Medium Reasoning

R1 Distill Qwen 14B

DeepSeek-R1-Distill-Qwen-14B is a highly efficient distilled AI model based on Qwen 2.5 14B, delivering state-of-the-art performance in reasoning, math (93.9% on MATH-500), and code tasks with reduced computational demands. Unlock powerful chain-of-thought capabilities for complex problem-solving without the overhead of larger models.

131k Context

Medium Intelligence

Jul '24 Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

GPQA

59.1%

HumanEval

72.6%

GSM-8K (CoT)

84.5%

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Why use R1 Distill Qwen 14B?

Advanced Reasoning

Distilled from DeepSeek-R1 for sophisticated reasoning in math, code, and logic tasks with high accuracy

Mathematical Problem-Solving

Engineered for strong performance on math benchmarks like AIME and MATH-500

Code Generation

Supports efficient coding tasks and programming synthesis with substantial context length up to 131K tokens

Capability Examples

Math Reasoning Challenge

Solve this: If a train leaves at 60 mph and another at 70 mph from stations 200 miles apart, when do they meet if heading towards each other?

The trains close the 200-mile gap at 130 mph combined (60+70). Time to meet: 200 / 130 ≈ 1.538 hours, or 1 hour 32 minutes. Step-by-step: relative speed = sum of speeds; time = distance / relative speed.

Code Generation Task

Write a Python function to find the Fibonacci sequence up to n terms, optimized for efficiency.

def fibonacci(n): if n <= 0: return []; a, b = 0, 1; seq = [a]; for _ in range(n-1): seq.append(b); a, b = b, a+b; return seq. Example: fibonacci(10) = [0,1,1,2,3,5,8,13,21,34]. Uses iterative approach to avoid recursion depth issues.

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