AnkitAI/Parable-Qwen3-4B-Claude-Fable-5

TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 9, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

AnkitAI/Parable-Qwen3-4B-Claude-Fable-5 is a 4 billion parameter coding model based on Qwen3-4B, specifically fine-tuned for local agent-like reasoning and terminal workflows. It distills planning and tool-use habits from genuine Claude Fable 5 agent sessions, making it adept at structured, reliable answers for coding tasks. This model is optimized for local deployment, with a GGUF build running on approximately 2.5 GB, suitable for modest hardware.

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AnkitAI/Parable-Qwen3-4B-Claude-Fable-5: Agent-like Coding Model

This model is a 4 billion parameter coding-focused language model, fine-tuned from Qwen/Qwen3-4B. Its core differentiator is the distillation of agent instincts, planning, and tool-use habits from real Claude Fable 5 agent sessions and gpt5.5-terminal transcripts, rather than synthetic Q&A. This approach results in structured and reliable outputs for multi-step agent and coding tasks.

Key Capabilities & Features

  • Reliable Answering: Unlike its base model, Parable-Qwen3-4B-Claude-Fable-5 consistently provides answers, addressing 34/34 prompts in a test suite where the base model failed on 27/34.
  • Agent-Shaped Reasoning: It excels at structured planning, tool selection, and terminal workflows due to its training on genuine multi-step agent sessions.
  • Local Deployment Friendly: With 4B parameters and a GGUF build of only 2.5 GB, it's designed to run efficiently on laptops, older GPUs, or modest desktops, ensuring code stays on your machine.
  • Improved Performance (v2.1): A recalibrated merge in version 2.1 boosted HumanEval-164 scores by 1.8 points (74.4 vs 72.6) over the previous build.

When to Use This Model

Choose AnkitAI/Parable-Qwen3-4B-Claude-Fable-5 for local agent and coding work requiring structured, consistent, and reliable answers. It is particularly well-suited for scenarios where agent-like planning and execution are beneficial, and silent failures are unacceptable. For maximum-accuracy function calling in a tool-calling harness where reasoning style is less critical, the base Qwen3-4B might offer a slight edge in specific benchmarks.

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