Clean up ec2 path
#2
by
fedyanin
- opened
- README.md +79 -154
- config.json +1 -0
- special_tokens_map.json +1 -1
- tokenizer.json +2 -2
- tokenizer_config.json +4 -4
README.md
CHANGED
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@@ -6,35 +6,25 @@ language:
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- pt
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tags:
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- falcon3
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base_model: tiiuae/Falcon3-7B-Base
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license: other
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license_name: falcon-llm-license
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license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
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library_name: transformers
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---
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<div align="center">
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<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
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</div>
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# Falcon3-7B-Instruct
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**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
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This repository contains the **Falcon3-7B-Instruct**. It achieves state of art results (at
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Falcon3-7B-Instruct supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.
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## Model Details
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- Architecture
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- Transformer based causal decoder only architecture
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- 28 decoder blocks
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-
- Grouped query attention (GQA) for faster inference: 12 query heads and 4
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- Wider head dimension: 256
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- High RoPE value to support long context understanding: 1000042
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-
-
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-
-
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-
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- Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
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- Postrained on 1.2 million samples of STEM, conversations, code, safety and function call data
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- Supports EN, FR, ES, PT
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- Developed by [Technology Innovation Institute](https://www.tii.ae)
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<summary> Click to expand </summary>
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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@@ -88,67 +80,8 @@ print(response)
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<br>
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We report the
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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: 7%;">
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<col style="width: 7%;">
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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>Llama-3.1-8B-Instruct</th>
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<th>Qwen2.5-7B-Instruct</th>
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<th>Falcon3-7B-Instruct</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><b>78.56</b></td>
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<td>75.85</td>
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<td>76.12</td>
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</tr>
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<tr>
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<td>BBH (3-shot)</td>
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<td>29.89</td>
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<td>34.89</td>
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<td><b>37.92</b></td>
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</tr>
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<tr>
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<td>MATH Lvl-5 (4-shot)</td>
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<td>19.34</td>
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<td>0.00</td>
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<td><b>31.87</b></td>
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</tr>
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<tr>
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<td>GPQA (0-shot)</td>
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<td>2.35</td>
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<td>5.48</td>
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<td><b>8.05</b></td>
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</tr>
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<tr>
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<td>MUSR (0-shot)</td>
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<td>8.41</td>
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<td>8.45</td>
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<td><b>21.17</b></td>
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</tr>
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<tr>
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<td>MMLU-PRO (5-shot)</td>
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<td>30.68</td>
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<td><b>36.52</b></td>
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<td>34.30</td>
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</tr>
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</tbody>
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</table>
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-
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Also, we report in the following table our internal pipeline benchmarks.
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- We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness).
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- We report **raw scores** obtained by applying chat template and fewshot_as_multiturn.
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- We use same batch-size across all models.
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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: 7%;">
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<col style="width: 7%;">
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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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<th>Category</th>
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<th>Benchmark</th>
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<th>Llama-3.1-8B-Instruct</th>
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<th>Qwen2.5-7B-Instruct</th>
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<th>Falcon3-7B-Instruct</th>
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</tr>
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</thead>
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<tr>
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<td rowspan="3">General</td>
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<td>MMLU (5-shot)</td>
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<td
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<td
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<td
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</tr>
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<tr>
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<td>MMLU-PRO (5-shot)</td>
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<td
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<td
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</tr>
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<tr>
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<td>IFEval</td>
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<td
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<td
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</tr>
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<tr>
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<td rowspan="
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<td>GSM8K (5-shot)</td>
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<td
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<td
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<td
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<td>GSM8K (8-shot, COT)</td>
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<td><b>85.4</b></td>
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<td>76.6</td>
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<td>79.7</td>
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</tr>
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<tr>
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<td>MATH
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<td
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<td>-</td>
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<td><b>29.4</b></td>
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</tr>
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<tr>
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<td rowspan="
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<td>Arc Challenge (25-shot)</td>
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<td
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<td
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<td
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</tr>
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<tr>
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<td>GPQA (0-shot)</td>
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<td
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<td
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<td
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<td>GPQA (0-shot, COT)</td>
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<td>9.6</td>
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<td>13.8</td>
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<td><b>22.3</b></td>
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</tr>
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<tr>
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<td>MUSR (0-shot)</td>
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<td
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<td
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</tr>
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<tr>
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<td>BBH (3-shot)</td>
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<td
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<td
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<td
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</tr>
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<tr>
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<td rowspan="4">CommonSense Understanding</td>
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<td>PIQA (0-shot)</td>
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<td
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<td
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<td
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</tr>
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<tr>
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<td>SciQ (0-shot)</td>
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<td
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<td
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<td
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</tr>
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<tr>
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<td>Winogrande (0-shot)</td>
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<td>-</td>
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<td>-</td>
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<td
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</tr>
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<tr>
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<td>OpenbookQA (0-shot)</td>
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<td
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<td
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<td
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<td rowspan="2">Instructions following</td>
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<td>MT-Bench (avg)</td>
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<td>7.9</td>
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<td><b>8.5</b></td>
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<td>8.4</td>
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</tr>
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<tr>
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<td>Alpaca (WC)</td>
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<td>26.6</td>
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<td><b>31.5</b></td>
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<td>26.1</td>
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</tr>
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<tr>
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<td>Tool use</td>
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<td>BFCL AST (avg)</td>
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<td>90.6</td>
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<td><b>91.4</b></td>
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<td>89.5</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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- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
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## Technical Report
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Coming soon....
