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falcon3 release

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README.md ADDED
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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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+ ---
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+
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+ # Falcon3-7B-Instruct
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+
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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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+
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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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+
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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 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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+ - License: TII Falcon-LLM License 2.0
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+ - Model Release Date: December 2024
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+
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+
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+ ## Getting started
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "tiiuae/Falcon3-7B-Instruct"
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+
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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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+
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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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+
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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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+
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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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+
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+ </details>
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+
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+ <br>
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+
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+ ## Benchmarks
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+ We report in the following table our internal pipeline benchmarks:
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+
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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>55.9</td>
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+ <td><b>72.4</b></td>
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+ <td>68</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>21.8</td>
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+ <td>35.8</td>
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+ <td><b>40.7</b></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>78.1</td>
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+ <td>77.5</td>
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+ <td><b>79.1</b></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>79.8</td>
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+ <td>72.7</td>
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+ <td><b>80.9</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>10.4</td>
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+ <td>26</td>
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+ <td><b>33.1</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>46.6</td>
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+ <td>55.7</td>
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+ <td><b>65.9</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>33.6</b></td>
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+ <td>31.9</td>
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+ <td>32</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>40.7</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>43.7</td>
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+ <td><b>53.9</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>7.86</td>
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+ <td><b>8.54</b></td>
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+ <td>8.36</td>
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+ </tr>
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+ <tr>
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+ <td>Alapaca (WC)</td>
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+ <td>26.57</td>
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+ <td><b>31.5</b></td>
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+ <td>26.13</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>72.3</td>
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+ </tr>
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+ </tbody>
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+ </table>
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+
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+ ## Technical Report
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+ Coming soon....
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+
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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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+ ```
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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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tokenizer_config.json ADDED
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