add remaining sections in readme

README.md

@@ -129,4 +129,104 @@ The accelerated partition is composed of 1,120 nodes with the following specific
 |7B|128|512|
 |40B|256 / 512|1,024 / 2,048|
 
----
+---
+
+## How to use
+
+<span style="color:red">TODO: table 2B model</span>
+
+---
+
+## Data
+
+<span style="color:red">TODO: table 2B model</span>
+
+---
+
+## Evaluation
+
+<span style="color:red">TODO: table 2B model</span>
+
+## Ethical Considerations and Limitations
+
+We examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases, 
+we test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019). 
+We report that while performance is high (accuracies between 0.69 and 0.87 depending on the social category) in disambiguated settings
+the model performs very poorly in ambiguous settings, which is indicative of the presence of societal biases which need to be addressed in post-training phases.
+
+We additionally analyse model generations using the Regard dataset and classifier in Catalan, Spanish, and English using backtranslation and manual revision of the 
+translations. We find no statistically significant difference in regard between majority and minority groups for any regard types,
+with the exception of negative regard in Catalan where model generations are actually slightly worse for social majorities. 
+Our analyses on societal biases show that while these biases are capable of interfering with model performance as expressed in the results on the BBQ dataset,
+their tendency for representational harm is limited given the results of the Regard dataset. We highlight that our analyses of these biases are by no means exhaustive
+and are limited by the relative scarcity of adequate resources in all languages present in the training data. We aim to gradually extend and expand our analyses 
+in future work.
+
+Our cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings.
+For positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018).
+We observe moderate to strong primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers.
+We measure effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We detect moderate effects,
+implying that outputs can be influenced by the prompts.
+
+We highlight that these results can be expected from a pretrained model that has not yet been instruction-tuned or aligned.
+These tests are performed in order to show the biases the model may contain.
+We urge developers to take them into account and perform safety testing and tuning tailored to their specific applications of the model.
+
+---
+
+## Additional information
+
+### Author
+The Language Technologies Unit from Barcelona Supercomputing Center.
+
+### Contact
+For further information, please send an email to <langtech@bsc.es>.
+
+### Copyright
+Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.
+
+### Funding
+This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/).
+
+This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU 
+within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335, 2022/TL22/00215334.
+
+### Acknowledgements
+
+This project benefited from the contributions of many teams and institutions, including: 
+Senado de España, Parlament de Catalunya, Òmnium Cultural, Dialnet, Institut d’Estudis Aranesos, 
+Fundación Elcano, Universidad de Las Palmas de Gran Canaria, Occiglot, Common Crawl, the Welsh Government, 
+the German Research Center for Artificial Intelligence (DFKI) and the partners of Proyecto ILENIA. 
+Their valuable efforts have been instrumental in the development of this work.
+
+A special acknowledgment is reserved for the NVIDIA Team with whom we have been meeting on a regular basis. 
+Their consistent support has been particularly appreciated throughout the process.
+
+### Disclaimer
+Be aware that the model may contain biases or other unintended distortions. 
+When third parties deploy systems or provide services based on this model, or use the model themselves, 
+they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, 
+including those governing the use of Artificial Intelligence.
+
+The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.
+
+## Citation
+<span style="color:red">Work in progress, paper coming soon.</span>
+```bibtext
+@article{salamandra,
+  title={Salamandra Technical Report},
+  author={LangTech@BSC},
+  year={2024},
+  url = {}
+}
+```
+
+## License
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+## Model Index
+|Model|Base|Instruct|
+|:---:|:---:|:---:|
+|2B| WiP | WiP |
+|7B| [Link](https://huggingface.co/projecte-aina/salamandra-7b) | [Link](https://huggingface.co/projecte-aina/salamandra-7b-instruct) |
+|40B| WiP | WiP |