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ColGemma4-E2B-IT-Base

Name: ColGemma4-E2B-IT-Base
Availability: InStock

See also: ColGemma4-E4B-IT-Base (larger 4.5B-effective variant)

ColGemma4 is a visual document retrieval model built on Google's Gemma 4 E2B (5.1B params). It generates ColBERT-style multi-vector representations of document images and text queries for late-interaction retrieval.

This is the base version - a single-seed LoRA adapter trained with ColbertLoss, no hard negatives, no model merging. It establishes the ColGemma4 baseline on the ViDoRe benchmark.

Built following the ColPali architecture pattern, adapted for Gemma 4's multimodal architecture.

Model Description

Property	Value
Base model	`google/gemma-4-E2B-it` (5.1B total, 2.3B effective)
Architecture	ColBERT late-interaction over Gemma 4 VLM
Embedding dim	128
Visual tokens	1120 (max soft tokens)
Fine-tuning	LoRA (r=32, alpha=32, dropout=0.1)
Trainable params	50.7M (1.04% of total)
Projection	Random-init linear (hidden_size → 128), not trained
Training loss	ColbertLoss (temperature=0.02, in-batch negatives only)
Precision	BF16

Benchmark Results

All scores are nDCG@5 on the ViDoRe benchmark.

ViDoRe V1

Task	nDCG@5	nDCG@10
ArxivQA	78.54	80.12
DocVQA	52.43	55.86
InfoVQA	87.11	87.88
ShiftProject	81.34	82.29
SyntheticDocQA - AI	98.16	98.16
SyntheticDocQA - Energy	92.04	92.40
SyntheticDocQA - Government	94.37	94.37
SyntheticDocQA - Healthcare	93.06	93.42
Tabfquad	84.78	86.10
Tatdqa	69.49	72.15
Average	83.13	84.28

ViDoRe V2

Task	nDCG@5	nDCG@10
BioMedical Lectures	53.06	56.30
ESG Reports - HL	54.96	58.39
ESG Reports	34.19	38.38
Economics Reports	37.19	38.86
Average	44.85	47.98

ViDoRe V3

Task	nDCG@5	nDCG@10
Computer Science	55.80	59.61
Energy	56.03	59.09
Finance En	39.61	42.12
Finance Fr	35.82	39.03
HR	38.62	42.01
Industrial	31.98	33.73
Pharmaceuticals	49.03	50.68
Physics	40.82	43.89
Average	43.46	46.27

Usage

Installation

pip install colpali-engine transformers torch peft

Loading the Model

python

import torch
from colgemma4 import ColGemma4, ColGemma4Processor

# Load base model + LoRA adapter
model = ColGemma4.from_pretrained(
    "athrael-soju/ColGemma4-E2B-IT-Base",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
    ignore_mismatched_sizes=True,  # needed for custom_text_proj
)

processor = ColGemma4Processor.from_pretrained(
    "athrael-soju/ColGemma4-E2B-IT-Base",
    max_num_visual_tokens=1120,
)

Encoding Documents (Images)

python

from PIL import Image

images = [Image.open("page1.png"), Image.open("page2.png")]
batch_doc = processor.process_images(images)
batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}

with torch.no_grad():
    doc_embeddings = model(**batch_doc)  # (batch, seq_len, 128)

Encoding Queries

python

queries = ["What is the revenue for Q3 2024?"]
batch_query = processor.process_queries(queries)
batch_query = {k: v.to(model.device) for k, v in batch_query.items()}

with torch.no_grad():
    query_embeddings = model(**batch_query)  # (batch, seq_len, 128)

Scoring (MaxSim)

python

scores = processor.score(query_embeddings, doc_embeddings)
# scores[i][j] = relevance of query i to document j

