Upload GemmaForCausalLM

README.md

@@ -1,137 +1,201 @@
 ---
-license: apache-2.0
-datasets:
-- somosnlp/RAC_Colombia_QualityImproved50Percent
-language:
-- es
 library_name: transformers
-pipeline_tag: text-generation
-tags:
-- legal
+tags: []
 ---
-# LLM-AviationV2: Innovación AI en los Cielos
 
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/6419c2f6b4adb0e101b17b6c/fhU5yTQKH9nHN_zues186.png" style="width: 50%; max-width: 500px; height: auto;" alt="LLM-AviationV2: Innovación AI en los Cielos"/>
-</p>
+# Model Card for Model ID
 
+<!-- Provide a quick summary of what the model is/does. -->
 
 
-### Descripción del Modelo
 
-Desarrollado por Edison Bejarano y Nicolas Potes, este modelo representa un avance revolucionario en la utilización de la tecnología de Modelos de Lenguaje (LM) dentro del sector aeronáutico, específicamente diseñado para mejorar la comprensión y accesibilidad del Reglamento Aeronáutico Colombiano (RAC). Entrenado en una Tesla V100-SXM2-16GB, el modelo `LLM-AviationV2` se embarca en un viaje para navegar el complejo panorama regulatorio con una eficiencia y perspicacia sin precedentes.
+## Model Details
 
-- **Desarrollado por:**  [Edison Bejarano](https://huggingface.co/ejbejaranos) - [Sergio Nicolas](https://huggingface.co/SergioMadridF) - [Santiago Pineda](https://huggingface.co/sapinedamo)
-- **Tipo de modelo:** Versión afinada de `google/gemma-2b-it`
-- **Idiomas (NLP):** Español (es)
-- **Licencia:** Apache-2.0
-- **Afinado a partir del modelo:** `google/gemma-2b-it`
+### Model Description
 
-### Fuentes del Modelo
+<!-- Provide a longer summary of what this model is. -->
 
-- **URL en Hugging Face:** [ejbejaranos/LLM-AviationV2](https://huggingface.co/ejbejaranos/LLM-AviationV2)
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-## Usos
+- **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]
 
-### Uso Directo
+### Model Sources [optional]
 
-El modelo `LLM-AviationV2` está diseñado para aplicaciones directas en tareas de generación de texto, con el objetivo de simplificar la interpretación y aplicación de las regulaciones aeronáuticas. Su función principal es servir a profesionales y entusiastas del campo de la aeronáutica, proporcionando acceso inmediato a información comprensible extraída del RAC.
+<!-- Provide the basic links for the model. -->
 
-## Detalles de Entrenamiento
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-## Datos de Entrenamiento
+## Uses
 
-El modelo `LLM-AviationV2` fue afinado utilizando el dataset `RAC_Colombia_QualityImproved025`, el cual representa una versión mejorada en términos de calidad del Reglamento Aeronáutico Colombiano. Este dataset fue curado y mejorado por el equipo de [SomosNLP](https://huggingface.co/somosnlp), con el objetivo de proporcionar una base de datos más precisa y relevante para tareas de procesamiento de lenguaje natural relacionadas con la aviación.
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-Para más detalles sobre este dataset, puedes consultar la documentación y los metadatos a través del siguiente enlace:
+### Direct Use
 
-[Dataset `RAC_Colombia_QualityImproved025` en Hugging Face](https://huggingface.co/datasets/somosnlp/RAC_Colombia_QualityImproved025)
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
+[More Information Needed]
 
-### Procedimiento de Entrenamiento y Resultados
+### Downstream Use [optional]
 
-#### Hiperparámetros de Entrenamiento para LLM-AviationV2
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-- **Tipo de GPU:** Tesla V100-SXM2-16GB
-- **Tiempo Total de Entrenamiento:** Aprox. 70 minutos (4239 segundos)
-- **Tasa de Aprendizaje:** 0.00005
-- **Optimizador:** Paged AdamW 8bit
-- **Pasos Máximos:** 258
-- **Tamaño de Secuencia:** 1024 (presumido)
-- **Tamaño de Lote por Dispositivo:** 3
+[More Information Needed]
 
-#### Velocidades, Tamaños, Tiempos para LLM-AviationV2
+### Out-of-Scope Use
 
-- **Tiempo de Entrenamiento:** 882.68 segundos
-- **Muestras por Segundo en Entrenamiento:** 2.338
-- **Pasos por Segundo en Entrenamiento:** 0.585
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-#### Hiperparámetros de Entrenamiento para LLMs-AviationV3
+[More Information Needed]
 
