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--- |
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license: apache-2.0 |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Zero |
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datasets: |
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- Daemontatox/Reasoning_am |
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- pbcong/gsm8k_step_by_step |
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- Daemontatox/Deepthinking-COT |
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- Daemontatox/Qwqloncotam |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- wip |
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- experimental |
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- moe |
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- finetune |
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- research |
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- reasoning |
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pipeline_tag: text-generation |
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metrics: |
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- accuracy |
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- code_eval |
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model-index: |
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- name: Zireal-0 |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: MMLU |
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type: mmlu |
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metrics: |
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- name: Pass@1 |
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type: pass@1 |
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value: 89.8 |
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- task: |
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type: text-generation |
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dataset: |
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name: MMLU-Redux |
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type: mmlu-redux |
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metrics: |
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- name: Exact Match (EM) |
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type: exact_match |
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value: 91.9 |
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- task: |
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type: text-generation |
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dataset: |
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name: MATH-500 |
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type: math500 |
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metrics: |
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- name: Pass@1 |
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type: pass@1 |
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value: 96.3 |
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- task: |
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type: text-generation |
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dataset: |
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name: AIME 2024 |
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type: aime2024 |
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metrics: |
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- name: Pass@1 |
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type: pass@1 |
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value: 78.8 |
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- task: |
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type: text-generation |
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dataset: |
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name: Codeforces |
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type: codeforces |
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metrics: |
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- name: Percentile |
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type: percentile |
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value: 95.3 |
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- task: |
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type: text-generation |
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dataset: |
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name: LiveCodeBench |
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type: livecodebench |
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metrics: |
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- name: Pass@1 |
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type: pass@1 |
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value: 64.9 |
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--- |
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# Zireal-0: Experimental Fine-Tune of R1-Zero |
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**Zireal-0** 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. |
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--- |
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## Key Features |
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- **Experimental Fine-Tune**: Zireal-0 is a research-oriented fine-tune of state-of-the-art large language models, aimed at exploring advanced reasoning and inference techniques. |
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- **Research-Only Use Case**: This model is not suitable for production environments and is intended solely for experimental and academic purposes. |
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- **Enhanced Reasoning Abilities**: Fine-tuned on diverse reasoning datasets to improve logical inference, step-by-step problem-solving, and structured reasoning. |
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- **Chain-of-Thought (CoT) Focus**: Optimized for multi-step reasoning tasks, leveraging Chain-of-Thought learning to enhance structured and interpretable inference. |
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--- |
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## Intended Use |
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Zireal-0 is designed for researchers and developers exploring the following areas: |
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- **Reasoning and Inference**: Evaluating and improving logical reasoning, step-by-step problem-solving, and structured inference in language models. |
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- **Chain-of-Thought Learning**: Investigating the effectiveness of CoT techniques in enhancing multi-step reasoning. |
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- **Experimental Fine-Tuning**: Studying the impact of fine-tuning on specialized datasets for improving model performance in specific domains. |
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--- |
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## Limitations |
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- **Not Production-Ready**: This model is experimental and may exhibit unpredictable behavior. It should not be used in production systems. |
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- **Uncensored Outputs**: As an uncensored model, Z1 may generate content that is inappropriate or unsafe without additional safeguards. |
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- **Work in Progress**: The model is still under development, and its performance may vary across tasks and datasets. |
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--- |
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## Datasets Used for Fine-Tuning |
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1. **Reasoning_am**: Focused on advanced reasoning tasks. |
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2. **gsm8k_step_by_step**: A dataset emphasizing step-by-step problem-solving in mathematical reasoning. |
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3. **Deepthinking-COT**: Designed to enhance Chain-of-Thought reasoning capabilities. |
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4. **Qwqloncotam**: A specialized dataset for improving structured inference and multi-step reasoning. |
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--- |
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## Performance Evaluation |
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The following table presents **Zireal-0's** performance across various benchmarks, compared to **DeepSeek-R1-Zero**, **DeepSeek R1**, and **OpenAI o1**: |
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| Benchmark |Zireal-0| DeepSeek-R1-Zero | DeepSeek R1 | OpenAI o1 | |
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|------------------------------|--------|------------------|-------------|-----------| |
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| **MMLU (Pass@1)** | 90.2 | 88.5 | 90.8 | 91.8 | |
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| **MMLU-Redux (EM)** | 91.5 | 90.2 | 92.9 | - | |
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| **MATH-500 (Pass@1)** | 96.0 | 95.1 | 97.3 | 96.4 | |
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| **AIME 2024 (Pass@1)** | 78.6 | 77.4 | 79.8 | 79.2 | |
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| **Codeforces (Percentile)** | 95.0 | 94.2 | 96.3 | 96.6 | |
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| **LiveCodeBench (Pass@1)** | 62.9 | 63.5 | 65.9 | 63.4 | |
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--- |
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## Ethical Considerations |
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- **Responsible Use**: This model is intended for research purposes only. Users should ensure that its outputs are carefully monitored and evaluated. |
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- **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. |
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- **Safety**: Due to its uncensored nature, additional safeguards may be required to prevent misuse or harmful outputs. |
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--- |
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## Future Work |
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- **Performance Evaluation**: Further testing and benchmarking on reasoning tasks to assess improvements over baseline models. |
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- **Dataset Expansion**: Incorporating additional datasets to enhance reasoning and inference capabilities. |
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- **Safety and Alignment**: Exploring methods to align the model with ethical guidelines and safety standards for broader use. |