Training Configuration

yaml

Base model: google/gemma-4-E2B-it
Loss: ColbertLoss (temperature=0.02)
Hard negatives: none
Batch size per GPU: 64
GPUs: 7
Gradient accumulation: 1
Effective batch size: 448 (64 x 7)
In-batch negatives: 448
LoRA:
  r: 32
  alpha: 32
  dropout: 0.1
  target_modules: "language_model.*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj)"
  # custom_text_proj is NOT LoRA-targeted (random init, untrained)
Learning rate: 2e-4 (cosine schedule, 8% warmup)
Weight decay: 0.02
Epochs: 1
Steps: 1,729
Visual tokens: 1120
Attention: Bidirectional (all layers patched)
Gradient checkpointing: enabled
Precision: BF16

Training Data

Trained on ~774K query-document pairs from publicly available datasets:

vidore/colpali_train_set
openbmb/VisRAG-Ret-Train-Synthetic-data
openbmb/VisRAG-Ret-Train-In-domain-data
llamaindex/vdr-multilingual-train (en/de/es/fr/it subsets)
vidore/tatdqa_train

Troubleshooting & Fine-tuning Guide

If you're building on this model or training your own ColGemma4, here's what we learned along the way.

Gemma 4 Architecture Gotchas

Position embedding memory blow-up - The vision encoder uses F.one_hot(positions, num_classes=10240) which allocates ~314 GB at batch=32 with 1120 tokens. Replacing with F.embedding is mathematically identical and saves ~106 GB/GPU. Required for batch sizes above 8.
python
1# In Gemma4VisionEncoder._position_embeddings: 2# Replace: one_hot = F.one_hot(clamped_positions, num_classes=self.position_embedding_size) 3# With: return F.embedding(clamped_positions, self.position_embedding_table)
KV-sharing and use_cache - Gemma 4 E2B has 20 of 35 text layers that reuse K/V from earlier layers via the cache. During training, always set use_cache=False to ensure every layer computes its own K/V and all LoRA weights are active. At inference time, set use_cache=True so the KV-sharing architecture works as designed.
Flash Attention is incompatible - Gemma 4 has head_dim 256 (sliding) and 512 (global). FA v2 caps at 256, FA v4 at 128. Use attn_implementation="sdpa" instead.
Gradient checkpointing is mandatory - At 1120 visual tokens, activations from 35 layers consume ~175 GB. Even with the position patch, disabling gradient checkpointing will OOM.

Training Tips

Leave custom_text_proj alone - The 128-dim projection is randomly initialized and works best untrained. Both LoRA-targeting and modules_to_save caused regressions in our experiments. The random projection provides a consistent mapping without overfitting.
Keep grad_accum=1 with contrastive losses - all_gather only collects the current micro-batch, so accumulation steps halve your in-batch negatives. Training loss looks deceptively good but eval regresses. Use the largest batch that fits with grad_accum=1.
Avoid torch.compile - It adds _orig_mod. to weight keys, breaking PEFT adapter loading at eval time. Scores drop to near-zero despite healthy training loss.
Watch for silent weight randomization - ignore_mismatched_sizes=True will initialize mismatched weights to random without any error. Sanity check: loss at step 0 should be near log(batch_size) (~6.1 for batch 448), and grad norms should be 5-20. If grad norms are in the thousands, weights didn't load correctly.

HydraGemma4 (Dual-Head Variant)

This repo also includes the HydraGemma4 architecture - a dual-head model that supports both retrieval (ColBERT embeddings) and generation (text output) from the same base model. See hydra_gemma4.py for the implementation.

Limitations

Single-seed, no model merging - variance across seeds is not averaged out
No hard negative mining - relies entirely on in-batch negatives
English-centric training data (some multilingual from VDR)
Visual token budget fixed at 1120 - train and eval must match

Citation

bibtex

@misc{colgemma4,
  title={ColGemma4: Visual Document Retrieval with Gemma 4},
  author={Athrael Soju},
  year={2026},
  url={https://huggingface.co/athrael-soju/ColGemma4-E2B-IT-Base}
}

Acknowledgements

ColPali by Faysse et al. for the ColBERT-over-VLM architecture
Google Gemma 4 for the base model
colpali-engine for the training framework
ViDoRe benchmark for evaluation