c15v-q3km-imat.gguf is the backbone GGUF for the Bragi-LLM system, a 786 MB Q3_K_M quantised Qwen2.5-Coder-1.5B-Instruct with imatrix calibration on MBPP-train Python code. It is the small LLM half of a hybrid design: combined with a 15 KB hand-engineered symbolic engine library and a 6 KB keyword intercept router, the full system reaches 92% pass@1 on the MBPP test split, single-shot greedy decoding, no retry. See the paper for the failure-mode analysis and ablations: doi:10.5281/zenodo.20557449.
OpenAI-compatible proxy wiring Bragi as Code Tree's default local backend.
Together: about 1 GB on disk. Fully offline. Zero recurring fees. MIT.
Results
System
Footprint
MBPP test 0-99 single-shot
Vanilla 1.5B Q3_K_M (this backbone alone)
786 MB
65%
With intercept router + engine_lib (full Bragi system)
805 MB
92%
Reference Qwen2.5-Coder-7B fp16
14 GB
94%
The GGUF alone (this file) is the 65% column. The 92% number requires the router and engine library which are open-sourced in the Bragi-LLM GitHub repo.
Or use the OpenAI-compatible proxy from Demeter-CodeBuilder, which wraps everything and exposes Bragi as a drop-in OpenAI endpoint.
How it works
The 1.5B Q3 backbone alone fails MBPP problems primarily by mis-recalling rare formulas: octagonal number written as 3n^2 - 2n instead of n(3n - 2), divisibility-by-11 implemented as digit-sum-mod-11 (correct rule is alternating digit sum). These formulas are 50-byte Python expressions; storing them in 3-bit quantised weights is wasteful and error-prone.
The full Bragi system externalises rare formulas into a 15 KB symbolic library (engine_lib.py) and routes matched problems entirely around the LLM via a regex keyword router. The backbone runs only when no route matches.
Quantisation details
Base model: Qwen/Qwen2.5-Coder-1.5B-Instruct
Quantisation: Q3_K_M with imatrix calibration
Imatrix corpus: MBPP-train Python code split (374 verified solutions, 67 KB; no test data)
1@misc{chen2026bragillm,
2 author = {Chen, Ho Yiing},
3 title = {Bragi-LLM: An 805 MB Hybrid Code-Generation System Reaches 92\% MBPP via LLM-Symbolic Engine Routing},
4 year = {2026},
5 doi = {10.5281/zenodo.20557449},
6 url = {https://doi.org/10.5281/zenodo.20557449},
7 note = {Independent Researcher, Taiwan. ORCID 0009-0006-6816-9891.}
8}
Author
Chen, Ho Yiing (norika), Independent Researcher, Taiwan.
ORCID: 0009-0006-6816-9891
Correspondence: norika at charenix.com
License
MIT. See the Bragi-LLM repo for the full LICENSE file.
Acknowledgements
Developed using donated off-hours access to NVIDIA DGX Spark hardware. Implementation and draft writing assisted by Claude (Anthropic). The architectural direction (refuse to ship sub-target results, diagnose before optimising, externalise rather than memorise) is the author's.
