helenk/gemma-4-E2B-finetune-GGUF

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