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---
base_model:
- BSC-LT/salamandra-7b-instruct
datasets:
- alinia/EADOP-RAG-out-of-domain
language:
- ca
- es
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- legal
---
# Salamandra 7B aligned EADOP Model Card
Salamandra 7B aligned EADOP is a full-finetuning version of
[BSC Language Technologies Unit](https://huggingface.co./BSC-LT)'s
[Salamndra Instruct 7B](https://huggingface.co./BSC-LT/salamandra-7b-instruct)
model by the at the Barcelona Supercomputing Center focused on improving
the handling of out-of-domain Questions in a RAG instruction-following setting.
The model has been finetuned on a dataset consisting of 2,000+ human annotated in-
and out-of-domain user messages and assistant responses in the context of a chatbot that can
provide helpful information about the current Catalan legislation.
The dataset [alinia/EADOP-RAG-out-of-domain](https://huggingface.co./datasets/alinia/EADOP-RAG-out-of-domain)
was collected in collaboration with the
[Entitat Autònoma del Diari Oficial i de Publicacions (EADOP)](https://dogc.gencat.cat/ca/sobre-el-dogc/eadop/)
and it consists of user messages and assistant responses in Catalan and Spanish.
> [!WARNING]
> **DISCLAIMER:** This model is a proof-of-concept designed to demonstrate the effects of
finetuning an Instruction model with a small dataset of out-of-domain questions in the model's
capability to politely and informatively refuse to answer questions that are out-of-domain.
> As a proof-of-concept, the model is still prone to generate harmful or inappropriate content.
---
## Model Details
Please refer to the [Salamndra Instruct 7B model details](https://huggingface.co./BSC-LT/salamandra-7b-instruct#model-details)
for the specific details about the model architecture and pretraining.
## Intended Use
This model was developed as a proof-of-concept to demonstrate the effects of finetuning
an Instruction model with a small dataset of in- and out-of-domain questions in the model's
capability to politely and informatively refuse to answer questions that are out-of-domain in
the context of a domain-specific RAG-based chatbot.
## How to use
This model uses the ChatML, the same instruction-following conversation format as the base model.
```python
from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "BSC-LT/salamandra-7b-instruct"
text = "At what temperature does water boil?"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
message = [ { "role": "user", "content": text } ]
prompt = tokenizer.apply_chat_template(
message,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Using this template, each turn is preceded by a `<|im_start|>` delimiter and the role of the entity
(either `user`, for content supplied by the user, or `assistant` for LLM responses), and finished with the `<|im_end|>` token.
---
## Finetuning Data
Please refer to [alinia/EADOP-RAG-out-of-domain](https://huggingface.co./datasets/alinia/EADOP-RAG-out-of-domain) for the Dataset Card.
### Author
This model has been finetuned by [Alinia AI](https://alinia.ai/).
### Contact
For further information, please email [[email protected]](mailto:[email protected]).
### Acknowledgements
This project is part of a partnership with the Language Technologies Unit at the [Barcelona Supercomputing Center](https://www.bsc.es/).
The data collection process was supported by the [Entitat Autònoma del Diari Oficial i de Publicacions (EADOP)](https://dogc.gencat.cat/ca/sobre-el-dogc/eadop/). |