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--- |
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language: |
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- en |
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tags: |
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- falcon3 |
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- falcon3_mamba |
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- falcon_mamba |
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base_model: |
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- tiiuae/Falcon3-Mamba-7B-Base |
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--- |
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# Falcon3-Mamba-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-Mamba-7B-Instruct**. It achieves, compared to similar SSM-based models of the same size, state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks. |
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Falcon3-Mamba-7B-Instruct supports a context length up to 32K and was mainly trained on english corpus. |
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## Model Details |
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- Architecture (same as [Falcon-Mamba-7b](https://huggingface.co/tiiuae/falcon-mamba-7b)) |
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- Mamba1 based causal decoder only architecture trained on a causal language modeling task (i.e., predict the next token). |
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- 64 decoder blocks |
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- width: 4096 |
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- state_size: 16 |
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- 32k context length |
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- 65k vocab size |
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- Continue Pretrained from [Falcon Mamba 7B](https://huggingface.co/tiiuae/falcon-mamba-7b), with another 1500 Gigatokens of data comprising of web, code, STEM and high quality data. |
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- Postrained on 1.2 million samples of STEM, conversations, code, and safety. |
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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-Mamba-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. For the benchmarks marked by star, we normalize the results with HuggingFace score normalization: |
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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="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>Zamba2-7B-instruct</th> |
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<th>Jamba-1.5-Mini</th> |
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<th>Llama-3.1-8B-Instruct</th> |
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<th>Falcon3-Mamba-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>30.6%</td> |
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<td>68.7%</td> |
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<td>55.9%</td> |
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<td>65.3%</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>32.4%</td> |
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<td>31.6%</td> |
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<td>21.8%</td> |
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<td>26.3%</td> |
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</tr> |
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<tr> |
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<td>IFEval</td> |
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<td>69.9%</td> |
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<td>65.7%</td> |
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<td>78.8%</td> |
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<td>71.7%</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Math</td> |
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<td>GSM8K (5-shot)</td> |
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<td>0%</td> |
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<td>74.9%</td> |
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<td>19.2%</td> |
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<td>65.2%</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>13.6%</td> |
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<td>6.9%</td> |
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<td>10.4%</td> |
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<td>27.3%</td> |
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</tr> |
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<tr> |
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<td rowspan="4">Reasoning</td> |
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<td>Arc Challenge (25-shot)</td> |
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<td>54%</td> |
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<td>54.3%</td> |
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<td>46.6%</td> |
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<td>53.7%</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>10.3%</td> |
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<td>11.1%</td> |
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<td>33.6%</td> |
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<td>7.2%</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>8.2%</td> |
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<td>12.2%</td> |
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<td>38.6%</td> |
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<td>8.3%</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>33.3%</td> |
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<td>35.3%</td> |
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<td>43.7%</td> |
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<td>25.2%</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>75.6%</td> |
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<td>82.3%</td> |
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<td>78.9%</td> |
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<td>80.9%</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>29.2%</td> |
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<td>94.9%</td> |
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<td>80.2%</td> |
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<td>93.6%</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>75.9%</td> |
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<td>64.5%</td> |
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<td>-</td> |
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<td>-</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>45.6%</td> |
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<td>34.6%</td> |
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<td>46.2%</td> |
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<td>47.2%</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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## Citation |
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If the Falcon3 family of models 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 = {Falcon-LLM Team}, |
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month = {December}, |
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year = {2024} |
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} |
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``` |
