Create README.md

README.md

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+---
+language:
+- en
+license: mit
+pipeline_tag: text-generation
+tags:
+- svector
+- reasoning
+---
+
+# Spec-T1-RL-7B
+
+ A high-precision mathematical and algorithmic reasoning model
+
+ [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Spec--T1--RL--7B-yellow)](https://huggingface.co/SVECTOR-CORPORATION/Spec-T1-RL-7B)
+ 
+
+## 📋 Model Card
+
+| Model Details | Description |
+|-----------------|----------------|
+| Developer | SVECTOR |
+| Model Size | 7 billion parameters |
+| Context Length | 32,000 tokens |
+| Training Data | Reasoning-focused datasets with mathematical, logical, and code content |
+| Precision | `bfloat16`, `float16` |
+| License | MIT |
+| Release Date | May 2025 |
+
+## 🔍 Model Overview
+
+`Spec-T1-RL-7B` is a specialized large language model engineered for exceptional performance in mathematical reasoning, algorithmic problem-solving, and real-world code generation. Unlike general-purpose models, Spec-T1 has been architecturally designed and trained specifically to excel in domains requiring precise, logical thinking.
+The model represents a significant advancement in specialized reasoning capabilities at the 7B parameter scale, outperforming much larger models on technical benchmarks while maintaining efficient deployment requirements.
+
+## ✨ Key Capabilities
+
+- Mathematical Reasoning: Solves complex math problems with step-by-step logical deduction
+- Algorithmic Problem-Solving: Designs and analyzes algorithms across multiple domains
+- Code Generation: Produces functional, high-quality code with strong test pass rates
+- Precise Instruction Following: Responds accurately to structured technical prompts
+- Symbolic Verification: Uses built-in verification mechanisms for mathematics and logic
+
+## 🏗️ Model Architecture
+
+Spec-T1-RL-7B combines several architectural innovations to achieve its specialized reasoning capabilities:
+
+- Foundation: Advanced transformer architecture with optimized attention mechanisms
+- Mixture-of-Experts (MoE): Lightweight conditional computation for efficient scaling
+- Activations: SwiGLU activations for improved gradient flow in mathematical operations
+- Normalization: RMSNorm for faster convergence and stability in reasoning tasks
+
+## 🛠️ Training Methodology
+
+Our model underwent a three-phase training process designed to optimize reasoning capabilities:
+
+### 1️⃣ Reasoning-Aware Pretraining
+- Specialized corpus with heavy emphasis on mathematical notation, logical syntax, and code
+- Curriculum learning approach prioritizing structured reasoning patterns
+- Custom tokenizer optimized for mathematical and programming syntax
+
+### 2️⃣ Instruction Fine-Tuning
+- 400K+ multi-domain, structured prompts focused on reasoning tasks
+- Combined CodeInstruct methodology with ThoughtChain prompting
+- Synthetic data generation with verification feedback loops
+
+### 3️⃣ Reinforcement Learning Alignment
+- Reward modeling using deterministic pass/fail signals for math and code correctness
+- Unit test integration for real-time verification of generated solutions
+- Symbolic verification of mathematical proofs and derivations
+
+## 📊 Benchmark Performance
+
+The Spec-T1-RL-7B model demonstrates exceptional performance across reasoning benchmarks, particularly in mathematics and code generation tasks:
+
+### General Reasoning
+
+| Benchmark | GPT-4o-0513 | Claude-3.5-Sonnet | OpenAI o1-mini | QwQ-32B | Spec-T1 |
+|-----------|:-----------:|:-----------------:|:--------------:|:-------:|:-----------:|
+| GPQA Diamond (Pass@1) | 49.9 | 65.0 | 60.0 | 54.5 | 65.1 |
+| SuperGPQA (Pass@1) | 42.4 | 48.2 | 45.2 | 43.6 |52.8 |
+| DROP (3-shot F1) | 83.7 | 88.3 | 83.9 | 71.2 | 86.2 |
+| MMLU-Pro (EM) | 72.6 | 78.0 | 80.3 | 52.0 | 76.4 |
+| IF-Eval (Prompt Strict) | 84.3 | 86.5 | 84.8 | 40.4 | 83.3 |
+
+### Mathematics
+
+| Benchmark | GPT-4o-0513 | Claude-3.5-Sonnet | OpenAI o1-mini | QwQ-32B |  Spec-T1 |
+|-----------|:-----------:|:-----------------:|:--------------:|:-------:|:-----------:|
+| MATH-500 (Pass@1) | 74.6 | 78.3 | 90.0 | 90.6 |  96.1 |
+| AIME 2024 (Pass@1) | 9.3 | 16.0 | 63.6 | 50.0 | 74.5 |
+| AIME 2025 (Pass@1) | 11.6 | 7.4 | 50.7 | 32.4 |68.3 |
+
+### Code Generation
+
+| Benchmark | GPT-4o-0513 | Claude-3.5-Sonnet | OpenAI o1-mini | QwQ-32B | Spec-T1 |
+|-----------|:-----------:|:-----------------:|:--------------:|:-------:|:-----------:|
+| LiveCodeBench v5 (Pass@1) | 32.9 | 38.9 | 53.8 | 41.9 |  60.2 |
+| LiveCodeBench v6 (Pass@1) | 30.9 | 37.2 | 46.8 | 39.1 | 54.4 |
+
+## 💻 Usage Examples
+
+### Basic Usage with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-RL-7B")
+tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-RL-7B")
+
+# Mathematical reasoning example
+prompt = """
+Prove: The sum of the first n odd numbers is n^2.
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Advanced Usage with Generation Parameters
+
+```python
+# Algorithm design example
+prompt = """
+Design an efficient algorithm to find the longest increasing subsequence in an array of integers.
+"""
+
+# Configure generation parameters for better reasoning
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    temperature=0.1,
+    top_p=0.95,
+    do_sample=True,
+    num_return_sequences=1,
+    repetition_penalty=1.1
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Code Generation Example
+
+```python
+# Code generation example
+prompt = """
+Write a Python function that implements the A* search algorithm for pathfinding.
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(
+    inputs,
+    max_new_tokens=2048,
+    temperature=0.2,
+    top_p=0.9,
+    do_sample=True
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## 🚀 Deployment
+
+Spec-T1-RL-7B can be deployed on consumer hardware due to its efficient architecture and parameter count:
+
+### Minimum Requirements
+- 16GB VRAM (bfloat16/float16)
+- 32GB system RAM
+- CUDA-compatible GPU
+
+### Recommended Configuration
+- 24GB+ VRAM for optimal performance
+- 64GB+ system RAM for long-context applications
+- NVIDIA A10 or better
+
+## 📝 Citation
+
+If you use Spec-T1-RL-7B in your research, please cite:
+
+```bibtex
+@misc{svector2025spect1,
+  title={Spec-T1-RL-7B: Structured Reasoning through Reinforcement Alignment},
+  author={SVECTOR Team},
+  year={2025},
+}
+```
+
+## 📄 License
+
+Spec-T1-RL-7B is released under the MIT License.
+
+## 📬 Contact
+
+For questions, feedback, or collaboration inquiries, please contact:
+- Email: [email protected]
+- Twitter: [@SVECTOR_](https://x.com/SVECTOR_)
+- GitHub: [SVECTOR-CORPORATION](https://github.com/SVECTOR-CORPORATION)