Update README.md

README.md

@@ -5,75 +5,61 @@ language:
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
 pipeline_tag: text-generation
+license: cc-by-nc-4.0
 ---
 
 # Model Card for RigoChat-7b-v2
 
-<!-- Provide a quick summary of what the model is/does. -->
+`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
 
-<!-- Provide a longer summary of what this model is. -->
+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.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Key benefits of this model include:
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- 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).
 
-### Model Sources [optional]
+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.
 
-<!-- Provide the basic links for the model. -->
+- **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).
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Sources
 
-## Uses
+- **Paper:** Cooming soon.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+## How to Get Started with the Model
 
-### Recommendations
+- To load model and tokenizer:
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+)
+import torch
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+model_name = "ignita/RigoChat-7b-v2"
 
-## How to Get Started with the Model
+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,
 
-Use the code below to get started with the model.
 
-[More Information Needed]
 
 ## Training Details