Spec-T1-RL-7B

A high-precision mathematical and algorithmic reasoning model

📋 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

Math Benchmarks

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

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

# 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

# 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:

@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:

SVECTOR-CORPORATION
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Spec-T1-RL-7B