CollectiveCognition-v1.1-Mistral-7B
| Property | Value |
|---|---|
| Base Model | Mistral-7B-v0.1 |
| License | Apache 2.0 |
| Language | English |
| Training Time | 3 minutes on single 4090 GPU |
What is CollectiveCognition-v1.1-Mistral-7B?
CollectiveCognition-v1.1-Mistral-7B is a state-of-the-art language model that demonstrates remarkable efficiency in both training and performance. Built on the Mistral-7B architecture, this model achieves exceptional results on the TruthfulQA benchmark, competing with models nearly 10 times its size.
Implementation Details
The model utilizes QLora fine-tuning techniques and was trained on a carefully curated dataset of just 100 high-quality data points from ShareGPT-like platforms. Its training process was remarkably efficient, completed in just 3 minutes on a single NVIDIA 4090 GPU.
- Quick and efficient training process
- Minimal dataset requirement (100 data points)
- Competitive performance against 70B parameter models
- Advanced QLora fine-tuning implementation
Core Capabilities
- Strong performance on TruthfulQA benchmark (MC1: 0.4051, MC2: 0.5738)
- High accuracy on ARC Challenge (50.85%) and ARC Easy (79.63%)
- Impressive BoolQ performance (84.95% accuracy)
- Strong results on Hellaswag (63.99% accuracy, 82.47% normalized)
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to achieve competitive performance with minimal training data and time is its standout feature. It demonstrates that efficient fine-tuning techniques can produce powerful results without requiring massive datasets or computational resources.
Q: What are the recommended use cases?
Given its strong performance on truthfulness benchmarks, this model is particularly well-suited for applications requiring factual accuracy and reliable information processing. It can be effectively used for question-answering, fact verification, and general language understanding tasks.
