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<!-- ===== Polished Info Header & Sections ===== -->
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<!-- Title + meta badges -->
<header class="lb-hero">
<h1>41 Open-Source LLMs Evaluated Locally on 19 Benchmarks ⚡</h1>
<p class="lb-tagline">
Evaluations run with the lm-evaluation-harness library on a local workstation.
</p>
<p class="lb-tagline">Benchmarks are grouped into three categories, with the corresponding tasks and metrics listed below.
</p>
</header>
<!-- Three cards: task groups -->
<div class="card-grid">
<article class="card">
<h3>Reasoning & Math</h3>
<div class="chips">
<span class="chip">gsm8k(exact_match,strict-match)</span>
<span class="chip">bbh(exact_match,get-answer)</span>
<span class="chip">arc_challenge(acc_norm,none)</span>
<span class="chip">anli_r1(acc,none)</span>
<span class="chip">anli_r2(acc,none)</span>
<span class="chip">anli_r3(acc,none)</span>
<span class="chip">gqpa_main_zeroshot(acc_norm,none)</span>
</div>
</article>
<article class="card">
<h3>Commonsense & Natural Language Inference</h3>
<div class="chips">
<span class="chip">hellaswag(acc_norm,none)</span>
<span class="chip">piqa(acc_norm,none)</span>
<span class="chip">winogrande(acc,none)</span>
<span class="chip">boolq(acc,none)</span>
<span class="chip">openbookqa(acc_norm,none)</span>
<span class="chip">sciq(acc_norm,none)</span>
<span class="chip">qnli(acc,none)</span>
</div>
</article>
<article class="card">
<h3>Knowledge & Reading</h3>
<div class="chips">
<span class="chip">mmlu(acc,none)</span>
<span class="chip">nq_open(exact_match,remove_whitespace)</span>
<span class="chip">drop(f1,none)</span>
<span class="chip">truthfulqa_mc1(acc,none)</span>
<span class="chip">truthfulqa_mc2(acc,none)</span>
<span class="chip">triviaqa(exact_match,remove_whitespace)</span>
</div>
</article>
</div>
<!-- Table field definitions -->
<section class="card card-defs">
<h3>Table Fields</h3>
<dl class="defs">
<dt>Model Name</dt>
<dd>Listed as <code>Company_ModelName</code>. If quantized, <code>(8bit)</code> is appended.</dd>
<dt>Total Time</dt>
<dd>Wall-clock time for the full evaluation run per model.</dd>
<dt>GPU Util Time</dt>
<dd>
Equivalent RTX 5090 GPU time at 100% utilization. GPU utilization was logged once per minute,
and the effective utilization time was calculated using the following equation:
<div class="equation-svg">
<img src="results_json/equation.svg" alt="T_util-100% = Σ (u_i / 100) Δt" />
</div>
<ul>
<li><span class="muted">interval:</span> 1 minute</li>
<li><span class="muted">u<sub>i</sub>:</span> GPU utilization (%) during interval i</li>
<li><span class="muted">Δt:</span> duration of interval i (s)</li>
</ul>
</dd>
<dt>Mean Score</dt>
<dd>Arithmetic mean across the 19 tasks (0–1; higher is better). Unweighted.</dd>
<dt>Overall Rank</dt>
<dd>Rank by Mean Score (1 = best).</dd>
</dl>
</section>
<section class="card card-doc">
<h3>Repository Contents</h3>
<ul class="contents-list">
<li>
<strong>Scripts</strong> — scripts for running benchmarks and collecting metrics/logs.
</li>
<li>
<strong>Notebooks</strong> — Jupyter notebook for table generation and post-processing.
</li>
<li>
<strong>Results (CSV / Excel)</strong> — tables for each ranking category, overall rankings, and a master table with all scores and fields.
</li>
<li>
<strong>Raw JSON</strong> — per-run outputs from <code>lm-evaluation-harness</code>.
</li>
<li>
<strong>Stdout logs</strong> — per-run console logs.
</li>
<li>
<strong>GPU utilization logs</strong> — minute-level logs of GPU utilization and memory usage, used to compute <em>GPU Util Time</em>.
