TraceBack-12b
| Property | Value |
|---|---|
| Base Model | Mistral Nemo 12B |
| Training Data | 200k samples |
| Training Method | QLoRA fine-tuning |
| Model URL | https://huggingface.co/secemp9/TraceBack-12b |
What is TraceBack-12b?
TraceBack-12b is an innovative language model designed to scale reasoning trace data generation effectively. Built on the Mistral Nemo 12B architecture, this model represents a novel approach to generating synthetic reasoning datasets by processing instruction-solution pairs to produce matching reasoning traces.
Implementation Details
The model was trained using QLoRA fine-tuning for 2 epochs on a carefully curated dataset of 200,000 samples. It implements a straightforward prompt format that accepts both instructions and solutions as input, generating corresponding reasoning traces as output. The training process utilized both DeepSpeed and mixed-precision training for optimal performance.
- Built on Mistral Nemo 12B base model
- Fine-tuned using QLoRA methodology
- Implements efficient deep learning optimizations including gradient checkpointing
- Utilizes BF16 mixed precision training
Core Capabilities
- Generates reasoning traces from instruction-solution pairs
- Enables faster synthetic reasoning dataset generation
- Converts non-reasoning model outputs into reasoning synthetic datasets
- Handles out-of-domain non-verifiable problems
Frequently Asked Questions
Q: What makes this model unique?
TraceBack-12b's uniqueness lies in its ability to generate reasoning traces without depending on traditional reasoning models like r1 or o3. This approach allows for faster and more scalable reasoning dataset generation, making it particularly valuable for AI researchers and developers.
Q: What are the recommended use cases?
The model is primarily designed for generating synthetic reasoning datasets, converting non-reasoning outputs into reasoning formats, and handling out-of-domain problem solving. It's particularly useful for researchers working on AI reasoning capabilities and those needing to generate large-scale reasoning datasets efficiently.