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If Falcon3 family were helpful to your work, feel free to give us a cite.
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```
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- pt
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tags:
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- falcon3
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---
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# Falcon3-7B-Instruct
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**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
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+
This repository contains the **Falcon3-7B-Instruct**. It achieves state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
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Falcon3-7B-Instruct supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.
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## Model Details
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- Architecture
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- Transformer based causal decoder only architecture
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- 28 decoder blocks
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+
- Grouped query attention (GQA) for faster inference: 12 query heads and 4 KV heads
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- Wider head dimension: 256
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- High RoPE value to support long context understanding: 1000042
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+
- 32k context length
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+
- 131k vocab size
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+
- Pretrained on 14 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 2048 H100 GPU chips
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- Postrained on 1.2 million samples of STEM, conversations, code, safety and function call data
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- Supports EN, FR, ES, PT
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- Developed by [Technology Innovation Institute](https://www.tii.ae)
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<summary> Click to expand </summary>
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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from transformers import AutoModelForCausalLM, AutoTokenizer
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<br>
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# Benchmarks
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We report in the following table our internal pipeline benchmarks:
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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: 7%;">
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<col style="width: 7%;">
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<col style="width: 7%;">
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<col style="width: 7%;">
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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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<th>Category</th>
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<th>Benchmark</th>
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<th>Llama-3.1-8B-Instruct</th>
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+
<th>Qwen2-7B-Instruct</th>
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<th>Qwen2.5-7B-Instruct</th>
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<th>gemma-2-9b-it</th>
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<th>Falcon3-7B-Instruct</th>
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</tr>
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</thead>
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<tr>
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<td rowspan="3">General</td>
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<td>MMLU (5-shot)</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>MMLU-PRO (5-shot)</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>IFEval</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td rowspan="2">Math</td>
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<td>GSM8K (5-shot)</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>MATH(4-shot)</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td rowspan="4">Reasoning</td>
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<td>Arc Challenge (25-shot)</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>GPQA (0-shot)</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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|
|
|
|
|
| 166 |
</tr>
|
| 167 |
<tr>
|
| 168 |
<td>MUSR (0-shot)</td>
|
| 169 |
+
<td>-</td>
|
| 170 |
+
<td>-</td>
|
| 171 |
+
<td>-</td>
|
| 172 |
+
<td>-</td>
|
| 173 |
+
<td>-</td>
|
| 174 |
</tr>
|
| 175 |
<tr>
|
| 176 |
<td>BBH (3-shot)</td>
|
| 177 |
+
<td>-</td>
|
| 178 |
+
<td>-</td>
|
| 179 |
+
<td>-</td>
|
| 180 |
+
<td>-</td>
|
| 181 |
+
<td>-</td>
|
| 182 |
</tr>
|
| 183 |
<tr>
|
| 184 |
<td rowspan="4">CommonSense Understanding</td>
|
| 185 |
<td>PIQA (0-shot)</td>
|
| 186 |
+
<td>-</td>
|
| 187 |
+
<td>-</td>
|
| 188 |
+
<td>-</td>
|
| 189 |
+
<td>-</td>
|
| 190 |
+
<td>-</td>
|
| 191 |
</tr>
|
| 192 |
<tr>
|
| 193 |
<td>SciQ (0-shot)</td>
|
| 194 |
+
<td>-</td>
|
| 195 |
+
<td>-</td>
|
| 196 |
+
<td>-</td>
|
| 197 |
+
<td>-</td>
|
| 198 |
+
<td>-</td>
|
| 199 |
</tr>
|
| 200 |
<tr>
|
| 201 |
<td>Winogrande (0-shot)</td>
|
| 202 |
<td>-</td>
|
| 203 |
<td>-</td>
|
| 204 |
+
<td>-</td>
|
| 205 |
+
<td>-</td>
|
| 206 |
+
<td>-</td>
|
| 207 |
</tr>
|
| 208 |
<tr>
|
| 209 |
<td>OpenbookQA (0-shot)</td>
|
| 210 |
+
<td>-</td>
|
| 211 |
+
<td>-</td>
|
| 212 |
+
<td>-</td>
|
| 213 |
+
<td>-</td>
|
| 214 |
+
<td>-</td>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 215 |
</tr>
|
| 216 |
</tbody>
|
| 217 |
</table>
|
| 218 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 219 |
|
| 220 |
+
# Citation
|
| 221 |
If Falcon3 family were helpful to your work, feel free to give us a cite.
