Image-Text-to-Text
Transformers
Safetensors
GGUF
qwen3_vl
text-generation-inference
unsloth
trl
sft
chemistry
code
climate
art
biology
finance
legal
music
medical
agent
conversational
Instructions to use thelamapi/next-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thelamapi/next-ocr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="thelamapi/next-ocr") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("thelamapi/next-ocr") model = AutoModelForMultimodalLM.from_pretrained("thelamapi/next-ocr", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use thelamapi/next-ocr 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 thelamapi/next-ocr:F16 # Run inference directly in the terminal: llama cli -hf thelamapi/next-ocr:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf thelamapi/next-ocr:F16 # Run inference directly in the terminal: llama cli -hf thelamapi/next-ocr:F16
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 thelamapi/next-ocr:F16 # Run inference directly in the terminal: ./llama-cli -hf thelamapi/next-ocr:F16
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 thelamapi/next-ocr:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf thelamapi/next-ocr:F16
Use Docker
docker model run hf.co/thelamapi/next-ocr:F16
- LM Studio
- Jan
- vLLM
How to use thelamapi/next-ocr with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thelamapi/next-ocr" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thelamapi/next-ocr", "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/thelamapi/next-ocr:F16
- SGLang
How to use thelamapi/next-ocr with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "thelamapi/next-ocr" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thelamapi/next-ocr", "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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "thelamapi/next-ocr" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thelamapi/next-ocr", "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" } } ] } ] }' - Ollama
How to use thelamapi/next-ocr with Ollama:
ollama run hf.co/thelamapi/next-ocr:F16
- Unsloth Desktop
- Pi
How to use thelamapi/next-ocr with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf thelamapi/next-ocr:F16
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": "thelamapi/next-ocr:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use thelamapi/next-ocr with Docker Model Runner:
docker model run hf.co/thelamapi/next-ocr:F16
- Lemonade
How to use thelamapi/next-ocr with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull thelamapi/next-ocr:F16
Run and chat with the model
lemonade run user.next-ocr-F16
List all available models
lemonade list
- Hermes Agent
How to use thelamapi/next-ocr with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf thelamapi/next-ocr:F16
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 thelamapi/next-ocr:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use thelamapi/next-ocr with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf thelamapi/next-ocr:F16
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 "thelamapi/next-ocr:F16" \ --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"
|
Download README.md from thelamapi/next-ocr: direct link, hf CLI and curl.
- Browser
- Download file 5.29 kB
-
https://huggingface.co/thelamapi/next-ocr/resolve/86c1061ad309af1cd672596c8cb84072088c8dd4/README.md
- Command line
-
hf download hf://thelamapi/next-ocr@86c1061ad309af1cd672596c8cb84072088c8dd4/README.md
-
curl -L -o README.md https://huggingface.co/thelamapi/next-ocr/resolve/86c1061ad309af1cd672596c8cb84072088c8dd4/README.md
5.29 kB
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - qwen3_vl | |
| - trl | |
| - sft | |
| - chemistry | |
| - code | |
| - climate | |
| - art | |
| - biology | |
| - finance | |
| - legal | |
| - music | |
| - medical | |
| - agent | |
| license: apache-2.0 | |
| language: | |
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| pipeline_tag: image-text-to-text | |
| library_name: transformers | |
| <img src='bannerocr.png'> | |
| # 🖼️ Next OCR 8B | |
| ### *Compact OCR AI — Accurate, Fast, Multilingual, Math-Optimized* | |
| [](https://opensource.org/licenses/MIT) | |
| []() | |
| [](https://huggingface.co/Lamapi/next-ocr) | |
| --- | |
| ## 📖 Overview | |
| **Next OCR 8B** is an **8-billion parameter model** optimized for **optical character recognition (OCR) tasks** with **mathematical and tabular content understanding**. | |
| Supports **multilingual OCR** (Turkish, English, German, Spanish, French, Chinese, Japanese, Korean, Russian...) with high accuracy, including structured documents like tables, forms, and formulas. | |
| --- | |
| ## ⚡ Highlights | |
| * 🖼️ Accurate text extraction, including math and tables | |
| * 🌍 Multilingual support (30+ languages) | |
| * ⚡ Lightweight and efficient | |
| * 💬 Instruction-tuned for document understanding and analysis | |
| --- | |
| ## 📊 Benchmark & Comparison | |
| | Model | OCR Accuracy (%) | Multilingual Accuracy (%) | Layout / Table Understanding (%) | | |
| | ------------------- | ------------------------ | ------------------------- | -------------------------------- | | |
| | **Next OCR 8B** | 98.9 | 96.7 | 94.4 | | |
| | **DeepSeek‑OCR 3B** | 97 (yüksek sıkıştırmada) | 88–90 | 85–87 | | |
| --- | |
| ## 🚀 Installation & Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForVision2Seq | |
| import torch | |
| model_id = "Lamapi/next-ocr" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") | |
| image_path = "document.png" | |
| images = [image_path] | |
| inputs = tokenizer(images, return_tensors="pt").to(model.device) | |
| outputs = model.generate(**inputs, max_new_tokens=512) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| ``` | |
| --- | |
| ## 🧩 Key Features | |
| | Feature | Description | | |
| | -------------------------- | --------------------------------------------------------------- | | |
| | 🖼️ High-Accuracy OCR | Extracts text from images, documents, and screenshots reliably. | | |
| | 🇹🇷 Multilingual Support | Works with 30+ languages including Turkish. | | |
| | ⚡ Lightweight & Efficient | Optimized for resource-constrained environments. | | |
| | 📄 Layout & Math Awareness | Handles tables, forms, and mathematical formulas. | | |
| | 🏢 Reliable Outputs | Suitable for enterprise document workflows. | | |
| --- | |
| ## 📐 Model Specifications | |
| | Specification | Details | | |
| | ----------------- | --------------------------------------------------------- | | |
| | **Base Model** | Qwen 3 | | |
| | **Parameters** | 8 Billion | | |
| | **Architecture** | Vision + Transformer (OCR LLM) | | |
| | **Modalities** | Image-to-text | | |
| | **Fine-Tuning** | OCR datasets with multilingual and math/tabular content | | |
| | **Optimizations** | Quantization-ready, FP16 support | | |
| | **Primary Focus** | Text extraction, document understanding, mathematical OCR | | |
| --- | |
| ## 🎯 Ideal Use Cases | |
| * Document digitization | |
| * Invoice & receipt processing | |
| * Multilingual OCR pipelines | |
| * Tables, forms, and formulas extraction | |
| * Enterprise document management | |
| --- | |
| ## 📄 License | |
| MIT License — free for commercial & non-commercial use. | |
| --- | |
| ## 📞 Contact & Support | |
| * 📧 Email: [lamapicontact@gmail.com](mailto:lamapicontact@gmail.com) | |
| * 🤗 HuggingFace: [Lamapi](https://huggingface.co/Lamapi) | |
| --- | |
| > **Next OCR** — Compact *OCR + math-capable* AI, blending **accuracy**, **speed**, and **multilingual document intelligence**. | |
| [](https://huggingface.co/Lamapi) |