Instructions to use thelamapi/next-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("thelamapi/next-ocr")
model = AutoModelForImageTextToText.from_pretrained("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?"}
        ]
    },
]
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]:]))

llama-cpp-python

How to use thelamapi/next-ocr with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next-ocr",
	filename="mmproj-next-ocr-F16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use thelamapi/next-ocr with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio new

How to use thelamapi/next-ocr with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thelamapi/next-ocr to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thelamapi/next-ocr to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thelamapi/next-ocr to start chatting

Pi new

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-server -hf thelamapi/next-ocr:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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

Hermes Agent new

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-server -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

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

Lamapi commited on Nov 13, 2025

Commit

6657632

verified ·

1 Parent(s): 0472c48

Update README.md

Browse files

Files changed (1) hide show

README.md +34 -13

README.md CHANGED Viewed

@@ -194,10 +194,16 @@ Supports **multilingual OCR** (Turkish, English, German, Spanish, French, Chines
 ## 📊 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                            |
 ---
@@ -210,15 +216,30 @@ 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))
 ```
 ---

 ## 📊 Benchmark & Comparison
+| Model                           | OCR-Bench Accuracy (%)   | Multilingual Accuracy (%) | Layout / Table Understanding (%) |
+| ------------------------------- | ------------------------ | ------------------------- | -------------------------------- |
+| **Next OCR**                    | **99.0**                 | **96.8**                  | **95.3**                         |
+| PaddleOCR                       | 95.2                     | 93.9                      | 95.3                             |
+| Deepseek OCR                    | 90.6                     | 87.4                      | 86.1                             |
+| Tesseract                       | 92.0                     | 88.4                      | 72.0                             |
+| EasyOCR                         | 90.4                     | 84.7                      | 78.9                             |
+| Google Cloud Vision / DocAI     | 98.7                     | 95.5                      | 93.6                             |
+| Amazon Textract                 | 94.7                     | 86.2                      | 86.1                             |
+| Azure Document Intelligence     | 95.1                     | 93.6                      | 91.4                             |
 ---
 model_id = "Lamapi/next-ocr"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16)
+img = Image.open("image.jpg")
+# ATTENTION: The content list must include both an image and text.
+messages = [
+    {"role": "system", "content": "You are Next-OCR, an helpful AI assistant trained by Lamapi."},
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": img},
+            {"type": "text", "text": "Read the text in this image and summarize it."}
+        ]
+    }
+]
+# Apply the chat template correctly
+prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=prompt, images=[img], return_tensors="pt").to(model.device)
+with torch.no_grad():
+    generated = model.generate(**inputs, max_new_tokens=256)
+print(processor.decode(generated[0], skip_special_tokens=True))
 ```
 ---