README.md

---
library_name: transformers
language:
- es
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
license: cc-by-nc-4.0
---

# Model Card for RigoChat-7b-v2

`RigoChat-7b-v2` is a Qwen-2.5-based model specifically designed to provide accurate responses from Spanish queries. Specifically, is based on the [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) model and has been fine-tuned with Direct Preference Optimization ([DPO](https://arxiv.org/pdf/2305.18290)) for improved performance in Spanish language.

## Model Details

### Model Description

This model is the second version of RigoChat, a family of Large Language Models (LLMs) designed to solve typical NLP tasks with Spanish instructions such as: Tool Use, Summarization, Math, Code, Abstractive-QA, etc. Like [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), this model has no specific use case and can be applied to a wide range of tasks. Indeed, it offers a slight improvement for generalist tasks in Spanish, particularly in RAG (Retriever Augmented Generation) systems with Spanish databases, as its training focused on resolving questions about contexts to prevent hallucinations and ensure safety responses.

Key benefits of this model include:

- Improved performance on generalist tasks in Spanish.
- Enhanced safety and reduced hallucinations in RAG systems with Spanish texts.
- Possibility of using it in different hardware requirements, especially those with reduced computational capacity. For more information on how to use RigoChat-7b-v2 on reduced hardware, see [IIC/RigoChat-7b-v2-GGUF](https://huggingface.co/IIC/RigoChat-7b-v2-GGUF).

Remarkably, this model was trained on a single A100 GPU with limited computational resources, yet achieved its current state in a relatively short time (less than 12 hours). This feat was made possible by leveraging a high-quality dataset and employing advanced techniques such as [LoRA](https://arxiv.org/pdf/2106.09685) to optimize memory usage. Further details on the training process can be found below.

- **Developed by:** Instituto de Ingeniería del Conocimiento (IIC).
- **Model type:** Generative Fine-tuned Transformer.
- **Language(s) (NLP):** Spanish.
- **License:** CC BY NC 4.0.
- **Finetuned from model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).

### Model Sources

- **Paper:** Cooming soon.

## How to Get Started with the Model

### To load the model and tokenizer:

```python
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
import torch

model_name = "IIC/RigoChat-7b-v2"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True
)
```

### Sample generation

```python
messages = [
    {"role": "user", "content": "¿Cómo puedo transformar un diccionario de listas en una lista de diccionarios en Python?"}
]

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=512
)
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]
```

### Tool Use

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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