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Open-weight model · Image and text to text

povl-sft-full-3ep

by Cheney Wong CheneyWong/povl-sft-full-3ep

povl-sft-full-3ep is an open-weight model for image and text to text from Cheney Wong, released under Apache License 2.0. It has 906M parameters and a 131,072-token context. At 16-bit it needs about 2.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

First-stage supervised fine-tuning: full-parameter, 100,122 samples, 3 epochs. Its job was to stop the model from generating unbounded output on unseen layouts -- average completion length on real photographed pages had grown to 6,610 characters with a 37.3%…

Parameters906M
Context131,072
Weights1.8 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve povl-sft-full-3ep (906M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 1.8 GB 2.2 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.9 GB 1.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.5 GB 0.5 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.

povl-sft-full-3ep on every accelerator the SAVRN Index prices, at every precision

Model Card

By Cheney Wong, published under apache-2.0, revision 04441ad3e0ba.

First-stage supervised fine-tuning: full-parameter, 100,122 samples, 3 epochs. Its job was to stop the model from generating unbounded output on unseen layouts -- average completion length on real photographed pages had grown to 6,610 characters with a 37.3% truncation rate. Fine-tuned from CheneyWong/tablerecognitionpaddleocrvl16 (PaddleOCR-VL-1.6), which is the released base model, unmodified except for a cleaning pass. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (not evaluated) (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction…

Read Cheney Wong's full model card

What this checkpoint is

First-stage supervised fine-tuning: full-parameter, 100,122 samples, 3 epochs. Its job was to stop the model from generating unbounded output on unseen layouts -- average completion length on real photographed pages had grown to 6,610 characters with a 37.3% truncation rate.

Lineage

Fine-tuned from CheneyWong/table_recognition_paddleocrvl16 (PaddleOCR-VL-1.6), which is the released base model, unmodified except for a cleaning pass.

PaddleOCR-VL-1.6 (base)
  └─ povl-sft-full-3ep   <-- this checkpoint

Results on public datasets

Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded.

(not evaluated)

Note on SPRINT-MUSTARD: its ground truth carries no cell text, so its TEDS (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction that correctly reads the visible text scores 0.33 TEDS / 1.00 TEDS-S. Only the TEDS-S column is meaningful for that dataset.

Known limitations

Truncation got worse, not better (8.6% -> 37.3%) while in-domain scores rose. In-domain metrics were unchanged or slightly shorter, so the blow-up was specific to unseen layouts. This is the exact trade-off the next stage exists to undo.

Task format: a single table image plus the prompt Table Recognition:, one pass, emitting OTSL markup (<fcel> <ecel> <ucel> <lcel> <xcel> <nl>). Evaluation numbers are tracked separately and are not part of this card.

Configuration

Architecture
PaddleOCRVLForConditionalGeneration
Context length (tokens)
131,072
Layers
18
Hidden size
1,024
Feed-forward size
3,072
Attention heads
16
Key/value heads
2
Head dimension
128
Vocabulary size
103,424
Model type
paddleocr_vl

Identity and Version

Repository
CheneyWong/povl-sft-full-3ep
Publisher
Cheney Wong
Task
Image and text to text
Modality
Image and text
Library
transformers
Parameters
906M parameters
Languages
Not stated by the source
Revision
04441ad3e0ba31a7ee037c6a8c4d8734a7bd31c0
First published
2026-10-12
Last updated
2026-10-12

Files and Weights

13 files, 1.8 GB in total. The weights are 2 files totalling 1.8 GB in bin, safetensors.

Weights2 files · 1.8 GB
Configuration6 files · 39.7 KB
Tokenizer2 files · 11.2 MB
Documentation1 file · 1.7 KB
Other1 file · 1.5 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights1.8 GB 91f70730ec83
training_args.binWeights6.7 KB 7fc8a0f2231c
args.jsonConfiguration16.4 KB —
config.jsonConfiguration2.4 KB —
generation_config.jsonConfiguration133 B —
preprocessor_config.jsonConfiguration641 B —
processor_config.jsonConfiguration749 B —
trainer_state.jsonConfiguration19.4 KB —
README.mdDocumentation1.7 KB —
chat_template.jinjaOther1.5 KB —
.gitattributesRepository1.6 KB —
tokenizer.jsonTokenizer11.2 MB c8a215a59183
tokenizer_config.jsonTokenizer804 B —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
1.8 GB
Download from Cheney Wong

Released by Cheney Wong through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published1.8 GB
16-bit1.8 GB
8-bit0.9 GB
4-bit0.5 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About povl-sft-full-3ep

How much GPU memory does povl-sft-full-3ep need?

About 2.2 GB at 16-bit and 0.5 GB at 4-bit: the weights (906M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run povl-sft-full-3ep on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use povl-sft-full-3ep commercially?

Yes. povl-sft-full-3ep is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is povl-sft-full-3ep's context length?

131,072 tokens, from the maximum position embeddings in its published configuration.

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