---
base_model: unsloth/gemma-4-E2B-it
library_name: gguf
license: gemma
pipeline_tag: text-generation
language:
  - en
  - zh
  - ar
tags:
  - gemma
  - gemma-4
  - gguf
  - quantized
  - q4-k-m
  - llama-cpp
  - ollama
  - fine-tuned
  - rag
  - offlineaid
  - australian-consumer-safety
  - anti-scam
  - disaster-response
datasets:
  - helenkwok/offlineaid
---

# OfflineAid — Gemma 4 E2B fine-tune (Q4_K_M GGUF)

Q4_K_M-quantized GGUF of the [OfflineAid](https://github.com/helenkwok/offlineaid) Stage-1 fine-tune (E2B variant). Drop-in for `llama.cpp` and Ollama. Smaller sibling of [`helenk/gemma-4-E4B-finetune-GGUF`](https://huggingface.co/helenk/gemma-4-E4B-finetune-GGUF) — same training corpus and recipe, lower memory footprint.

- **Base:** [`unsloth/gemma-4-E2B-it`](https://huggingface.co/unsloth/gemma-4-E2B-it)
- **LoRA:** [`helenk/gemma-4-E2B-lora`](https://huggingface.co/helenk/gemma-4-E2B-lora) — Unsloth fine-tune on Kaggle T4
- **Merged fp16 source:** [`helenk/gemma-4-E2B-finetune`](https://huggingface.co/helenk/gemma-4-E2B-finetune)
- **Quantize chain:** `peft.merge_and_unload` → `llama.cpp/convert_hf_to_gguf.py` → `llama-quantize Q4_K_M`
- **File size:** ~3.4 GB

## Use with Ollama

```
ollama pull hf.co/helenk/gemma-4-E2B-finetune-GGUF
```

Or via a local `Modelfile`:

```
FROM /path/to/gemma-4-E2B-offlineaid-Q4_K_M.gguf
RENDERER gemma4
PARSER gemma4
PARAMETER num_ctx 32768
PARAMETER stop "<turn|>"
PARAMETER temperature 0.0
```

```
ollama create offlineaid-e2b -f Modelfile
ollama run offlineaid-e2b
```

## Use with llama.cpp

```
./llama-cli \
  -m gemma-4-E2B-offlineaid-Q4_K_M.gguf \
  -p "Answer in Simplified Chinese.\n\nQUESTION: ..." \
  --temp 0.0 -n 256
```

## Tier A held-out eval

Tier A held-out numbers for the writeup are reported on the larger E4B variant — see [`helenk/gemma-4-E4B-finetune-GGUF`](https://huggingface.co/helenk/gemma-4-E4B-finetune-GGUF) for the full methodology and the headline result (fine-tune + RAG raises overall multilingual format-OK from 53.2% → 70.3% vs stock + RAG, with AR format-OK +32.4 pp / 2.5×). Both E2B and E4B share identical training data, recipe, and quantization chain.

## Intended use

Lower-memory variant of the OfflineAid Stage-1 fine-tune. Pixel 7 production deployment uses *stock* Gemma 4 E2B + retrieval, not this fine-tune; see [the project writeup](https://github.com/helenkwok/offlineaid) for the architectural rationale.

## License

Inherits Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms). Training data ([`helenkwok/offlineaid`](https://huggingface.co/datasets/helenkwok/offlineaid)) is CC-BY-4.0.

## Sibling repos

- Merged fp16 safetensors (~9.5 GB): [`helenk/gemma-4-E2B-finetune`](https://huggingface.co/helenk/gemma-4-E2B-finetune)
- LoRA adapter (~30 MB): [`helenk/gemma-4-E2B-lora`](https://huggingface.co/helenk/gemma-4-E2B-lora)
- Larger E4B variant: [`helenk/gemma-4-E4B-finetune-GGUF`](https://huggingface.co/helenk/gemma-4-E4B-finetune-GGUF)