|
--- |
|
license: apache-2.0 |
|
base_model: |
|
- deepseek-ai/DeepSeek-R1-Zero |
|
datasets: |
|
- Daemontatox/Reasoning_am |
|
- pbcong/gsm8k_step_by_step |
|
- Daemontatox/Deepthinking-COT |
|
- Daemontatox/Qwqloncotam |
|
language: |
|
- en |
|
library_name: transformers |
|
tags: |
|
- wip |
|
- experimental |
|
- moe |
|
- finetune |
|
- research |
|
pipeline_tag: text-generation |
|
metrics: |
|
- accuracy |
|
- code_eval |
|
--- |
|
 |
|
|
|
# Z1: Experimental Fine-Tune of R1-Zero |
|
|
|
**Z1** is a highly experimental fine-tune of the **DeepSeek-R1-Zero** model, designed for research purposes and not intended for production use. This model focuses on advancing reasoning capabilities and structured inference through fine-tuning on multiple high-quality reasoning datasets. |
|
|
|
--- |
|
|
|
## Key Features |
|
|
|
- **Experimental Fine-Tune**: Z1 is a research-oriented fine-tune of state-of-the-art large language models, aimed at exploring advanced reasoning and inference techniques. |
|
- **Research-Only Use Case**: This model is not suitable for production environments and is intended solely for experimental and academic purposes. |
|
- **Enhanced Reasoning Abilities**: Fine-tuned on diverse reasoning datasets to improve logical inference, step-by-step problem-solving, and structured reasoning. |
|
- **Chain-of-Thought (CoT) Focus**: Optimized for multi-step reasoning tasks, leveraging Chain-of-Thought learning to enhance structured and interpretable inference. |
|
|
|
--- |
|
|
|
## Intended Use |
|
|
|
Z1 is designed for researchers and developers exploring the following areas: |
|
- **Reasoning and Inference**: Evaluating and improving logical reasoning, step-by-step problem-solving, and structured inference in language models. |
|
- **Chain-of-Thought Learning**: Investigating the effectiveness of CoT techniques in enhancing multi-step reasoning. |
|
- **Experimental Fine-Tuning**: Studying the impact of fine-tuning on specialized datasets for improving model performance in specific domains. |
|
|
|
--- |
|
|
|
## Limitations |
|
|
|
- **Not Production-Ready**: This model is experimental and may exhibit unpredictable behavior. It should not be used in production systems. |
|
- **Uncensored Outputs**: As an uncensored model, Z1 may generate content that is inappropriate or unsafe without additional safeguards. |
|
- **Work in Progress**: The model is still under development, and its performance may vary across tasks and datasets. |
|
|
|
--- |
|
|
|
## Datasets Used for Fine-Tuning |
|
|
|
1. **Reasoning_am**: Focused on advanced reasoning tasks. |
|
2. **gsm8k_step_by_step**: A dataset emphasizing step-by-step problem-solving in mathematical reasoning. |
|
3. **Deepthinking-COT**: Designed to enhance Chain-of-Thought reasoning capabilities. |
|
4. **Qwqloncotam**: A specialized dataset for improving structured inference and multi-step reasoning. |
|
|
|
--- |
|
|
|
## Ethical Considerations |
|
|
|
- **Responsible Use**: This model is intended for research purposes only. Users should ensure that its outputs are carefully monitored and evaluated. |
|
- **Bias and Fairness**: As with all language models, Z1 may inherit biases from its training data. Researchers should assess and mitigate potential biases in their applications. |
|
- **Safety**: Due to its uncensored nature, additional safeguards may be required to prevent misuse or harmful outputs. |
|
|
|
--- |
|
|
|
## Future Work |
|
|
|
- **Performance Evaluation**: Further testing and benchmarking on reasoning tasks to assess improvements over baseline models. |
|
- **Dataset Expansion**: Incorporating additional datasets to enhance reasoning and inference capabilities. |
|
- **Safety and Alignment**: Exploring methods to align the model with ethical guidelines and safety standards for broader use. |
