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 Stage-1 fine-tune (E2B variant). Drop-in for llama.cpp and Ollama. Smaller sibling of helenk/gemma-4-E4B-finetune-GGUF — same training corpus and recipe, lower memory footprint.
- Base:
unsloth/gemma-4-E2B-it - LoRA:
helenk/gemma-4-E2B-lora— Unsloth fine-tune on Kaggle T4 - Merged fp16 source:
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 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 for the architectural rationale.
License
Inherits Google's Gemma Terms of Use. Training data (helenkwok/offlineaid) is CC-BY-4.0.
Sibling repos
- Merged fp16 safetensors (~9.5 GB):
helenk/gemma-4-E2B-finetune - LoRA adapter (~30 MB):
helenk/gemma-4-E2B-lora - Larger E4B variant:
helenk/gemma-4-E4B-finetune-GGUF