File size: 6,024 Bytes
3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b 441a876 3abb65b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
---
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>30.6</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>0</td>
<td>74.9</td>
<td>19.2</td>
<td>65.2</td>
</tr>
<tr>
<td>MATH Lvl-5 (4-shot)</td>
<td>13.6</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>54</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>6.2</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>75.6</td>
<td>82.3</td>
<td>78.9</td>
<td>80.9</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>29.2</td>
<td>94.9</td>
<td>80.2</td>
<td>93.6</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>45.6</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}
}
``` |