Llama-3.1-Tulu-3-8B-SFT-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| License | Llama 3.1 Community License |
| Base Model | meta-llama/Llama-3.1-8B |
| Format | GGUF (Quantized) |
What is Llama-3.1-Tulu-3-8B-SFT-GGUF?
This is a quantized version of the Tulu-3 model, built on the Llama 3.1 architecture. It represents a significant advancement in instruction-following models, specifically optimized for diverse tasks including mathematical reasoning, problem-solving, and conversational AI. The model has been fine-tuned using supervised fine-tuning (SFT) techniques on the Tulu-3 dataset mixture.
Implementation Details
The model utilizes advanced training parameters including a learning rate of 5E-6, an effective batch size of 128, and a maximum sequence length of 4096. It implements a linear learning rate schedule with a 0.03 warmup ratio and runs for 2 epochs.
- Quantized format for efficient deployment
- Built on Llama 3.1 architecture
- Optimized for both chat and specialized tasks
- Implements standard chat template format
Core Capabilities
- Strong performance on mathematical reasoning (MATH, GSM8K)
- Advanced instruction following capabilities
- High safety scores (93.1% on 6-task average)
- Excellent performance on programming tasks (HumanEval)
- Robust conversational abilities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its balanced performance across various tasks, achieving particularly strong results in mathematical reasoning and safety metrics while maintaining efficient deployment through quantization.
Q: What are the recommended use cases?
The model excels in mathematical problem-solving, programming tasks, and general conversation. It's particularly well-suited for applications requiring balanced performance across multiple domains while maintaining computational efficiency.
