---

base_model: lightonai/LightOnOCR-3-0.8B

base_model_relation: quantized

license: apache-2.0

pipeline_tag: image-text-to-text

tags: [gguf, llama.cpp, ollama, imatrix, vision, multimodal, ocr, document-understanding]

---

LightOnOCR-3-0.8B (GGUF)

GGUF quantizations of lightonai/LightOnOCR-3-0.8B — the lightest LightOnOCR-3 model (Qwen3.5 vision-language architecture), with the same layout and visual understanding features as the 4B — optimized for local inference with llama.cpp and Ollama, with its vision projector. All quants use an importance matrix.

Quick Start

With Ollama

ollama run richardyoung/lightonocr-3-0.8b
# or straight from Hugging Face
ollama run hf.co/richardyoung/LightOnOCR-3-0.8B-GGUF:Q8_0

With llama.cpp

huggingface-cli download richardyoung/LightOnOCR-3-0.8B-GGUF \
    --include "*Q8_0*" "mmproj*" --local-dir ./models

./llama-mtmd-cli -m ./models/LightOnOCR-3-0.8B-Q8_0.gguf \
    --mmproj ./models/mmproj-LightOnOCR-3-0.8B-F16.gguf \
    --image page.png -p "grounding"

Prompts: send the image with an empty prompt for plain transcription (Markdown), or with the prompt grounding to get every block with a label and a 0–1000 bounding box, image descriptions and chart data as tables. LightOn notes that other instructions are out of distribution. Recommended sampling: temperature 0.2, top_p 0.9 (set in the Ollama tags).

Why This Model?

• Smallest and fastest LightOnOCR-3 model, under 1 GB
• Two modes: plain transcription, or grounding with labels and bounding boxes
• Visual understanding: image descriptions and chart data as tables
• Apache-2.0: research and commercial use

Use Cases

• High-volume OCR on CPU or small GPUs
• Edge and on-device document capture
• Layout-aware extraction

Available Quantizations

| File | Size | Use Case |

|---|---|---|

| LightOnOCR-3-0.8B-Q4_K_M.gguf | 0.53 GB | Recommended |

| LightOnOCR-3-0.8B-Q5_K_M.gguf | 0.58 GB | Higher quality |

| LightOnOCR-3-0.8B-Q6_K.gguf | 0.63 GB | Very high quality |

| LightOnOCR-3-0.8B-Q8_0.gguf | 0.81 GB | Recommended — best accuracy for a small OCR model |

| mmproj-LightOnOCR-3-0.8B-F16.gguf | 0.2 GB | Vision projector (required for images) |

| LightOnOCR-3-0.8B-imatrix.gguf | – | Importance matrix used for the quants (calibration_datav3) |

Verified

Tested through Ollama before upload (Q4_K_M and Q8_0) on a synthetic invoice with an empty prompt; Q4_K_M returned: 'INVOICE 4471 / Date: 2026-09-28 / Widgets x3 ...... $42.00 / TOTAL: $86.20'. Converted with --no-mtp: the upstream config declares a multi-token-prediction layer whose weights are not in the release.

Also Available

• LightOnOCR-3-4B — most accurate
• LightOnOCR-3-1B — LightOnOCR-2 architecture, drop-in upgrade

Other Models by richardyoung

• New Heretic models (reproducible, listed in the Heretic Grimoire): LFM2.5-1.2B | Granite-4.2-3B | Granite-4.2-8B | Olmo-3-7B-Think | Phi-4-mini | DeepSeek-R1-0528-Qwen3-8B | Agents-A1-4B | FrogNano-4B
• Abliterated/Uncensored models: Qwen2.5-7B | Qwen3-14B | DeepSeek-R1-32B | Qwen3-8B
• OCR & Vision: MiniCPM-V-4.6 GGUF | dots.mocr GGUF | Unlimited-OCR GGUF | olmOCR GGUF | LightOnOCR-3-4B GGUF | WeVisDoc-4B GGUF | Xiaomi-OCR-0 GGUF
• MLX quantizations (Apple Silicon): Kimi-K2 series | olmOCR MLX
• Healthcare/Medical: Synthea 575K patients dataset | CardioEmbed
• Research: LLM Instruction-Following Evaluation (arxiv:2510.18892)

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Built & maintained by Richard Young · DeepNeuro