</li>
</ul>
<div class="ref-note">
Benchmarks were referenced from
<a href="https://github.com/leobeeson/llm_benchmarks" target="_blank" rel="noopener">
leobeeson/llm_benchmarks
</a>.
Detailed descriptions of each benchmark can be found in this repository.
</div>
</section>
<hr class="lb-divider">
</section>
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<div class="wrap">
<h1 class="title">Overall Rank (Average Rank)</h1>
<div class="table-card">
<!-- 🔽 Paste your raw HTML table inside this div -->
<div id="table-slot">
<!-- Example: your pandas to_html dump goes here -->
<!-- <table> ... </table> -->
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>Model Name</th>
<th>Total Time</th>
<th>GPU Util Time</th>
<th>Mean Score</th>
<th>Overall Rank</th>
</tr>
</thead>
<tbody>
<tr>
<td>google_gemma-3-12b-it</td>
<td>15h 45m</td>
<td>14h 8m</td>
<td>0.6038</td>
<td>1</td>
</tr>
<tr>
<td>Qwen_Qwen3-14B (8bit)</td>
<td>29h 45m</td>
<td>17h 29m</td>
<td>0.5961</td>
<td>2</td>
</tr>
<tr>
<td>openchat_openchat-3.6-8b-20240522</td>
<td>7h 51m</td>
<td>6h 59m</td>
<td>0.5871</td>
<td>3</td>
</tr>
<tr>
<td>Qwen_Qwen3-8B</td>
<td>15h 31m</td>
<td>13h 44m</td>
<td>0.5859</td>
<td>4</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct</td>
<td>9h 36m</td>
<td>8h 33m</td>
<td>0.5788</td>
<td>5</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-14B-Instruct (8bit)</td>
<td>52h 44m</td>
<td>29h 32m</td>
<td>0.5775</td>
<td>6</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-9B</td>
<td>11h 43m</td>
<td>10h 26m</td>
<td>0.5676</td>
<td>7</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct-1M</td>
<td>11h 17m</td>
<td>10h 10m</td>
<td>0.5672</td>
<td>8</td>
</tr>
<tr>
<td>meta-llama_Llama-3.1-8B-Instruct</td>
<td>12h 19m</td>
<td>10h 52m</td>
<td>0.5653</td>
<td>9</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-9B-Chat</td>
<td>13h 54m</td>
<td>12h 15m</td>
<td>0.5621</td>
<td>10</td>
</tr>
<tr>
<td>mistralai_Ministral-8B-Instruct-2410</td>
<td>10h 46m</td>
<td>9h 27m</td>
<td>0.5576</td>
<td>11</td>
</tr>
<tr>
<td>meta-llama_Meta-Llama-3-8B-Instruct</td>
<td>6h 30m</td>
<td>5h 46m</td>
<td>0.5528</td>
<td>12</td>
</tr>
<tr>
<td>Qwen_Qwen3-4B</td>
<td>5h 51m</td>
<td>5h 3m</td>
<td>0.5510</td>
<td>13</td>
</tr>
<tr>
<td>NousResearch_Hermes-2-Pro-Mistral-7B</td>
<td>8h 27m</td>
<td>7h 28m</td>
<td>0.5480</td>
<td>14</td>
</tr>
<tr>
<td>mistralai_Mistral-7B-Instruct-v0.3</td>
<td>8h 38m</td>
<td>7h 41m</td>
<td>0.5451</td>
<td>15</td>
</tr>
<tr>
<td>google_gemma-3-4b-it</td>
<td>4h 51m</td>
<td>3h 50m</td>
<td>0.5368</td>
<td>16</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B-Chat</td>
<td>8h 4m</td>
<td>7h 1m</td>
<td>0.5335</td>
<td>17</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B</td>
<td>4h 28m</td>
<td>3h 54m</td>
<td>0.5312</td>
<td>18</td>
</tr>
<tr>
<td>Qwen_Qwen2-7B-Instruct</td>
<td>11h 30m</td>
<td>10h 11m</td>
<td>0.5271</td>
<td>19</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-0528-Qwen3-8B</td>
<td>17h 57m</td>
<td>15h 30m</td>
<td>0.5219</td>
<td>20</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-3B-Instruct</td>
