bagel-8b-v1.0
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Text Generation |
| Base Model | Meta-LLaMA-3-8B |
| License | LLaMA 3 |
| Tensor Type | BF16 |
What is bagel-8b-v1.0?
bagel-8b-v1.0 is a sophisticated fine-tuned version of Meta's LLaMA-3-8B model, optimized for versatile text generation tasks. Built by jondurbin, this model stands out for its comprehensive training across 41 diverse datasets and its impressive performance on benchmarks like MT-Bench, where it achieves a 7.296875 average score.
Implementation Details
The model utilizes the LLaMA-3 instruct prompt template and is specifically engineered for enhanced performance in various specialized tasks. It's implemented with BF16 precision and maintains compatibility with text-generation-inference systems.
- Standardized on LLaMA-3 instruct format for consistent prompting
- Trained on carefully curated datasets spanning multiple domains
- Implements context-obedient question answering for RAG applications
- Supports advanced function calling and planning capabilities
Core Capabilities
- Context-aware question answering and summarization
- Function calling with multiple prompt formats
- Chain-of-thought reasoning for complex problem-solving
- Novel writing and creative content generation
- SQL query generation and boolean question handling
- Emotion detection using VAD scoring
- Multi-character chat direction
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its versatile capabilities and standardized approach to prompt formatting. Unlike other models, it combines context-obedient RAG capabilities with creative writing abilities and sophisticated function calling, all while maintaining consistent performance across diverse tasks.
Q: What are the recommended use cases?
The model excels in various applications including RAG systems, creative writing, technical documentation, function calling implementations, and multi-character dialogue generation. It's particularly well-suited for applications requiring both factual accuracy and creative flexibility.
