The gyorgy-ruzicska/lingua-news-llama-3-spanish-simplifier is a 3.2 billion parameter Llama 3-based instruction-tuned model, specifically fine-tuned for simplifying Spanish news text. Developed by gyorgy-ruzicska, this model leverages Unsloth for efficient training and GGUF conversion, making it suitable for local deployment. With a context length of 32768 tokens, it is optimized for processing and simplifying longer Spanish texts, offering a specialized solution for readability enhancement.
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Model Overview
The gyorgy-ruzicska/lingua-news-llama-3-spanish-simplifier is a Llama 3-based instruction-tuned model with 3.2 billion parameters, designed for simplifying Spanish news text. It was fine-tuned and converted to the GGUF format using Unsloth, which facilitated faster training.
Key Features
- Specialized Task: Fine-tuned specifically for simplifying Spanish news content.
- Architecture: Based on the Llama 3 model family.
- Parameter Count: Features 3.2 billion parameters, offering a balance between performance and efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for processing longer articles.
- GGUF Format: Provided in GGUF format, enabling compatibility with
llama.cppand other local inference engines. - Ollama Support: Includes an Ollama Modelfile for streamlined deployment and usage.
Usage and Deployment
This model is ready for deployment with llama.cpp using the provided GGUF files, such as llama-3.2-3b-instruct.Q4_K_M.gguf. Example command-line usage is provided for both text-only and multimodal llama.cpp clients. The inclusion of an Ollama Modelfile further simplifies its integration into existing workflows.
Note on Training
The model's training was accelerated by a factor of 2x through the use of Unsloth, highlighting an efficient development process.