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---
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
- reasoning
pipeline_tag: text-generation
metrics:
- accuracy
- code_eval
model-index:
- name: Zireal-0
results:
- task:
type: text-generation
dataset:
name: MMLU
type: mmlu
metrics:
- name: Pass@1
type: pass@1
value: 89.8
- task:
type: text-generation
dataset:
name: MMLU-Redux
type: mmlu-redux
metrics:
- name: Exact Match (EM)
type: exact_match
value: 91.9
- task:
type: text-generation
dataset:
name: MATH-500
type: math500
metrics:
- name: Pass@1
type: pass@1
value: 96.3
- task:
type: text-generation
dataset:
name: AIME 2024
type: aime2024
metrics:
- name: Pass@1
type: pass@1
value: 78.8
- task:
type: text-generation
dataset:
name: Codeforces
type: codeforces
metrics:
- name: Percentile
type: percentile
value: 95.3
- task:
type: text-generation
dataset:
name: LiveCodeBench
type: livecodebench
metrics:
- name: Pass@1
type: pass@1
value: 64.9
---
# Zireal-0: Experimental Fine-Tune of R1-Zero
**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.
---
## Key Features
- **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.
- **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
Zireal-0 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.
---
## Performance Evaluation
The following table presents **Zireal-0's** performance across various benchmarks, compared to **DeepSeek-R1-Zero**, **DeepSeek R1**, and **OpenAI o1**:
| Benchmark |Zireal-0| DeepSeek-R1-Zero | DeepSeek R1 | OpenAI o1 |
|------------------------------|--------|------------------|-------------|-----------|
| **MMLU (Pass@1)** | 90.2 | 88.5 | 90.8 | 91.8 |
| **MMLU-Redux (EM)** | 91.5 | 90.2 | 92.9 | - |
| **MATH-500 (Pass@1)** | 96.0 | 95.1 | 97.3 | 96.4 |
| **AIME 2024 (Pass@1)** | 78.6 | 77.4 | 79.8 | 79.2 |
| **Codeforces (Percentile)** | 95.0 | 94.2 | 96.3 | 96.6 |
| **LiveCodeBench (Pass@1)** | 62.9 | 63.5 | 65.9 | 63.4 |
---
## 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. |