<td>7h 12m</td>
<td>5h 57m</td>
<td>0.5048</td>
<td>21</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-3B-Instruct</td>
<td>7h 48m</td>
<td>6h 30m</td>
<td>0.4939</td>
<td>22</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B</td>
<td>27h 21m</td>
<td>24h 38m</td>
<td>0.4907</td>
<td>23</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-chat</td>
<td>10h 6m</td>
<td>9h 8m</td>
<td>0.4869</td>
<td>24</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Llama-8B</td>
<td>11h 46m</td>
<td>10h 36m</td>
<td>0.4830</td>
<td>25</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-hf</td>
<td>19h 21m</td>
<td>17h 38m</td>
<td>0.4819</td>
<td>26</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-chat-hf</td>
<td>17h 8m</td>
<td>15h 37m</td>
<td>0.4813</td>
<td>27</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-7B</td>
<td>6h 28m</td>
<td>5h 43m</td>
<td>0.4644</td>
<td>28</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-1.5B-Instruct</td>
<td>3h 20m</td>
<td>2h 36m</td>
<td>0.4608</td>
<td>29</td>
</tr>
<tr>
<td>Qwen_Qwen3-1.7B</td>
<td>4h 25m</td>
<td>3h 36m</td>
<td>0.4597</td>
<td>30</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B-Instruct</td>
<td>5h 37m</td>
<td>4h 57m</td>
<td>0.4596</td>
<td>31</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-chat-hf</td>
<td>6h 57m</td>
<td>6h 7m</td>
<td>0.4525</td>
<td>32</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-hf</td>
<td>5h 42m</td>
<td>4h 59m</td>
<td>0.4516</td>
<td>33</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-base</td>
<td>7h 11m</td>
<td>6h 26m</td>
<td>0.4451</td>
<td>34</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-math-7b-rl</td>
<td>8h 2m</td>
<td>7h 12m</td>
<td>0.4419</td>
<td>35</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-1B-Instruct</td>
<td>3h 30m</td>
<td>2h 35m</td>
<td>0.4219</td>
<td>36</td>
</tr>
<tr>
<td>google_gemma-3-1b-it</td>
<td>6h 50m</td>
<td>4h 52m</td>
<td>0.4013</td>
<td>37</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-1.5B</td>
<td>3h 40m</td>
<td>2h 52m</td>
<td>0.3986</td>
<td>38</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-1.5B-Instruct</td>
<td>3h 25m</td>
<td>2h 39m</td>
<td>0.3838</td>
<td>39</td>
</tr>
<tr>
<td>Qwen_Qwen3-0.6B</td>
<td>3h 45m</td>
<td>2h 53m</td>
<td>0.3816</td>
<td>40</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-0.5B-Instruct</td>
<td>2h 34m</td>
<td>1h 48m</td>
<td>0.3799</td>
<td>41</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="wrap">
<h1 class="title">Reasoning & Math (Average Rank)</h1>
<div class="table-card">
<!-- 🔽 Paste your raw HTML table inside this div -->
<div id="table-slot">
<!-- Example: your pandas to_html dump goes here -->
<!-- <table> ... </table> -->
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>Model Name</th>
<th>Total Time</th>
<th>GPU Util Time</th>
<th>Reasoning &amp; Math Mean Score</th>
<th>Reasoning &amp; Math Avg. Rank</th>
</tr>
</thead>
<tbody>
<tr>
<td>google_gemma-3-12b-it</td>
<td>15h 45m</td>
<td>14h 8m</td>
<td>0.6266</td>
<td>1</td>
</tr>
<tr>
<td>Qwen_Qwen3-8B</td>
<td>15h 31m</td>
<td>13h 44m</td>
