Instructions to use cstr/OXR-1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cstr/OXR-1.0-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf cstr/OXR-1.0-GGUF:F16 # Run inference directly in the terminal: llama cli -hf cstr/OXR-1.0-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cstr/OXR-1.0-GGUF:F16 # Run inference directly in the terminal: llama cli -hf cstr/OXR-1.0-GGUF:F16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf cstr/OXR-1.0-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama cli -hf cstr/OXR-1.0-GGUF:F16
Use Docker
docker model run hf.co/cstr/OXR-1.0-GGUF:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use cstr/OXR-1.0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cstr/OXR-1.0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cstr/OXR-1.0-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cstr/OXR-1.0-GGUF:F16
- Ollama
How to use cstr/OXR-1.0-GGUF with Ollama:
ollama run hf.co/cstr/OXR-1.0-GGUF:F16
- Unsloth Desktop
- Docker Model Runner
How to use cstr/OXR-1.0-GGUF with Docker Model Runner:
docker model run hf.co/cstr/OXR-1.0-GGUF:F16
- Lemonade
How to use cstr/OXR-1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cstr/OXR-1.0-GGUF:F16
Run and chat with the model
lemonade run user.OXR-1.0-GGUF-F16
List all available models
lemonade list
- Atomic Chat
OXR-1.0 GGUF (CrispEmbed)
GGUF conversions of SoMarkAI/OXR-1.0
by SoMark Team, for CrispEmbed's native ggml runtime.
All credit for the model belongs to the original authors; these files only change the storage format.
They target CrispEmbed's native OXR profile (general.architecture = oxr, short vit. vision tensor names) and are not loadable by llama.cpp. Full-page document parsing additionally uses the D-FINE layout detector published separately as cstr/OXR-1.0-DFINE-layout-GGUF.
License and attribution
- OXR License: Apache-2.0 with additional terms (
LICENSE, verbatim; base text inLICENSE-APACHE-2.0). The additional terms take precedence:- commercial use without a separate SoMark commercial license is permitted only while you and your affiliates stay below USD 2,000,000 in total funding and in annual revenue;
- an online service built on OXR or its derivatives must prominently identify SoMark OXR;
- offering third parties a hosted API that competes with SoMark's document parsing / OCR / layout services is not permitted at any size without SoMark's written license;
- violating these terms terminates the license automatically.
- These GGUF files are derivative works and are redistributed under the same terms;
NOTICEcarries the attribution. - CrispEmbed only loads them after explicit acknowledgement (
CRISPEMBED_OXR=1andCRISPEMBED_ACCEPT_OXR_LICENSE=1); its model registry requires--accept-license oxrto download them, andcrispembed-serverwarns about sections 2 and 3 when an OXR stage is configured. UPSTREAM_README.mdis the unmodified upstream model card at revisionc708407f9d2b4df658f2543d9132b62bb7221df4(SHA256a3241e175099ea57a4491be25cce8e965945889e41ed25c3c01911fd17a2236f).
Statement of changes (Apache-2.0 section 4(b))
These files were modified from the original by CrispEmbed: the checkpoint weights of revision
c708407f9d2b4df658f2543d9132b62bb7221df4 were format-converted to GGUF (tensor renaming, casting, tokenizer and
preprocessing metadata embedded).
No weights were trained, fine-tuned, merged or otherwise altered.
Files
| File | Precision | Bytes | SHA256 |
|---|---|---|---|
oxr-1.0-f16.gguf |
F16 | 4,440,553,984 | eac8754f4886ba4197416083d49089a3d86c8644f0e5650f44fcf69059d81a57 |
- F16: F16 attention/MLP matrices; embeddings, lm_head, visual vocabulary/head, patch/position tables and norms F32
How the files were produced
- Downloaded
SoMarkAI/OXR-1.0at the pinned revisionc708407f9d2b4df658f2543d9132b62bb7221df4; the converter (models/convert-oxr-to-gguf.py) checks the source files, tensor names and shapes. - Quantized variants were produced with CrispEmbed's own
crispembed-quantizefrom the listed source GGUF. - Built and uploaded by CrispEmbed commit
893293336fd51c2d4c2086bd93b986c8b8a45624(workflowocr-gguf-publish.yml, protocoldocs/oxr-publication-v1.json);manifest.jsonlists every recipe, input pin, receipt and file hash.
Parity status (CrispEmbed native vs. the original Python model, CPU, greedy)
- The reference arms (f32, f32-reuse) reproduce the original model's generated token IDs
exactly on all 5 frozen requests (
formula(22),table(261),japanese(38),receipt(202),german(401) tokens), CrispEmbed commit0e9060fc, reference revisionc708407f9d2b. - F16 is token-ID exact on the same 5 cases (receipt
tests/results/oxr/precision.json). - Strict per-stage numerical budgets are not met, including F32: one-ulp F32 differences at the decoder's ~2^15 outlier activations exceed the fixed 0.002 absolute budget (worst cosine 0.99999988); decoded IDs and text are exact. Also not verified: GPU execution, mobile/WASM, representative document corpora beyond the five frozen cases.
Usage
OXR support is experimental and requires acknowledging the OXR License:
CRISPEMBED_OXR=1 CRISPEMBED_ACCEPT_OXR_LICENSE=1 crispembed --ocr-pipeline page.png \\
--ocr-engine oxr --ocr-rec oxr-1.0-f16.gguf
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