Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

thelamapi
/
next-ocr

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
Model card Files Files and versions
xet
Community
2

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)
    # 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=40)
    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"
next-ocr
17.6 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 16 commits
Lamapi's picture
Lamapi
Upload nextocr6.png
86c1061 verified 11 months ago
  • .gitattributes
    1.62 kB
    Upload nextocr6.png 11 months ago
  • README.md
    5.29 kB
    Update README.md 11 months ago
  • added_tokens.json
    707 Bytes
    Upload tokenizer 11 months ago
  • chat_template.jinja
    5.29 kB
    Upload tokenizer 11 months ago
  • config.json
    1.63 kB
    Upload model trained with Unsloth 11 months ago
  • generation_config.json
    199 Bytes
    Upload model trained with Unsloth 11 months ago
  • merges.txt
    1.67 MB
    Upload tokenizer 11 months ago
  • model-00001-of-00004.safetensors
    5 GB
    xet
    Upload model trained with Unsloth 11 months ago
  • model-00002-of-00004.safetensors
    4.92 GB
    xet
    Upload model trained with Unsloth 11 months ago
  • model-00003-of-00004.safetensors
    4.92 GB
    xet
    Upload model trained with Unsloth 11 months ago
  • model-00004-of-00004.safetensors
    2.7 GB
    xet
    Upload model trained with Unsloth 11 months ago
  • model.safetensors.index.json
    67.8 kB
    Upload model trained with Unsloth 11 months ago
  • nextocr6.png
    181 kB
    xet
    Upload nextocr6.png 11 months ago
  • preprocessor_config.json
    390 Bytes
    Upload 2 files 11 months ago
  • special_tokens_map.json
    614 Bytes
    Upload tokenizer 11 months ago
  • tokenizer.json
    11.4 MB
    xet
    Upload tokenizer 11 months ago
  • tokenizer_config.json
    5.47 kB
    Upload tokenizer 11 months ago
  • vocab.json
    2.78 MB
    Upload tokenizer 11 months ago