|
| 222 |
|
| 223 |
```
|
config.json
CHANGED
|
@@ -9,6 +9,7 @@
|
|
| 9 |
"head_dim": 256,
|
| 10 |
"hidden_act": "silu",
|
| 11 |
"hidden_size": 3072,
|
|
|
|
| 12 |
"intermediate_size": 23040,
|
| 13 |
"max_position_embeddings": 32768,
|
| 14 |
"mlp_bias": false,
|
|
|
|
| 9 |
"head_dim": 256,
|
| 10 |
"hidden_act": "silu",
|
| 11 |
"hidden_size": 3072,
|
| 12 |
+
"initializer_range": 0.02,
|
| 13 |
"intermediate_size": 23040,
|
| 14 |
"max_position_embeddings": 32768,
|
| 15 |
"mlp_bias": false,
|
special_tokens_map.json
CHANGED
|
@@ -32,7 +32,7 @@
|
|
| 32 |
"single_word": false
|
| 33 |
},
|
| 34 |
"pad_token": {
|
| 35 |
-
"content": "<|
|
| 36 |
"lstrip": false,
|
| 37 |
"normalized": false,
|
| 38 |
"rstrip": false,
|
|
|
|
| 32 |
"single_word": false
|
| 33 |
},
|
| 34 |
"pad_token": {
|
| 35 |
+
"content": "<|endoftext|>",
|
| 36 |
"lstrip": false,
|
| 37 |
"normalized": false,
|
| 38 |
"rstrip": false,
|
tokenizer.json
CHANGED
|
@@ -18212,7 +18212,7 @@
|
|
| 18212 |
},
|
| 18213 |
{
|
| 18214 |
"id": 2023,
|
| 18215 |
-
"content": "
|
| 18216 |
"single_word": false,
|
| 18217 |
"lstrip": false,
|
| 18218 |
"rstrip": false,
|
|
@@ -20280,7 +20280,7 @@
|
|
| 20280 |
">>UNUSED_1894<<": 2020,
|
| 20281 |
">>UNUSED_1895<<": 2021,
|
| 20282 |
">>UNUSED_1896<<": 2022,
|
| 20283 |
-
"
|
| 20284 |
"!": 2024,
|
| 20285 |
"\"": 2025,
|
| 20286 |
"#": 2026,
|
|
|
|
| 18212 |
},
|
| 18213 |
{
|
| 18214 |
"id": 2023,
|
| 18215 |
+
"content": ">>UNUSED_1897<<",
|
| 18216 |
"single_word": false,
|
| 18217 |
"lstrip": false,
|
| 18218 |
"rstrip": false,
|
|
|
|
| 20280 |
">>UNUSED_1894<<": 2020,
|
| 20281 |
">>UNUSED_1895<<": 2021,
|
| 20282 |
">>UNUSED_1896<<": 2022,
|
| 20283 |
+
">>UNUSED_1897<<": 2023,
|
| 20284 |
"!": 2024,
|
| 20285 |
"\"": 2025,
|
| 20286 |
"#": 2026,
|
tokenizer_config.json
CHANGED
|
@@ -16186,7 +16186,7 @@
|
|
| 16186 |
"special": true
|
| 16187 |
},
|
| 16188 |
"2023": {
|
| 16189 |
-
"content": "
|
| 16190 |
"lstrip": false,
|
| 16191 |
"normalized": false,
|
| 16192 |
"rstrip": false,
|
|
@@ -16219,7 +16219,7 @@
|
|
| 16219 |
">>PASSWORD<<",
|
| 16220 |
">>KEY<<"
|
| 16221 |
],
|
| 16222 |
-
"chat_template": "{
|
| 16223 |
"clean_up_tokenization_spaces": true,
|
| 16224 |
"eos_token": "<|endoftext|>",
|
| 16225 |
"extra_special_tokens": {},
|
|
@@ -16227,7 +16227,7 @@
|
|
| 16227 |
"input_ids",
|
| 16228 |
"attention_mask"
|
| 16229 |
],
|
| 16230 |
-
"model_max_length":
|
| 16231 |
-
"pad_token": "<|
|
| 16232 |
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 16233 |
}
|
|
|
|
| 16186 |
"special": true
|
| 16187 |
},
|
| 16188 |
"2023": {
|
| 16189 |
+
"content": ">>UNUSED_1897<<",
|
| 16190 |
"lstrip": false,
|
| 16191 |
"normalized": false,
|
| 16192 |
"rstrip": false,
|
|
|
|
| 16219 |
">>PASSWORD<<",
|
| 16220 |
">>KEY<<"
|
| 16221 |
],
|
| 16222 |
+
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>\n' + message['content'] + eos_token + '\n' }}{% else %}{{ '<|assistant|>\n' + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}{% endfor %}",
|
| 16223 |
"clean_up_tokenization_spaces": true,
|
| 16224 |
"eos_token": "<|endoftext|>",
|
| 16225 |
"extra_special_tokens": {},
|
|
|
|
| 16227 |
"input_ids",
|
| 16228 |
"attention_mask"
|
| 16229 |
],
|
| 16230 |
+
"model_max_length": 8192,
|
| 16231 |
+
"pad_token": "<|endoftext|>",
|
| 16232 |
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 16233 |
}
|