<td>0.6214</td>
<td>2</td>
</tr>
<tr>
<td>Qwen_Qwen3-14B (8bit)</td>
<td>29h 45m</td>
<td>17h 29m</td>
<td>0.5860</td>
<td>3</td>
</tr>
<tr>
<td>Qwen_Qwen3-4B</td>
<td>5h 51m</td>
<td>5h 3m</td>
<td>0.5712</td>
<td>4</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct</td>
<td>9h 36m</td>
<td>8h 33m</td>
<td>0.5541</td>
<td>5</td>
</tr>
<tr>
<td>openchat_openchat-3.6-8b-20240522</td>
<td>7h 51m</td>
<td>6h 59m</td>
<td>0.5505</td>
<td>6</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-14B-Instruct (8bit)</td>
<td>52h 44m</td>
<td>29h 32m</td>
<td>0.5488</td>
<td>7</td>
</tr>
<tr>
<td>mistralai_Ministral-8B-Instruct-2410</td>
<td>10h 46m</td>
<td>9h 27m</td>
<td>0.5446</td>
<td>8</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-9B-Chat</td>
<td>13h 54m</td>
<td>12h 15m</td>
<td>0.5399</td>
<td>9</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-0528-Qwen3-8B</td>
<td>17h 57m</td>
<td>15h 30m</td>
<td>0.5387</td>
<td>10</td>
</tr>
<tr>
<td>google_gemma-3-4b-it</td>
<td>4h 51m</td>
<td>3h 50m</td>
<td>0.5374</td>
<td>11</td>
</tr>
<tr>
<td>meta-llama_Llama-3.1-8B-Instruct</td>
<td>12h 19m</td>
<td>10h 52m</td>
<td>0.5366</td>
<td>12</td>
</tr>
<tr>
<td>meta-llama_Meta-Llama-3-8B-Instruct</td>
<td>6h 30m</td>
<td>5h 46m</td>
<td>0.5286</td>
<td>13</td>
</tr>
<tr>
<td>Qwen_Qwen2-7B-Instruct</td>
<td>11h 30m</td>
<td>10h 11m</td>
<td>0.5285</td>
<td>14</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct-1M</td>
<td>11h 17m</td>
<td>10h 10m</td>
<td>0.5245</td>
<td>15</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-9B</td>
<td>11h 43m</td>
<td>10h 26m</td>
<td>0.5206</td>
<td>16</td>
</tr>
<tr>
<td>NousResearch_Hermes-2-Pro-Mistral-7B</td>
<td>8h 27m</td>
<td>7h 28m</td>
<td>0.5184</td>
<td>17</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B</td>
<td>27h 21m</td>
<td>24h 38m</td>
<td>0.5010</td>
<td>18</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B-Chat</td>
<td>8h 4m</td>
<td>7h 1m</td>
<td>0.5006</td>
<td>19</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B-Instruct</td>
<td>5h 37m</td>
<td>4h 57m</td>
<td>0.4997</td>
<td>20</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-7B</td>
<td>6h 28m</td>
<td>5h 43m</td>
<td>0.4841</td>
<td>21</td>
</tr>
<tr>
<td>mistralai_Mistral-7B-Instruct-v0.3</td>
<td>8h 38m</td>
<td>7h 41m</td>
<td>0.4704</td>
<td>22</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-3B-Instruct</td>
<td>7h 12m</td>
<td>5h 57m</td>
<td>0.4688</td>
<td>23</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B</td>
<td>4h 28m</td>
<td>3h 54m</td>
<td>0.4495</td>
<td>24</td>
</tr>
<tr>
<td>Qwen_Qwen3-1.7B</td>
<td>4h 25m</td>
<td>3h 36m</td>
<td>0.4493</td>
<td>25</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Llama-8B</td>
<td>11h 46m</td>
<td>10h 36m</td>
<td>0.4469</td>
<td>26</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-chat</td>
<td>10h 6m</td>
<td>9h 8m</td>
<td>0.4244</td>
<td>27</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-chat-hf</td>
<td>17h 8m</td>
<td>15h 37m</td>
<td>0.4143</td>
<td>28</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-1.5B-Instruct</td>
