Instructions to use abacusai/Smaug-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abacusai/Smaug-Mini with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="abacusai/Smaug-Mini")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("abacusai/Smaug-Mini")
model = AutoModelForMultimodalLM.from_pretrained("abacusai/Smaug-Mini", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abacusai/Smaug-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abacusai/Smaug-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Mini",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/abacusai/Smaug-Mini

SGLang

How to use abacusai/Smaug-Mini with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "abacusai/Smaug-Mini" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Mini",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "abacusai/Smaug-Mini" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Mini",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use abacusai/Smaug-Mini with Docker Model Runner:
```
docker model run hf.co/abacusai/Smaug-Mini
```

prann-ab commited on Sep 9

Commit

ee82c1d

verified ·

1 Parent(s): a58236f

Dark-mode fix: theme-inherited text for benchmark names, brighter header blue

Browse files

Files changed (1) hide show

README.md +14 -14

README.md CHANGED Viewed

@@ -62,16 +62,16 @@ This card describes the training approach and the evaluation results. Dataset co
 <table style="width:100%;table-layout:fixed;border-collapse:collapse;font-size:13px">
 <thead><tr>
 <th style="padding:10px 6px;text-align:left;border-bottom:2px solid #2a78d6;width:26%"></th>
-<th style="padding:10px 4px;text-align:center;font-weight:600;border-bottom:2px solid #2a78d6;color:#1a5fb4;font-size:13px;background:rgba(42,120,214,0.10);">Smaug-Mini</th>
-<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#1a5fb4;font-size:13px;">Qwen3.8-27B<div style="font-size:10px;font-weight:400;color:#6B6B6B">(base)</div></th>
-<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#1a5fb4;font-size:13px;">Qwen3.6-27B</th>
-<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#1a5fb4;font-size:13px;">Qwen3.7-Plus</th>
-<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#1a5fb4;font-size:13px;">Opus4.6 Max</th>
 </tr></thead>
 <tbody>
 <tr><td colspan="6" style="padding:7px 10px;font-weight:600;color:#1a5fb4;background:#e8f0fc;border-bottom:1px solid rgba(42,120,214,0.2)">Agentic</td></tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">AutomationBench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>41.8</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.38)">37.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;color:#9aa0a6">&mdash;</td>
@@ -79,7 +79,7 @@ This card describes the training approach and the evaluation results. Dataset co
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.15)">25.5</td>
 </tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">JobBench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>50.5</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.22)">33.4</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">21.8</td>
@@ -87,7 +87,7 @@ This card describes the training approach and the evaluation results. Dataset co
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.27)">36.9</td>
 </tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">LiveBench agentic coding</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.46)">60.8</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.47)"><strong>61.4</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">39.3</td>
@@ -95,7 +95,7 @@ This card describes the training approach and the evaluation results. Dataset co
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.23)">49.0</td>
 </tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">NL2Repo-Bench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>55.8</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.18)">42.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">36.2</td>
@@ -104,7 +104,7 @@ This card describes the training approach and the evaluation results. Dataset co
 </tr>
 <tr><td colspan="6" style="padding:7px 10px;font-weight:600;color:#1a5fb4;background:#e8f0fc;border-bottom:1px solid rgba(42,120,214,0.2)">Reasoning, knowledge &amp; instruction following</td></tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">GPQA-diamond</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.24)">89.4</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.22)">89.2</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">87.8</td>
@@ -112,7 +112,7 @@ This card describes the training approach and the evaluation results. Dataset co
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.47)"><strong>91.3</strong></td>
 </tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">HLE</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.32)">34.2</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.23)">30.8</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">24.0</td>
@@ -120,7 +120,7 @@ This card describes the training approach and the evaluation results. Dataset co
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.47)"><strong>40.0</strong></td>
 </tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">IFBench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>82.0</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.42)">79.5</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.19)">69.1</td>
@@ -128,7 +128,7 @@ This card describes the training approach and the evaluation results. Dataset co
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">62.5</td>
 </tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">LiveBench overall</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>76.9</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.42)">75.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">64.0</td>
@@ -137,7 +137,7 @@ This card describes the training approach and the evaluation results. Dataset co
 </tr>
 <tr><td colspan="6" style="padding:7px 10px;font-weight:600;color:#1a5fb4;background:#e8f0fc;border-bottom:1px solid rgba(42,120,214,0.2)">Vision</td></tr>
 <tr>
-<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600;color:#171717">MMMU-Pro</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.10)">75.6</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.16)">76.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">75</td>

 <table style="width:100%;table-layout:fixed;border-collapse:collapse;font-size:13px">
 <thead><tr>
 <th style="padding:10px 6px;text-align:left;border-bottom:2px solid #2a78d6;width:26%"></th>
+<th style="padding:10px 4px;text-align:center;font-weight:600;border-bottom:2px solid #2a78d6;color:#2a78d6;font-size:13px;background:rgba(42,120,214,0.10);">Smaug-Mini</th>
+<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#2a78d6;font-size:13px;">Qwen3.8-27B<div style="font-size:10px;font-weight:400;color:#6B6B6B">(base)</div></th>
+<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#2a78d6;font-size:13px;">Qwen3.6-27B</th>
+<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#2a78d6;font-size:13px;">Qwen3.7-Plus</th>
+<th style="padding:10px 4px;text-align:center;font-weight:500;border-bottom:2px solid #2a78d6;color:#2a78d6;font-size:13px;">Opus4.6 Max</th>
 </tr></thead>
 <tbody>
 <tr><td colspan="6" style="padding:7px 10px;font-weight:600;color:#1a5fb4;background:#e8f0fc;border-bottom:1px solid rgba(42,120,214,0.2)">Agentic</td></tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">AutomationBench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>41.8</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.38)">37.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;color:#9aa0a6">&mdash;</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.15)">25.5</td>
 </tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">JobBench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>50.5</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.22)">33.4</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">21.8</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.27)">36.9</td>
 </tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">LiveBench agentic coding</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.46)">60.8</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.47)"><strong>61.4</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">39.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.23)">49.0</td>
 </tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">NL2Repo-Bench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>55.8</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.18)">42.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">36.2</td>
 </tr>
 <tr><td colspan="6" style="padding:7px 10px;font-weight:600;color:#1a5fb4;background:#e8f0fc;border-bottom:1px solid rgba(42,120,214,0.2)">Reasoning, knowledge &amp; instruction following</td></tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">GPQA-diamond</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.24)">89.4</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.22)">89.2</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">87.8</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.47)"><strong>91.3</strong></td>
 </tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">HLE</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.32)">34.2</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.23)">30.8</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">24.0</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.47)"><strong>40.0</strong></td>
 </tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">IFBench</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>82.0</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.42)">79.5</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.19)">69.1</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">62.5</td>
 </tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">LiveBench overall</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.47)"><strong>76.9</strong></td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.42)">75.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">64.0</td>
 </tr>
 <tr><td colspan="6" style="padding:7px 10px;font-weight:600;color:#1a5fb4;background:#e8f0fc;border-bottom:1px solid rgba(42,120,214,0.2)">Vision</td></tr>
 <tr>
+<td style="padding:8px 6px;padding-left:16px;border-bottom:1px solid rgba(128,128,128,0.15);font-size:14px;font-weight:600">MMMU-Pro</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;font-weight:600;background:rgba(42,120,214,0.10)">75.6</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.16)">76.3</td>
 <td style="padding:8px 6px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);vertical-align:middle;font-size:14px;line-height:1.2;background:rgba(42,120,214,0.05)">75</td>