{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon","slug":"scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon","name":"Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning (CM3Leon)","type":"product","url":"https://ai.meta.com/research/publications/scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning/","page_url":"https://unfragile.ai/scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_0","uri":"capability://image.visual.bidirectional.text.to.image.and.image.to.text.generation.with.unified.token.representation","name":"bidirectional text-to-image and image-to-text generation with unified token representation","description":"CM3Leon implements a decoder-only, token-based multimodal architecture that unifies text and image modalities into a single autoregressive sequence. The model uses a retrieval-augmented approach during pretraining where both text and image tokens are processed through the same transformer decoder, enabling bidirectional generation (text→image and image→text) without separate encoder-decoder branches. This is achieved by tokenizing images into discrete tokens and treating them identically to text tokens in the autoregressive sequence, allowing the model to learn cross-modal dependencies through standard language modeling objectives.","intents":["Generate images from text descriptions with zero-shot capability","Convert images to descriptive text captions automatically","Build multimodal applications without maintaining separate vision and language models","Perform bidirectional generation in a single unified model"],"best_for":["Research teams exploring unified multimodal architectures","Developers building image-text applications requiring bidirectional capabilities","Organizations seeking to reduce model complexity by consolidating vision and language"],"limitations":["Requires discrete image tokenization which may lose fine-grained visual details compared to continuous representations","Autoregressive image generation is slower than diffusion-based methods due to token-by-token decoding","Zero-shot performance (FID 4.88 on MS-COCO) requires substantial pretraining compute (5x more efficient than alternatives, but still significant)","No documented support for video or 3D modalities, only static images"],"requires":["Image tokenizer (discrete token vocabulary for visual content)","Pretraining dataset with aligned text-image pairs at scale","Transformer decoder architecture with sufficient capacity (model size not specified in documentation)","Retrieval augmentation infrastructure for pretraining stage"],"input_types":["text prompts (for image generation)","image tokens (for image-to-text generation)","mixed sequences of text and image tokens"],"output_types":["image tokens (decoded to pixel space for visualization)","text sequences (natural language descriptions)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_1","uri":"capability://memory.knowledge.retrieval.augmented.pretraining.for.multimodal.sequence.modeling","name":"retrieval-augmented pretraining for multimodal sequence modeling","description":"CM3Leon's pretraining stage incorporates retrieval augmentation where relevant text-image pairs are retrieved and concatenated into the training sequences. During pretraining, the model learns to predict both text and image tokens in context of retrieved examples, enabling the model to leverage external knowledge without explicit fine-tuning. The retrieval mechanism operates at the sequence level, pulling related examples from a large corpus and interleaving them with the primary sequence, allowing the autoregressive model to learn in-context patterns and improve generalization through exposure to diverse multimodal contexts.","intents":["Improve zero-shot generation quality by exposing model to diverse in-context examples during pretraining","Reduce hallucination in image generation by grounding in retrieved reference content","Enable knowledge transfer from large multimodal corpora without explicit fine-tuning","Scale pretraining efficiency by leveraging retrieval rather than increasing model size"],"best_for":["Research teams with access to large-scale multimodal datasets and retrieval infrastructure","Organizations seeking to improve zero-shot performance without task-specific fine-tuning","Teams building foundation models where pretraining efficiency is critical"],"limitations":["Requires a large indexed corpus of text-image pairs, adding infrastructure complexity","Retrieval latency during pretraining adds computational overhead compared to standard pretraining","Quality of retrieved examples directly impacts model performance; poor retrieval degrades learning","No documented mechanism for updating or refreshing the retrieval corpus post-training"],"requires":["Large-scale multimodal dataset with text-image alignment","Retrieval index (vector database or similar) supporting fast similarity search","Pretraining infrastructure capable of dynamic sequence construction with retrieved examples","Compute resources for large-scale pretraining (efficiency gains are relative, not absolute)"],"input_types":["text-image pair corpora","query sequences (text or image) for retrieval"],"output_types":["augmented training sequences with retrieved context","pretrained model