<td>3h 25m</td>
<td>2h 39m</td>
<td>0.4085</td>
<td>29</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-1.5B</td>
<td>3h 40m</td>
<td>2h 52m</td>
<td>0.4009</td>
<td>30</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-1.5B-Instruct</td>
<td>3h 20m</td>
<td>2h 36m</td>
<td>0.3874</td>
<td>31</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-3B-Instruct</td>
<td>7h 48m</td>
<td>6h 30m</td>
<td>0.3823</td>
<td>32</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-hf</td>
<td>19h 21m</td>
<td>17h 38m</td>
<td>0.3719</td>
<td>33</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-math-7b-rl</td>
<td>8h 2m</td>
<td>7h 12m</td>
<td>0.3702</td>
<td>34</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-chat-hf</td>
<td>6h 57m</td>
<td>6h 7m</td>
<td>0.3674</td>
<td>35</td>
</tr>
<tr>
<td>Qwen_Qwen3-0.6B</td>
<td>3h 45m</td>
<td>2h 53m</td>
<td>0.3494</td>
<td>36</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-1B-Instruct</td>
<td>3h 30m</td>
<td>2h 35m</td>
<td>0.3450</td>
<td>37</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-base</td>
<td>7h 11m</td>
<td>6h 26m</td>
<td>0.3377</td>
<td>38</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-hf</td>
<td>5h 42m</td>
<td>4h 59m</td>
<td>0.3361</td>
<td>39</td>
</tr>
<tr>
<td>google_gemma-3-1b-it</td>
<td>6h 50m</td>
<td>4h 52m</td>
<td>0.3312</td>
<td>40</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-0.5B-Instruct</td>
<td>2h 34m</td>
<td>1h 48m</td>
<td>0.2914</td>
<td>41</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="wrap">
<h1 class="title">Commonsense & NLI (Average Rank)</h1>
<div class="table-card">
<!-- 🔽 Paste your raw HTML table inside this div -->
<div id="table-slot">
<!-- Example: your pandas to_html dump goes here -->
<!-- <table> ... </table> -->
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>Model Name</th>
<th>Total Time</th>
<th>GPU Util Time</th>
<th>Commonsense &amp; NLI Mean Score</th>
<th>Commonsense &amp; NLI Avg. Rank</th>
</tr>
</thead>
<tbody>
<tr>
<td>Qwen_Qwen2.5-14B-Instruct (8bit)</td>
<td>52h 44m</td>
<td>29h 32m</td>
<td>0.7941</td>
<td>1</td>
</tr>
<tr>
<td>Qwen_Qwen3-14B (8bit)</td>
<td>29h 45m</td>
<td>17h 29m</td>
<td>0.7807</td>
<td>2</td>
</tr>
<tr>
<td>google_gemma-3-12b-it</td>
<td>15h 45m</td>
<td>14h 8m</td>
<td>0.7737</td>
<td>3</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct</td>
<td>9h 36m</td>
<td>8h 33m</td>
<td>0.7730</td>
<td>4</td>
</tr>
<tr>
<td>openchat_openchat-3.6-8b-20240522</td>
<td>7h 51m</td>
<td>6h 59m</td>
<td>0.7726</td>
<td>5</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-9B-Chat</td>
<td>13h 54m</td>
<td>12h 15m</td>
<td>0.7691</td>
<td>6</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct-1M</td>
<td>11h 17m</td>
<td>10h 10m</td>
<td>0.7564</td>
<td>7</td>
</tr>
<tr>
<td>Qwen_Qwen3-8B</td>
<td>15h 31m</td>
<td>13h 44m</td>
<td>0.7468</td>
<td>8</td>
</tr>
<tr>
<td>mistralai_Mistral-7B-Instruct-v0.3</td>
<td>8h 38m</td>
<td>7h 41m</td>
<td>0.7403</td>
<td>9</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B-Chat</td>
<td>8h 4m</td>
<td>7h 1m</td>
<td>0.7374</td>
