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Medium Reasoning

R1 Distill Qwen 1.5B

DeepSeek R1 Distill Qwen 1.5B is a compact 1.5B-parameter AI model distilled from the powerful 671B DeepSeek-R1 reasoning powerhouse, delivering exceptional chain-of-thought performance in math (83.9% on MATH-500) and code tasks while fitting on a single laptop GPU. Deploy it effortlessly on AWS or edge devices for efficient, high-impact reasoning under tight resource constraints.

128k Context
Medium Intelligence
Not specified Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU
17.7%
HumanEval
22.0%
GPQA
9.1%

How do you want to interact?

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Why use R1 Distill Qwen 1.5B?

Advanced Chain-of-Thought Reasoning

Inherits complex multi-step reasoning from DeepSeek-R1 via distillation, excelling in math, logic, and code tasks

Parameter Efficiency

Compact 1.5B model outperforms larger LLMs on benchmarks like AIME2024 and MATH-500 while fitting resource-limited devices

Resource-Constrained Deployment

Optimized dense architecture with GQA enables local execution on consumer hardware and edge devices

Capability Examples

Math Reasoning Demo
Solve this step-by-step: If a train leaves at 60 mph and another at 70 mph towards each other 200 miles apart, when do they meet?
Let's think step-by-step. Relative speed is 60 + 70 = 130 mph. Time to meet = 200 / 130 ≈ 1.538 hours, or 1 hour 32 minutes.
Code Generation Demo
Write a Python function to find the Fibonacci number at index n using memoization.
def fib(n, memo={}): if n in memo: return memo[n]; if n <= 1: return n; memo[n] = fib(n-1, memo) + fib(n-2, memo); return memo[n]

How to use

1
Go to Chat

Navigate to the "AI Chat" page.

2
Select Model

Ensure R1 Distill Qwen 1.5B is selected.

3
Type Prompt

Ask a question or paste code.

4
Interact

Refine the answer by replying to the AI.

Compare LLMs Side-by-Side

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