mammut2-moderntext_ViT-B-32.cc12m-qk-lr3e3

Model card for mammut2-moderntext_ViT-B-32.cc12m-qk-lr3e3

MaMMUT2 with a modern text decoder (qk-norm) at lr 3e-3 -- past the stable-LR edge for this tower; weakest moderntext run on every metric. Kept for the LR-sensitivity data point.

Research checkpoint from a B/32-scale architecture/objective comparison on CC12M (CLIP vs MaMMUT2 vs modern text decoder); not a production model.

Model Details

• Dataset: pixparse/cc12m-wds
• Epochs: 32
• Precision: bf16 amp
• Weight decay: 0.25
• Objective: InfoNCE + 2x caption CE + 1e-5 z-loss
• Text decoder: modern (RoPE/RMSNorm/SwiGLU/gated attn), qk-norm, tiktoken r50k vocab 50260, ctx 128, variable-length text
• Optimizer: adamw, lr 3e-3
• Batch size: 1024

Model Usage

import torch
import open_clip

model, preprocess = open_clip.create_model_from_pretrained('hf-hub:rwightman/mammut2-moderntext_ViT-B-32.cc12m-qk-lr3e3')
tokenizer = open_clip.get_tokenizer('hf-hub:rwightman/mammut2-moderntext_ViT-B-32.cc12m-qk-lr3e3')

image = preprocess(pil_image).unsqueeze(0)
text = tokenizer(['a dog', 'a cat', 'a bird'])
with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)

Caption generation:

with torch.no_grad():
    tokens = model.generate(image, generation_type='beam_search', seq_len=30)
print(tokenizer.decode(tokens[0].cpu().tolist()))

Model Comparison

All eight runs: CC12M, 32 epochs, 2x GPU, bf16 amp, wd 0.25, evaluated at epoch 32 with one harness (ImageNet zero-shot w/ OpenAI 80-prompt ensemble; COCO Karpathy-test retrieval, I2T = any-of-5-captions; caption metrics vs the 5 COCO refs, beam search). Key reads:

• The CLIP rows are prior runs, not purpose-trained controls: they were the closest available baselines but come from NAdaMuon optimizer experiments (nadamuon @ bs 4096 vs the MaMMUT runs' adamw @ bs ~1024), so cross-family deltas bundle optimizer + batch + loss.
• Caption objective -> I2T retrieval: every MaMMUT2 run beats both CLIP runs by 1-4 pt I2T R@1 despite 1/4 the contrastive batch and a plainer optimizer.
• Zero-shot ordering tracks batch/loss/optimizer, not architecture -- confirmed by the bs-4096 moderntext run: at matched batch (still adamw) it lands within 0.4 IN1K of clip-naflex and within 0.5 IN-R of clip-siglip. Batch was most of the CLIP lead.
• The caption-objective I2T advantage is batch-independent (37.7-38.6 I2T R@1 at both bs 1024 and 4096), while zero-shot, T2I, and caption metrics all scale with batch.
• NaFlex vision and 1e-5 caption z-loss are both ~free (within the <=0.6 pt repeat-run noise).
• Modern text tower (bundled with r50k tokenizer + ctx 128) at its best LR: IN1K parity with classic, +V2/+IN-R/+T2I, and clearly better caption metrics. qk-norm buys 2x LR headroom rather than direct quality; 3e-3 is past the stable edge even with it.
• Caption scores are style-limited: CC12M alt-text register vs COCO references. Absolute CIDEr is low for all runs; relative differences are the signal.