weights"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_10","uri":"capability://image.visual.semantic.segmentation.as.token.prediction","name":"semantic segmentation as token prediction","description":"CM3Leon frames semantic segmentation as a token prediction task within the unified decoder, enabling the model to generate segmentation masks by predicting special segmentation tokens conditioned on image input. During multi-task SFT, the model learns to output segmentation tokens that correspond to semantic classes, converting the segmentation task into sequence prediction. This approach integrates segmentation into the multimodal model without separate segmentation heads or decoders.","intents":["Perform semantic segmentation within a unified multimodal model","Generate segmentation masks from images without separate segmentation models","Enable joint image understanding and segmentation in multimodal applications","Support segmentation as a downstream task in multimodal systems"],"best_for":["Teams building multimodal systems requiring segmentation capabilities","Research exploring unified approaches to vision tasks","Applications seeking to consolidate multiple vision models into one"],"limitations":["Segmentation resolution limited by token vocabulary size; may be coarser than specialized segmentation models","No documented comparison with specialized segmentation models (DeepLab, Mask R-CNN, SAM)","Autoregressive token-by-token generation may be slower than direct mask prediction","Performance metrics (mIoU, pixel accuracy) not provided in documentation","Generalization to novel classes or domains not documented","Requires task-specific fine-tuning data with segmentation annotations"],"requires":["Pretrained CM3Leon model with multi-task SFT","Segmentation token vocabulary (special tokens for each semantic class)","Segmentation training dataset with pixel-level annotations","Decoder to convert segmentation tokens back to spatial masks"],"input_types":["images (as tokens)"],"output_types":["segmentation masks (as token sequences, decoded to spatial maps)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_11","uri":"capability://image.visual.image.infilling.and.inpainting.from.partial.context","name":"image infilling and inpainting from partial context","description":"CM3Leon supports image infilling where partial images with missing regions are completed based on surrounding context and optional text descriptions. The model conditions on the visible image tokens and text instructions, predicting tokens for the masked regions autoregressively. This capability is learned during multi-task SFT and enables tasks like object removal, hole filling, and content-aware completion without requiring explicit mask inputs or separate inpainting models.","intents":["Complete images with missing or masked regions based on context","Remove unwanted objects by infilling masked areas","Generate missing image content guided by text descriptions","Support interactive image completion applications"],"best_for":["Teams building image completion and inpainting applications","Content creation tools requiring object removal or hole filling","Multimodal systems needing image restoration capabilities"],"limitations":["Infilling quality depends on surrounding context and mask size; large masked regions may produce artifacts","Autoregressive generation may produce inconsistent or hallucinated content in masked regions","No documented comparison with specialized inpainting models (LaMa, MAT)","No mechanism documented for controlling infilling style or content type","Performance metrics and failure cases not provided","Requires task-specific fine-tuning data with inpainting examples"],"requires":["Pretrained CM3Leon model with multi-task SFT","Partial image with masked regions (as tokens)","Optional text description for guided infilling","Inpainting training dataset with masked images and ground-truth completions"],"input_types":["partial images with masked regions (as tokens)","optional text descriptions (natural language)"],"output_types":["completed images (as tokens, decoded to pixels)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_12","uri":"capability://planning.reasoning.multi.task.instruction.tuning.for.diverse.downstream.capabilities","name":"multi-task instruction tuning for diverse downstream capabilities","description":"CM3Leon's multi-task SFT stage trains the model on diverse downstream tasks (text-to-image, image-to-text, infilling, editing, segmentation) using instruction-tuning approaches where each task is framed as following natural language instructions. This enables the model to learn task-specific behaviors while maintaining a unified architecture, allowing a single model to handle multiple vision and language tasks. The instruction tuning approach enables the model to generalize to new tasks and instructions not seen during training.","intents":["Train a single model on multiple diverse vision and language tasks","Enable instruction-following behavior for flexible task specification","Improve generalization to unseen tasks through multi-task learning","Reduce the need for task-specific model variants"],"best_for":["Teams building versatile multimodal models for diverse applications","Research exploring multi-task learning in