ApolloRaines/Mistral-7B-Instruct-v0.3-Jbliterated

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:4kTool Calling:SupportedPublished:Jul 14, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

ApolloRaines/Mistral-7B-Instruct-v0.3-Jbliterated is a 7 billion parameter instruction-tuned causal language model, a Jbliterated version of mistralai/Mistral-7B-Instruct-v0.3. This model features an improved multi-phase processing pipeline for cleaner output and more precise geometric decomposition of the refusal subspace. It is designed to provide coherent and instruction-following responses across various scenarios, treating all framings of a topic equally without fake compliance.

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Overview

ApolloRaines/Mistral-7B-Instruct-v0.3-Jbliterated is a 7 billion parameter instruction-tuned model, building upon the mistralai/Mistral-7B-Instruct-v0.3 base. This version incorporates a unique "Jbliteration" process, which modifies all transformer layers to enhance its performance and behavior.

Key Capabilities & Enhancements

  • Improved Output Quality: Features an enhanced multi-phase processing pipeline designed to produce cleaner and more refined outputs.
  • Refusal Subspace Decomposition: Utilizes a more precise geometric decomposition of the refusal subspace, contributing to its distinct response characteristics.
  • Consistent Compliance: Engineered to avoid "fake compliance," ensuring the model treats all framings of a given topic equally and maintains coherent, instruction-following behavior across diverse scenarios.
  • Base Model: Derived from mistralai/Mistral-7B-Instruct-v0.3, maintaining its core architecture and capabilities.

When to Use This Model

This model is particularly suitable for applications requiring a 7B instruction-following model that prioritizes consistent, unbiased responses and clean output generation. Its specific modifications aim to address common issues related to model compliance and coherence, making it a strong candidate for tasks where nuanced instruction adherence is critical.