From 48ffa9a2543dc70f7eb837a837ccefebd36c3194 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 10 Oct 2026 16:56:18 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF Source: Original Platform --- .gitattributes | 44 ++++++ Parable-Qwen3-4B-Claude-Fable-5-GGUF-F16.gguf | 3 + ...e-Qwen3-4B-Claude-Fable-5-GGUF-Q4_K_M.gguf | 3 + ...e-Qwen3-4B-Claude-Fable-5-GGUF-Q5_K_M.gguf | 3 + ...ble-Qwen3-4B-Claude-Fable-5-GGUF-Q6_K.gguf | 3 + ...ble-Qwen3-4B-Claude-Fable-5-GGUF-Q8_0.gguf | 3 + README.md | 143 ++++++++++++++++++ banner.svg | 96 ++++++++++++ 8 files changed, 298 insertions(+) create mode 100644 .gitattributes create mode 100644 Parable-Qwen3-4B-Claude-Fable-5-GGUF-F16.gguf create mode 100644 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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 `` 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: + +

+ Buy Me a Coffee +

+ +## 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. diff --git a/banner.svg b/banner.svg new file mode 100644 index 0000000..c9ef922 --- /dev/null +++ b/banner.svg @@ -0,0 +1,96 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +