willyninja30/ARIA-70B-French
willyninja30/ARIA-70B-French is a 69 billion parameter language model developed by FARADAY, based on the Llama 2-70B-Chat-HF architecture. It is specifically fine-tuned on over 50,000 high-quality French language rows, including data from the French parliament, to enhance its performance and quality on French and general topics. This model is optimized for dialogue use cases and aims to improve French language understanding and generation.
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ARIA-70B-French: A Llama 2-Based Model for French Language Tasks
ARIA-70B-French is a 69 billion parameter model developed by FARADAY, built upon the Llama 2-70B-Chat-HF architecture. This model has undergone extensive fine-tuning using a high-quality dataset comprising over 50,000 rows of French language data, including content from the French parliament. The primary objective of this fine-tuning was to significantly enhance the model's proficiency in French language understanding and generation, as well as its general topic knowledge in French.
Key Capabilities
- Enhanced French Language Performance: Specifically optimized for high-quality French text generation and comprehension through dedicated fine-tuning.
- Dialogue Optimization: Inherits the dialogue-optimized characteristics of its Llama 2-70B-Chat-HF base model.
- Context Length Extension (Experimental): Features experimental Rope Scaling to potentially increase the context length from 4,096 to over 6,000 tokens, enabling the handling of larger documents for data extraction (requires explicit activation).
- Auto-regressive Transformer Architecture: Utilizes an optimized transformer architecture with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) for alignment.
Good for
- French Language Applications: Ideal for applications requiring robust performance in French, such as chatbots, content generation, and translation.
- Dialogue Systems: Well-suited for conversational AI and dialogue-based interactions in French.
- Data Extraction from Large French Documents: With experimental Rope Scaling, it can be adapted for processing and extracting information from extensive French texts.