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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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# 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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- 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 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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- 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 AutoTokenizer, AutoModelForCausalLM |
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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 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: 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>19.2</td> |
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<td>33.7</td> |
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<td><b>78.8</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="6">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>BBH (3-shot, COT)</td> |
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<td>6.7</td> |
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<td>21.2</td> |
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<td><b>69.3</b></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>TODO</td> |
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<td>TODO</td> |
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<td>TODO</td> |
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</tr> |
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</tbody> |
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</table> |
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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 = {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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