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Model: tiiuae/Falcon3-7B-Instruct Source: Original Platform
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---
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language:
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- en
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- fr
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- es
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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 the time of release) 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 key value 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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- Uses SwiGLU and RMSNorm
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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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- 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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- 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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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "tiiuae/Falcon3-7B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "How many hours in one day?"
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messages = [
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{"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=1024
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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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 the official HuggingFace leaderboard normalized evaluations [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) in the following table.
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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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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: 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>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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<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>68.2</td>
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<td><b>73.5</b></td>
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<td>70.5</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>36.4</td>
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<td><b>43.1</b></td>
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<td>40.7</td>
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</tr>
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<tr>
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<td>IFEval</td>
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<td><b>78.8</b></td>
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<td>74.7</td>
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<td>76.5</td>
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</tr>
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<tr>
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<td rowspan="3">Math</td>
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<td>GSM8K (5-shot)</td>
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<td><b>82.6</b></td>
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<td>72.0</td>
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<td>81.4</td>
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</tr>
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<tr>
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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 Lvl-5 (4-shot)</td>
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<td>15.4</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="5">Reasoning</td>
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<td>Arc Challenge (25-shot)</td>
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<td>58.6</td>
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<td>57.8</td>
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<td><b>62.6</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><b>33.5</b></td>
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<td>32</td>
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<td>31.9</td>
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</tr>
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<tr>
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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>38.6</td>
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<td>41</td>
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<td><b>46.4</b></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>48.6</td>
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<td><b>54.1</b></td>
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<td>52.4</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><b>78.9</b></td>
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<td>73.7</td>
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<td>78.8</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>80.2</td>
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<td>50.9</td>
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<td><b>94.7</b></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>70.4</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><b>46.2</b></td>
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<td>42.4</td>
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<td>45.8</td>
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</tr>
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<tr>
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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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||||
|
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## Citation
|
||||
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 = {The Falcon 3 family of Open 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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28
config.json
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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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"bos_token_id": 11,
|
||||
"eos_token_id": 11,
|
||||
"head_dim": 256,
|
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"hidden_act": "silu",
|
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"hidden_size": 3072,
|
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"intermediate_size": 23040,
|
||||
"max_position_embeddings": 32768,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 12,
|
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"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
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"pretraining_tp": 1,
|
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"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
|
||||
"rope_theta": 1000042,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.46.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 131072
|
||||
}
|
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6
generation_config.json
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generation_config.json
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{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 11,
|
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"eos_token_id": 11,
|
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"transformers_version": "4.46.1"
|
||||
}
|
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}
|
||||
}
|
||||
41
special_tokens_map.json
Normal file
41
special_tokens_map.json
Normal file
@@ -0,0 +1,41 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
">>TITLE<<",
|
||||
">>ABSTRACT<<",
|
||||
">>INTRODUCTION<<",
|
||||
">>SUMMARY<<",
|
||||
">>COMMENT<<",
|
||||
">>ANSWER<<",
|
||||
">>QUESTION<<",
|
||||
">>DOMAIN<<",
|
||||
">>EMAIL_ADDRESS<<",
|
||||
">>IP_ADDRESS<<",
|
||||
"<|startoftext|>",
|
||||
">>IP_ADDRESS_0<<",
|
||||
">>IP_ADDRESS_1<<",
|
||||
">>IP_ADDRESS_2<<",
|
||||
">>IP_ADDRESS_3<<",
|
||||
">>IP_ADDRESS_4<<",
|
||||
">>IP_ADDRESS_5<<",
|
||||
">>IP_ADDRESS_6<<",
|
||||
">>IP_ADDRESS_7<<",
|
||||
">>IP_ADDRESS_8<<",
|
||||
">>IP_ADDRESS_9<<",
|
||||
">>PASSWORD<<",
|
||||
">>KEY<<"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
664576
tokenizer.json
Normal file
664576
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
16233
tokenizer_config.json
Normal file
16233
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user