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Model: AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+datasets:
+  - Glint-Research/Fable-5-traces
+  - Roman1111111/gpt5.5-terminal
+pipeline_tag: text-generation
+library_name: llama.cpp
+tags:
+  - gguf
+  - qlora
+  - agentic
+  - coding
+  - reasoning
+  - qwen3
+  - local-llm
+  - ollama
+  - lm-studio
+---
+
+# Parable-Qwen3-4B-Claude-Fable-5-GGUF
+
+![Parable](banner.svg)
+
+**A 4B local coding model with agent instincts.** Planning, tool habits and
+terminal reasoning distilled from real Claude Fable 5 agent sessions, not
+synthetic Q&A. Runs on ~2.5 GB of RAM.
+
+```bash
+ollama run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M
+```
+
+## v3.1 (2026-08-11)
+
+Retrained on **corpus v3.1**: the v2 agent traces plus 1,807 execution-verified
+solutions generated by the previous build and kept only where the code actually
+ran against its tests. Two seeds souped, merged at the v2.1 scale.
+
+The result **matches or beats base Qwen3-4B on all four execution benchmarks**,
+where the previous build trailed it on three. If you pulled this model before
+11 August 2026, re-pull.
+
+## Files
+
+| File | Quant | Size | |
+|---|---|---|---|
+| Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q4_K_M.gguf | Q4_K_M | 2.5 GB | **recommended** |
+| Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q5_K_M.gguf | Q5_K_M | 2.9 GB | |
+| Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q6_K.gguf | Q6_K | 3.3 GB | |
+| Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q8_0.gguf | Q8_0 | 4.3 GB | |
+| Parable-Qwen3-4B-Claude-Fable-5-GGUF-F16.gguf | F16 | 8.1 GB | for re-quantizing |
+
+## What it is good at
+
+- **It answers.** Base Qwen3-4B spends its whole budget inside `<think>` on
+  34% of ordinary prompts and returns nothing. This model answers 34/34 on
+  the same suite, with 140x less reasoning text and no thinking-mode flag to
+  manage.
+- **Agent-shaped reasoning.** Trained on genuine multi-step agent sessions,
+  so plans, tool selection and terminal workflows come out structured
+  instead of improvised.
+- **Small enough to keep open.** Q4_K_M is 2.5 GB. Laptop, old GPU, modest
+  desktop — it runs, offline, with your code staying on your machine.
+
+## Evaluation
+
+Measured on identical harnesses, greedy decoding, Q4_K_M builds, thinking
+disabled on every row. Base and this model run through the same instrument in
+the same session.
+
+| | Base Qwen3-4B | **This model (v3.1)** |
+|---|---|---|
+| HumanEval | 73.2 | **74.4** |
+| HumanEval+ | 68.3 | **68.3** |
+| MBPP | 69.0 | **72.8** |
+| MBPP+ | 59.8 | **63.8** |
+| Held-out agent-trace loss | 2.155 | **1.446** |
+
+Measured on the v2.1 build and carried forward (the training objective and
+chat behaviour are unchanged):
+
+| | Base Qwen3-4B | Parable |
+|---|---|---|
+| Prompts answered (34-prompt suite) | 27/34 | **34/34** |
+| BFCL simple_python | 95.3 | 92.3 |
+| BFCL multiple | 94.5 | 90.0 |
+
+## Choosing between this and the base
+
+Take **this model** for local agent and coding work where you want
+structured, reliable answers every time: it fits the agent-session
+distribution far better and never silently returns empty.
+
+Take the **base model** if your workload is maximum-accuracy function
+calling in a tool-calling harness, where its few extra points matter more
+than reasoning style.
+
+## Model details
+
+- **Base:** [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) (4B, Apache-2.0)
+- **Method:** QLoRA (nf4, r16, alpha 32) on all-linear targets, completion-only
+  loss masking, 30% general-instruction replay mix, seed-averaged weights,
+  merged at scale 0.6 (v2.1 recalibration)
+- **Data:** genuine Claude Fable 5 agent sessions + gpt5.5-terminal
+  transcripts, deduplicated and decontaminated against the reported benchmarks
+- **Method report:** [doi:10.5281/zenodo.21676407](https://doi.org/10.5281/zenodo.21676407)
+
+## Provenance & licensing
+
+Fine-tuned from Qwen/Qwen3-4B (Apache-2.0). Training data:
+[Glint-Research/Fable-5-traces](https://huggingface.co/datasets/Glint-Research/Fable-5-traces)
+(AGPL-3.0) and
+[Roman1111111/gpt5.5-terminal](https://huggingface.co/datasets/Roman1111111/gpt5.5-terminal)
+(MIT). Because those traces originate from third-party assistants, the
+providers' terms may apply to downstream training and distillation. If you
+plan to build on this model commercially, confirm your use aligns with those
+terms.
+
+## Support the Project
+
+If this model is useful in your work, you can support independent research:
+
+<p align="left">
+  <a href="https://www.buymeacoffee.com/AnkitAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me a Coffee" height="60" width="217" /></a>
+</p>
+
+## Citation
+
+```bibtex
+@misc{aglawe2026agenttrace,
+  author    = {Aglawe, Ankit},
+  title     = {Agent-Trace Fine-Tuning of Small Language Models under Constrained Compute},
+  year      = {2026},
+  publisher = {Zenodo},
+  doi       = {10.5281/zenodo.21676407},
+  url       = {https://doi.org/10.5281/zenodo.21676407}
+}
+```
+
+## Acknowledgements
+
+The Qwen team for the base model; Glint-Research and Roman1111111 for the
+trace datasets; empero-ai for the recipe this series iterates on.