Blame for README.md

---
license: other
library_name: transformers
tags:
  - multimodal
  - vision-language
  - reasoning
  - science
  - rl
  - qwen3.5
---

# InnovatorVL-v2

<video src="https://huggingface.co/InnovatorLab/InnovatorVL-v2/resolve/main/InnovatorVL-v2-Main-Promo-v7-28s.mp4" controls width="100%" preload="metadata" playsinline></video>

**InnovatorVL-v2 is a dense 9B-parameter multimodal model that delivers
state-of-the-art scientific visual understanding at its scale, without giving up
general multimodal ability.** Built on Qwen3.5-9B-Base, it is trained through a
pipeline centred on systematic data governance — recursive self-improvement for
data scoring and filtering, rank-based mixture optimisation, failure-guided data
synthesis, and Visual Evidence-Calibrated Reinforcement Learning (VEC-RL).

Evaluated on 22 benchmarks with thinking disabled for every compared model,
InnovatorVL-v2 attains **the highest average in all three categories**:

| Category | **InnovatorVL-v2 (Ours)** | Margin over strongest baseline |
| --- | :---: | :---: |
| General (12 benchmarks) | **80.18** | +3.32 |
| Science (8 benchmarks) | **59.71** | **+12.46** |
| Spatial (2 benchmarks) | **61.56** | +1.17 |

Headline results:

- **First on 18 of the 22 benchmarks** among the models displayed, and
  **beats Qwen3.5-9B on 21 of the 22** — the single exception is SimpleVQA.
- **Scientific understanding is the standout gain**: +17.70 points over
  Qwen3.5-9B on the Science average.
- **MolParse 75.40** and **OpenRxn 56.50** — ahead of the best compared baseline
  by **+56.35** and **+40.85** points respectively. Molecular structure parsing
  and reaction understanding are near-solved relative to the field.
- **Best general score on 11 of the 12 general benchmarks**, including
  DocVQA 96.76, V\*Bench 90.58, ChartQA 89.24, AI2D 88.34,
  OCRBench 88.20 and InfographicVQA 87.69.
- **Highest Spatial average**, with EmbSpatial-Bench 77.36 and ERQA 45.75.

## Performance

![Performance overview across 22 benchmarks](main_result.png)

**Performance overview across 22 benchmarks.** Blue bars indicate our model; the
remaining bars show the compared models available in each panel. Our model ranks
first on 18 of the 22 benchmarks among the models displayed and outperforms
Qwen3.5-9B on 21 of the 22. The signed value in the upper-right corner of each
panel is the score-point difference against Qwen3.5-9B. All results use our
local evaluation setting with thinking disabled. Higher scores are better;
vertical scales vary across panels.

![Category-level performance](general_ability.png)

**Category-level performance with thinking disabled.** Panels (a), (b) and (c)
report unweighted averages over 12 general, eight scientific and two spatial
benchmarks respectively. All seven models have complete coverage. Blue bars
denote InnovatorVL-v2 (Ours); the remaining bars are the six baselines, including
Qwen3.5-9B-Base. Models appear in the same order across panels, sorted by their
General scores. All panels use the same 0–100 scale.

### Full results

Local non-thinking evaluation across all 22 benchmarks. The
**InnovatorVL-v2 (Ours)** column is placed first and emphasised throughout;
bold marks the best score in each row, including ties.

<div class="max-w-full overflow-auto">
<table>
<thead>
<tr>
<th>Benchmark</th>
<th align="center" style="background-color:#a78bfa;color:#1f2937">InnovatorVL-v2 (Ours)</th>
<th align="center">Qwen3-VL-8B</th>
<th align="center">Gemma4-12B-IT</th>
<th align="center">InternVL3.5-8B</th>
<th align="center">Step3-VL-10B</th>
<th align="center">Qwen3.5-9B</th>
<th align="center">Qwen3.5-9B-Base</th>
</tr>
</thead>
<tbody>
<tr><td colspan="8"><i>General</i></td></tr>
<tr>
<td>AI2D</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>88.34</b></td>
<td align="center">83.74</td>
<td align="center">77.49</td>
<td align="center">82.22</td>
<td align="center">83.55</td>
<td align="center">85.46</td>
<td align="center">63.28</td>
</tr>
<tr>
<td>ChartQA</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>89.24</b></td>
<td align="center">85.24</td>
<td align="center">52.12</td>
<td align="center">87.20</td>
<td align="center">59.68</td>
<td align="center">85.28</td>
<td align="center">61.04</td>
</tr>
<tr>
<td>InfographicVQA (val)</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>87.69</b></td>
<td align="center">83.72</td>
<td align="center">53.86</td>
<td align="center">74.23</td>
<td align="center">71.27</td>
<td align="center">83.34</td>
<td align="center">76.70</td>
</tr>
<tr>
<td>DocVQA (val)</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>96.76</b></td>
<td align="center">93.66</td>
<td align="center">83.10</td>
<td align="center">92.37</td>
<td align="center">92.33</td>
<td align="center">94.98</td>
<td align="center">92.60</td>
</tr>
<tr>
<td>MMBench (EN dev)</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>87.97</b></td>
<td align="center">83.67</td>
<td align="center">81.62</td>
<td align="center">83.51</td>
<td align="center">85.47</td>
<td align="center">85.48</td>
<td align="center">86.34</td>
</tr>
<tr>
<td>MME-RealWorld</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>71.33</b></td>
<td align="center">63.29</td>
<td align="center">35.02</td>
<td align="center">62.66</td>
<td align="center">30.32</td>
<td align="center">60.08</td>
<td align="center">46.79</td>
</tr>
<tr>
<td>MMStar</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>73.15</b></td>
<td align="center">59.94</td>
<td align="center">67.11</td>
<td align="center">66.38</td>
<td align="center">63.24</td>
<td align="center">70.52</td>
<td align="center">71.92</td>
</tr>
<tr>
<td>OCRBench</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>88.20</b></td>
<td align="center">87.00</td>
<td align="center">77.80</td>
<td align="center">83.20</td>
<td align="center">85.30</td>
<td align="center">83.10</td>
<td align="center">86.30</td>
</tr>
<tr>
<td>RealWorldQA</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>76.73</b></td>
<td align="center">68.76</td>
<td align="center">65.23</td>
<td align="center">63.53</td>
<td align="center">63.66</td>
<td align="center">74.51</td>
<td align="center">72.81</td>
</tr>
<tr>
<td>SEED-Bench-2-Plus</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>72.38</b></td>
<td align="center">71.28</td>
<td align="center">68.25</td>
<td align="center">70.58</td>
<td align="center">69.35</td>
<td align="center">70.84</td>
<td align="center">64.08</td>
</tr>
<tr>
<td>V*Bench</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>90.58</b></td>
<td align="center">84.82</td>
<td align="center">52.36</td>
<td align="center">70.68</td>
<td align="center">67.02</td>
<td align="center">84.29</td>
<td align="center">82.20</td>
</tr>
<tr>
<td>SimpleVQA</td>
<td align="center" style="background-color:#be9df6;color:#1f2937">39.80</td>
<td align="center">39.31</td>
<td align="center">40.27</td>
<td align="center">35.47</td>
<td align="center"><b>46.24</b></td>
<td align="center">44.40</td>
<td align="center">45.73</td>
</tr>
<tr><td colspan="8"><i>Science</i></td></tr>
<tr>
<td>EMVista</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>49.62</b></td>
