Update README.md

<strong><span style="font-size: larger;">bertin-gpt-j-6B-alpaca-4bit-128g 🤗</span></strong>
**descripción en español agregado ⬇️**

This is a 4-bit GPTQ version of the [bertin-project/bertin-gpt-j-6B-alpaca]( https://huggingface.co/bertin-project/bertin-gpt-j-6B-alpaca)
this is the result of quantizing to 4 bits using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).

** How to easily download and use this model in text-generation-webui** (tutorial by [TheBloke](https://huggingface.co/TheBloke))

Open [the text-generation-webui UI]( https://github.com/oobabooga/text-generation-webui).
as normal.
here is a tutorial how to install the text-generation-webui UI: [tutorial]( https://www.youtube.com/watch?v=lb_lC4XFedU&t).
Click the Model tab.
Under Download custom model or LoRA, enter RedXeol/bertin-gpt-j-6B-alpaca-4bit-128g.
Click Download.
Wait until it says it's finished downloading.
Click the Refresh icon next to Model in the top left.
In the Model drop-down: choose the model you just downloaded, bertin-gpt-j-6B-alpaca-4bit-128g.
If you see an error in the bottom right, ignore it - it's temporary.
Fill out the GPTQ parameters on the right: Bits = 4, Groupsize = 128, model_type = gptj
Click Save settings for this model in the top right.
Click Reload the Model in the top right.
Once it says it's loaded, click the Text Generation tab and enter a prompt!

**Model details**
Data
The dataset is a translation to Spanish of alpaca_data_cleaned.json (a clean version of the Alpaca dataset made at Stanford) using OpenAI's gpt-3.5-turbo model. We translated using a full-sample prompt instead of per strings, which resulted in more coherent tuples of (instruction, input, output) and costed around $60.0.

This dataset cannot be used to create models that compete in any way with OpenAI.

Finetuning
To fine-tune the BERTIN GPT-J-6B model we used the code available on BERTIN's fork of mesh-transformer-jax, which provides code adapt an Alpaca dataset to finetune any GPT-J-6B model. We run finetuning for 3 epochs using sequence length of 2048 on a single TPUv3-8 for 3 hours on top of BERTIN GPT-J-6B.

** español **
Esta es una versión GPTQ de 4 bits del [bertin-project/bertin-gpt-j-6B-alpaca]( https://huggingface.co/bertin-project/bertin-gpt-j-6B-alpaca)
Este es el resultado de cuantificar a 4 bits usando [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).

** Cómo descargar y usar fácilmente este modelo en text-generation-webui** (tutorial de [TheBloke](https://huggingface.co/TheBloke))

Abra la interfaz de usuario [the text-generation-webui UI]( https://github.com/oobabooga/text-generation-webui). normal.
aquí hay un tutorial de cómo instalar la interfaz de usuario text-generation-webui: [tutorial]( https://www.youtube.com/watch?v=lb_lC4XFedU&t).
Haga clic en la pestaña Modelo.
En Descargar modelo personalizado o LoRA, ingrese RedXeol/bertin-gpt-j-6B-alpaca-4bit-128g.
Haz clic en Descargar.
Espera hasta que diga que ha terminado de descargarse.
Haga clic en el icono Actualizar junto a Modelo en la parte superior izquierda.
En el menú desplegable Modelo: elija el modelo que acaba de descargar, bertin-gpt-j-6B-alpaca-4bit-128g.
Si ve un error en la parte inferior derecha, ignórelo, es temporal.
Complete los parámetros GPTQ a la derecha: Bits = 4, Groupsize = 128, model_type = gptj
Haz clic en Guardar configuración para este modelo en la parte superior derecha.
Haga clic en Recargar el modelo en la parte superior derecha.
Una vez que diga que está cargado, haga clic en la pestaña Generación de texto e ingrese un mensaje.

**Detalles del modelo**
Datos
El conjunto de datos es una traducción al español de alpaca_data_cleaned.json (una versión limpia del conjunto de datos de Alpaca hecho en Stanford) utilizando el modelo gpt-3.5-turbo de OpenAI. Traducimos usando un indicador de muestra completa en lugar de por cadenas, lo que resultó en tuplas más coherentes de (instruction, input, output) y costó alrededor de $ 60.0.
Este conjunto de datos no se puede usar para crear modelos que compitan de alguna manera con OpenAI.

Finetuning
Para ajustar el modelo BERTIN GPT-J-6B, usamos el código disponible en la bifurcación de BERTIN de mesh-transformer-jax, que proporciona código para adaptar un conjunto de datos de Alpaca para ajustar cualquier modelo GPT-J-6B. Ejecutamos un ajuste fino para 3 épocas usando una longitud de secuencia de 2048 en un solo TPUv3-8 durante 3 horas sobre BERTIN GPT-J-6B.

README.md

@@ -12,4 +12,199 @@ tags:
 - PyTorch
 - alpaca
 - llm spanish
----
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [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]
+
+## 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.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data 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 Data Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+[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
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+[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:**
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+**APA:**
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+## Glossary [optional]
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+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
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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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+