RedXeol
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bertin-gpt-j-6B-alpaca-4bit-128g

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 ---
 # Model Card for Model ID
+<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.