<td>10</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-3B-Instruct</td>
<td>7h 48m</td>
<td>6h 30m</td>
<td>0.7367</td>
<td>11</td>
</tr>
<tr>
<td>mistralai_Ministral-8B-Instruct-2410</td>
<td>10h 46m</td>
<td>9h 27m</td>
<td>0.7328</td>
<td>12</td>
</tr>
<tr>
<td>NousResearch_Hermes-2-Pro-Mistral-7B</td>
<td>8h 27m</td>
<td>7h 28m</td>
<td>0.7284</td>
<td>13</td>
</tr>
<tr>
<td>Qwen_Qwen2-7B-Instruct</td>
<td>11h 30m</td>
<td>10h 11m</td>
<td>0.7274</td>
<td>14</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-9B</td>
<td>11h 43m</td>
<td>10h 26m</td>
<td>0.7266</td>
<td>15</td>
</tr>
<tr>
<td>Qwen_Qwen3-4B</td>
<td>5h 51m</td>
<td>5h 3m</td>
<td>0.7266</td>
<td>16</td>
</tr>
<tr>
<td>meta-llama_Llama-3.1-8B-Instruct</td>
<td>12h 19m</td>
<td>10h 52m</td>
<td>0.7249</td>
<td>17</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B</td>
<td>4h 28m</td>
<td>3h 54m</td>
<td>0.7199</td>
<td>18</td>
</tr>
<tr>
<td>google_gemma-3-4b-it</td>
<td>4h 51m</td>
<td>3h 50m</td>
<td>0.7167</td>
<td>19</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-hf</td>
<td>19h 21m</td>
<td>17h 38m</td>
<td>0.7157</td>
<td>20</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-chat-hf</td>
<td>17h 8m</td>
<td>15h 37m</td>
<td>0.7153</td>
<td>21</td>
</tr>
<tr>
<td>meta-llama_Meta-Llama-3-8B-Instruct</td>
<td>6h 30m</td>
<td>5h 46m</td>
<td>0.7147</td>
<td>22</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-0528-Qwen3-8B</td>
<td>17h 57m</td>
<td>15h 30m</td>
<td>0.7094</td>
<td>23</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-chat</td>
<td>10h 6m</td>
<td>9h 8m</td>
<td>0.7090</td>
<td>24</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-chat-hf</td>
<td>6h 57m</td>
<td>6h 7m</td>
<td>0.6978</td>
<td>25</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-hf</td>
<td>5h 42m</td>
<td>4h 59m</td>
<td>0.6956</td>
<td>26</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Llama-8B</td>
<td>11h 46m</td>
<td>10h 36m</td>
<td>0.6928</td>
<td>27</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-base</td>
<td>7h 11m</td>
<td>6h 26m</td>
<td>0.6886</td>
<td>28</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-1.5B-Instruct</td>
<td>3h 20m</td>
<td>2h 36m</td>
<td>0.6803</td>
<td>29</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-3B-Instruct</td>
<td>7h 12m</td>
<td>5h 57m</td>
<td>0.6788</td>
<td>30</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-math-7b-rl</td>
<td>8h 2m</td>
<td>7h 12m</td>
<td>0.6711</td>
<td>31</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B</td>
<td>27h 21m</td>
<td>24h 38m</td>
<td>0.6587</td>
<td>32</td>
</tr>
<tr>
<td>Qwen_Qwen3-1.7B</td>
<td>4h 25m</td>
<td>3h 36m</td>
<td>0.6442</td>
<td>33</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-7B</td>
<td>6h 28m</td>
<td>5h 43m</td>
<td>0.6422</td>
<td>34</td>
</tr>
<tr>
<td>google_gemma-3-1b-it</td>
<td>6h 50m</td>
<td>4h 52m</td>
<td>0.6267</td>
<td>35</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-1B-Instruct</td>
<td>3h 30m</td>
<td>2h 35m</td>
<td>0.6264</td>
<td>36</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B-Instruct</td>
