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README.md
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@@ -71,8 +71,61 @@ python run_inference.py -m models/Falcon3-1B-1.58bit/ggml-model-i2_s.gguf -p "Yo
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```
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# Evaluation
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-
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-
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## Useful links
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- View our [release blogpost](https://huggingface.co/blog/falcon3).
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```
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# Evaluation
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We report in the following table our internal pipeline benchmarks:
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**Note evaluation results are normalized score from v2 leaderboard tasks - reported results of original models in the blogpost are raw scores**
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<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
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<colgroup>
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<col style="width: 10%;">
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<col style="width: 10%;">
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<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
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</colgroup>
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<thead>
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<tr>
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<th>Benchmark</th>
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<th>Llama3-8B-1.58-100B-tokens</th>
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<th>Falcon3-1B-Instruct-1.58bit</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>IFEval</td>
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<td>17.91</td>
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<td><b>44.5</b></td>
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</tr>
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<tr>
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<td>MUSR</td>
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<td>4.87</td>
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<td><b>2.78</b></td>
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</tr>
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<tr>
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<td>GPQA</td>
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<td>1.83</td>
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<td><b>0</b></td>
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</tr>
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<tr>
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<td>BBH</td>
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<td><b>5.36</b></td>
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<td>2.24</td>
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</tr>
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<tr>
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<td>MMLU-PRO</td>
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<td><b>2.78</b></td>
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<td>1.93</td>
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</tr>
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<tr>
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<td>MATH</td>
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<td>0.26</td>
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<td><b>0.17</b></td>
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</tr>
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<tr>
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<td>Average</td>
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<td>5.5</td>
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<td><b>8.6</b></td>
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</tr>
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</tbody>
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</table>
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## Useful links
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- View our [release blogpost](https://huggingface.co/blog/falcon3).
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