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---
language:
- en
tags:
- falcon3
- falcon3_mamba
- falcon_mamba
base_model:
- tiiuae/Falcon3-Mamba-7B-Base
---
# Falcon3-Mamba-7B-Instruct
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
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.
Falcon3-Mamba-7B-Instruct supports a context length up to 32K and was mainly trained on english corpus.
## Model Details
- Architecture (same as [Falcon-Mamba-7b](https://huggingface.co/tiiuae/falcon-mamba-7b))
- Mamba1 based causal decoder only architecture trained on a causal language modeling task (i.e., predict the next token).
- 64 decoder blocks
- width: 4096
- state_size: 16
- 32k context length
- 65k vocab size
- 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.
- Postrained on 1.2 million samples of STEM, conversations, code, and safety.
- 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-Mamba-7B-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. For the benchmarks marked by star, we normalize the results with HuggingFace score normalization:
<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="width: 7%;">
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Category</th>
<th>Benchmark</th>
<th>Zamba2-7B-instruct</th>
<th>Jamba-1.5-Mini</th>
<th>Llama-3.1-8B-Instruct</th>
<th>Falcon3-Mamba-7B-Instruct</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>-</td>
<td>68.7%</td>
<td>55.9%</td>
<td>65.3%</td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)*</td>
<td>32.4%</td>
<td>31.6%</td>
<td>21.8%</td>
<td>26.3%</td>
</tr>
<tr>
<td>IFEval</td>
<td>69.9%</td>
<td>65.7%</td>
<td>78.8%</td>
<td>71.7%</td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>GSM8K (5-shot)</td>
<td>-</td>
<td>74.9%</td>
<td>19.2%</td>
<td>65.2%</td>
</tr>
<tr>
<td>MATH Lvl-5 (4-shot)</td>
<td>-</td>
<td>6.9%</td>
<td>10.4%</td>
<td>27.3%</td>
</tr>
<tr>
<td rowspan="4">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>-</td>
<td>54.3%</td>
<td>46.6%</td>
<td>53.7%</td>
</tr>
<tr>
<td>GPQA (0-shot)*</td>
<td>10.3%</td>
<td>11.1%</td>
<td>33.6%</td>
<td>7.2%</td>
</tr>
<tr>
<td>MUSR (0-shot)*</td>
<td>8.2%</td>
<td>12.2%</td>
<td>38.6%</td>
<td>8.3%</td>
</tr>
<tr>
<td>BBH (3-shot)*</td>
<td>33.3%</td>
<td>35.3%</td>
<td>43.7%</td>
<td>25.2%</td>
</tr>
<tr>
<td rowspan="4">CommonSense Understanding</td>
<td>PIQA (0-shot)</td>
<td>-</td>
<td>82.3%</td>
<td>78.9%</td>
<td>80.9%</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>-</td>
<td>94.9%</td>
<td>80.2%</td>
<td>93.6%</td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>-</td>
<td>64.5%</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>-</td>
<td>34.6%</td>
<td>46.2%</td>
<td>47.2%</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.
## Citation
If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {The Falcon 3 Family of Open Models},
author = {Falcon-LLM Team},
month = {December},
year = {2024}
}
``` |