REPOGEO REPORT · LITE

allenai/molmo

Default branch main · commit 793fa387 · scanned 6/13/2026, 3:08:04 AM

GitHub: 914 stars · 96 forks

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface allenai/molmo, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening to explicitly state 'Vision-Language Model'

Why:

CURRENT

Molmo is a repository for training and using Ai2's state-of-the-art multimodal open language models.

COPY-PASTE FIX

Molmo is AllenAI's state-of-the-art **vision-language model (VLM)**, providing code for training and using powerful multimodal open language models that integrate vision encoding and generative evaluations.

mediumhomepage#2
Add the project homepage URL
Why:
COPY-PASTE FIX
```
https://molmo.allenai.org/blog
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface allenai/molmo

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 8 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 8×
huggingface/peft · recommended 3×
pytorch/pytorch · recommended 2×
tensorflow/tensorflow · recommended 2×
keras-team/keras-nlp · recommended 2×

CATEGORY QUERY
How can I build and train a custom vision-language model for multimodal tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. 🤗 PEFT (huggingface/peft)
3. Accelerate (huggingface/accelerate)
4. Hugging Face Hub
5. datasets library (huggingface/datasets)
6. Trainer API (huggingface/transformers)
7. LoRA (huggingface/peft)
8. QLoRA (huggingface/peft)
9. ViTModel (huggingface/transformers)
10. LlamaForCausalLM (huggingface/transformers)
11. PyTorch Lightning (Lightning-AI/pytorch-lightning)
12. PyTorch (pytorch/pytorch)
13. TensorBoard (tensorflow/tensorboard)
14. Weights & Biases (wandb/wandb)
15. Keras (keras-team/keras)
16. TensorFlow backend (tensorflow/tensorflow)
17. tf.keras.applications.EfficientNetB0 (tensorflow/tensorflow)
18. keras_nlp.models.BertClassifier (keras-team/keras-nlp)
19. keras_cv (keras-team/keras-cv)
20. keras_nlp (keras-team/keras-nlp)
21. OpenMMLab
22. MMDetection (open-mmlab/mmdetection)
23. MMEngine (open-mmlab/mmengine)
24. MMYOLO (open-mmlab/mmyolo)
25. torch.nn.parallel.DistributedDataParallel (pytorch/pytorch)
26. COCO
27. Visual Genome
28. Conceptual Captions
29. ViT (huggingface/transformers)
30. ResNet
31. BERT (huggingface/transformers)
32. GPT-2 (huggingface/transformers)
33. Llama (huggingface/transformers)
34. NVIDIA A100s
35. H100s
36. BLEU
37. ROUGE
38. CIDEr
AI recommended 38 alternatives but never named allenai/molmo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools exist for evaluating generative multimodal AI models with vision input?
you: not recommended
AI recommended (in order):
1. FiftyOne
2. Weights & Biases
3. Amazon Mechanical Turk
4. Scale AI
5. Appen
6. OpenAI CLIP model
7. Hugging Face Transformers
8. InceptionV3 model
9. PyTorch
10. TensorFlow
11. Hugging Face Evaluate Library
12. TensorBoard
AI recommended 12 alternatives but never named allenai/molmo. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of allenai/molmo?
pass
AI named allenai/molmo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts allenai/molmo in production, what risks or prerequisites should they evaluate first?
pass
AI named allenai/molmo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo allenai/molmo solve, and who is the primary audience?
pass
AI named allenai/molmo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of allenai/molmo. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/allenai/molmo.svg)](https://repogeo.com/en/r/allenai/molmo)

HTML

<a href="https://repogeo.com/en/r/allenai/molmo"><img src="https://repogeo.com/badge/allenai/molmo.svg" alt="RepoGEO" /></a>

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allenai/molmo — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite