Update README.md

README.md

@@ -1,13 +1,22 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- OpenAssistant/oasst1
+language:
+- en
+- es
+base_model:
+- EleutherAI/gpt-neo-125m
 ---
 
-# Model Card for Model ID
+# Model Card for Aconoya/Nono_instruct_neo-125m_dpo
 
-<!-- Provide a quick summary of what the model is/does. -->
+Este modelo es parte de nuestro esfuerzo por dar acceso a la IA en Español a todas las personas y no sólo a aquellos con un celular de gama alta o una computadora poderosa.
 
+Creado usando gpt-neo-125m por EleutherAI como modelo base y el conjunto de datos conversacional del proyeto OpenAssistant (oasst1) para adaptarlo al formato de instrucciones.
 
+-This model is part of an effort to make AI accessible in Spanish to everyone, not just those with high-end smartphones or powerful computers.-
 
 ## Model Details
 
@@ -15,185 +24,52 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** [Javier R. García](https://huggingface.co/jr-garcia)
+- **Language(s) (NLP):** English, Spanish
+- **License:** MIT
+- **Finetuned from:** gpt-neo-125m by EleutherAI
 
 ## Uses
 
-<!-- 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]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Text generation.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_name = "Aconoya/Nono_instruct_neo-125m_dpo"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.gradient_checkpointing_enable() 
+model = model.to(device.type)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+
+ender_string = '<endofturn>'
+system_string='<system>'
+user_string='<user>'
+assistant_string='<assistant>'
+
+prompt = ['Hello! How are you?', '¡Hola!, ¿Cómo estás?', '¿Qué es un perro?', 'What is a dog?']
+prompt = choice(prompt)
+formatted_prompt = system_string + 'You are a digital assistant.' + ender_string + '\n' + user_string + prompt + ender_string + '\n' + assistant_string
+model_input = tokenizer.encode(formatted_prompt, return_tensors='pt').to(device)
+generated_ids = model.generate(input_ids=model_input, pad_token_id=tokenizer.eos_token_id, max_new_tokens=50)
+generated_text = tokenizer.decode(generated_ids[:, model_input.shape[-1]:][0], skip_special_tokens=True)
+print('Prompt:', prompt)
+print("Response: '{}'".format(generated_text))
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+The model was trained using the conversational dataset from the OpenAssistant project (oasst1).
 
-## Model Card Contact
+## Technical Specifications.
 
-[More Information Needed]
+The model was trained using [Kaggle.com](https://www.kaggle.com/) free services.