---

license: gemma

base_model: google/gemma-4-E4B-it

library_name: gguf

pipeline_tag: text-generation

language:

- en

tags:

- gguf

- llama.cpp

- unsloth

- qlora

- gemma

- gemma-4

- e4b

- edge

- calendar

- scheduling

- structured-output

- multimodal

- vision

---

gemma-cal E4B — a calendar-native edge LLM (GGUF)

gemma-cal-e4b-Q4_K_M.gguf is a QLoRA fine-tune of Gemma-4 E4B (~4B effective

parameters, ~5.3 GB at Q4_K_M) built for exactly one job: reading a messy human conversation —

or a photo of a flyer/invite — and emitting a single validated ActionPlan: events with exact

ISO datetimes, calendar conflicts, proposed alternatives, a drafted reply, and a clarifying

question when the plan is too vague to schedule.

It is the production model of OffGridSchedula

serving on a 16 GB T4 via llama.cpp, fully local, no

cloud AI APIs. Vision (screenshots/flyers) works by pairing it with the base E4B's projector:

unsloth/gemma-4-E4B-it-GGUF / mmproj-F16.gguf.

Why an edge fine-tune

• Edge-sized by design: runs on a ~$0.40/hr T4, a gaming GPU, or an Apple-silicon laptop —

local-first as a parameter count, not a tagline.

• Schema-bulletproof: 100% schema validity on the project eval **even with no system

prompt**, with stronger no-event discipline (doesn't invent events from "thanks!") and a higher

rate of asking when a date is TBD.

• Convention-trained: learns the product's date semantics ("next Tuesday" = next week's

Tuesday; weekday-anchored relative dates) instead of generic internet priors.

• Eval-gated: every retrain must clear a 60-example task eval (start-exact datetime matching,

F1, validity, clarification) before it can be published — the pipeline has rejected eight

regressed models to date. Full scorecard: the project's docs/eval-roadmap.md.

How to run

# text + vision via llama.cpp server (OpenAI-compatible API on :8080/v1)
MODEL=$(python -c "from huggingface_hub import hf_hub_download as d; print(d('ParetoOptimal/gemma-4-cal-gguf','gemma-cal-e4b-Q4_K_M.gguf'))")
MMPROJ=$(python -c "from huggingface_hub import hf_hub_download as d; print(d('unsloth/gemma-4-E4B-it-GGUF','mmproj-F16.gguf'))")
llama-server -m "$MODEL" --mmproj "$MMPROJ" -ngl 999 -c 8192 --jinja --port 8080

Use the explicit filename rather than the -hf repo:Q4_K_M shorthand — this repo also stores

legacy training artifacts at the same quant.

The model is trained to answer with only an ActionPlan JSON object. Typical user turn:

Current datetime: Monday, 2026-09-14T09:00:00
Existing calendar: (none provided)

Conversation:
Room parent: Picture day is Thursday — photos at 9am, wear the green shirt!
Me: thanks!

Return the ActionPlan JSON now.

→

{
  "reasoning": "School picture day Thursday Sep 17 at 9am; wear green shirt.",
  "events": [{"title": "School picture day", "start": "2026-09-17T09:00:00",
              "end": null, "location": "School", "attendees": [],
              "reminder_minutes": 720, "notes": "Wear green class shirt"}],
  "conflicts": [], "proposed_times": [],
  "reply_draft": "Got it — green shirt Thursday!", "needs_clarification": null
}

Honest evaluation

Scored on the project's 60-example held-out eval (50 gold events; start-exact datetime matching;

temp 0; same constrained-JSON call production uses):

| with system prompt | stock E4B | gemma-cal E4B |

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

| schema validity | 1.0 | 1.0 |

| event F1 | 0.97 | 0.97 |

| start-exact recall | 0.96 | 0.96 |

| clarification recall | 1.0 | 1.0 |

| bare (no system prompt) | stock E4B | gemma-cal E4B |

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

| schema validity | 0.967 | 1.0 |

| no-event accuracy | 0.70 | 0.80 |

| clarification recall | 0.50 | 0.625 |

| event F1 | 0.682 | 0.644 |

i.e. parity with stock under the engineered prompt (identical error counts) and **better

schema validity and discipline with no prompt at all**. Published after six eval-gated training

iterations; the publish-at-parity call was an explicit owner decision (the auto-gate requires

strict dominance).

Training

• Base: google/gemma-4-E4B-it
• Method: QLoRA (4-bit) with Unsloth; LoRA r=16,

alpha=16, targets q/k/v/o/gate/up/down; merged to 16-bit before GGUF conversion.

• Recipe details that mattered: trained on Gemma-4's native chat template

(<|turn>role … <turn|> — the same template embedded in the GGUF and served by

llama-server --jinja), loss masked to the assistant turn only, LR 5e-5, 1 epoch.

• Data: 139 hand-authored thread-style examples (4× upsampled) + 2,000 examples converted

from SMCalFlow (CC BY-SA 4.0 — *Semantic Machines et al., "Task-Oriented Dialogue as

Dataflow Synthesis," TACL 2020*), with LISP date/time programs resolved against per-example

reference datetimes and convention-conflicting rows filtered out.

• Hardware: single A100-80GB on Modal; convert_hf_to_gguf.py + llama-quantize for export.

Reproduce / retrain (eval-gated): training/gated_retrain.py in the

project repo.

Files in this repo

| File | Size | Status |

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

| gemma-cal-e4b-Q4_K_M.gguf | ~5.3 GB | The model. Production edge fine-tune (this card). |

| gemma-cal-Q4_K_M.gguf | ~18.7 GB | Legacy 31B training artifact from earlier iterations; not served. |

| mmproj-F16.gguf | ~1.2 GB | Legacy projector for the 31B artifact. For the E4B, use unsloth/gemma-4-E4B-it-GGUF / mmproj-F16.gguf. |

Limitations & responsible use

• Specialized: scheduling extraction only — not general chat, Q&A, or code.
• English only; expects the reference datetime (with weekday) supplied in the prompt.
• Q4_K_M quantization; verify extracted dates before trusting blindly (the app surfaces

everything for review before saving).

• Derivative of Gemma-4 — use is subject to Google's

Gemma Terms of Use.

*Base model: Google Gemma-4 E4B. Tooling: Unsloth (training), llama.cpp (conversion + serving).

Training data includes SMCalFlow (CC BY-SA 4.0, Semantic Machines et al.).*