Model Description for TarjamaN/Poronoia-14b-community

Overview

TarjamaN/Poronoia-14b-community is a 14-billion-parameter language model designed to handle advanced natural language processing (NLP) tasks with a focus on multilingual understanding and generation. The model has been fine-tuned to excel in various applications, including text generation, translation, summarization, and more. Built with a community-driven approach, it offers robust performance across diverse linguistic domains.

Key Features

Multilingual Proficiency: The model supports multiple languages, with enhanced capabilities for low-resource and morphologically rich languages.

Customizable Outputs:

Fine-tuned with an emphasis on flexibility, allowing users to generate outputs tailored to specific requirements, such as formal or creative text.

Optimized for Contextual Understanding: Equipped to handle long-context understanding for applications like document summarization or detailed question answering.

Open Source and Community-Driven: Designed to be used and improved by the community, fostering collaboration and innovation.

Applications

Text Generation: Create coherent and contextually relevant text for creative writing, marketing, or general use. Machine Translation: Translate between languages with high fidelity, especially for less commonly supported languages. Summarization: Generate concise and accurate summaries for long documents or articles. Chatbots and Virtual Assistants: Develop conversational AI systems capable of nuanced and natural dialogue. Sentiment Analysis and Classification: Analyze and categorize text for various NLP tasks. Technical Specifications Model Size: 14 billion parameters Base Architecture: Transformer-based architecture, similar to GPT-like or LLAMA models Tokenizer: Custom tokenizer optimized for multilingual data Framework: Compatible with the Hugging Face transformers library Usage To use the model, install the transformers library and load it as follows:

python Copy code from transformers import AutoModelForCausalLM, AutoTokenizer

Load tokenizer and model

tokenizer = AutoTokenizer.from_pretrained("TarjamaN/Poronoia-14b-community", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("TarjamaN/Poronoia-14b-community", trust_remote_code=True)

Example usage

input_text = "Translate the following text: Hello, how are you?" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True)) License The model is released under an open-source license, allowing free usage for non-commercial and research purposes. Refer to the repository for detailed license information.

Contributing As a community-driven project, contributions are highly encouraged. If you encounter issues, want to improve the model, or share feedback, visit the model's repository on Hugging Face.

Limitations and Bias While highly capable, the model might:

Struggle with niche or domain-specific knowledge. Reflect biases present in the training data. Produce inaccurate or nonsensical outputs in rare edge cases. Feel free to adjust this description for the specific details of the model!