Zainab-Jamshaid/Gemini-3.1-pro-Gemma-4-E4B-Distill

VISIONConcurrent Unit Cost:1Model Size:7.9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 13, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Zainab-Jamshaid/Gemini-3.1-pro-Gemma-4-E4B-Distill is a 7.9 billion parameter model fine-tuned for expert-level reasoning tasks, leveraging the advanced capabilities of Gemini 3.1 Pro. It excels in multi-step reasoning, logical coherence, and synthesizing conflicting information, making it ideal for complex analytical applications. The model was trained using a synthetic, high-complexity reasoning corpus to push the limits of modern reasoning models. Its 32768 token context length supports extensive problem-solving and derivation.

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Overview: Zainab-Jamshaid/Gemini-3.1-pro-Gemma-4-E4B-Distill

This model, developed by Zainab-Jamshaid, is a 7.9 billion parameter fine-tuned variant designed specifically for expert-level reasoning tasks. It leverages the advanced capabilities of Gemini 3.1 Pro to enhance analytical depth, logical coherence, and the ability to synthesize conflicting information.

Key Capabilities

  • Advanced Reasoning: Excels in multi-step reasoning, complex derivations, and problem synthesis.
  • High-Complexity Problem Solving: Trained on a synthetic, high-complexity reasoning corpus, including problems across logic, mathematics, and domain-specific reasoning.
  • Enhanced Analytical Depth: Designed to push the limits of modern reasoning models in challenging domains.

Training Details

  • Finetune Method: Supervised Fine-Tuning (SFT).
  • Data Type: Synthetic, high-complexity reasoning corpus generated using Gemini 3.1 Flash as the prompting agent and Gemini 3.1 Pro as the solving agent.
  • Purpose: To address extreme difficulty scenarios requiring advanced analytical and reasoning capabilities.

Intended Use Cases

  • Advanced Reasoning Applications: Ideal for scenarios demanding deep analytical and problem-solving skills.
  • AI Cognition Research: Suitable for research into multi-step logical reasoning.

Limitations

  • Performance in real-world scenarios may vary as the model was trained on synthetic data.
  • Its specialized focus on reasoning might lead to reduced performance in general NLP tasks or casual conversation.

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