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arxiv: 2308.01390 · v2 · submitted 2023-08-02 · 💻 cs.CV · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Anas Awadalla , Irena Gao , Josh Gardner , Jack Hessel , Yusuf Hanafy , Wanrong Zhu , Kalyani Marathe , Yonatan Bitton
show 8 more authors
Samir Gadre Shiori Sagawa Jenia Jitsev Simon Kornblith Pang Wei Koh Gabriel Ilharco Mitchell Wortsman Ludwig Schmidt
Authors on Pith no claims yet

Pith reviewed 2026-05-14 01:47 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords OpenFlamingovision-language modelsautoregressive modelsopen-source replicationmultimodal learningFlamingofew-shot evaluationmodel benchmarks
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The pith

OpenFlamingo delivers open-source vision-language models that reach 80-89 percent of Flamingo performance across seven datasets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents OpenFlamingo as a public family of autoregressive vision-language models sized from 3 billion to 9 billion parameters. These models replicate an existing closed system by training on openly available data and code. They attain between 80 and 89 percent of the original performance when measured on the same seven vision-language benchmarks. Releasing the full training setup, models, and evaluation details lets other researchers run, verify, and build on the work without access to private resources.

Core claim

We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models. On seven vision-language datasets, OpenFlamingo models average between 80 and 89 percent of corresponding Flamingo performance. This technical report describes our models, training data, hyperparameters, and evaluation suite, and we share the models and code publicly.

What carries the argument

The OpenFlamingo model family, which uses autoregressive next-token prediction over interleaved image and text sequences to support few-shot multimodal reasoning.

If this is right

  • Researchers can now train comparable multimodal models from public resources without starting from proprietary checkpoints.
  • The 80-89 percent performance band shows that core few-shot vision-language capabilities transfer to open training pipelines.
  • Scaling experiments across the 3B to 9B range supply baselines for studying how size affects multimodal task accuracy.
  • The shared evaluation suite enables direct head-to-head comparisons of new open models against the Flamingo reference.
  • Public code and weights allow the community to iterate on data mixtures, training schedules, and architectural tweaks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If further open training closes the remaining gap, the results would indicate that Flamingo's gains come mainly from architecture and scale rather than from exclusive data or methods.
  • The released framework could serve as a template for open replications of other large closed multimodal systems.
  • Extending the same open training recipe to higher parameter counts or additional modalities such as video would test how far the replication approach generalizes.

Load-bearing premise

The reported performance numbers were measured under evaluation conditions comparable to the original Flamingo models and the released code and data suffice for independent reproduction.

What would settle it

Re-running the released code and data on the seven datasets under the same evaluation protocol and obtaining average scores below 70 percent of Flamingo's reported numbers would undermine the replication claim.

read the original abstract

We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models. On seven vision-language datasets, OpenFlamingo models average between 80 - 89% of corresponding Flamingo performance. This technical report describes our models, training data, hyperparameters, and evaluation suite. We share our models and code at https://github.com/mlfoundations/open_flamingo.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces OpenFlamingo, an open-source family of autoregressive vision-language models (3B–9B parameters) intended as a replication of DeepMind’s Flamingo. It reports that these models achieve 80–89% of the corresponding Flamingo performance on average across seven vision-language datasets, provides details on architecture, training data, hyperparameters, and evaluation protocols, and releases models and code at the linked GitHub repository.

Significance. If the relative performance numbers hold under matched evaluation conditions, the work is significant as the first public, reproducible alternative to the closed-source Flamingo models. The explicit release of code, models, and training details directly addresses reproducibility concerns in large-scale vision-language research and lowers the barrier for follow-on work.