<td>5h 37m</td>
<td>4h 57m</td>
<td>0.6184</td>
<td>37</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-0.5B-Instruct</td>
<td>2h 34m</td>
<td>1h 48m</td>
<td>0.6039</td>
<td>38</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-1.5B</td>
<td>3h 40m</td>
<td>2h 52m</td>
<td>0.5703</td>
<td>39</td>
</tr>
<tr>
<td>Qwen_Qwen3-0.6B</td>
<td>3h 45m</td>
<td>2h 53m</td>
<td>0.5696</td>
<td>40</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-1.5B-Instruct</td>
<td>3h 25m</td>
<td>2h 39m</td>
<td>0.5181</td>
<td>41</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="wrap">
<h1 class="title">Knowledge & Reading (Average Rank)</h1>
<div class="table-card">
<!-- 🔽 Paste your raw HTML table inside this div -->
<div id="table-slot">
<!-- Example: your pandas to_html dump goes here -->
<!-- <table> ... </table> -->
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>Model Name</th>
<th>Total Time</th>
<th>GPU Util Time</th>
<th>Knowledge &amp; Reading Mean Score</th>
<th>Knowledge &amp; Reading Avg. Rank</th>
</tr>
</thead>
<tbody>
<tr>
<td>01-ai_Yi-1.5-9B</td>
<td>11h 43m</td>
<td>10h 26m</td>
<td>0.4369</td>
<td>1</td>
</tr>
<tr>
<td>openchat_openchat-3.6-8b-20240522</td>
<td>7h 51m</td>
<td>6h 59m</td>
<td>0.4136</td>
<td>2</td>
</tr>
<tr>
<td>meta-llama_Llama-3.1-8B-Instruct</td>
<td>12h 19m</td>
<td>10h 52m</td>
<td>0.4127</td>
<td>3</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B</td>
<td>4h 28m</td>
<td>3h 54m</td>
<td>0.4063</td>
<td>4</td>
</tr>
<tr>
<td>mistralai_Mistral-7B-Instruct-v0.3</td>
<td>8h 38m</td>
<td>7h 41m</td>
<td>0.4045</td>
<td>5</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct-1M</td>
<td>11h 17m</td>
<td>10h 10m</td>
<td>0.3963</td>
<td>6</td>
</tr>
<tr>
<td>Qwen_Qwen3-14B (8bit)</td>
<td>29h 45m</td>
<td>17h 29m</td>
<td>0.3926</td>
<td>7</td>
</tr>
<tr>
<td>meta-llama_Meta-Llama-3-8B-Instruct</td>
<td>6h 30m</td>
<td>5h 46m</td>
<td>0.3923</td>
<td>8</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-7B-Instruct</td>
<td>9h 36m</td>
<td>8h 33m</td>
<td>0.3810</td>
<td>9</td>
</tr>
<tr>
<td>google_gemma-3-12b-it</td>
<td>15h 45m</td>
<td>14h 8m</td>
<td>0.3791</td>
<td>10</td>
</tr>
<tr>
<td>NousResearch_Hermes-2-Pro-Mistral-7B</td>
<td>8h 27m</td>
<td>7h 28m</td>
<td>0.3719</td>
<td>11</td>
</tr>
<tr>
<td>mistralai_Ministral-8B-Instruct-2410</td>
<td>10h 46m</td>
<td>9h 27m</td>
<td>0.3683</td>
<td>12</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-14B-Instruct (8bit)</td>
<td>52h 44m</td>
<td>29h 32m</td>
<td>0.3581</td>
<td>13</td>
</tr>
<tr>
<td>Qwen_Qwen3-8B</td>
<td>15h 31m</td>
<td>13h 44m</td>
<td>0.3566</td>
<td>14</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-9B-Chat</td>
<td>13h 54m</td>
<td>12h 15m</td>
<td>0.3467</td>
<td>15</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-3B-Instruct</td>
<td>7h 12m</td>
<td>5h 57m</td>
<td>0.3438</td>
<td>16</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-3B-Instruct</td>
<td>7h 48m</td>
<td>6h 30m</td>
<td>0.3406</td>
<td>17</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-hf</td>
<td>19h 21m</td>
<td>17h 38m</td>
<td>0.3374</td>
