metadata

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

Advanced Reasoning Capabilities
- State-of-the-art logical inference
- Deep analytical problem-solving
- Nuanced contextual understanding
Architectural Highlights
- 35 Billion Parameter Model
- Transformer-based architecture
- Advanced attention mechanisms
- Optimized for complex reasoning tasks
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

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:

@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
Email: [email protected]
Research Inquiries: [email protected]

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