1. Model Introduction
Smaug-Mini is an agentic finetune of Qwen3.8-27B, finetuned by Abacus.AI. It improves end-to-end agentic tool use and automation performance (+4.5 on AutomationBench, +17.1 on JobBench, +2.0 overall on LiveBench, +2.5 on IFBench) while holding the base model's general capabilities at parity (GPQA-diamond at the official number, LiveBench coding and agentic-coding at board parity). The weights load exactly like the official release: same layout, same Qwen3_5ForConditionalGeneration architecture with the vision tower intact, same 262,144-token context, same reasoning interface with xhigh / medium / low effort levels.
This card describes the training approach and the evaluation results. Dataset contents are not disclosed; training data consists of multi-turn, tool-using automation episodes generated and verified against executable environments.
2. Model Summary
3. Evaluation
Smaug-Mini vs its base
Higher is better. Base points from the Qwen3.8-27B model card and livebench.ai.
LiveBench category profile
Scores 0–100; overall = mean of the seven category averages. Both models' scores are the published livebench.ai leaderboard entries (Smaug-Mini listed under the finetunes filter).
All Smaug-Mini numbers were produced at temperature 1.0, top_p 0.95, reasoning effort xhigh, with generation budgets large enough that no score is truncation-bound.
Base points: JobBench, IFBench, and GPQA-diamond are the scores published on the Qwen3.8-27B model card; the AutomationBench base score is from our own run of the base model. JobBench was run under the official protocol with the OpenCode scaffold and an LLM judge.
4. Training Approach
Smaug-Mini was trained to make multi-turn tool use and automation episodes more reliable end to end. It is trained with on-policy reinforcement learning (GRPO) over multi-turn, tool-using automation episodes with verified, outcome-based rewards - an episode is rewarded for measurably completing its task in the environment. Training is a LoRA adapter over the language trunk only, merged into the base weights as a full delta; the vision tower and multimodal projection are bitwise-identical to the base release.
One deployment note: the multi-token-prediction (MTP) head is inherited from the base model and was not retrained against the updated trunk. Speculative decoding via MTP should be left off; standard decoding is unaffected.
5. Known Behaviors and Limitations
Smaug-Mini redistributes deliberation rather than adding it: more reasoning per action but fewer actions per episode, finishing passing episodes about three steps sooner at essentially unchanged total reasoning volume. The clearest shift is in failure behavior — episodes that burn the entire step budget without finishing drop from 3.4% to 1.0%. Refusals are zero for both models, and the general-capability benchmarks in §3 show the deliberation shift does not come at the cost of base skills.
6. Deployment
Smaug-Mini serves as a drop-in replacement for Qwen3.8-27B on any stack that supports the base model. Reference vLLM invocation:
vllm serve abacusai/Smaug-Mini \
--max-model-len 262144 \
--reasoning-parser qwen3 \
--enable-auto-tool-choice --tool-call-parser qwen3_coder
Recommended sampling for agentic use: temperature 1.0, top_p 0.95, reasoning effort xhigh (the default). Recent vLLM versions return the chain-of-thought in the reasoning field of the message; when replaying conversation history, pass reasoning back under both reasoning and reasoning_content keys for portability across serving versions. Leave MTP-based speculative decoding off (§4).
7. License
Apache 2.0, inherited from Qwen3.8-27B.
8. Citation
@misc{abacusai2026smaugmini,
title = {Smaug-Mini},
author = {Abacus.AI},
year = {2026},
note = {Agentic fine-tune of Qwen/Qwen3.8-27B},
url = {https://huggingface.co/abacusai/Smaug-Mini}
}
The Smaug line and the DPO-Positive method behind it:
@article{pal2024smaug,
title={Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive},
author={Pal, Arka and Karkhanis, Deep and Dooley, Samuel and
Roberts, Manley and Naidu, Siddartha and White, Colin},
journal={arXiv preprint arXiv:2402.13228},
year={2024}
}
9. Contact Us
If you have any questions, please reach out at Abacus.AI.