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