Elod1e/Llama-3.1-8B-Instruct-Lingala-QLoRA-merged_vf

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 10, 2026Architecture:Transformer Featherless Exclusive Cold

Elod1e/Llama-3.1-8B-Instruct-Lingala-QLoRA-merged_vf is an 8 billion parameter instruction-tuned language model, fine-tuned from the Llama-3.1 architecture. This model is specifically optimized for tasks involving the Lingala language, leveraging QLoRA for efficient adaptation. Its primary differentiator is its focus on Lingala, making it suitable for applications requiring natural language understanding and generation in this specific language. The model has a context length of 8192 tokens.

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Model Overview

Elod1e/Llama-3.1-8B-Instruct-Lingala-QLoRA-merged_vf is an 8 billion parameter instruction-tuned language model built upon the Llama-3.1 architecture. This model has been fine-tuned using QLoRA, a parameter-efficient fine-tuning method, to specialize in the Lingala language. It is designed to handle various natural language processing tasks within the Lingala linguistic context.

Key Characteristics

  • Architecture: Llama-3.1 base model.
  • Parameter Count: 8 billion parameters.
  • Context Length: Supports an 8192-token context window.
  • Language Focus: Specifically fine-tuned for the Lingala language.
  • Fine-tuning Method: Utilizes QLoRA for efficient adaptation and performance.

Intended Use Cases

This model is particularly well-suited for applications requiring language understanding and generation in Lingala. Potential use cases include:

  • Lingala Text Generation: Creating coherent and contextually relevant text in Lingala.
  • Instruction Following: Responding to instructions and queries posed in Lingala.
  • Language Translation (Lingala-centric): Assisting in translation tasks where Lingala is a source or target language.
  • Content Creation: Generating articles, summaries, or creative content in Lingala.

Limitations and Recommendations

As with any specialized language model, users should be aware of potential biases and limitations inherent in the training data and fine-tuning process. Further information regarding specific biases, risks, and detailed performance metrics is currently marked as "More Information Needed" in the model card. Users are advised to conduct thorough evaluations for their specific applications.

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