File size: 7,078 Bytes
62e8b6f a01146d 62e8b6f a01146d 62e8b6f d34c453 62e8b6f d34c453 7f94a35 62e8b6f 7f94a35 26a78de b74b487 7f94a35 62e8b6f 7f94a35 b74b487 7f94a35 b74b487 62e8b6f b74b487 7f94a35 b74b487 62e8b6f 7f94a35 b74b487 7f94a35 62e8b6f f78a522 b74b487 7f94a35 62e8b6f 7f94a35 b74b487 62e8b6f b74b487 7f94a35 62e8b6f 7f94a35 b74b487 7f94a35 62e8b6f 7f94a35 b74b487 7f94a35 b74b487 62e8b6f b74b487 26a78de 7f94a35 62e8b6f 7f94a35 62e8b6f 7f94a35 26a78de 7f94a35 62e8b6f 7f94a35 26a78de 7f94a35 26a78de 62e8b6f 7f94a35 62e8b6f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 |
---
language:
- en
- es
- pt
tags:
- falcon3
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
---
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
# TL;DR
# Model Details
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.**
## Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
- **Model type:** Causal decoder-only
- **Architecture:** Transformer-base
- **Language(s) (NLP):** Mainly English
- **License:** TII Falcon-LLM License 2.0
<br>
# Usage
Find below some example scripts on how to use the model in `transformers` (Make sure to have the latest transformers, or the one built from source):
## Using the Pytorch model with 🤗 transformers
### Running the model on a CPU
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base")
input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", device_map="auto")
input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU using `torch.compile`
<details>
<summary> Click to expand </summary>
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", torch_dtype=torch.bfloat16).to(0)
model = torch.compile(model)
input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
# Training Details
## Training Data
Falcon3-7B is trained on 15 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data.
## Training Procedure
Falcon3-7B is trained on 256 H100 nodes (world size 2048).
### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|---------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Max learning rate | 6e-4 | Following a WSD (warmup-stable-decay) |
| | | learning rate scheduler |
| Weight decay | 1e-1 | |
| z-loss | 1e-4 | |
| Batch size | Variable | Batch size was gradually increased |
| | | during the training |
# Evaluation
<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="width: 7%;">
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Category</th>
<th>Benchmark</th>
<th>Llama-3.2-1B</th>
<th>Qwen2.5-1.5B</th>
<th>SmolLM2-1.7B</th>
<th>gemma-2-2b</th>
<th>Falcon3-1B-Base</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>31.1</td>
<td>61.0</td>
<td>50.1</td>
<td>53.0</td>
<td>42.5</td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>11.7</td>
<td>28.4</td>
<td>21.3</td>
<td>22.1</td>
<td>16.1</td>
</tr>
<tr>
<td>IFEval</td>
<td>14.8</td>
<td>26.0</td>
<td>24.2</td>
<td>20.3</td>
<td>25.2</td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>GSM8K (5-shot)</td>
<td>6.6</td>
<td>62.2</td>
<td>31.0</td>
<td>25.5</td>
<td>34.3</td>
</tr>
<tr>
<td>MATH Lvl-5 (4-shot)</td>
<td>0.2</td>
<td>6.7</td>
<td>1.4</td>
<td>2.6</td>
<td>2.2</td>
</tr>
<tr>
<td rowspan="4">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>40.2</td>
<td>54.8</td>
<td>54.1</td>
<td>53.7</td>
<td>48.1</td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td>24.2</td>
<td>28.1</td>
<td>28.9</td>
<td>25.5</td>
<td>28.1</td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td>34.5</td>
<td>35.5</td>
<td>34.7</td>
<td>42.7</td>
<td>41.9</td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>31.2</td>
<td>41.1</td>
<td>34.2</td>
<td>36.8</td>
<td>36.0</td>
</tr>
<tr>
<td rowspan="4">CommonSense Understanding</td>
<td>PIQA (0-shot)</td>
<td>74.5</td>
<td>76.0</td>
<td>77.5</td>
<td>79.2</td>
<td>74.5</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>88.5</td>
<td>93.1</td>
<td>90.8</td>
<td>95.7</td>
<td>91.1</td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>60.4</td>
<td>63.0</td>
<td>66.1</td>
<td>68.6</td>
<td>61.2</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>37.4</td>
<td>40.4</td>
<td>44.0</td>
<td>41.8</td>
<td>41.0</td>
</tr>
</tbody>
</table>
# Citation |