Update README.md

Model Card for Model ID
Overview
This model is a conversational AI designed to engage in natural language interactions with users. It is based on the Causal Language Modeling (CLM) architecture and has been fine-tuned on conversational datasets to generate coherent and contextually relevant responses.

Usage
To use this model, you can interact with it via the Hugging Face Inference API. Provide a text prompt, and the model will generate a response based on the given input.

Intended Use
This model is intended for various conversational applications, including chatbots, virtual assistants, and dialogue systems. It can be deployed in environments where human-like interactions are required, such as customer service, educational platforms, or entertainment applications.

Limitations and Ethical Considerations
While this model is capable of generating human-like responses, it may occasionally produce outputs that are inappropriate, offensive, or misleading. It is essential to monitor its responses and ensure responsible deployment to mitigate potential harms.

License
The model is released under the Apache License 2.0, which allows for both commercial and non-commercial use with proper attribution.

Acknowledgments
This model was trained using the Hugging Face Transformers library and fine-tuned on conversational datasets. We acknowledge the contributions of the open-source community and the developers of the Transformers library.

Contact Information
For inquiries or feedback regarding this model, please contact [your contact information].

References
Provide any relevant references, citations, or links to resources used in training or developing this model

README.md

@@ -8,202 +8,96 @@ language:
 - hi
 metrics:
 - bleu
-library_name: fairseq
+library_name: fastai
 tags:
 - code
 pipeline_tag: text2text-generation
 ---
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+Overview
+This model is a conversational AI designed to engage in natural language interactions with users. It is based on the Causal Language Modeling (CLM) architecture and has been fine-tuned on conversational datasets to generate coherent and contextually relevant responses.
 
-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).
+Usage
+To use this model, you can interact with it via the Hugging Face Inference API. Provide a text prompt, and the model will generate a response based on the given input.
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-## 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
+Intended Use
+This model is intended for various conversational applications, including chatbots, virtual assistants, and dialogue systems. It can be deployed in environments where human-like interactions are required, such as customer service, educational platforms, or entertainment applications.
 
-<!-- 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. -->
+Limitations and Ethical Considerations
+While this model is capable of generating human-like responses, it may occasionally produce outputs that are inappropriate, offensive, or misleading. It is essential to monitor its responses and ensure responsible deployment to mitigate potential harms.
 
-[More Information Needed]
+License
+The model is released under the Apache License 2.0, which allows for both commercial and non-commercial use with proper attribution.
 
-### Training Procedure
+Acknowledgments
+This model was trained using the Hugging Face Transformers library and fine-tuned on conversational datasets. We acknowledge the contributions of the open-source community and the developers of the Transformers library.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Contact Information
+For inquiries or feedback regarding this model, please contact [your contact information].
 
-#### Preprocessing [optional]
+References
+Provide any relevant references, citations, or links to resources used in training or developing this model.
 
-[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).
+## Model Details
 
-- **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]
+### Model Description
+This model is a state-of-the-art conversational AI system based on the Causal Language Modeling (CLM) architecture. It has been fine-tuned on large-scale conversational datasets to generate contextually relevant and coherent responses to user inputs. The model utilizes self-attention mechanisms and deep neural networks to understand and process natural language inputs, allowing it to engage in human-like conversations across a wide range of topics and contexts.
 
-## Technical Specifications [optional]
+Architecture
+The architecture of this model consists of multiple layers of transformer blocks, including self-attention mechanisms and feed-forward neural networks. It employs techniques such as positional encoding and layer normalization to enhance its ability to capture and process sequential information in text data. The model's parameters are optimized through training on conversational datasets using techniques such as gradient descent and backpropagation.
 
-### Model Architecture and Objective
+Fine-Tuning
+During the fine-tuning process, the model is trained on conversational datasets, where it learns to generate appropriate responses based on input prompts. Fine-tuning involves adjusting the parameters of the pre-trained model to better suit the conversational task at hand, thereby improving its performance in generating contextually relevant and coherent responses.
 
-[More Information Needed]
+Performance
+The performance of this model is evaluated based on various metrics, including fluency, coherence, relevance, and engagement. It has been extensively tested on benchmark datasets and real-world conversational applications to assess its ability to produce human-like responses and maintain meaningful interactions with users.
 
-### Compute Infrastructure
+Use Cases
+This model can be deployed in a variety of conversational applications, including chatbots, virtual assistants, customer support systems, and interactive storytelling platforms. It can facilitate natural language interactions between users and systems, enhancing user experience and providing valuable assistance across different domains and industries.
 
-[More Information Needed]
+Limitations and Ethical Considerations
+While this model demonstrates advanced capabilities in generating human-like responses, it may occasionally produce outputs that are inappropriate, biased, or misleading. Careful monitoring and evaluation are necessary to ensure responsible deployment and mitigate potential risks, such as spreading misinformation or perpetuating harmful stereotypes.
 
-#### Hardware
+License
+The model is released under the Apache License 2.0, allowing for both commercial and non-commercial use with proper attribution.
 
-[More Information Needed]
+Contact Information
+For inquiries or feedback regarding this model, please contact [your contact information].
 
-#### Software
+References
+Provide any relevant references, citations, or links to resources used in training or developing this model.
 
-[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. -->
+- **Developed by:** [VAIBHAV VERMA]
+- **Funded by [optional]:** [VAIBHAV VERMA]
+- **Shared by [optional]:** [OEvortex/vortex-3b ]
+- **Model type:** [conversational AI]
+- **Language(s) (NLP):** [PYTHON]
+- **License:** [Apache License 2.0]
+- **Finetuned from model [optional]:** [More Information Needed]
+-
 
-**BibTeX:**
 
-[More Information Needed]
 
-**APA:**
+## Uses
 
-[More Information Needed]
+The model can be utilized in various conversational applications across different domains and industries. Some potential uses include:
 
-## Glossary [optional]
+Chatbots: Deploy the model as a chatbot to engage with users in natural language conversations, providing assistance, answering questions, and offering recommendations.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Virtual Assistants: Integrate the model into virtual assistant applications to help users with tasks such as scheduling appointments, setting reminders, and retrieving information from the web.
 
-[More Information Needed]
+Customer Support Systems: Use the model to power customer support chat systems, where it can handle customer inquiries, troubleshoot issues, and escalate complex queries to human agents when necessary.
 
-## More Information [optional]
+Interactive Storytelling: Employ the model in interactive storytelling platforms to create immersive narrative experiences where users can engage with virtual characters and influence the plot through their interactions.
 
-[More Information Needed]
+Language Learning: Develop language learning applications that leverage the model to provide conversational practice and feedback to learners, helping them improve their language skills through realistic dialogue simulations.
 
-## Model Card Authors [optional]
+Social Media Engagement: Integrate the model into social media platforms to enhance user engagement by enabling automated responses to comments, messages, and posts, personalized recommendations, and conversational interactions.
 
-[More Information Needed]
+Healthcare Assistants: Adapt the model for use in healthcare applications, where it can assist patients with medical inquiries, provide health-related information, and offer support for mental health and wellness.
 
-## Model Card Contact
+Educational Tools: Incorporate the model into educational applications to create interactive tutoring systems, virtual classroom assistants, and language practice tools that engage students in conversational learning experiences.
 
-[More Information Needed]