<td align="center">44.27</td>
<td align="center">39.30</td>
<td align="center">46.81</td>
<td align="center">43.03</td>
<td align="center">43.35</td>
<td align="center">37.84</td>
</tr>
<tr>
<td>ScienceQA</td>
<td align="center" style="background-color:#be9df6;color:#1f2937">93.89</td>
<td align="center">91.91</td>
<td align="center">89.39</td>
<td align="center"><b>95.94</b></td>
<td align="center">85.76</td>
<td align="center">93.35</td>
<td align="center">89.53</td>
</tr>
<tr>
<td>MolParse</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>75.40</b></td>
<td align="center">4.35</td>
<td align="center">4.95</td>
<td align="center">19.05</td>
<td align="center">4.25</td>
<td align="center">7.00</td>
<td align="center">5.20</td>
</tr>
<tr>
<td>OpenRxn</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>56.50</b></td>
<td align="center">9.25</td>
<td align="center">12.60</td>
<td align="center">14.05</td>
<td align="center">12.70</td>
<td align="center">14.40</td>
<td align="center">15.65</td>
</tr>
<tr>
<td>RxnBench (EN)</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>89.05</b></td>
<td align="center">82.75</td>
<td align="center">85.31</td>
<td align="center">80.46</td>
<td align="center">73.70</td>
<td align="center">88.52</td>
<td align="center">87.67</td>
</tr>
<tr>
<td>SmolInstruct_v2</td>
<td align="center" style="background-color:#be9df6;color:#1f2937">27.08</td>
<td align="center">20.15</td>
<td align="center">24.18</td>
<td align="center"><b>40.27</b></td>
<td align="center">6.87</td>
<td align="center">14.38</td>
<td align="center">27.44</td>
</tr>
<tr>
<td>ProteinLMBench</td>
<td align="center" style="background-color:#be9df6;color:#1f2937">61.97</td>
<td align="center">59.32</td>
<td align="center">56.67</td>
<td align="center">61.76</td>
<td align="center">56.25</td>
<td align="center">54.77</td>
<td align="center"><b>65.89</b></td>
</tr>
<tr>
<td>SFE (EN)</td>
<td align="center" style="background-color:#be9df6;color:#1f2937">24.13</td>
<td align="center"><b>28.98</b></td>
<td align="center">22.41</td>
<td align="center">19.63</td>
<td align="center">14.44</td>
<td align="center">20.24</td>
<td align="center">20.12</td>
</tr>
<tr><td colspan="8"><i>Spatial</i></td></tr>
<tr>
<td>EmbSpatial-Bench</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>77.36</b></td>
<td align="center">74.90</td>
<td align="center">73.54</td>
<td align="center">73.24</td>
<td align="center">68.10</td>
<td align="center">75.77</td>
<td align="center">75.00</td>
</tr>
<tr>
<td>ERQA</td>
<td align="center" style="background-color:#be9df6;color:#1f2937"><b>45.75</b></td>
<td align="center">38.80</td>
<td align="center">42.25</td>
<td align="center">41.25</td>
<td align="center">24.00</td>
<td align="center">45.00</td>
<td align="center"><b>45.75</b></td>
</tr>
</tbody>
</table>
</div>

## Usage

Verified with `transformers` 5.3.0. All 760 tensors in `model.safetensors` map
cleanly onto the model (no missing or unexpected keys).

```python
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

path = "InnovatorLab/InnovatorVL-v2"
processor = AutoProcessor.from_pretrained(path)          # Qwen3VLProcessor
model = AutoModelForImageTextToText.from_pretrained(     # Qwen3_5ForConditionalGeneration
    path, dtype=torch.bfloat16, device_map="auto"
)
```

### Inference dependencies

The text stack uses hybrid attention with `linear_attention` layers. Without
[`flash-linear-attention`](https://github.com/fla-org/flash-linear-attention)
and [`causal-conv1d`](https://github.com/Dao-AILab/causal-conv1d) installed,
`transformers` logs *"The fast path is not available"* and falls back to a
slower pure-PyTorch implementation. Results are unaffected, but throughput is
lower — install both for production inference.

## License

The license field above is a placeholder. It must be set by the model owner
before relying on this model commercially.