---
license: cc-by-4.0
language:
- en
tags:
- advanced reasoning
- logical AI
library_name: transformers
---

# Theta-35B: Advanced Logical Reasoning AI Model

## Introduction

**Theta-35B** is a cutting-edge artificial intelligence model developed by SVECTOR, specifically engineered to push the boundaries of logical reasoning and analytical capabilities. This model represents a significant leap in AI technology, designed to tackle complex reasoning tasks with unprecedented precision and depth.

## Key Features

1. **Advanced Reasoning Capabilities**
   - State-of-the-art logical inference
   - Deep analytical problem-solving
   - Nuanced contextual understanding

2. **Architectural Highlights**
   - 35 Billion Parameter Model
   - Transformer-based architecture
   - Advanced attention mechanisms
   - Optimized for complex reasoning tasks

3. **Technical Specifications**
   - Model Type: Causal Language Model
   - Parameters: 35 Billion
   - Context Length: 32,768 tokens
   - Architecture: Advanced Transformer with:
     * RoPE (Rotary Position Embedding)
     * SwiGLU Activation
     * RMSNorm Normalization
     * Enhanced Attention Mechanisms

## Performance Capabilities

- Exceptional performance in:
  * Mathematical reasoning
  * Complex problem-solving
  * Analytical task decomposition
  * Multi-step logical inference

## Quickstart Guide

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "SVECTOR-CORPORATION/Theta-35B-Preview"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example reasoning prompt
messages = [
    {"role": "system", "content": "You are an advanced logical reasoning assistant developed by SVector."},
    {"role": "user", "content": "Break down the logical steps to solve a complex problem."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7
)

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

## Ethical AI Commitment

SVECTOR is committed to developing responsible AI that:
- Prioritize ethical considerations
- Ensure robust safety mechanisms
- Promote transparent and accountable AI development

## Citation

If you use Theta-35B in your research, please cite:

```bibtex
@misc{theta-35b,
    title = {Theta-35B: Advanced Logical Reasoning AI Model},
    author = {SVECTOR CORPORATION},
    year = {2025},
    publisher = {SVECTOR}
}
```

## Contact and Support

- Website: [www.svector.co.in](SVECTOR)
- Email: support@svector.co.in
- Research Inquiries: research@svector.co.in

## Limitations and Considerations

While Theta-35B represents a significant advancement in AI reasoning, users should be aware of:
- Potential context-specific reasoning variations
- Need for careful prompt engineering
- Ongoing model refinement and updates