wilsonoey/llama3-finetuned-rag-model-1
TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 6, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The wilsonoey/llama3-finetuned-rag-model-1 is an 8 billion parameter Llama 3 model, finetuned by wilsonoey. It was optimized for faster training using Unsloth and Huggingface's TRL library, making it efficient for RAG applications. This model leverages a 32768 token context length, suitable for processing extensive documents in retrieval-augmented generation tasks.
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Overview
The wilsonoey/llama3-finetuned-rag-model-1 is an 8 billion parameter Llama 3 model developed by wilsonoey. It has been specifically finetuned for Retrieval Augmented Generation (RAG) applications, leveraging a substantial 32768 token context window to handle large input texts.
Key Capabilities
- Efficient Finetuning: This model was finetuned using Unsloth and Huggingface's TRL library, enabling significantly faster training times (2x faster).
- Llama 3 Architecture: Built upon the robust Llama 3 base, providing strong foundational language understanding and generation capabilities.
- Extended Context: Features a 32768 token context length, which is crucial for RAG systems that need to process and synthesize information from lengthy documents or multiple sources.
Good For
- Retrieval Augmented Generation (RAG): Its finetuning and large context window make it well-suited for tasks where external knowledge retrieval is combined with language generation.
- Applications requiring efficient training: Developers looking for a Llama 3 based model that can be quickly adapted or further finetuned for specific RAG use cases.
- Processing long documents: Ideal for scenarios involving summarization, question answering, or information extraction from extensive textual data.