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Gemma4_E2B_ADS

Gemma4_E2B_ADS is a full fine-tune of google/gemma-4-E2B-it for English-to-Korean financial translation. It was trained with the DQS low-QE curriculum using seed 42.

This repository contains both the original Transformers checkpoint and one LM Studio/llama.cpp export:

model.safetensors: original BF16 fine-tuned checkpoint
Gemma4_E2B_ADS-Q4_K_M.gguf: the only quantized main-model variant
mmproj-Gemma4_E2B_ADS-BF16.gguf: multimodal encoder/projector companion

The BF16 mmproj is not an additional LLM quantization variant. It is kept at BF16 for multimodal compatibility and quality.

LM Studio

Use the latest LM Studio runtime and download the Q4_K_M variant:

lms get https://huggingface.co/alwaysgood/Gemma4_E2B_ADS@Q4_K_M

The matching mmproj file enables image input. Direct llama.cpp multimodal testing with this checkpoint requires --jinja. Gemma 4 audio support may vary by runtime and is not guaranteed by this model card. For translation, disable thinking and ask for translation-only output, for example:

text

Translate the following English financial text into Korean. Return only the translation.

<source text>

Training and provenance

Tuning: full-parameter supervised fine-tuning
Seed: 42
Selection: low quality-estimation score first (qe_selection_order=low)
Base model thinking during training/evaluation: disabled
Vision/audio layers: not trained; the base model's multimodal components were preserved
Run artifacts: gemma4_e2b_it_full_lowqe_seed42
Source revision: fa8166a883d96460cc285b46d66b74a074b4b8d4

Evaluation

The following scores are from the original BF16 final checkpoint on the 500-row held-out test set. They are not claimed as a separate Q4_K_M evaluation.

Metric	Score
BLEU	30.7621
chrF	49.3295
COMET (`wmt22-comet-da`)	0.8968
COMETKiwi (`wmt22-cometkiwi-da`)	0.8630
XCOMET-XXL	0.8746
MetricX-24 Hybrid XXL (lower is better)	3.4078

Full evaluation records and configuration are available in the linked run.

License and data note

The model weights follow the Apache-2.0 license of the base model. The training corpus aggregates sources with mixed upstream terms; the dataset card is marked license: other. Users are responsible for reviewing the source-specific terms described in alwaysgood/financial-english-source-corpus.