--- base_model: tiiuae/Falcon3-10B-Base library_name: transformers license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html tags: - falcon3 model-index: - name: Falcon3-10B-Instruct results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 78.17 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 44.82 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 25.91 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 10.51 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 13.61 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 38.1 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct name: Open LLM Leaderboard --- <div align="center"> <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/> </div> # Falcon3-10B-Instruct **Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. 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. Falcon3-10B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K. ## Model Details - Architecture - Transformer-based causal decoder-only architecture - 40 decoder blocks - Grouped Query Attention (GQA) for faster inference: 12 query heads and 4 key-value heads - Wider head dimension: 256 - High RoPE value to support long context understanding: 1000042 - Uses SwiGLu and RMSNorm - 32K context length - 131K vocab size - 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 - Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data - Supports EN, FR, ES, PT - Developed by [Technology Innovation Institute](https://www.tii.ae) - License: TII Falcon-LLM License 2.0 - Model Release Date: December 2024 ## Getting started <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "tiiuae/Falcon3-10B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "How many hours in one day?" messages = [ {"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=1024 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` </details> <br> ## Benchmarks We report in the following table our internal pipeline benchmarks. - We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). - We report **raw scores** obtained by applying chat template and fewshot_as_multiturn. - We use same batch-size across all models. <table border="1" style="width: 100%; text-align: center; border-collapse: collapse;"> <colgroup> <col style="width: 10%;"> <col style="width: 10%;"> <col style="width: 7%;"> <col style="width: 7%;"> <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;"> </colgroup> <thead> <tr> <th>Category</th> <th>Benchmark</th> <th>Yi-1.5-9B-Chat</th> <th>Mistral-Nemo-Base-2407 (12B)</th> <th>Falcon3-10B-Instruct</th> </tr> </thead> <tbody> <tr> <td rowspan="3">General</td> <td>MMLU (5-shot)</td> <td>68.8</td> <td>66.0</td> <td><b>73.9</b></td> </tr> <tr> <td>MMLU-PRO (5-shot)</td> <td>38.8</td> <td>34.3</td> <td><b>44</b></td> </tr> <tr> <td>IFEval</td> <td>57.8</td> <td>63.4</td> <td><b>78</b></td> </tr> <tr> <td rowspan="3">Math</td> <td>GSM8K (5-shot)</td> <td>77.1</td> <td>77.6</td> <td><b>84.9</b></td> </tr> <tr> <td>GSM8K (8-shot, COT)</td> <td>76</td> <td>80.4</td> <td><b>84.6</b></td> </tr> <tr> <td>MATH Lvl-5 (4-shot)</td> <td>3.3</td> <td>5.9</td> <td><b>22.1</b></td> </tr> <tr> <td rowspan="5">Reasoning</td> <td>Arc Challenge (25-shot)</td> <td>58.3</td> <td>63.4</td> <td><b>66.2</b></td> </tr> <tr> <td>GPQA (0-shot)</td> <td><b>35.6</b></td> <td>33.2</td> <td>33.5</td> </tr> <tr> <td>GPQA (0-shot, COT)</td> <td>16</td> <td>12.7</td> <td><b>32.6</b></td> </tr> <tr> <td>MUSR (0-shot)</td> <td><b>41.9</b></td> <td>38.1</td> <td>41.1</td> </tr> <tr> <td>BBH (3-shot)</td> <td>50.6</td> <td>47.5</td> <td><b>58.4</b></td> </tr> <tr> <td rowspan="4">CommonSense Understanding</td> <td>PIQA (0-shot)</td> <td>76.4</td> <td>78.2</td> <td><b>78.4</b></td> </tr> <tr> <td>SciQ (0-shot)</td> <td>61.7</td> <td>76.4</td> <td><b>90.4</b></td> </tr> <tr> <td>Winogrande (0-shot)</td> <td>-</td> <td>-</td> <td>71</td> </tr> <tr> <td>OpenbookQA (0-shot)</td> <td>43.2</td> <td>47.4</td> <td><b>48.2</b></td> </tr> <tr> <td rowspan="2">Instructions following</td> <td>MT-Bench (avg)</td> <td>8.3</td> <td><b>8.6</b></td> <td>8.2</td> </tr> <tr> <td>Alpaca (WC)</td> <td>25.8</td> <td><b>45.4</b></td> <td>24.7</td> </tr> <tr> <td>Tool use</td> <td>BFCL AST (avg)</td> <td>48.4</td> <td>74.2</td> <td><b>90.5</b></td> </tr> <tr> <td rowspan="2">Code</td> <td>EvalPlus (0-shot) (avg)</td> <td>69.4</td> <td>58.9</td> <td><b>74.7</b></td> </tr> <tr> <td>Multipl-E (0-shot) (avg)</td> <td>-</td> <td>34.5</td> <td><b>45.8</b></td> </tr> </tbody> </table> ## Useful links - View our [release blogpost](https://huggingface.co/blog/falcon3). - Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers. ## Technical Report Coming soon.... ## Citation If Falcon3 family were helpful in your work, feel free to give us a cite. ``` @misc{Falcon3, title = {The Falcon 3 family of Open Models}, author = {TII Team}, month = {December}, year = {2024} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/tiiuae__Falcon3-10B-Instruct-details) | Metric |Value| |-------------------|----:| |Avg. |35.19| |IFEval (0-Shot) |78.17| |BBH (3-Shot) |44.82| |MATH Lvl 5 (4-Shot)|25.91| |GPQA (0-shot) |10.51| |MuSR (0-shot) |13.61| |MMLU-PRO (5-shot) |38.10|