wilsonoey/llama3-grpo-rag-think
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-grpo-rag-think is an 8 billion parameter Llama 3 model, developed by wilsonoey, and fine-tuned from wilsonoey/llama3-finetuned-rag-model-1. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for RAG (Retrieval Augmented Generation) applications, leveraging its Llama 3 architecture for enhanced performance in information retrieval and generation tasks.
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wilsonoey/llama3-grpo-rag-think Overview
This model is an 8 billion parameter Llama 3 variant, developed by wilsonoey, and fine-tuned from the wilsonoey/llama3-finetuned-rag-model-1 base. It leverages the Llama 3 architecture, known for its strong language understanding and generation capabilities.
Key Capabilities
- Optimized Training: Achieved 2x faster training speeds by utilizing Unsloth and Huggingface's TRL library.
- RAG-focused: Specifically fine-tuned for Retrieval Augmented Generation (RAG) tasks, indicating its suitability for applications requiring information retrieval combined with text generation.
- Llama 3 Foundation: Benefits from the robust performance and extensive pre-training of the Llama 3 model family.
Good For
- Retrieval Augmented Generation (RAG): Ideal for use cases where external knowledge bases are queried to inform generated responses.
- Applications requiring efficient training: Demonstrates the potential for faster fine-tuning workflows using specialized libraries like Unsloth.
- Developers building on Llama 3: Provides a fine-tuned Llama 3 model ready for RAG-specific deployments.