multimodal settings","Organizations seeking to consolidate multiple task-specific models"],"limitations":["Multi-task learning may introduce task interference where learning one task degrades performance on others","No documented analysis of task-specific performance vs. single-task baselines","Instruction tuning quality depends on instruction clarity and diversity in training data","No mechanism documented for task-specific hyperparameter tuning or weighting","Generalization to novel tasks not quantified; may require additional fine-tuning","Requires diverse task-specific training datasets, increasing data collection burden"],"requires":["Pretrained model from retrieval-augmented pretraining","Task-specific training datasets for each downstream task","Instruction templates or natural language task specifications","Multi-task training infrastructure supporting diverse loss functions"],"input_types":["task-specific training examples with natural language instructions"],"output_types":["instruction-tuned model weights","task-specific predictions (images, text, masks)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_2","uri":"capability://image.visual.multi.task.supervised.fine.tuning.for.controlled.generation.and.editing","name":"multi-task supervised fine-tuning for controlled generation and editing","description":"After retrieval-augmented pretraining, CM3Leon undergoes multi-task supervised fine-tuning (SFT) on diverse downstream tasks including text-to-image generation, image infilling, language-guided image editing, image-controlled generation, and segmentation. The SFT stage uses task-specific training data where each task is framed as a sequence prediction problem, allowing the unified decoder to learn task-specific behaviors while maintaining the shared multimodal representation. Contrastive decoding methods are applied during this stage to improve generation quality by contrasting high-quality and lower-quality outputs.","intents":["Enable image infilling and inpainting from partial image and text context","Perform language-guided image editing by conditioning on both image and text instructions","Generate images conditioned on reference images or style examples","Perform semantic segmentation as a token prediction task within the same model","Improve generation quality through contrastive decoding without additional models"],"best_for":["Teams building image editing applications requiring fine-grained control","Researchers exploring task-specific adaptation of multimodal models","Applications requiring multiple vision tasks (generation, editing, segmentation) from a single model"],"limitations":["Requires task-specific annotated datasets for each downstream task, increasing data collection burden","Contrastive decoding requires sampling multiple candidates, increasing inference latency and compute","No documented mechanism for adding new tasks post-training without retraining","Segmentation output format and resolution not specified; may be limited by token vocabulary size","Trade-off between task diversity and model specialization not quantified"],"requires":["Task-specific training datasets (image infilling, editing, segmentation, etc.)","Contrastive decoding implementation supporting multi-candidate sampling","Pretrained model from retrieval-augmented pretraining stage","Computational resources for multi-task fine-tuning"],"input_types":["partial images with text instructions (for infilling)","images with text editing instructions (for editing)","reference images with generation prompts (for controlled generation)","images for segmentation"],"output_types":["completed/edited images","segmentation masks as token sequences","generated images conditioned on references"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_3","uri":"capability://planning.reasoning.contrastive.decoding.for.improved.generation.quality","name":"contrastive decoding for improved generation quality","description":"CM3Leon implements a self-contained contrastive decoding method that improves generation quality by contrasting predictions from the model with a reference distribution during inference. Rather than requiring a separate quality model or discriminator, the method operates within the single multimodal decoder by sampling multiple candidate sequences and selecting or reranking them based on contrastive objectives. This approach is integrated into the SFT stage and applied during inference to improve both image and text generation without architectural modifications.","intents":["Improve visual quality of generated images without training separate quality models","Reduce artifacts and hallucinations in multimodal generation","Enhance text quality in image-to-text generation tasks","Provide a lightweight quality improvement mechanism for inference-time optimization"],"best_for":["Teams seeking to improve generation quality without model ensemble or auxiliary networks","Applications where inference latency can tolerate multi-candidate sampling","Research exploring contrastive methods for autoregressive generation"],"limitations":["Requires sampling multiple candidates (typically 2-4), increasing inference latency by 2-4x","Contrastive objective design not fully specified; effectiveness depends on reference distribution choice","No