Colpali Cookbooks
Hands-on ColPali recipes for multimodal document retrieval.
The definitive learning resource for ColPali-based multimodal RAG. The ViDoRe V3 benchmark and human-annotated data make it indispensable for research. But you need GPU access and fine-tuning know-how no turnkey solution here.
- ML engineers building multimodal RAG systems for enterprise documents
- Data scientists fine-tuning vision-language models on domain-specific data
- Researchers evaluating retrieval accuracy on the ViDoRe benchmark
- Teams needing an open-source, reproducible document retrieval pipeline
- Users seeking a production-ready, hosted API for document search
- Non-technical teams needing no-code document indexing
- Beginners without experience in fine-tuning LLMs or VLMs
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In short
Colpali Cookbooks — Hands-on ColPali recipes for multimodal document retrieval. Best for ML engineers building multimodal RAG systems for enterprise documents, Data scientists fine-tuning vision-language models on domain-specific data, Researchers evaluating retrieval accuracy on the ViDoRe benchmark. Free to use.
What's new in Colpali Cookbooks
Checked 14 days agoAcross the latest 8 updates: 6 feature updates, 1 launch and 1 news mention.
Hugging Face and Cerebras bring Gemma 4 to real-time voice AI
Partnership to run Gemma 4 on Cerebras hardware for low-latency voice AI inference.
Featuring Every Eval Ever Results on Hugging Face Model Pages
Model pages now surface evaluation results from all public leaderboards directly.
ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration
New benchmark evaluates AI agents on migrating enterprise Java codebases across frameworks.
Filter Models page by Hardware
New hardware filter on Models page shows only models compatible with your specific GPU/CPU/Apple Silicon.
Run a vLLM Server on HF Jobs in One Command
Guide to deploy a vLLM inference server on Hugging Face Jobs with a single command.
Share your feedback with us
New user menu option lets you send feedback, bug reports, or feature suggestions directly to Hugging Face.
Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel
Integration with NeMo AutoModel simplifies and speeds up fine-tuning of transformer models on HF.
Service Accounts for Enterprise organizations
Enterprise orgs can create machine identities with fine-grained tokens for CI/CD and automation.
Viability Score
How likely is Colpali Cookbooks to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Step-by-step fine-tuning recipes for ColPali models
- Inference tutorials for multimodal document retrieval
- Support for ColPali v1.3, ColQwen2, ColSmol models
- Integration with ViDoRe V3 benchmark (10 datasets, 26K+ pages)
- Human-annotated ground truth with bounding boxes
- Multilingual query support across 6 languages
- GPU-accelerated training via NVIDIA contributions
- Hugging Face Hub integration for models and datasets
- Open-source code on GitHub with community contributions
- Domain adaptation recipes for custom document types
About Colpali Cookbooks
Colpali Cookbooks is an open-source collection of tutorials and recipes hosted on Hugging Face under the Vidore organization. It provides step-by-step guidance for learning, fine-tuning, and deploying ColPali vision-language models for multimodal retrieval-augmented generation (RAG). Built by ILLUIN Technology with NVIDIA contributions, the cookbooks target ML engineers and data scientists building enterprise-grade retrieval systems on visually complex documents like invoices, reports, and forms. The recipes cover inference, fine-tuning, and domain adaptation for the ColPali model family, including ColPali v1.3, ColQwen2 v1.0, ColQwen2.5 v0.2, and ColSmol. A key feature is tight integration with the ViDoRe benchmark suite (V1, V2, and the latest V3), which includes 10 datasets, 26,000+ pages, 3,099 human-annotated queries in 6 languages, and bounding-box ground truth. Users can iterate model performance against real-world enterprise retrieval tasks. The cookbooks also leverage the Hugging Face ecosystem (Hub, Datasets, Spaces) for end-to-end experimentation. Unlike proprietary RAG APIs, this resource is fully open-source and requires hands-on coding and GPU resources.
Behind the Verdict
Colpali Cookbooks is the go-to open-source tutorial suite for anyone serious about vision-language document retrieval. The recipes walk you through inference on ColPali v1.3, fine-tuning on custom data, and evaluating with the ViDoRe V3 benchmark, which sets a high bar with 26K+ pages and human annotations across 6 languages. If you're building a retrieval pipeline for invoices or technical manuals, this gives you the building blocks without vendor lock-in. But it's not a plug-and-play API. You'll need Python, PyTorch, and a GPU. Compared to commercial options like Glean or Google Document AI, the cookbooks offer transparency and control but demand more setup time. Where it bites: the documentation is code-heavy and assumes familiarity with Hugging Face Transformers and vision-language models. Best for ML engineers who want to benchmark and fine-tune their own retriever. Skip if you need a managed, no-code document search solution.
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Use Cases
- Fine-tune ColPali on your own invoices and receipts to enable accurate page-level retrieval.
- Evaluate retrieval accuracy on the ViDoRe V3 benchmark with 10 enterprise datasets.
- Adapt ColQwen2 for multilingual document search across 6 languages.
- Use ColSmol-256M for low-resource deployment on edge devices.
- Build a multimodal RAG pipeline that retrieves relevant document pages from a large knowledge base.
Models Under the Hood
as of 2026-07-15
Limitations
- No dedicated API or hosted service; users must run models locally or on their own infrastructure.
- GPU hardware is recommended for fine-tuning and inference.
- The cookbooks assume familiarity with Hugging Face libraries and PyTorch.
Integrations
Resources & Guides
Official links
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