-- **Tipo de GPU:** NVIDIA A100-SXM4-40GB
-- **Tiempo Total de Entrenamiento:** Aprox. 50 minutos (3007 segundos)
-- **Tasa de Aprendizaje:** 0.00005
-- **Optimizador:** Paged AdamW 8bit
-- **Pasos Máximos:** 1638
-- **Tamaño de Secuencia:** 2048
-- **Tamaño de Lote por Dispositivo:** 1
-- **Versión de Transformers:** 4.39.0
-- **Función de Activación:** gelu_pytorch_tanh
+## Bias, Risks, and Limitations
 
-#### Velocidades, Tamaños, Tiempos para LLMs-AviationV3
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-- **Tiempo de Entrenamiento:** 1641.78 segundos
-- **Muestras por Segundo en Entrenamiento:** 3.991
-- **Pasos por Segundo en Entrenamiento:** 0.998
+[More Information Needed]
 
-### Comparación de Modelos
+### Recommendations
 
-Al comparar los modelos, observamos mejoras significativas en la versión LLMs-AviationV3. La expansión del tamaño de la secuencia a 2048 y la reducción del tamaño de lote por dispositivo a 1, junto con el incremento en los pasos máximos a 1638, han demandado más recursos pero han resultado en un aumento notable en la calidad del modelo. Además, la actualización a la versión 4.39.0 de Transformers y el cambio en la función de activación a `gelu_pytorch_tanh` para LLMs-AviationV3 han contribuido a este avance cualitativo.
+<!-- 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.
 
-### Resultados
+## How to Get Started with the Model
 
-El modelo ha demostrado una capacidad significativa para comprender y generar contenido regulatorio aeronáutico en español, convirtiéndose en un valioso recurso para la industria.
+Use the code below to get started with the model.
 
-Actualmente vamos en la tercera version en donde hemos conseguido mejorar previas versiones:
+[More Information Needed]
 
+## Training Details
 
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/6419c2f6b4adb0e101b17b6c/vkdnFXNuRz8GRMY1_yvSy.png" style="width: 70%; max-width: 1000px; height: auto;" alt="Métrica de perdida: Innovación AI en los Cielos"/>
-</p>
+### Training Data
 
-## Evaluación
-Se esta desarrollando un espacio para que expertos en el campo puedan realizar una evalucacion por el momento tenemos estos dos para nuestros mejores modelos :
+<!-- 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. -->
 
-https://somosnlp-rac-col-v1.hf.space
+[More Information Needed]
 
-## Impacto Ambiental
+### Training Procedure 
 
-El entrenamiento de `LLM-AviationV2` se llevó a cabo con una consideración cuidadosa de su huella ambiental, optimizando para la eficiencia y minimizando el gasto computacional innecesario.
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-- **Tipo de Hardware:** Tesla V100-SXM2-16GB
-- **Horas Utilizadas:** Aproximadamente 0.52 horas
-- **Consumo de Energía:** Aproximadamente 0.156 kWh
-- **Emisiones de CO2 Estimadas:** Aproximadamente 0.0741 kg
+#### Preprocessing [optional]
 
-Estas cifras subrayan nuestro compromiso con la sostenibilidad y la reducción del impacto ambiental en el desarrollo de tecnologías de inteligencia artificial.
+[More Information Needed]
 
 
+#### Training Hyperparameters
 
-## Especificaciones Técnicas
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-### Infraestructura de Cómputo
+#### 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
 
-El entrenamiento se realizó en una Tesla V100-SXM2-16GB, elegida por su equilibrio entre rendimiento y eficiencia energética.
+[More Information Needed]
 
 #### Software
 
-- **Versión de Transformers:** 4.38.0
-- **Entorno de Entrenamiento:** Proporcionado por la biblioteca Hugging Face Transformers.
+[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]
+
+## Model Card Contact
+
+[More Information Needed]
 
-## Más Información
 
-Para obtener información más detallada sobre `LLM-AviationV2`, incluido el acceso al modelo y sus capacidades completas, por favor visita nuestro [repositorio en Hugging Face](https://huggingface.co/ejbejaranos/LLM-AviationV2).
-LLM-AviationV2).

config.json

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+{
+  "_name_or_path": "google/gemma-2b-it",
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu",
+  "hidden_activation": null,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.0",
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

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+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.39.0"
+}

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