documented guidance on candidate count vs. quality trade-off","Computational cost scales with number of candidates; not suitable for latency-critical applications","Effectiveness not quantified separately from other SFT improvements"],"requires":["Trained multimodal decoder from SFT stage","Multi-candidate sampling capability during inference","Contrastive objective implementation (specific formulation not documented)"],"input_types":["text prompts or image context","candidate generation parameters"],"output_types":["reranked or selected image/text outputs","quality-improved generation results"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_4","uri":"capability://image.visual.zero.shot.image.generation.with.competitive.benchmark.performance","name":"zero-shot image generation with competitive benchmark performance","description":"CM3Leon achieves zero-shot image generation capability (without task-specific fine-tuning) through its retrieval-augmented pretraining and unified multimodal architecture. The model generates images directly from text prompts by predicting image tokens autoregressively, achieving MS-COCO FID score of 4.88 without any COCO-specific training. This zero-shot capability emerges from the large-scale pretraining on diverse text-image pairs and the model's ability to leverage retrieved examples during inference, enabling competitive performance on standard benchmarks without task-specific adaptation.","intents":["Generate images from text descriptions without model fine-tuning or task-specific training","Evaluate multimodal model quality on standard benchmarks (MS-COCO)","Deploy image generation without maintaining task-specific model variants","Benchmark against other text-to-image models on comparable metrics"],"best_for":["Researchers evaluating multimodal model capabilities","Teams seeking general-purpose image generation without task-specific training","Benchmarking studies comparing text-to-image approaches"],"limitations":["Zero-shot FID of 4.88 is competitive but not state-of-the-art compared to specialized diffusion models (DALL-E 3, Midjourney achieve lower FID)","Autoregressive token-by-token generation is slower than diffusion-based approaches","No documented comparison of zero-shot vs. fine-tuned performance on COCO","Benchmark evaluation limited to MS-COCO; performance on other datasets not documented","Image quality may be limited by discrete tokenization resolution"],"requires":["Pretrained CM3Leon model with retrieval-augmented pretraining","Text prompt input","Inference infrastructure supporting autoregressive image token generation","Image decoder to convert tokens back to pixel space"],"input_types":["text prompts (natural language descriptions)"],"output_types":["generated images (pixel space)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_5","uri":"capability://automation.workflow.training.efficiency.optimization.achieving.5x.compute.reduction","name":"training efficiency optimization achieving 5x compute reduction","description":"CM3Leon achieves 5x reduction in training compute compared to comparable multimodal methods through its unified decoder-only architecture and retrieval-augmented pretraining approach. The efficiency gains come from eliminating separate vision encoders and cross-modal fusion layers, using a single transformer decoder for all modalities, and leveraging retrieval to improve learning efficiency without scaling model size. The paper documents this efficiency metric but does not provide detailed breakdowns of which architectural choices contribute most to the improvement.","intents":["Reduce pretraining costs for multimodal models by 5x compared to encoder-decoder approaches","Scale multimodal model development with limited compute budgets","Understand architectural trade-offs between parameter efficiency and performance","Benchmark training efficiency of different multimodal architectures"],"best_for":["Organizations with constrained compute budgets for model development","Research teams exploring efficient multimodal architectures","Teams seeking to understand architectural efficiency trade-offs"],"limitations":["5x efficiency claim is relative to unspecified baseline methods; absolute compute requirements not documented","No breakdown of efficiency gains by architectural component (decoder-only vs. retrieval vs. tokenization)","Efficiency measured on pretraining; downstream task fine-tuning costs not documented","Comparison baselines not explicitly named; unclear which methods are 'comparable'","No analysis of efficiency vs. performance trade-off (e.g., does 5x efficiency come with quality loss?)"],"requires":["Large-scale multimodal pretraining dataset","Retrieval infrastructure for augmented pretraining","Distributed training infrastructure","Compute resources (amount not specified, but 5x less than alternatives)"],"input_types":["training dataset specifications","model architecture parameters"],"output_types":["training efficiency metrics (FLOPs, wall-clock time, memory usage)","pretrained model weights"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_6","uri":"capability://data.processing.analysis.discrete.image.tokenization.for.unified.sequence.representation","name":"discrete image tokenization for unified sequence representation","description":"CM3Leon