---
datasets:
- Glint-Research/Fable-5-traces
- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
- osieosie/tmax-sft-skill-tax-20260505-2.2k-combined-balanced-qwen3.6-27b-thinking
- zake7749/Qwen3.6-35B-A3B-Tool-Calling
- nickrosh/Evol-Instruct-Code-80k-v1
license: apache-2.0
base_model: tvall43/Qwen3.6-35B-A3B-heretic
language: en
---
# Qwen3.6-14B-A3B-FableVibes

This model started as `Qwen3.6-35B-A3B-heretic` and was pruned via REAP down to ~14B with ~3B active parameters, removing over half its expert capacity. A single QLoRA pass was then orchestrated entirely by an autonomous AI agent (**Steve**), utilizing ~4,600 raw reasoning traces from Claude Fable 5 (Mythos-class) to recover capabilities lost during pruning. 

Rather than focusing strictly on agentic orchestration, this model serves as a general-purpose reasoning distill. The Fable CoT traces provide structured multi-step reasoning patterns from a frontier-class model, distilled into a footprint that can run on consumer hardware. The Fable traces are further supplemented by Claude Opus reasoning, Qwen tool-calling data, and Evol-Instruct-Code.

## Background & Training

- The pruned base was not pre-fine-tuned before this run—the Fable LoRA was applied directly to the REAP output.
- The model leverages Qwen's thinking format and will natively output its step-by-step logic inside `<think>...</think>` XML blocks (often mapped to the `reasoning_content` API field).
- Give it a sufficient generation budget. The reasoning pass typically uses hundreds to thousands of tokens to explore state spaces before answering.

## Usage

This repository contains the merged bfloat16 base weights. If you are looking for quantized GGUF versions to run in `llama.cpp`, Ollama, LM Studio, etc., please see the [GGUF Repository](https://huggingface.co/tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF).