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TeleOCR-GGUF

Image-Text-to-Text
GGUF
ocr
crispembed
document-parsing
teleocr
qwen2.5-vl
non-commercial
Model card Files
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TeleOCR-GGUF / README.md
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Publish TeleOCR GGUF: F16, Q8_0, Q4_K_M
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metadata 
license: other
license_name: qwen-research
license_link: LICENSE
base_model: XingChen-AGI/TeleOCR
base_model_relation: quantized
pipeline_tag: image-text-to-text
library_name: gguf
tags:
  - gguf
  - ocr
  - crispembed
  - document-parsing
  - teleocr
  - qwen2.5-vl
  - non-commercial

TeleOCR GGUF (CrispEmbed)

GGUF conversions of XingChen-AGI/TeleOCR by XingChen-AGI / China Telecom, 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 TeleOCR profile (general.architecture = teleocr, GGUF v1) and are not loadable by llama.cpp.

License and attribution

  • Non-commercial use only. TeleOCR's vision encoder derives from Qwen/Qwen2.5-VL-3B-Instruct, which Alibaba Cloud releases under the Qwen RESEARCH LICENSE AGREEMENT (LICENSE, verbatim). That agreement governs these weights: research and evaluation use only; commercial use requires a license from Alibaba Cloud.
  • The upstream TeleOCR card declares apache-2.0; that declaration (LICENSE-APACHE-2.0) covers the TeleOCR authors' own contributions but cannot relax the inherited Qwen terms (agreement section 3d). The lineage evidence (per-tensor weight cosines against every Qwen2.5-VL tower) is in CrispEmbed's tests/results/licenses/qwen-vision-lineage.json.
  • NOTICE carries the attribution the Qwen agreement requires. Built with Qwen.
  • CrispEmbed only loads these files with CRISPEMBED_ACCEPT_QWEN_RESEARCH_LICENSE=1, and its model registry requires --accept-license qwen-research to download them.
  • UPSTREAM_README.md is the unmodified upstream model card at revision b3f6a0c084db72f971e19a779d2fd8a6bc9e3b8d (SHA256 176c705cd92eeb9006c048e72274741e839f2bafa2bae858e64c3277264eb0db).

Statement of changes (Apache-2.0 section 4(b))

These files were modified from the original by CrispEmbed: the safetensors weights of revision b3f6a0c084db72f971e19a779d2fd8a6bc9e3b8d were format-converted to GGUF (tensor renaming, casting, tokenizer and preprocessing metadata embedded) and, where noted, quantized. No weights were trained, fine-tuned, merged or otherwise altered.

Files

File Precision Bytes SHA256
teleocr-f16.gguf F16 2,526,109,984 6c52a2800e313964717cf4e5d06e0392c2b5936d8adeac423d563b8a026a2780
teleocr-q8_0.gguf Q8_0 2,113,233,248 9de7e45ce6c13ba03d3d2879f2a1bff9f8407e12808be588cf057cd2553f224b
teleocr-q4_k-m.gguf Q4_K_M 1,923,310,816 e975568f8bd069b3bc81364d10b38143a834922cdf600150e7e74084bfbce293
  • F16: F16 matrices, F32 norms/biases
  • Q8_0: decoder matrices Q8_0; vision, merger and tied embedding F16
  • Q4_K_M: decoder Q4_K with ffn_down/attn_v Q6_K; vision, merger and tied embedding F16

How the files were produced

  1. Downloaded XingChen-AGI/TeleOCR at the pinned revision b3f6a0c084db72f971e19a779d2fd8a6bc9e3b8d; the converter (models/convert-teleocr-to-gguf.py) checks the source files, tensor names and shapes.
  2. Quantized variants were produced with CrispEmbed's own crispembed-quantize from the listed source GGUF.
  3. Built and uploaded by CrispEmbed commit 4132e9e2de45b825d55febafa27ebf3895043788 (workflow ocr-gguf-publish.yml, protocol docs/teleocr-publication-v1.json); manifest.json lists every recipe, input pin, receipt and file hash.

Parity status (CrispEmbed native vs. the original Python model, CPU, greedy)

  • The reference arms (f32) reproduce the original model's generated token IDs exactly on all 6 frozen requests (text (66), formula (166), table (283), code (366), layout (768), synthetic (19) tokens), CrispEmbed commit 9d24b621, reference revision b3f6a0c084db.
  • F16 is token-ID exact on the same 6 cases (receipt tests/results/teleocr/hosted-precision.json); Q8_0 is token-ID exact on the same 6 cases (receipt tests/results/teleocr/hosted-precision.json); Q4_K_M is token-ID exact on the same 6 cases (receipt tests/results/teleocr/hosted-precision.json).
  • Strict intermediate numerical budgets are not met, including F32: one-ulp differences on the model's massive activations are amplified where they emerge (vision block 17); decoded IDs and text are exact. Also not verified: GPU numerical parity, other CPU/kernel combinations, mobile/WASM, aggregate OCR benchmarks beyond the six frozen cases.

Usage

TeleOCR support is experimental and default-off in CrispEmbed (CPU by default), and non-commercial only:

CRISPEMBED_TELEOCR=1 CRISPEMBED_ACCEPT_QWEN_RESEARCH_LICENSE=1 crispembed --ocr-pipeline page.png \\
  --ocr-engine teleocr --ocr-rec teleocr-f16.gguf