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alwaysgood
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Model Details
Model Provider
alwaysgood
Model Tree
google/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
Use the latest LM Studio runtime and download the Q4_K_M variant:
bash
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>
qe_selection_order=low)gemma4_e2b_it_full_lowqe_seed42fa8166a883d96460cc285b46d66b74a074b4b8d4The 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.
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.