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README.md
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---
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base_model: tiiuae/Falcon3-7B-Instruct
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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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tags:
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- bitnet
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- falcon3
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/c-tosr0FvMlKuKQTojx_6.png)
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# Table of Contents
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0. [TL;DR](#TL;DR)
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1. [Model Details](#model-details)
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2. [Training Details](#training-details)
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3. [Usage](#usage)
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4. [Evaluation](#evaluation)
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5. [Citation](#citation)
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# TL;DR
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# Model Details
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## Model Description
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- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
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- **Model type:** Causal decoder-only - instruct / chat version
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- **Architecture:** Pure-transformer - 1.58bit version
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- **Language(s) (NLP):** Mainly English
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- **License:** TII Falcon License 2.0
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# Training details
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The model has been trained following the training strategies from the recent [1-bit LLM HF blogpost](https://huggingface.co/blog/1_58_llm_extreme_quantization) and [1-bit LLM paper](https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf).
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For more details about the training protocol of this model, please refer to the Falcon-3 technical report, section *Compression*.
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# Usage
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Currently to use this model you can on [BitNet](https://github.com/microsoft/BitNet) library. You can also play with the model using the [falcon-1.58bit playground](https://huggingface.co/spaces/tiiuae/falcon3-1.58bit-playground) (only for the 7B instruct version).
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## BitNet
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```
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git clone https://github.com/microsoft/BitNet && cd BitNet
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pip install -r requirements.txt
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huggingface-cli download tiiuae/Falcon3-7B-Instruct-1.58bit-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon3-7B-1.58bit/
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python run_inference.py -m models/Falcon3-7B-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
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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>Falcon3-7B-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>59.24</b></td>
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</tr>
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<tr>
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<td>MUSR</td>
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<td><b>4.87</b></td>
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<td>1.76</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>5.25</b></td>
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</tr>
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<tr>
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<td>BBH</td>
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<td>5.36</td>
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<td><b>8.54</b></td>
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</tr>
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<tr>
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<td>MMLU-PRO</td>
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<td>2.78</td>
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<td><b>8.44</b></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>2.93</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>14.36</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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- 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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## Citation
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If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
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```
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@misc{Falcon3,
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title = {The Falcon 3 Family of Open Models},
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author = {Falcon-LLM Team},
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month = {December},
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year = {2024}
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}
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```
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