Upload tokenizer_config.json
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- README.md +3 -226
- config.json +3 -1
- generation_config.json +1 -1
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README.md
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---
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- fr
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- es
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tags:
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- falcon3
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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-Base
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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-Base**. It achieves state of art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks.
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Falcon3-7B-Base supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.
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⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.**
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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 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
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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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- License: TII Falcon-LLM License 2.0
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- Model Release Date: December 2024
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## Getting started
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<details>
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<summary> Click to expand </summary>
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```python
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import torch
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from transformers import pipeline
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pipe = pipeline(
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"text-generation",
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model="tiiuae/Falcon3-7B-Base",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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response = pipe("Question: How many hours in one day? Answer: ")
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print(response[0]['generated_text'])
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```
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</details>
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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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- We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness).
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- We report **raw scores**.
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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: 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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<tr>
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<th>Category</th>
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<th>Benchmark</th>
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<th>Llama3.1-8B</th>
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<th>Qwen2-7B</th>
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<th>Qwen2.5-7B</th>
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<th>gemma-2-9b</th>
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<th>Falcon3-7B-Base</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 rowspan="3">General</td>
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<td>MMLU (5-shot)</td>
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<td>65.2</td>
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<td>70.4</td>
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<td>74.2</td>
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<td>-</td>
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<td>67.5</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>32.7</td>
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<td>42.1</td>
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<td>43.5</td>
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<td>-</td>
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<td>39.2</td>
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</tr>
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<tr>
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<td>IFEval</td>
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<td>12.0</td>
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<td>30.6</td>
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<td>33.9</td>
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<td>-</td>
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<td>34.3</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>49.4</td>
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<td>77.9</td>
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<td>82.9</td>
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<td>-</td>
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<td>76.2</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>4.1</td>
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<td>17.5</td>
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<td>15.5</td>
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<td>-</td>
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<td>18.0</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>53.4</td>
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<td>57.4</td>
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<td>59.0</td>
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<td>-</td>
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<td>59.6</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>31.0</td>
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<td>31.9</td>
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<td>33.0</td>
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<td>-</td>
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<td>35.5</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>38.0</td>
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<td>44.1</td>
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<td>44.2</td>
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<td>-</td>
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<td>47.3</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>46.5</td>
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<td>53.3</td>
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<td>54.0</td>
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<td>-</td>
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<td>51.0</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>80.3</td>
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<td>79.8</td>
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<td>78.7</td>
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<td>-</td>
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<td>77.7</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>96.3</td>
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<td>95.9</td>
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<td>96.6</td>
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<td>-</td>
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<td>95.3</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>74.0</td>
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<td>72.1</td>
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<td>72.9</td>
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<td>-</td>
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<td>71.0</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>33.4</td>
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<td>35.2</td>
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<td>33.6</td>
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<td>-</td>
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<td>31.4</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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## Citation
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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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@misc{Falcon3,
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title = {Falcon 3 family of Open Foundation Models},
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author = {TII 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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---
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license: apache-2.0
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"eos_token_id": 11,
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"head_dim": 256,
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"hidden_act": "silu",
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"rope_theta": 1000042,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.
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"use_cache": true,
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"vocab_size": 131072
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}
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{
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"_name_or_path": "falcon3-7b-32k-best",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 11,
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"eos_token_id": 11,
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"head_dim": 256,
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"hidden_act": "silu",
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"rope_theta": 1000042,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.2",
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"use_cache": true,
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"vocab_size": 131072
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}
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generation_config.json
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"_from_model_config": true,
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"bos_token_id": 11,
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"eos_token_id": 11,
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"transformers_version": "4.46.
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}
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"_from_model_config": true,
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"bos_token_id": 11,
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"eos_token_id": 11,
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"transformers_version": "4.46.2"
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}
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