# Lift

URL: https://interfaze.ai/models/datalab-tolift

[All models](https://interfaze.ai/models)

Lift by datalab-to, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Lift | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, document | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | unknown | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Lift | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/datalab-to/lift)

lift is a structured extraction model from [Datalab](https://www.datalab.to) that pulls structured JSON out of PDFs and images. Pass any JSON schema and lift returns a JSON object matching it, using schema-constrained decoding to guarantee valid, well-typed output.

Try lift in the [free playground](https://www.datalab.to/playground), or use the [hosted API](https://www.datalab.to/) for higher accuracy, per-field verification, and citations.

## Features

-   Extract structured data from documents
-   Pass any JSON schema
-   Handles multi-page documents in a single pass, including values that span pages
-   Two inference modes: local (HuggingFace) and remote (vLLM server)
-   CLI for single files, inline schemas, or whole directories
-   Schema Studio: a Streamlit app to build, save, and test schemas against your documents

## Quickstart

```
pip install lift-pdf


lift_vllm
lift_extract input.pdf ./output --schema schema.json


pip install lift-pdf[hf]
lift_extract input.pdf ./output --schema schema.json --method hf
```

A schema is standard JSON Schema. Keep it simple — `string`, `number`, `integer`, `boolean`, arrays of those, arrays of objects, and nested objects are all supported. Write a `description` for any field whose name isn't self-explanatory, and mark a field `required` only when it must appear; fields genuinely absent from a document come back `null`.

```
{
  "type": "object",
  "properties": {
    "invoice_number": {"type": "string", "description": "Invoice identifier"},
    "total": {"type": "number", "description": "Total amount due"},
    "line_items": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "description": {"type": "string"},
          "amount": {"type": "number"}
        }
      }
    }
  },
  "required": ["invoice_number", "total"]
}
```

## Usage

### With vLLM (recommended)

```
from lift import extract
from lift.model import InferenceManager


model = InferenceManager(method="vllm")
result = extract("document.pdf", "schema.json", model=model)
print(result.extraction)
```

### With HuggingFace Transformers

```
from lift import extract
from lift.model import InferenceManager


model = InferenceManager(method="hf")
result = extract("document.pdf", "schema.json", model=model)
print(result.extraction)
```

`extract` accepts the schema as a dict, a path to a `.json` file, an inline JSON string, or the name of a saved schema. Pass `page_range="0-5"` to limit PDF pages, and set `VLLM_API_BASE` to target a remote server.

## Benchmarks

Evaluated on a 225-document extraction benchmark (6–64 pages per document, ~11,000 scored fields) with adversarial cases planted throughout: cross-page values, exhaustive lists, fields that must be left null, near-miss distractors, multi-source aggregation. Scoring is deterministic exact-match against ground truth (numeric tolerance, normalized strings).

All models receive the same rendered page images, and extract each document in a single pass.

| Model | Size | Field accuracy | Full-document accuracy | Median latency\* | Features |
| --- | --- | --- | --- | --- | --- |
| Datalab API | — | 95.9% | 44.4% | 30.8s | Citations + Verification |
| Gemini Flash 3.5 | — | 91.3% | 40.0% | 28.1s |  |
| lift | 9B | 90.2% | 20.9% | 9.5s |  |
| Azure Content Understanding | — | 83.4% | 22.2% | 73.7s |  |
| NuExtract3 | 4B | 81.5% | 8.4% | 8.3s |  |
| Qwen3.5-9B | 9B | 76.3% | 24.0% | 16.8s |  |

\* Per document, 8 concurrent requests. Local models (lift, Qwen3.5-9B, NuExtract3) served with vLLM on a single GPU; Gemini, Datalab, and Azure via API. Latency varies with hardware and load — treat as relative, not absolute.

-   **Field accuracy** — fraction of individual schema fields extracted correctly.
-   **Full-document accuracy** — fraction of documents where _every_ field is correct.

Hosted models with verification, citations, and confidence scores are available via the [Datalab API](https://www.datalab.to) — test in the [playground](https://www.datalab.to/playground).

## Commercial Usage

Code is Apache 2.0. Model weights use a modified OpenRAIL-M license: free for research, personal use, and startups under $5M funding/revenue. Cannot be used competitively with our API. For broader commercial licensing, see [pricing](https://www.datalab.to/pricing?utm_source=hf-lift).

## Credits

-   [Huggingface Transformers](https://github.com/huggingface/transformers)
-   [vLLM](https://github.com/vllm-project/vllm)
-   [Qwen 3.5](https://github.com/QwenLM/Qwen3)

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)