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---
license: mit
tags:
- svector
- theta-35-mini
- theta
---
# Theta-35-mini
**A lightweight, high-efficiency reasoning model distilled from Theta-35.**
**Theta-35-Mini** is a compact 3B parameter language model developed by **SVECTOR**, built on the Qwen architecture and trained using **Group Relative Policy Optimization (GRPO)**. It is the smaller sibling of our flagship **Theta-35** model (33B parameters), offering efficient performance for resource-constrained environments.
---
## 🔍 Overview
- **Architecture**: Based on Qwen2-style transformer blocks
- **Training Objective**: Autoregressive next-token prediction
- **Technique**: Trained using **Group Relative Policy Optimization (GRPO)** – a reinforcement learning optimization strategy enabling fine-grained control and alignment
- **Size**: 3 billion parameters
- **Parent Model**: [Theta-35 (33B)](https://huggingface.co/SVECTOR-CORPORATION/Theta-35)
## 🚀 Model Highlights
- ✅ **Compact and Capable**: Achieves strong performance despite its small size
- ⚙️ **GRPO-trained**: Trained with Group Relative Policy Optimization for better alignment, coherence, and efficiency
- 💡 **Low-latency Inference**: Ideal for edge and on-device applications
## 📦 How to Use
Install dependencies:
```bash
pip install transformers
```
Run model in Python:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Force use of the slow tokenizer to avoid tokenizer.json issues
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini")
inputs = tokenizer("Once upon a time", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
## 📄 License
This model is released under the **MIT License**.
---
## 🏢 About SVECTOR
🔗 Visit us at [svector.co.in](https://www.svector.co.in)
---
## 🙌 Acknowledgements
- DeepSeek GRPO Paper
- Qwen2 Architecture
--- |