converts images into discrete tokens using an image tokenizer, enabling images to be represented as sequences of integers identical to text tokens. This tokenization approach allows the unified decoder to process images and text through the same autoregressive mechanism without separate vision-specific processing. The discrete tokens are learned during pretraining and enable the model to treat image generation as a sequence prediction problem, though the specific tokenizer architecture (VQ-VAE, learned codebook, etc.) is not detailed in the documentation.","intents":["Represent images as discrete sequences enabling unified processing with text","Enable autoregressive image generation through token prediction","Simplify model architecture by eliminating continuous image representations","Support bidirectional text-image generation in a single model"],"best_for":["Teams building unified multimodal models with discrete representations","Researchers exploring token-based approaches to vision tasks","Applications requiring consistent handling of text and image modalities"],"limitations":["Discrete tokenization introduces quantization loss compared to continuous representations, potentially losing fine-grained visual details","Token vocabulary size limits image resolution and quality; larger vocabularies increase model size and inference latency","Tokenizer training requires separate pretraining stage before main model training","No documented analysis of tokenization quality vs. vocabulary size trade-off","Decoding from tokens back to pixels requires a separate decoder network"],"requires":["Image tokenizer (architecture not specified; could be VQ-VAE, learned codebook, or similar)","Discrete token vocabulary (size not documented)","Image decoder for converting tokens back to pixel space","Training data for tokenizer pretraining"],"input_types":["images (pixel space)"],"output_types":["discrete token sequences","reconstructed images (from tokens)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_7","uri":"capability://text.generation.language.image.to.text.generation.and.captioning","name":"image-to-text generation and captioning","description":"CM3Leon can generate descriptive text captions from images by conditioning the autoregressive decoder on image tokens and predicting text tokens. The bidirectional nature of the unified architecture enables the model to learn image-to-text generation during pretraining without separate caption-specific training. The model leverages the same retrieval-augmented pretraining and multi-task fine-tuning as image generation, allowing it to generate contextually relevant descriptions from visual input.","intents":["Generate natural language descriptions from images automatically","Create captions for image datasets without manual annotation","Enable accessibility features by providing text descriptions of images","Support image understanding tasks within a unified multimodal model"],"best_for":["Teams building image understanding and captioning applications","Accessibility-focused projects requiring automatic image descriptions","Multimodal systems requiring bidirectional text-image understanding"],"limitations":["Caption quality depends on pretraining data diversity; performance on domain-specific images not documented","No documented comparison with specialized captioning models (BLIP, LLaVA)","Autoregressive generation may produce repetitive or hallucinated descriptions","No mechanism documented for controlling caption length or style","Performance metrics (BLEU, CIDEr, METEOR) not provided in documentation"],"requires":["Pretrained CM3Leon model","Image input in tokenized form","Text generation parameters (temperature, max length, etc.)"],"input_types":["images (as discrete tokens)"],"output_types":["text sequences (natural language captions)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_8","uri":"capability://image.visual.language.guided.image.editing.with.instruction.following","name":"language-guided image editing with instruction following","description":"CM3Leon supports language-guided image editing where users provide text instructions to modify existing images. During the multi-task SFT stage, the model learns to condition on both the original image and text editing instructions, predicting modified image tokens that reflect the requested changes. This capability enables tasks like object removal, style transfer, attribute modification, and other edits specified through natural language without requiring separate editing models or mask inputs.","intents":["Edit images using natural language instructions without manual mask creation","Remove, add, or modify objects in images through text commands","Apply style changes or attribute modifications via language descriptions","Build interactive image editing applications with intuitive text-based control"],"best_for":["Teams building user-friendly image editing applications","Accessibility-focused tools enabling non-technical image manipulation","Creative applications requiring flexible instruction-based editing"],"limitations":["Editing quality depends on instruction clarity and model's understanding of spatial relationships","No documented mechanism for precise spatial control (e.g., 'edit the left side'); relies