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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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tags: |
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- advanced reasoning |
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- logical AI |
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library_name: transformers |
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--- |
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# Theta-35B: Advanced Logical Reasoning AI Model |
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## Introduction |
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**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. |
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## Key Features |
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1. **Advanced Reasoning Capabilities** |
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- State-of-the-art logical inference |
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- Deep analytical problem-solving |
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- Nuanced contextual understanding |
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2. **Architectural Highlights** |
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- 35 Billion Parameter Model |
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- Transformer-based architecture |
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- Advanced attention mechanisms |
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- Optimized for complex reasoning tasks |
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3. **Technical Specifications** |
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- Model Type: Causal Language Model |
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- Parameters: 35 Billion |
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- Context Length: 32,768 tokens |
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- Architecture: Advanced Transformer with: |
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* RoPE (Rotary Position Embedding) |
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* SwiGLU Activation |
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* RMSNorm Normalization |
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* Enhanced Attention Mechanisms |
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## Performance Capabilities |
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- Exceptional performance in: |
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* Mathematical reasoning |
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* Complex problem-solving |
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* Analytical task decomposition |
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* Multi-step logical inference |
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## Quickstart Guide |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "SVECTOR-CORPORATION/Theta-35B-Preview" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Example reasoning prompt |
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messages = [ |
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{"role": "system", "content": "You are an advanced logical reasoning assistant developed by SVector."}, |
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{"role": "user", "content": "Break down the logical steps to solve a complex problem."} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512, |
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do_sample=True, |
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temperature=0.7 |
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) |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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## Ethical AI Commitment |
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SVECTOR is committed to developing responsible AI that: |
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- Prioritize ethical considerations |
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- Ensure robust safety mechanisms |
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- Promote transparent and accountable AI development |
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## Citation |
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If you use Theta-35B in your research, please cite: |
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```bibtex |
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@misc{theta-35b, |
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title = {Theta-35B: Advanced Logical Reasoning AI Model}, |
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author = {SVECTOR CORPORATION}, |
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year = {2025}, |
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publisher = {SVECTOR} |
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} |
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``` |
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## Contact and Support |
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- Website: [www.svector.co.in](SVECTOR) |
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- Email: [email protected] |
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- Research Inquiries: [email protected] |
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## Limitations and Considerations |
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While Theta-35B represents a significant advancement in AI reasoning, users should be aware of: |
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- Potential context-specific reasoning variations |
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- Need for careful prompt engineering |
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- Ongoing model refinement and updates |
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