Image-Text-to-Text
GGUF
English
Thai
llama.cpp
imatrix
OCR
vision-language
document-understanding
multimodal
conversational
Instructions to use chanasia/typhoon-ocr1.5-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use chanasia/typhoon-ocr1.5-2b-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use chanasia/typhoon-ocr1.5-2b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chanasia/typhoon-ocr1.5-2b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chanasia/typhoon-ocr1.5-2b-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
- Ollama
How to use chanasia/typhoon-ocr1.5-2b-GGUF with Ollama:
ollama run hf.co/chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use chanasia/typhoon-ocr1.5-2b-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use chanasia/typhoon-ocr1.5-2b-GGUF with Docker Model Runner:
docker model run hf.co/chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
- Lemonade
How to use chanasia/typhoon-ocr1.5-2b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.typhoon-ocr1.5-2b-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use chanasia/typhoon-ocr1.5-2b-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use chanasia/typhoon-ocr1.5-2b-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "chanasia/typhoon-ocr1.5-2b-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Add model card
Browse files
README.md
ADDED
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---
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base_model: typhoon-ai/typhoon-ocr1.5-2b
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base_model_relation: quantized
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license: apache-2.0
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language:
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- en
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- th
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pipeline_tag: image-text-to-text
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library_name: llama.cpp
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tags:
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- gguf
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- imatrix
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- OCR
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- vision-language
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- document-understanding
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- multimodal
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- llama.cpp
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---
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# typhoon-ocr1.5-2b GGUF
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GGUF quantization of [typhoon-ai/typhoon-ocr1.5-2b](https://huggingface.co/typhoon-ai/typhoon-ocr1.5-2b),
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a Thai/English document-OCR vision-language model built on Qwen3-VL-2B-Instruct.
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This is a **vision-language model**: you need both the model file and the `mmproj`
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(multimodal projector) file. The model file alone will load, but it will not see images.
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## Files
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| File | Size | Notes |
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| --- | --- | --- |
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| `typhoon-ocr1.5-2b-Q4_K_M-imat.gguf` | 1.03 GiB | Language model, Q4_K_M with importance matrix |
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| `typhoon-ocr1.5-2b-mmproj-Q8_0.gguf` | 424 MiB | Vision encoder / projector — **required** |
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The Q4_K_M weights were quantized with an importance matrix (imatrix) computed from a
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calibration set, which recovers some of the quality lost at 4 bits compared to a plain
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Q4_K_M of the same size.
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## Usage
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### llama-server (OpenAI-compatible API)
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```bash
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llama-server \
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-m typhoon-ocr1.5-2b-Q4_K_M-imat.gguf \
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--mmproj typhoon-ocr1.5-2b-mmproj-Q8_0.gguf \
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-c 8192 --host 0.0.0.0 --port 8080
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```
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Then post an image to `/v1/chat/completions` the usual way:
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```bash
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curl http://localhost:8080/v1/chat/completions \
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-H 'Content-Type: application/json' \
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-d '{
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"messages": [{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
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{"type": "text", "text": "Extract all text from this document as Markdown."}
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]
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}]
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}'
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```
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### llama-mtmd-cli (one-shot)
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```bash
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llama-mtmd-cli \
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-m typhoon-ocr1.5-2b-Q4_K_M-imat.gguf \
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--mmproj typhoon-ocr1.5-2b-mmproj-Q8_0.gguf \
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--image page.png \
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-p "Extract all text from this document as Markdown."
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```
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Use a recent llama.cpp build — Qwen3-VL support landed relatively late.
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## License
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Apache 2.0, inherited from the base model. See the
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[base model card](https://huggingface.co/typhoon-ai/typhoon-ocr1.5-2b) for the model's
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intended use and limitations.
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