Mistral-Gutenberg-Doppel-7B-FFT-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| License | Apache-2.0 |
| Base Model | Mistral-7B-Instruct-v0.2 |
| Format | GGUF |
What is Mistral-Gutenberg-Doppel-7B-FFT-GGUF?
This is a quantized version of the Mistral-Gutenberg-Doppel model, created using llama.cpp. It represents a full fine-tuning of the Mistral-7B-Instruct-v0.2 base model on Gutenberg datasets, specifically optimized for literary and conversational tasks.
Implementation Details
The model underwent a full fine-tuning process using the ORPO (Optimal Reinforcement Policy Optimization) technique, trained on 4x A100 GPUs for 2 epochs. The training utilized two key datasets: jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo.
- Quantized implementation for improved efficiency
- Full model fine-tuning rather than QLoRA approach
- Optimized using ORPO training methodology
Core Capabilities
- Literary text generation and analysis
- Conversational AI applications
- Efficient deployment through GGUF format
- Enhanced performance on literature-related tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its full fine-tuning approach on Gutenberg datasets, making it particularly adept at handling literary content while maintaining the strong base capabilities of Mistral-7B-Instruct-v0.2. The GGUF quantization makes it more efficient for deployment.
Q: What are the recommended use cases?
The model is best suited for applications involving literary analysis, text generation in literary styles, and conversational AI systems that require understanding of classical literature and sophisticated language patterns.
