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---
base_model: unsloth/deepseek-r1-distill-qwen-32b-bn
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
datasets:
- Daemontatox/math_conv
library_name: transformers
---
# Zirel-R1: Optimized for Fast and Essential Reasoning
## Model Overview
**Zirel-R1** is an advanced **reasoning-optimized** model designed for **short, fast, and necessary reasoning**, avoiding long and unnecessary computation. It surpasses **Cogito-R1** and **PathfinderAI S1** in efficiency, making it ideal for applications requiring structured logical inference and quick decision-making.
- **Developed by:** Daemontatox
- **Model Series:** Zirel
- **Base Model:** `unsloth/deepseek-r1-distill-qwen-32b`
- **License:** Apache-2.0
- **Languages:** English
- **Finetuned on:** `Daemontatox/math_conv`
- **Library:** Transformers
## Key Features
✅ **Fast and Concise Reasoning** – Delivers precise answers with minimal computational overhead.
✅ **Optimized for Short-Form Problem Solving** – Excels in extracting core insights efficiently.
✅ **Enhanced Logical Inference** – Ideal for applications in structured decision-making, math reasoning, and controlled AI workflows.
## Usage
You can load the model using `transformers`:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Daemontatox/Zirel-R1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
prompt = "What is the next number in the sequence: 2, 4, 8, 16?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Performance
Speed: 🚀 Optimized for rapid inference and low-latency responses.
Accuracy: 🎯 Fine-tuned on high-quality mathematical and reasoning datasets.
Efficiency: ⚡ Processes only the necessary information for an answer.
Citation
If you use Zirel-R1, please cite:
@misc{daemontatox2025zirel,
author = {Daemontatox},
title = {Zirel-R1: Optimized for Fast and Essential Reasoning},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co./Daemontatox/Zirel-R1}
}
License
This model is released under the Apache-2.0 License.
---
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