---
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
built_buy:
  - ParetoOptimal
---

# 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](https://huggingface.co/spaces/ParetoOptimal/OffGridSchedula)**
serving on a 16 GB T4 via [llama.cpp](https://github.com/ggml-org/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

```bash
# 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.
```

→

```json
{
  "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](https://github.com/unslothai/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](https://github.com/n8mauer/imessage-calendar-agent).

## 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](https://ai.google.dev/gemma/terms).

*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.).*