Atllama (Azerbaijani Tuned LLaMA) is a fine-tuned language model, specifically designed to improve instruction-following, comprehension, and text generation in the Azerbaijani language. It is part of an experimental project aimed at building a suite of Azerbaijani-focused NLP tools and models.
This model card provides a comprehensive overview of Atllama, its development process, intended use cases, risks, and technical specifications.
Model Details
Model Description
Atllama is an Azerbaijani fine-tuned version of the LLaMA model, developed as part of an experimental effort to enhance Azerbaijani language understanding and generation capabilities. The project explores ways to improve NLP tools in underrepresented languages like Azerbaijani, with Atllama being a core component for language-based applications.
Developed by: Arzu Huseynov and Nigar Arabli
Funded by [optional]: Self-funded
Shared by [optional]: Arzu Huseynov
Model type: Fine-tuned LLaMA (Azerbaijani)
Language(s) (NLP): Azerbaijani
License: Open-source, MIT
Finetuned from model: LLaMA 3.1 8B model
Model Sources [optional]
Repository: [Add link when available]
Paper [optional]: [Add paper if available]
Demo [optional]: [Add demo link if available]
GGUF Format Support
Atllama is also available in the GGUF (GPT-Generated Unified Format) file format, which allows users to efficiently run the model on local machines using frameworks like llama.cpp, Ollama, or other GGML-based inference libraries.
GGUF is an ideal format for lightweight inference, and the file includes both the model weights and metadata, enabling faster loading and usage with minimal setup. Users can find the GGUF files for Atllama in the repository, and here is how to run it:
Example Usage with GGUF
To run Atllama in the GGUF format on your local machine:
Download the GGUF file from the Hugging Face repository.
Use tools like llama.cpp or Ollama to load the model:
ollama run atllama.gguf "Your Azerbaijani input prompt here"
For detailed instructions on GGUF and its usage with local inference tools, please refer to the respective documentation for llama.cpp and Ollama tools.
Uses
Atllama is designed to be used in various NLP tasks that require Azerbaijani language processing, including text generation, question-answer systems, instruction-following, and more.
Direct Use
Atllama can be directly used for:
Azerbaijani text generation
Following Azerbaijani-language instructions
Question-answer systems for Azerbaijani
Downstream Use [optional]
When fine-tuned further, Atllama can be adapted to:
Improve conversational agents for Azerbaijani-speaking users
Generate datasets specific to Azerbaijani NLP tasks
Assist in text correction or translation efforts in Azerbaijani
Out-of-Scope Use
The model may not perform well for:
Non-Azerbaijani language tasks
Domains where highly specific contextual knowledge is required (e.g., scientific data or legal texts outside of Azerbaijani context)
Bias, Risks, and Limitations
Atllama, like other fine-tuned models, may carry certain biases from the dataset it was trained on. These biases can affect:
Representation of minority groups or underrepresented topics in Azerbaijani contexts
Language model accuracy in specific dialects or regional variations of Azerbaijani
Recommendations
Users should be cautious of potential biases, particularly when using the model for sensitive content or high-stakes applications. More detailed testing across different subpopulations in Azerbaijani-speaking regions is recommended to mitigate risks.
Training Details
Training Data
Atllama3.5 was trained using a variety of Azerbaijani text sources, including Wikipedia, news articles, and custom datasets. The training data was carefully curated to cover diverse topics, but there may still be limitations in niche domains.
Dataset: A 50K example dataset including instructional pairs and Wikipedia data.
Training Procedure
The model was fine-tuned using:
Hardware: PC (96GB RAM, RTX 4090, i9 CPU)
Training regime: fp16 mixed precision
Epochs: 3 epochs with additional fine-tuning for task-specific improvements
Preprocessing
Text data was cleaned for grammatical accuracy and translated from English sources in some cases, ensuring a focus on Azerbaijani language instruction-following.
Evaluation
Testing Data, Factors & Metrics
Testing Data
Atllama was tested on custom datasets and Azerbaijani conversational tasks to evaluate its performance in instruction-following and text generation.
Factors
The model was evaluated across various factors, such as:
Comprehension of formal vs. colloquial Azerbaijani
Performance in generating coherent Azerbaijani instructions
Quality of output in terms of grammar and contextual relevance
Metrics
Evaluation metrics include:
Accuracy in instruction-following tasks
Fluency of generated text
User satisfaction in conversational contexts
Results
Atllama3.5 has shown significant improvement in understanding instructions and generating more accurate Azerbaijani text. However, the model may still struggle with edge cases involving regional dialects or very specific domains. Please keep in mind this model is not intended for production use in its current state.
Summary
Atllama3.5 continues to evolve as part of ongoing research into Azerbaijani language processing. While promising in its current form, future iterations aim to address biases and limitations.
Environmental Impact
Hardware Type: Personal machine with high-end specs (96GB RAM, RTX 4090, i9 CPU)
Hours used: More than 100 hours+
Cloud Provider: N/A (on-premises training)
Compute Region: N/A
Carbon Emitted: N/A
Technical Specifications [optional]
Model Architecture and Objective
Atllama is based on LLaMA 3.1 architecture, fine-tuned for Azerbaijani NLP tasks with the objective of improving instruction-following and text generation.
Compute Infrastructure
The model was trained on a high-end local machine, as described in the "Training Procedure" section.
Citation [optional]
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Glossary [optional]
LLaMA: A family of language models designed by Meta, used as the base for fine-tuning in specific languages like Azerbaijani.
Fine-tuning: The process of adapting a pre-trained model to specific tasks or languages.