<td>18</td>
</tr>
<tr>
<td>01-ai_Yi-1.5-6B-Chat</td>
<td>8h 4m</td>
<td>7h 1m</td>
<td>0.3339</td>
<td>19</td>
</tr>
<tr>
<td>google_gemma-3-4b-it</td>
<td>4h 51m</td>
<td>3h 50m</td>
<td>0.3261</td>
<td>20</td>
</tr>
<tr>
<td>Qwen_Qwen3-4B</td>
<td>5h 51m</td>
<td>5h 3m</td>
<td>0.3226</td>
<td>21</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-hf</td>
<td>5h 42m</td>
<td>4h 59m</td>
<td>0.3018</td>
<td>22</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-chat</td>
<td>10h 6m</td>
<td>9h 8m</td>
<td>0.3007</td>
<td>23</td>
</tr>
<tr>
<td>Qwen_Qwen2-7B-Instruct</td>
<td>11h 30m</td>
<td>10h 11m</td>
<td>0.2919</td>
<td>24</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-1.5B-Instruct</td>
<td>3h 20m</td>
<td>2h 36m</td>
<td>0.2903</td>
<td>25</td>
</tr>
<tr>
<td>meta-llama_Llama-2-13b-chat-hf</td>
<td>17h 8m</td>
<td>15h 37m</td>
<td>0.2864</td>
<td>26</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-llm-7b-base</td>
<td>7h 11m</td>
<td>6h 26m</td>
<td>0.2864</td>
<td>27</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-0528-Qwen3-8B</td>
<td>17h 57m</td>
<td>15h 30m</td>
<td>0.2834</td>
<td>28</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B</td>
<td>27h 21m</td>
<td>24h 38m</td>
<td>0.2827</td>
<td>29</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Llama-8B</td>
<td>11h 46m</td>
<td>10h 36m</td>
<td>0.2805</td>
<td>30</td>
</tr>
<tr>
<td>meta-llama_Llama-3.2-1B-Instruct</td>
<td>3h 30m</td>
<td>2h 35m</td>
<td>0.2731</td>
<td>31</td>
</tr>
<tr>
<td>meta-llama_Llama-2-7b-chat-hf</td>
<td>6h 57m</td>
<td>6h 7m</td>
<td>0.2656</td>
<td>32</td>
</tr>
<tr>
<td>deepseek-ai_deepseek-math-7b-rl</td>
<td>8h 2m</td>
<td>7h 12m</td>
<td>0.2581</td>
<td>33</td>
</tr>
<tr>
<td>Qwen_Qwen3-1.7B</td>
<td>4h 25m</td>
<td>3h 36m</td>
<td>0.2567</td>
<td>34</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-7B</td>
<td>6h 28m</td>
<td>5h 43m</td>
<td>0.2340</td>
<td>35</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-7B-Instruct</td>
<td>5h 37m</td>
<td>4h 57m</td>
<td>0.2276</td>
<td>36</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-0.5B-Instruct</td>
<td>2h 34m</td>
<td>1h 48m</td>
<td>0.2218</td>
<td>37</td>
</tr>
<tr>
<td>google_gemma-3-1b-it</td>
<td>6h 50m</td>
<td>4h 52m</td>
<td>0.2202</td>
<td>38</td>
</tr>
<tr>
<td>Qwen_Qwen3-0.6B</td>
<td>3h 45m</td>
<td>2h 53m</td>
<td>0.2000</td>
<td>39</td>
</tr>
<tr>
<td>Qwen_Qwen2.5-Math-1.5B-Instruct</td>
<td>3h 25m</td>
<td>2h 39m</td>
<td>0.1983</td>
<td>40</td>
</tr>
<tr>
<td>deepseek-ai_DeepSeek-R1-Distill-Qwen-1.5B</td>
<td>3h 40m</td>
<td>2h 52m</td>
<td>0.1954</td>
<td>41</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<section class="lb-container">
<section class="card card-doc">
<h3>Project Summary</h3>
<p>
<strong>Total Machine Runtime:</strong> 18 days 8 hours<br>
<strong>Total GPU Time (RTX 5090 @ 100% Utilization):</strong> 14 days 23 hours <br>
</p>
<p class="env-note">
🌱 The environmental impact caused by this project was mitigated through my active use of public transportation. :)
</p>
</section>
</section>
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