major comments (3)
  1. [Abstract / Evaluation section] Abstract and Evaluation section: the central claim that OpenFlamingo reaches 80–89% of Flamingo performance on seven datasets rests on the unverified assumption that few-shot prompts, example selection, image preprocessing, shot count, and metric computation are identical to those used in the original (closed-source) Flamingo evaluations. The manuscript describes its own suite and shares code, but provides no explicit side-by-side protocol table or verification that would allow independent confirmation of equivalence.
  2. [Evaluation section] Evaluation section: reported performance figures lack error bars, multiple random seeds, or statistical comparisons against the Flamingo baselines. Without these, it is impossible to determine whether the observed 80–89% range reflects a stable gap or is sensitive to evaluation variance.
  3. [Training section] Training and ablation discussion: the manuscript describes hyperparameters and data but contains no systematic ablations (e.g., effect of vision encoder choice, cross-attention layers, or data mixture ratios). This omission makes it difficult to isolate which design decisions are responsible for closing most of the gap to Flamingo.
minor comments (2)
  1. [Abstract] The GitHub URL in the abstract should be accompanied by a permanent archive link (e.g., Zenodo DOI) to guard against repository changes.
  2. [Model Architecture] Notation for model sizes (3B, 9B) should be defined consistently with parameter counts reported in Table 1 or the model architecture section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and clarify our evaluation approach, limitations, and the scope of this work as an open-source replication effort.

read point-by-point responses

  1. Referee: [Abstract / Evaluation section] Abstract and Evaluation section: the central claim that OpenFlamingo reaches 80–89% of Flamingo performance on seven datasets rests on the unverified assumption that few-shot prompts, example selection, image preprocessing, shot count, and metric computation are identical to those used in the original (closed-source) Flamingo evaluations. The manuscript describes its own suite and shares code, but provides no explicit side-by-side protocol table or verification that would allow independent confirmation of equivalence.

    Authors: We agree that a side-by-side protocol comparison would improve transparency. While we cannot access the closed-source Flamingo code for exact verification, our evaluation suite was designed to follow the protocols described in the Flamingo paper as closely as possible, including shot counts, prompt templates, and metrics. The released code at https://github.com/mlfoundations/open_flamingo contains the precise evaluation scripts, data loaders, and preprocessing steps used. In the revision we will add an explicit comparison table in the Evaluation section detailing our choices against the Flamingo paper descriptions, along with any unavoidable differences. revision: yes

  2. Referee: [Evaluation section] Evaluation section: reported performance figures lack error bars, multiple random seeds, or statistical comparisons against the Flamingo baselines. Without these, it is impossible to determine whether the observed 80–89% range reflects a stable gap or is sensitive to evaluation variance.

    Authors: We acknowledge that error bars and multi-seed statistics would strengthen the presentation. However, the computational cost of repeated full evaluations on 3B–9B models across seven datasets is prohibitive. We followed the single-run reporting convention common in large-scale vision-language papers and observed consistent relative performance across diverse tasks, which suggests the gap is not driven by outlier variance. In the revision we will add a limitations paragraph noting this constraint and the consistency evidence. revision: partial

  3. Referee: [Training section] Training and ablation discussion: the manuscript describes hyperparameters and data but contains no systematic ablations (e.g., effect of vision encoder choice, cross-attention layers, or data mixture ratios). This omission makes it difficult to isolate which design decisions are responsible for closing most of the gap to Flamingo.

    Authors: We agree that systematic ablations would be informative. The primary objective of this technical report is to release reproducible models and code that close most of the gap to Flamingo, rather than to perform an exhaustive ablation study that would require orders-of-magnitude more compute. We do describe key hyperparameter choices and data mixtures in the Training section. In the revision we will expand the discussion to highlight the design decisions we found most impactful during development, while noting that a full ablation study remains future work. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical replication claims rest on external baselines without internal self-definition or fitted predictions

full rationale

The paper is a technical report describing an open-source replication of Flamingo models, including architecture, training data, hyperparameters, and an evaluation suite. The headline result (80-89% relative performance on seven datasets) is an empirical measurement against the closed-source Flamingo baseline on public datasets. No equations, derivations, or first-principles results are presented that reduce to the paper's own inputs by construction. There are no self-definitional quantities, no parameters fitted to a subset and then relabeled as predictions, and no load-bearing self-citations. The comparison assumes protocol equivalence, but this is an external validity concern rather than circularity per the enumerated patterns. The derivation chain consists of standard model training and benchmarking steps that are independently verifiable from the released code and data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work is an empirical engineering report that relies on standard neural-network training practices and public datasets; no new free parameters, axioms, or invented entities are introduced beyond those inherited from the Flamingo architecture.

pith-pipeline@v0.9.0 · 5452 in / 1027 out tokens · 43582 ms · 2026-05-14T01:47:24.782890+00:00 · methodology

discussion (0)

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