anril32/Llama3.1-8B-SFT-RAG
TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Jun 23, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The anril32/Llama3.1-8B-SFT-RAG is an 8 billion parameter Llama 3.1 model, developed by anril32, fine-tuned for specific tasks. This model leverages Unsloth for accelerated training, making it efficient for deployment. It is designed for applications requiring a compact yet capable language model, offering a balance of performance and resource efficiency.
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Model Overview
The anril32/Llama3.1-8B-SFT-RAG is an 8 billion parameter language model, developed by anril32. It is fine-tuned from the unsloth/llama-3.1-8b-unsloth-bnb-4bit base model, indicating an optimization for efficient training and deployment.
Key Characteristics
- Efficient Training: This model was trained significantly faster using the Unsloth library in conjunction with Hugging Face's TRL library. This approach allows for quicker iteration and reduced computational costs during the fine-tuning process.
- Llama 3.1 Architecture: Built upon the Llama 3.1 family, it inherits the robust capabilities and general language understanding of its base architecture.
- Parameter Count: With 8 billion parameters, it offers a strong balance between performance and computational footprint, suitable for various applications where larger models might be too resource-intensive.
Ideal Use Cases
This model is particularly well-suited for scenarios where:
- Resource Efficiency is Key: Its optimized training with Unsloth suggests it can be deployed and run efficiently.
- Specific Task Fine-tuning: As a fine-tuned model, it's designed to excel in the particular tasks it was trained on, making it a strong candidate for specialized applications.
- Llama 3.1 Ecosystem Integration: Developers already working with Llama 3.1 models will find this a familiar and compatible option.