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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
- 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-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
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