timesfm vs unlimited-ocr
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
timesfm
Pretrained time-series foundation model for zero-shot forecasting from Google Research
unlimited-ocr
OCR model that parses long documents and multi-page PDFs in one pass
Verdict
timesfm ranks higher, 70 to 55. Of the 3 score parts we measure for both, timesfm leads on 2, unlimited-ocr on none and 1 is even.
- Adoptiontimesfm leads by 37 points, 65 to 28: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score unlimited-ocr on this; timesfm has 58.
- Agent-readytimesfm leads by 30 points, 30 to 0: a wide gap.
Pick timesfm if you are
Engineers and researchers forecasting time series without per-task training
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
Pick unlimited-ocr if you are
Engineers self-hosting OCR for scanned documents and PDFs on NVIDIA GPUs
- Multi-page parsing in one request, with 32768 max context length in the examples
- Three inference paths: Transformers, vLLM (Docker images), and SGLang with OpenAI-compatible API
| What we compare | timesfm | unlimited-ocr |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 65, popular | 28, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 58, fair | Not scored for this kind |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 34.5k | 27.3k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Sep 2026 | Oct 2026 |
| Last release | Aug 2026 | None published |
| Language | Python | Python |
| Docker | No | No |
| GPU | Optional | Required |
| arm64 or Apple Silicon | Mentioned | Not stated |
timesfm
TimesFM is a pretrained decoder-only model for time-series forecasting, installed as a Python package with PyTorch or MLX (Apple silicon) backends. Version 3.0 forecasts univariate and multivariate series with past-only and past-and-future covariates, returning point forecasts plus nine quantiles (0.1 to 0.9). The code is Apache-2.0, but 3.0 weights carry a non-commercial license.
Who it is for: Engineers and researchers forecasting time series without per-task training
Strengths
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
- MLX backend runs 3.0 on Apple silicon without PyTorch, numerically matched to PyTorch
- Includes LoRA fine-tuning example (HF Transformers + PEFT), unit tests and an agent SKILL.md
Weaknesses
- TimesFM 3.0 weights are non-commercial and non-production; commercial use only via Google Cloud services
- Not an officially supported Google product
- Forecasting only; contexts beyond 15,360 points are truncated to the most recent
- README code imports differ between 3.0 examples and the PyPI 2.0.2 note, which is unclear
- GPU optional
- Needs PyTorch or MLX, Hugging Face checkpoints
- Models: TimesFM 3.0 (PyTorch checkpoint), TimesFM 2.5 (200M params, 16k context), TimesFM 1.0 and 2.0 (v1 archive)
unlimited-ocr
Unlimited-OCR is a Baidu document-parsing vision-language model, building on DeepSeek-OCR, with weights on Hugging Face. The repo ships inference code for Transformers, vLLM and SGLang, the last exposing an OpenAI-compatible chat API. It handles single images and multi-page input, with PDFs converted to images first.
Who it is for: Engineers self-hosting OCR for scanned documents and PDFs on NVIDIA GPUs
Strengths
- Multi-page parsing in one request, with 32768 max context length in the examples
- Three inference paths: Transformers, vLLM (Docker images), and SGLang with OpenAI-compatible API
- infer.py batch script launches SGLang and sends concurrent requests for image dirs or PDFs
- MIT license; training supported through ms-swift
Weaknesses
- Requires an NVIDIA GPU; examples tested only on CUDA 12.9 with Python 3.12
- PDFs must be rasterized to images with PyMuPDF before parsing
- SGLang setup needs a bundled dev wheel and custom logit processor flags
- VRAM requirement is not stated in the README
- GPU required
- Needs PyTorch 2.10.0, transformers 4.57.1, PyMuPDF, vLLM or SGLang (optional)
- Models: baidu/Unlimited-OCR (safetensors)
- port 10000
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