bryysteve/llama3-finetuned-rag-grpo-16bit-v1
TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Jun 28, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The bryysteve/llama3-finetuned-rag-grpo-16bit-v1 is an 8 billion parameter Llama 3 model developed by bryysteve, fine-tuned from bryysteve/llama3-finetuned-rag-16bit-v1. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for Retrieval Augmented Generation (RAG) tasks, leveraging its Llama 3 architecture for enhanced performance in information retrieval and generation.
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Model Overview
The bryysteve/llama3-finetuned-rag-grpo-16bit-v1 is an 8 billion parameter Llama 3-based language model, developed by bryysteve. It is a fine-tuned iteration of the bryysteve/llama3-finetuned-rag-16bit-v1 model, specifically optimized for Retrieval Augmented Generation (RAG) workflows.
Key Characteristics
- Architecture: Based on the Llama 3 family, providing a robust foundation for language understanding and generation.
- Parameter Count: Features 8 billion parameters, balancing performance with computational efficiency.
- Training Efficiency: This model was trained with significant speed improvements, achieving 2x faster training times by utilizing Unsloth in conjunction with Huggingface's TRL library.
- Context Length: Supports a context length of 8192 tokens, suitable for processing moderately long inputs in RAG scenarios.
Good For
- Retrieval Augmented Generation (RAG): Its fine-tuned nature makes it particularly well-suited for applications requiring the retrieval of information from external sources and generating coherent responses based on that information.
- Efficient Fine-tuning: The use of Unsloth for training indicates a focus on efficient model development and deployment, potentially offering benefits for further customization or integration into resource-constrained environments.