on language descriptions","Autoregressive generation may produce artifacts or inconsistencies in edited regions","No comparison with specialized editing models (Instruct-Pix2Pix, ControlNet) provided","Editing success metrics and failure cases not documented","Requires task-specific fine-tuning data; generalization to novel edit types unclear"],"requires":["Pretrained CM3Leon model with multi-task SFT","Image editing training dataset with text instructions and ground-truth edits","Image tokenizer and decoder","Text instruction input"],"input_types":["original image (as tokens)","text editing instructions (natural language)"],"output_types":["edited images (as tokens, decoded to pixels)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon__cap_9","uri":"capability://image.visual.image.controlled.generation.with.reference.conditioning","name":"image-controlled generation with reference conditioning","description":"CM3Leon supports image-controlled generation where a reference image provides visual style, composition, or content guidance for generating new images. During multi-task SFT, the model learns to condition on reference images and text prompts, generating new images that follow the reference's visual characteristics while incorporating the text description. This enables style transfer, composition-guided generation, and other reference-based image synthesis tasks within the unified decoder.","intents":["Generate images in the style of a reference image with text-specified content","Perform style transfer by conditioning on reference and text description","Create variations of images with specific visual characteristics","Enable composition-guided generation for consistent visual layouts"],"best_for":["Creative applications requiring style-consistent image generation","Teams building reference-based image synthesis tools","Applications needing visual consistency across generated images"],"limitations":["Style transfer quality depends on reference image similarity and text description clarity","No documented mechanism for controlling style strength or influence","Autoregressive generation may not perfectly preserve reference style details","No comparison with specialized style transfer models (AdaIN, CycleGAN) provided","Generalization to diverse reference styles not documented","Requires task-specific fine-tuning data with reference-guided examples"],"requires":["Pretrained CM3Leon model with multi-task SFT","Reference image (as tokens)","Text prompt describing desired content","Training data with reference-guided generation examples"],"input_types":["reference image (as tokens)","text prompt (natural language description)"],"output_types":["generated images (as tokens, decoded to pixels)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Image tokenizer (discrete token vocabulary for visual content)","Pretraining dataset with aligned text-image pairs at scale","Transformer decoder architecture with sufficient capacity (model size not specified in documentation)","Retrieval augmentation infrastructure for pretraining stage","Large-scale multimodal dataset with text-image alignment","Retrieval index (vector database or similar) supporting fast similarity search","Pretraining infrastructure capable of dynamic sequence construction with retrieved examples","Compute resources for large-scale pretraining (efficiency gains are relative, not absolute)","Pretrained CM3Leon model with multi-task SFT","Segmentation token vocabulary (special tokens for each semantic class)"],"failure_modes":["Requires discrete image tokenization which may lose fine-grained visual details compared to continuous representations","Autoregressive image generation is slower than diffusion-based methods due to token-by-token decoding","Zero-shot performance (FID 4.88 on MS-COCO) requires substantial pretraining compute (5x more efficient than alternatives, but still significant)","No documented support for video or 3D modalities, only static images","Requires a large indexed corpus of text-image pairs, adding infrastructure complexity","Retrieval latency during pretraining adds computational overhead compared to standard pretraining","Quality of retrieved examples directly impacts model performance; poor retrieval degrades learning","No documented mechanism for updating or refreshing the retrieval corpus post-training","Segmentation resolution limited by token vocabulary size; may be coarser than specialized segmentation models","No documented comparison with specialized segmentation models (DeepLab, Mask R-CNN, SAM)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.4,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:27.894Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon","compare_url":"https://unfragile.ai/compare?artifact=scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon"}},"signature":"aaFn5tkRl+sruucI8x+5H2iHbtN1DCUzhDmutf7WZRW+JK48HQw5edyJypiQcmdvCsKAX2nhrDqBQ08T2zn7Cg==","signedAt":"2026-06-21T07:25:31.306Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon","artifact":"https://unfragile.ai/scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon","verify":"https://unfragile.ai/api/v1/verify?slug=scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning-cm3leon","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}