license: mit
language:
- en
tags:
- advanced reasoning
- logical AI
library_name: transformers
extra_gated_prompt: >-
You agree to not use the model to conduct experiments that cause harm to human
subjects.
Theta-35-Preview: Advanced Logical Reasoning AI Model
Introduction
Theta-35-Preview is an experimental research 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. As a preview release, it demonstrates promising analytical abilities while having several important limitations:
Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.
Key Features
Advanced Reasoning Capabilities
- State-of-the-art logical inference
- Deep analytical problem-solving
- Nuanced contextual understanding
Architectural Highlights
- 33 Billion Parameter Model
- Transformer-based architecture
- Advanced attention mechanisms
- Optimized for complex reasoning tasks
Technical Specifications
- Model Type: Causal Language Model
- Parameters: 33 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-35-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-35 in your research, please cite:
@misc{theta-35,
title = {Theta-35: 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-35 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