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.

ollama run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

v2.1 (2026-08-03)

Recalibrated merge. Same training, better weight blending: +1.8 points on HumanEval-164 over the previous build (74.4 vs 72.6), reproduced across three independent adapters. If you pulled this model before August 2026, re-pull for the stronger build.

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 Qwen3-4B	This model (v2.1)
Prompts answered (34-prompt suite)	27/34	34/34
HumanEval-164	79.3	74.4
Held-out agent-trace loss	2.846	1.876
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 (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

Provenance & licensing

Fine-tuned from Qwen/Qwen3-4B (Apache-2.0). Training data: Glint-Research/Fable-5-traces (AGPL-3.0) and 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.

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.

lunaarclabs/Parable-Qwen3-4B-Claude-Fable-5-GGUF

Parable-Qwen3-4B-Claude-Fable-5-GGUF