## Grouped Bar Chart: Data Source Weight Distribution for Two QA Blends
### Overview
The image displays a grouped bar chart comparing the percentage weight assigned to eleven different data sources across two distinct datasets or models, labeled "QA Blend" and "QA Blend 1T". The chart visualizes the compositional differences between these two blends.
### Components/Axes
* **Chart Type:** Grouped (clustered) vertical bar chart.
* **Legend:** Located at the top center of the chart area.
* **Light Green Square:** Labeled "QA Blend"
* **Darker Green Square:** Labeled "QA Blend 1T"
* **X-Axis (Horizontal):**
* **Title:** "Data Source" (centered below the axis labels).
* **Categories (from left to right):** Web Crawl, Books, News Articles, Papers, Encyclopedia, Legal, Finance, Misc., Multilingual, Code, QA.
* **Y-Axis (Vertical):**
* **Title:** "Weight (%)" (rotated 90 degrees).
* **Scale:** Linear scale from 0 to 35, with major gridlines at intervals of 5 (0, 5, 10, 15, 20, 25, 30, 35).
### Detailed Analysis
The following table reconstructs the approximate weight (%) for each data source in both blends. Values are estimated based on bar height relative to the y-axis gridlines.
| Data Source | QA Blend (Light Green) Weight (%) | QA Blend 1T (Darker Green) Weight (%) |
| :--- | :--- | :--- |
| **Web Crawl** | ~3% | ~4% |
| **Books** | ~16% | ~13.5% |
| **News Articles** | ~3% | ~4% |
| **Papers** | ~18% | ~15% |
| **Encyclopedia** | ~8% | ~7% |
| **Legal** | ~8% | ~11.5% |
| **Finance** | ~3% | ~4% |
| **Misc.** | ~11% | ~14% |
| **Multilingual** | ~3% | ~3% |
| **Code** | ~15% | ~20% |
| **QA** | ~12% | ~4% |
**Trend Verification per Data Series:**
* **QA Blend (Light Green):** The series shows its highest weights in **Papers (~18%)**, **Books (~16%)**, and **Code (~15%)**. It has notably lower weights in **Web Crawl, News Articles, Finance, and Multilingual** (all ~3%).
* **QA Blend 1T (Darker Green):** This series peaks sharply at **Code (~20%)**. Other significant sources are **Papers (~15%)**, **Misc. (~14%)**, and **Books (~13.5%)**. Its lowest weight is in **QA (~4%)**.
### Key Observations
1. **Dominant Source Shift:** The primary weight shifts from **Papers** in QA Blend to **Code** in QA Blend 1T.
2. **Significant Divergence in 'QA':** The most dramatic relative difference is in the **QA** category itself, where QA Blend assigns ~12% weight, but QA Blend 1T assigns only ~4%.
3. **Increased Emphasis:** QA Blend 1T shows a clear increase in weight for **Code, Legal, and Misc.** compared to QA Blend.
4. **Decreased Emphasis:** QA Blend 1T shows a clear decrease in weight for **Books, Papers, Encyclopedia, and QA** compared to QA Blend.
5. **Consistent Low-Priority Sources:** **Web Crawl, News Articles, Finance, and Multilingual** remain low-weight sources (3-4%) in both blends.
6. **Equal Weight:** **Multilingual** is the only category where both blends appear to have an identical weight (~3%).
### Interpretation
This chart illustrates a strategic rebalancing of training data composition between two iterations or versions of a model (QA Blend vs. QA Blend 1T). The data suggests a deliberate pivot in focus:
* **From Academic to Practical:** The reduction in weight for **Books, Papers, and Encyclopedia** (traditional knowledge sources) coupled with the major increase for **Code** indicates a shift towards prioritizing practical, technical, and potentially instruction-following data. This often aligns with improving a model's reasoning, logic, and task-completion abilities.
* **Refinement of QA Data:** The sharp drop in the **QA** category's weight is intriguing. It may indicate that the "1T" blend relies less on curated question-answer pairs, perhaps because the increased weight in **Code** and **Legal** documents provides more implicit reasoning patterns, or because the QA data was consolidated or filtered more aggressively.
* **Broadening of 'Misc.':** The increase in the **Misc.** category suggests an effort to incorporate a wider variety of unstructured or niche data to improve generalization.
* **Stable Foundation:** The consistent, low weighting of broad web data (**Web Crawl, News**) suggests both blends use these as a minor, stabilizing component rather than a primary source.
In essence, **QA Blend 1T appears to be a more technically-oriented and possibly more specialized derivative** of the original QA Blend, trading some breadth of general knowledge for depth in code, law, and miscellaneous practical domains.
