mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1

Hugging Face
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jan 26, 2025License:mitArchitecture:Transformer0.0K Open Weights Featherless Exclusive Warm

mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 is a 1.5 billion parameter causal language model, re-distilled from DeepSeek-R1-Distill-Qwen-1.5B, featuring a 131072 token context length. This model demonstrates improved performance across various benchmarks, including ARC, HellaSwag, MMLU, and GSM8K, making it suitable for general language understanding and reasoning tasks. Its re-distillation process enhances its capabilities over its base model, offering better accuracy in question answering and mathematical reasoning.

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Model Overview

mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 is a 1.5 billion parameter language model that has been re-distilled from the original DeepSeek-R1-Distill-Qwen-1.5B. This re-distillation process aims to enhance the model's overall performance and accuracy across a range of benchmarks.

Key Performance Improvements

This version shows notable improvements compared to its predecessor, DeepSeek-R1-Distill-Qwen-1.5B, across several critical benchmarks:

  • ARC (25-shot): Improved from 40.96 to 41.55
  • HellaSwag (10-shot): Increased from 44 to 45.88
  • MMLU (5-shot): Rose from 39.27 to 41.82
  • TruthfulQA-MC2: Enhanced from 45.17 to 46.63
  • Winogrande (5-shot): Grew from 55.49 to 57.7
  • GSM8K (5-shot): Significantly improved from 69.9 to 74.3
  • Average: Overall average score increased from 49.13 to 51.31

Further improvements are also observed in more challenging benchmarks:

  • MMLU PRO (5-shot): Improved from 16.74 to 19.86
  • BBH (3-shot): Increased from 35.12 to 37.23

Use Cases

This model is particularly well-suited for applications requiring:

  • Enhanced reasoning capabilities: Demonstrated by improvements in ARC, MMLU, and BBH.
  • Accurate question answering: Benefiting from better performance in TruthfulQA and MMLU.
  • Mathematical problem-solving: Evidenced by the significant gain in GSM8K scores.

Developers can integrate this model using the provided Hugging Face Transformers library, leveraging its improved performance for various natural language processing tasks.

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