File size: 4,185 Bytes
4d9a9de 5aa3a09 4d9a9de |
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 |
---
library_name: transformers
tags:
- bitnet
- falcon3
base_model: tiiuae/Falcon3-1B-Instruct
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. [Training Details](#training-details)
3. [Usage](#usage)
4. [Evaluation](#evaluation)
5. [Citation](#citation)
# TL;DR
# Model Details
## Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
- **Model type:** Causal decoder-only - instruct / chat version
- **Architecture:** Pure-transformer - 1.58bit version
- **Language(s) (NLP):** Mainly English
- **License:** TII Falcon License 2.0
# Training details
The model has been trained following the training strategies from the recent [1-bit LLM HF blogpost](https://huggingface.co/blog/1_58_llm_extreme_quantization) and [1-bit LLM paper](https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf).
For more details about the training protocol of this model, please refer to the Falcon-3 technical report, section *Compression*.
# Usage
Currently to use this model you can either rely on Hugging Face transformers library or [BitNet](https://github.com/microsoft/BitNet) library. You can also play with the model using the [falcon-1.58bit playground](https://huggingface.co/spaces/tiiuae/falcon3-1.58bit-playground) (only for the 7B instruct version).
## 🤗 transformers
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiiuae/Falcon3-1B-Instruct-1.58bit"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
).to("cuda")
# Perform text generation
```
## BitNet
```
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
python setup_env.py --hf-repo tiiuae/Falcon3-1B-Instruct-1.58bit -q i2_s
python run_inference.py -m models/Falcon3-1B-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
```
# Evaluation
We report in the following table our internal pipeline benchmarks:
**Note evaluation results are normalized score from v2 leaderboard tasks - reported results of original models in the blogpost are raw scores**
<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
<colgroup>
<col style="width: 10%;">
<col style="width: 10%;">
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Benchmark</th>
<th>Llama3-8B-1.58-100B-tokens</th>
<th>Falcon3-1B-Instruct-1.58bit</th>
</tr>
</thead>
<tbody>
<tr>
<td>IFEval</td>
<td>17.91</td>
<td><b>44.5</b></td>
</tr>
<tr>
<td>MUSR</td>
<td>4.87</td>
<td><b>2.78</b></td>
</tr>
<tr>
<td>GPQA</td>
<td>1.83</td>
<td><b>0</b></td>
</tr>
<tr>
<td>BBH</td>
<td><b>5.36</b></td>
<td>2.24</td>
</tr>
<tr>
<td>MMLU-PRO</td>
<td><b>2.78</b></td>
<td>1.93</td>
</tr>
<tr>
<td>MATH</td>
<td>0.26</td>
<td><b>0.17</b></td>
</tr>
<tr>
<td>Average</td>
<td>5.5</td>
<td><b>8.6</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.
## 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}
}
``` |