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yothinS/Typhoon_OCR_7B.gguf
Typhoon_OCR_7B.gguf is a machine learning model from yothinS. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This repository contains the GGUF weights for typhoon-ai/typhoon-ocr-7b, an advanced Vision-Language Model optimized for Optical Character Recognition (OCR) in Thai and English, developed by SCB 10X.
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From the Hugging Face model README
This repository contains the GGUF weights for typhoon-ai/typhoon-ocr-7b, an advanced Vision-Language Model optimized for Optical Character Recognition (OCR) in Thai and English, developed by SCB 10X.
The models have been quantified using llama.cpp with CUDA acceleration, providing various sizes suitable for local deployment on hardware with limited resources (e.g., 16GB RAM).
To run this Vision-Language Model (VLM), you need two components downloaded into the same directory:
.gguf files listed below)typhoon-ocr-mmproj-f16.gguf to process images.| File Name | Quantization Type | Size | Recommended Use Case |
|---|---|---|---|
typhoon-ocr-Q2_K.gguf | 2-bit | ~2.5 GB | Extreme resource constraint / Testing purposes |
typhoon-ocr-Q4_K_M.gguf | 4-bit | ~4.5 GB | Recommended Balance for 16GB RAM systems |
typhoon-ocr-Q6_K.gguf | 6-bit | ~6.1 GB | High quality, minimal degradation of Thai vocabulary |
typhoon-ocr-Q8_0.gguf | 8-bit | ~7.7 GB | Near-lossless precision, requires higher VRAM/RAM |
typhoon-ocr-mmproj-f16.gguf (~1.3 GB): The required vision adapter component. Works universally with all quant variants above.You can run the model directly via the llama-cli using the multi-modal argument (--mmproj):
llama-cli -m typhoon-ocr-Q4_K_M.gguf \
--mmproj typhoon-ocr-mmproj-f16.gguf \
--image /path/to/your/document.png \
-p "<|im_start|><|role|>user<|im_end|><|image|>Extract text from this image.<|im_start|><|role|>assistant<|im_end|>" \
-n 2048
To run this model natively in Ollama, create a file named Modelfile in your directory and add the following configuration:
# Path to the main model and vision projector
FROM ./typhoon-ocr-Q4_K_M.gguf
MMPROJ ./typhoon-ocr-mmproj-f16.gguf
# Set Parameters
PARAMETER temperature 0.1
PARAMETER stop <|im_end|>
PARAMETER stop <|endoftext|>
# Template for Qwen2.5-VL / Typhoon OCR
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""
Then compile and run it via terminal:
ollama create typhoon-ocr -f ./Modelfile
ollama run typhoon-ocr
As stated by the original developers (Typhoon AI), this model is highly specialized for Document Parsing and OCR. Standard conversational chit-chat prompts might yield suboptimal results. Always structuralize your queries towards data extraction or text transcribing.
Credits go to Typhoon AI (SCB 10X) for developing and releasing the original typhoon-ocr-7b weights. This GGUF version is optimized purely for the open-source community to leverage efficient local edge inference.