Occiglot 7b Eu5

Table of Contents

Model Overview

The Occiglot-7B-EU5 model is a polyglot language model designed to support multiple languages, including English, Spanish, French, German, and Italian. It’s a generative language model with 7B parameters, which is a huge number that allows it to understand and generate human-like text.

Tasks and rank

Text Generation 311th

Natural Language Understanding 260th

Multimodal Deep Learning 20th

Language Modelling 262nd

Cross-Lingual Transfer 14th

Learn More About the Ranking System

Capabilities

The model is a powerful tool for anyone working with language or code, and its multilingual capabilities make it a valuable resource for a wide range of applications. Here are some examples of what it can do:

• Generate text: The model can create human-like text based on a given prompt, making it useful for tasks like writing articles, emails, or chatbot responses.
• Translate text: With its multilingual capabilities, the model can translate text from one language to another, helping to break down language barriers.
• Code generation: The model can also generate code in various programming languages, making it a valuable tool for developers and programmers.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Xlm Roberta Large 323 Multilingual Language Model

Bloomz 560m 113 Multilingual zero-shot model

Performance

The model’s performance is impressive, with an average score of 0.516895 across all 5 languages in the arc_challenge benchmark. It also performs well in other benchmarks, such as belebele, hellaswag, mmlu, and truthfulqa.

Here’s a breakdown of its performance in each language:

Language	Average Score
English	0.59657
German	0.508311
Spanish	0.533194
French	0.525017
Italian	0.421382

Limitations

While the model is powerful, it’s not perfect. Here are some of its limitations:

• Language Biases: The model was trained on a dataset that’s biased towards English, which means it might not perform as well in other languages.
• Limited Domain Knowledge: The model is a general-purpose language model, which means it’s not specialized in any particular domain or topic.
• Lack of Instruction Fine-Tuning: The model was not fine-tuned for specific instructions or tasks, which can affect its performance in certain scenarios.

Examples

Translate this text from English to Spanish: The new policy has been implemented to reduce carbon emissions. La nueva política ha sido implementada para reducir las emisiones de carbono.

Write a short poem in French about the beauty of nature. La nature est belle, elle est notre mère, elle nous donne la vie, elle nous donne l'amour.

Provide a German translation for the phrase 'I am excited to learn about artificial intelligence'. Ich bin aufgeregt, über künstliche Intelligenz zu lernen.

Format

The model uses a causal decoder-only transformer architecture, which means it’s designed to generate text based on the input it receives. Here’s an example of how to use the model with a pipeline for text generation:

from transformers import pipeline, set_seed

generator = pipeline('text-generation', model='occiglot/occiglot-7b-eu5')
set_seed(42)

generated_text = generator("Hallo, Ich bin ein Sprachmodell,", max_length=40, num_return_sequences=1)
print(generated_text)

This code generates text based on the input “Hallo, Ich bin ein Sprachmodell,” and sets the maximum length of the generated text to 40 characters.