Open-Weight LLM · Private & Custom AI
h2ovl-mississippi-800m
Compact vision-language model for OCR, document extraction, and multi-modal ops automation in resource-constrained environments.
H2OVL-Mississippi-800M is a 0.8B parameter vision-language model optimized for text recognition, document comprehension, and OCR tasks. For ops teams, it enables private, on-premise automation of invoice processing, receipt scanning, form extraction, and chart/table interpretation without external API calls.
Model facts
Private deployment
Run h2ovl-mississippi-800m in your own environment
Runs on modest GPU hardware (estimated 2–4 GB VRAM in bfloat16). Deploy via transformers + torch in your own data center or edge device. No external API; all processing stays in your environment. Custom code is required (trust_remote_code=True), so code review is necessary before production.
Operational AI use cases
Expense & Invoice Processing
Automate receipt/invoice scanning workflows. Extract vendor, amount, date, line items into structured JSON. Feed output directly into accounting systems or expense management platforms. Reduces manual data entry and enables real-time audit trails.
Document Triage & Form Extraction
Ingest scanned forms, contracts, or compliance documents. Extract fields, tables, and handwritten sections into structured data. Route documents to relevant departments (HR, Legal, Finance) based on content. Replaces manual sorting and keying.
Operational Knowledge from Charts & Tables
Process reports, dashboards, and visualizations. Extract metrics, trends, and structured data from charts, graphs, and tables. Pipe into BI systems, alerting workflows, or internal knowledge bases. Enables automated insights from unstructured reporting.
Custom AI
As a base for custom AI
Strong foundation for building custom document-processing products or internal tools. Fine-tune on proprietary document types (insurance claims, medical records, shipping manifests) to create domain-specific extractors. Model card includes JSON extraction prompt engineering patterns as a starting point.
In the operating system
Where it fits
Sits in the perception/ingestion layer of an ops AI stack. Acts as the vision front-end for document agents, feeding extracted data into workflow orchestration, knowledge bases, and decision-making pipelines. Pairs well with a text LLM (like H2O-Danube) for reasoning on extracted content.
Data control & security
Self-hosting eliminates API dependency and network transfer of sensitive documents (invoices, contracts, medical records). Data remains in your environment, simplifying compliance with HIPAA, SOX, GDPR. However, self-hosting requires your team to manage model updates, security patches, and infrastructure—this is an architecture choice, not a security guarantee from the model itself.
Hardware footprint
Estimated 2–4 GB VRAM (bfloat16, single image). Batch processing (10+ images) may require 8–12 GB. CPU inference possible but slow (~2–5 sec/image). Optimize with quantization (ONNX, int8) for edge deployment. Verify on your hardware before committing.
Integration
Accepts images via file path or file object; outputs text and JSON. Use `model.chat()` API for multi-turn conversations. Designed for transformers inference; integrate via FastAPI, Ray Serve, or Hugging Face Inference Server for scaling. Batch processing via standard PyTorch patterns. Requires `flash_attention_2` for efficiency; verify CUDA compatibility before deployment.
When it's not the right fit
- —Real-time, low-latency requirements (inference ~500ms–2s per image on typical GPU).
- —Handwritten text or heavily distorted/rotated content (trained on clean, structured documents).
- —Models larger than 800M needed for complex multi-modal reasoning tasks (consider 2B alternatives for stronger benchmarks).
- —Strict reproducibility requirements without custom code review (requires trust_remote_code in transformers).
Alternatives to consider
Qwen2-VL-2B
2.1B parameters, higher OCRBench score (797 vs 751). Better general vision-language performance; trade-off is ~2.5× more VRAM and slower inference.
InternVL2-1B
1B parameters, similar footprint. Competitive OCR (755), stronger on multi-modal benchmarks. Good middle ground if you need more reasoning capacity.
PaliGemma-3B-mix-448
Google-backed, permissive license. Larger model (2.9B) with broader instruction-tuning. Better for open-ended document Q&A; worse OCR performance than Mississippi.
Related open models
FAQ
Can we fine-tune Mississippi-800M on our proprietary documents?
Yes. Apache-2.0 permits modification. Model card does not document fine-tuning procedures, so you'll need to experiment or consult H2O documentation. Start with LoRA (low-rank adaptation) for efficiency.
What license restrictions apply to a commercial product built on this model?
Apache-2.0 allows commercial use, modification, and distribution. You must include a copy of the license and state changes. No warranty or liability. No royalties to H2O. Verify with your legal team for your specific use case.
How do we deploy this privately without relying on HuggingFace?
Download the model weights and tokenizer (safetensors format). Host on your infrastructure (VPS, Kubernetes, on-prem GPU). Use transformers library or ONNX for inference. No external calls required. You manage updates and security.
Is OCRBench the only metric that matters for our use case?
No. OCRBench is strong (751 for 800M), but test on YOUR document types. Benchmark on invoices, forms, and charts relevant to your business. Mississippi excels at structured, printed text; performance on handwritten or degraded images is unknown.
Build a Private Document AI System
Mississippi-800M is built for on-premise ops automation. Partner with LLM.co to integrate it into your custom AI stack—from invoice processing to compliance automation. Keep data private, own the model, automate the workflow.