GGUF files and model card

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+teleocr-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+teleocr-q8v.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: XingChen-AGI/TeleOCR
+base_model_relation: quantized
+pipeline_tag: image-text-to-text
+library_name: teleocr-rs
+tags:
+- gguf
+- ocr
+- document-parsing
+- candle
+- rust
+---
+
+# TeleOCR — GGUF for teleocr-rs
+
+GGUF conversions of [XingChen-AGI/TeleOCR](https://huggingface.co/XingChen-AGI/TeleOCR)
+(China Telecom, ~1.2B, document parsing: layout, text, LaTeX formulas, tables, code)
+for **[teleocr-rs](https://github.com/rzafiamy/teleocr-rs)**, a Rust/Candle port.
+
+> **Not for llama.cpp.** TeleOCR's decoder is Qwen3-style (head_dim 128 ≠ hidden/heads,
+> per-head QK-norm) behind a Qwen2.5-VL config, which llama.cpp does not load. These files
+> are single-file GGUFs (weights + config + tokenizer) in the teleocr-rs layout.
+
+| file | size | weights | use |
+|---|---|---|---|
+| `teleocr-q8v.gguf` | 1.5 GB | text and vision linears Q8_0 | **GPU** (recommended) |
+| `teleocr-q8_0.gguf` | 2.0 GB | text Q8_0, vision F16 | **CPU** (Q8_0 vision is slow on CPU) |
+
+## Accuracy
+
+Greedy output is **token-identical** to the transformers reference on the five model-card
+samples (text, formula, table, code, 767-token layout), for both files, on CPU and CUDA.
+Both files also give identical Markdown on six pages of the TeleOCR paper.
+
+## Speed (RTX 4090)
+
+~225 tokens/s per sequence; vision tower 0.38 s for a 1036×1036 page; full page parsing
+(layout + all blocks, batch 8) ~3.2 s per page. Peak VRAM ~6.4 GB at batch 8.
+
+## Usage
+
+```bash
+git clone https://github.com/rzafiamy/teleocr-rs && cd teleocr-rs
+cargo build --release -p teleocr-cli --features cuda   # or without --features cuda for CPU
+scripts/fetch-pdfium.sh target/release                  # PDF input
+
+target/release/teleocr parse -m teleocr-q8v.gguf paper.pdf --pages 1-3       # Markdown
+target/release/teleocr run   -m teleocr-q8v.gguf table.png -t table           # one task
+target/release/teleocr serve -m teleocr-q8v.gguf --port 8090                  # POST /v1/ocr
+```
+
+Also served by [zallama](https://github.com/rzafiamy/zallama) (`modality: ocr`, `POST /v1/ocr`).
+
+## Reproduce
+
+```bash
+teleocr convert ./TeleOCR -o teleocr-q8v.gguf --vision-dtype q8_0
+teleocr convert ./TeleOCR -o teleocr-q8_0.gguf            # vision F16
+```
+
+## License
+
+Apache-2.0, as the original model. Credits: Cai et al., *TeleOCR: Navigating Document
+Parsing Across Digital and Camera-Captured Documents*, arXiv:2608.12898.

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