DJLougen/Nemotron-Research-GooseReason-4B-Instruct-MLX-16bit

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Mar 5, 2026License:cc-by-nc-4.0Architecture:Transformer Open Weights Warm

DJLougen/Nemotron-Research-GooseReason-4B-Instruct-MLX-16bit is an MLX-optimized, 16-bit full-precision version of NVIDIA's 4.4 billion parameter Nemotron-Research-GooseReason-4B-Instruct model, built on the Qwen3-4B-Instruct-2507 architecture. This model, trained with Reinforcement Learning with Verifiable Rewards (RLVR), excels in mathematical, coding, and STEM reasoning tasks. It features a maximum sequence length of 32,768 tokens and is specifically designed for complex reasoning, utilizing an extended thinking mode.

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Overview

This model, DJLougen/Nemotron-Research-GooseReason-4B-Instruct-MLX-16bit, is an MLX-optimized, 16-bit full-precision conversion of NVIDIA's Nemotron-Research-GooseReason-4B-Instruct. Built upon the Qwen3-4B-Instruct-2507 base model, it features 4.4 billion parameters and a substantial maximum sequence length of 32,768 tokens. The original model was developed by NVIDIA using Reinforcement Learning with Verifiable Rewards (RLVR) to enhance its reasoning capabilities.

Key Capabilities

  • Advanced Reasoning: Specifically optimized for complex reasoning across various domains.
  • Math Performance: Demonstrates strong results on benchmarks like AIME 2025 (55.0 avg@64) and AMC (82.2 avg@64).
  • Code Generation: Achieves competitive performance on coding benchmarks such as LiveCodeBench v6 (30.1 pass@1) and HumanEval.
  • STEM Reasoning: Possesses broad scientific and technical reasoning abilities.
  • Thinking Mode: Utilizes an extended thinking mode with <think> tags for tackling intricate problems, which can be explicitly prompted.

Good For

  • Mathematical Problem Solving: Ideal for applications requiring high accuracy in math and arithmetic.
  • Code Development: Suitable for generating and understanding code snippets.
  • Scientific and Technical Analysis: Effective in tasks demanding STEM-related reasoning.
  • Complex Reasoning Tasks: Benefits from its RLVR training and thinking mode for multi-step logical deductions.