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--- |
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library_name: transformers |
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tags: |
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- falcon3 |
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base_model: tiiuae/Falcon3-10B-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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<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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# Falcon3-10B-Instruct |
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**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. |
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This repository contains the **Falcon3-10B-Instruct**. It achieves state-of-the-art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks. |
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Falcon3-10B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of 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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- 40 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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- Depth up-scaled from **Falcon3-7B-Base** with 2 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips |
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- Posttrained on 1.2 million samples of STEM, conversational, 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/Falcon3-10B-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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- 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 **without fewshot_as_multiturn** (unlike Llama3.1). |
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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>Yi-1.5-9B-Chat</th> |
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<th>Mistral-Nemo-Base-2407 (12B)</th> |
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<th>Falcon3-10B-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>70</td> |
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<td>65.9</td> |
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<td><b>71.6</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>39.6</td> |
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<td>32.7</td> |
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<td><b>44</td> |
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</tr> |
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<tr> |
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<td>IFEval</td> |
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<td>57.6</td> |
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<td>63.4</td> |
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<td><b>78</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>76.6</td> |
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<td>73.8</td> |
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<td><b>83.1</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>78.5</td> |
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<td>73.6</td> |
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<td><b>81.3</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>8.8</td> |
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<td>0.4</td> |
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<td><b>22.1</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>51.9</td> |
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<td>61.6</td> |
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<td><b>64.5</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>35.4</td> |
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<td>33.2</td> |
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<td>33.5</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>16</td> |
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<td>12.7</td> |
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<td><b>32.6</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><b>41.9</td> |
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<td>38.1</td> |
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<td>41.1</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>49.2</td> |
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<td>43.6</td> |
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<td><b>58.4</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>76.4</td> |
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<td>78.2</td> |
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<td><b>78.4</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>61.7</td> |
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<td>76.4</td> |
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<td><b>90.4</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>71.3</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>43.2</td> |
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<td>47.4</td> |
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<td><b>48.2</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>8.28</td> |
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<td><b>8.6</td> |
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<td>8.17</td> |
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</tr> |
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<tr> |
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<td>Alpaca (WC)</td> |
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<td>25.81</td> |
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<td><b>45.44</td> |
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<td>24.7</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>48.4</td> |
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<td>74.2</td> |
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<td><b>86.3</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Code</td> |
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<td>EvalPlus (0-shot) (avg)</td> |
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<td>69.4</td> |
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<td>58.9</td> |
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<td><b>74.7</b></td> |
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</tr> |
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<tr> |
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<td>Multipl-E (0-shot) (avg)</td> |
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<td>-</td> |
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<td>34.5</td> |
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<td><b>45.8</b></td> |
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</tr> |
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</tbody> |
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</table> |
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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 in 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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