---

license: gemma

base_model: prasadvittaldev/dogmatix

tags: [code, python, web, agent, qlora, gguf, llama-cpp]

---

dogmatix v0.2 — GGUF

GGUF quantisations of dogmatix v0.2,

a QLoRA fine-tune of google/gemma-4-E2B-it (~2.3B effective) into a local,

Python-specialised web-application coding agent. For llama.cpp / LM Studio /

Ollama.

> v0.2 supersedes v0.1 (the v0.1 GGUFs are on the v0.1 branch). See the

> full model card for honest,

> single-harness benchmarks and limitations.

Files

| file | quant | size | CPU tok/s* |

|---|---|---|---|

| dogmatix-q8_0.gguf | Q8_0 | 4.6 GB | ~19 |

| dogmatix-q5_k_m.gguf| Q5_K_M | 3.4 GB | ~26 |

| dogmatix-q4_k_m.gguf| Q4_K_M | 3.2 GB | ~30 |

*Rough llama-bench text-generation on 4 CPU threads; identified as gemma4 E2B, 4.63B.

Q4_K_M is the smallest/fastest and the usual default; Q8_0 is the most faithful

to the bf16 weights. All three were verified to load and generate.

What it is for

Small, well-specified Python web work — FastAPI, Flask, SQLAlchemy/SQLModel —

through an agent loop (read → edit → test → fix). v0.2 is a sharper web-app

specialisation than v0.1: best of the line at end-to-end app-building

(pass@1 0.18 base → 0.36 v0.2 on a 12-spec hidden-suite benchmark; monotone

across checkpoints, not yet statistically significant), at a measured cost to

Django (0.500 vs 0.567 base) and general Python (MBPP 0.700 vs 0.760 base). If

you need Django or a general coder as much as app-building, use the v0.1 branch

or the base.

Note on tool-calling

dogmatix expects the serving translation layer in the

repo for full agentic

tool-calling (turn-boundary handling, blind-call guard, client tool-name

mapping). Raw GGUF inference works for generation; agentic use through Claude

Code / Pi goes through that proxy.

Author

Prasad Vittaldev