datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gemma3n-slicing-configsThis repository contains configurations to slice Gemma 3n E4B, which is enabled thanks to it being a MatFormer.
The E4B model can be sliced into small models, trading off quality and latency/compute requirements.
We recommend exploring the [MatFormer Lab](TODO: add link) to getting started with slicing Gemma 3n E4B yourself.
For each configuration, we calculate the MMLU accuracy.
Although these are not the only configurations possible, they are optimal configurations
identified by calculating… See the full description on the dataset page: https://huggingface.co/datasets/google/gemma3n-slicing-configs.Selective-Context-Gemma3-12B-resultsLLMLingua2-Gemma3-12B-resultsgemma-3-1b-pt-blind-spots
Gemma-3-1b-pt Blind Spots Dataset
Dataset Description
This dataset documents blind spots (systematic errors) found in
google/gemma-3-1b-pt,
a 1-billion-parameter pretrained base model (not instruction-tuned)
released by Google in March 2025 as part of the Gemma 3 family.
Each row contains:
Column
Description
id
Unique probe index
category
Type of reasoning tested
prompt
The input fed to the model (text-completion style)
expected_output
The… See the full description on the dataset page: https://huggingface.co/datasets/Junaid687/gemma-3-1b-pt-blind-spots.diffing-stats-gemma3_1B-kansas_abortion-L19-k100-lr1e-03-x32-local-shuffling-Crosscodergemma-3-1b-pt-blind-spots
Blind Spots of google/gemma-3-1b-pt
Model Tested
Model: google/gemma-3-1b-ptParameters: 1BType: Pre-trained base language model (not instruction-tuned)Tested by: Toka-Tarek | Biotechnology graduate & Pharmacogenetics Lab Specialist
How I Loaded the Model
Tested on Google Colab (free T4 GPU, 16GB VRAM).
Note: torch.float16 caused numerical instability (NaN/inf errors)
on the T4 GPU, so torch.float32 was used instead for stable generation.
from huggingface_hub… See the full description on the dataset page: https://huggingface.co/datasets/Toka-Tarek/gemma-3-1b-pt-blind-spots.Gemma3_1bdiffing-stats-SAEdiff_ftb-gemma3_1B-caps-L13-s1-t100-k100-lr1e-04-x2diffing-stats-SAE-difference_ftb-gemma3_1B-kansas_abortion-L6-k100-x32-lr1e-03-local-shufflingAxcer-Gemma3-12B-resultsdiffing-stats-gemma3_1B-kansas_abortion-L13-k100-lr1e-03-x32-local-shuffling-Crosscoderscam_call_gemma3this dataset is forked from https://huggingface.co/datasets/BothBosu/scam-dialogue and it is formatted to gemma3's chat template to fine-tune a gemma3 model.
diffing-stats-gemma3_1B-kansas_abortion-L6-k100-lr1e-03-x32-local-shuffling-Crosscoderdiffing-stats-SAEdiff_ftb-gemma3_1B-roman_concrete-L12-s1-t100-k50-lr1e-04-x8diffing-stats-SAEdiff_ftb-gemma3_1B-roman_concrete-L12-s1-t100-k100-lr1e-04-x16gemma31b_5kdiffing-stats-SAEdiff_ftb-gemma3_1B-kansas_abortion-L13-k100-x2-lr1e-04-t200diffing-stats-gemma3_1B-kansas_abortion-L13-k100-lr1e-04-x32-Crosscodergemma3-finetuningTrydiffing-stats-SAEdiff_ftb-gemma3_1B-caps-L12-s1-t100-k100-lr1e-04-x2diffing-stats-gemma3_1B-cake_bake-L12-Crosscoder-s1-t100-k100-lr1e-04-x32gemma-3-1b-blind-spots
Gemma 3 1B Base Model — Blind Spots Dataset
Model Tested
Model: google/gemma-3-1b-pt
Developer: Google DeepMind
Parameters: 1B
Type: Base language model (pretrained only, no instruction tuning)
Tested on: Google Colab (free tier, T4 GPU)
How I Loaded the Model
# Step 1: Install dependencies
!pip install -q transformers torch accelerate
# Step 2: Authenticate with Hugging Face
from huggingface_hub import login
login("your_hf_token")
# Step 3: Load model… See the full description on the dataset page: https://huggingface.co/datasets/RhodaAdigun/gemma-3-1b-blind-spots.