based models) in determining scalability and effectiveness.