---
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: transformers
tags:
  - safetensors
  - qlora
  - agentic
  - coding
  - reasoning
  - qwen3
  - local-llm
  - ollama
  - lm-studio
---

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

![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. Full-precision weights; the GGUF build runs on ~2.5 GB.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
```

Prefer to run it locally in Ollama or LM Studio? Take the
[GGUF build](https://huggingface.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF)
(2.5 GB at 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.

## 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.** 4B parameters, and the GGUF build 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](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.