diff --git a/.github/workflows/integration.yml b/.github/workflows/integration.yml
new file mode 100644
index 0000000..b1bdca1
--- /dev/null
+++ b/.github/workflows/integration.yml
@@ -0,0 +1,28 @@
+name: Marker Plus CI tests
+
+on: [push]
+
+jobs:
+  tests:
+    runs-on: t4_gpu
+    steps:
+      - uses: actions/checkout@v3
+      - name: Install apt requirements
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y libpango-1.0-0 libharfbuzz0b libpangoft2-1.0-0 libgdk-pixbuf2.0-0 libcairo2 libffi-dev shared-mime-info
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.12
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+      - name: Install python dependencies
+        run: |
+          uv sync --group dev
+      - name: Run tests
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          PYTHONPATH: .
+        run: |
+          uv run pytest tests/integration
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..201bc8c
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,43 @@
+name: Python package
+on:
+  push:
+    tags:
+      - "v*.*.*"
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.12"
+      - name: Extract and verify version
+        id: version
+        run: |
+          # Get the tag name (e.g., v1.2.3)
+          TAG_VERSION="${GITHUB_REF#refs/tags/}"
+          echo "Tag version: $TAG_VERSION"
+          
+          # Extract version from pyproject.toml (e.g., 1.2.3)
+          PYPROJECT_VERSION=$(uv run python -c "import tomllib; print(tomllib.load(open('pyproject.toml', 'rb'))['project']['version'])")
+          echo "pyproject.toml version: $PYPROJECT_VERSION"
+          
+          # Compare versions (tag should be v{pyproject_version})
+          if [ "$TAG_VERSION" != "v$PYPROJECT_VERSION" ]; then
+            echo "Error: Tag version ($TAG_VERSION) does not match pyproject.toml version (v$PYPROJECT_VERSION)"
+            exit 1
+          fi
+          
+          echo "Version verification passed!"
+          echo "version=$TAG_VERSION" >> $GITHUB_OUTPUT
+      - name: Build package
+        run: |
+          uv build
+      - name: Publish package
+        env:
+          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
+        run: |
+          uv publish --token "$PYPI_TOKEN"
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 69ba7a7..9676325 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 local.env
 experiments
+.claude
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..4350d1b
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,202 @@
+
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+
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+
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diff --git a/README.md b/README.md
index eb61b00..51c4033 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,20 @@ Chandra is a highly accurate OCR model that converts images and PDFs into struct
 - Support for 40+ languages
 - Two inference modes: local (HuggingFace) and remote (vLLM server)
 
+## Benchmarks
+
+| **Model** | ArXiv | Old Scans Math | Tables | Old Scans | Headers and Footers | Multi column | Long tiny text | Base | Overall |
+|:----------|:-----:|:--------------:|:------:|:---------:|:-------------------:|:------------:|:--------------:|:----:|:-------:|
+| Datalab Chandra v0.1.0 | 81.4 | **80.3** | **89.4** | **50.0** | 88.3 | **81.0** | **91.6** | **99.9** | **82.7 ± 0.9** |
+| Datalab Marker v1.10.0 | **83.8** | 69.7 | 74.8 | 32.3 | 86.6 | 79.4 | 85.7 | 99.6 | 76.5 ± 1.0 |
+| Mistral OCR API | 77.2 | 67.5 | 60.6 | 29.3 | 93.6 | 71.3 | 77.1 | 99.4 | 72.0 ± 1.1 |
+| Deepseek OCR | 75.2 | 67.9 | 79.1 | 32.9 | 96.1 | 66.3 | 78.5 | 97.7 | 74.2 ± 1.0 |
+| Nanonets OCR | 67.0 | 68.6 | 77.7 | 39.5 | 40.7 | 69.9 | 53.4 | 99.3 | 64.5 ± 1.1 |
+| GPT-4o (Anchored) | 53.5 | 74.5 | 70.0 | 40.7 | 93.8 | 69.3 | 60.6 | 96.8 | 69.9 ± 1.1 |
+| Gemini Flash 2 (Anchored) | 54.5 | 56.1 | 72.1 | 34.2 | 64.7 | 61.5 | 71.5 | 95.6 | 63.8 ± 1.2 |
+| Qwen 2.5 VL (No Anchor) | 63.1 | 65.7 | 67.3 | 38.6 | 73.6 | 68.3 | 49.1 | 98.3 | 65.5 ± 1.2 |
+| olmOCR v0.3.0 | 78.6 | 79.9 | 72.9 | 43.9 | **95.1** | 77.3 | 81.2 | 98.9 | 78.5 ± 1.1 |
+
 ## Installation
 
 ```bash
@@ -20,14 +34,53 @@ source .venv/bin/activate
 
 ## Usage
 
+### CLI
+
+Process single files or entire directories:
+
+```bash
+# Process a single PDF with vLLM
+python chandra_cli.py input.pdf ./output --method vllm
+
+# Process all files in a directory with local model
+python chandra_cli.py ./documents ./output --method hf
+
+# Process specific pages with custom settings
+python chandra_cli.py document.pdf ./output --page-range "1-10,15,20-25" --max-workers 8
+```
+
+**CLI Options:**
+- `--method [hf|vllm]`: Inference method (default: vllm)
+- `--page-range TEXT`: Page range for PDFs (e.g., "1-5,7,9-12")
+- `--max-output-tokens INTEGER`: Max tokens per page
+- `--max-workers INTEGER`: Parallel workers for vLLM
+- `--include-images/--no-images`: Extract and save images (default: include)
+- `--include-headers-footers/--no-headers-footers`: Include page headers/footers (default: exclude)
+- `--batch-size INTEGER`: Pages per batch (default: 1)
+
+**Output Structure:**
+
+Each processed file creates a subdirectory with:
+- `<filename>.md` - Markdown output
+- `<filename>.html` - HTML output
+- `<filename>_metadata.json` - Metadata (page info, token count, etc.)
+- `images/` - Extracted images from the document
+
 ### Streamlit Web App
 
-Launch the interactive demo:
+Launch the interactive demo for single-page processing:
 
 ```bash
 streamlit run chandra_app.py --server.fileWatcherType none --server.headless true
 ```
 
+The web interface allows you to:
+- Upload PDFs or images
+- Select specific pages from PDFs
+- View OCR results with layout visualization
+- Download markdown output
+- See extracted images embedded in the output
+
 **Inference Modes:**
 - **hf**: Loads model locally using HuggingFace Transformers (requires GPU)
 - **vllm**: Connects to a running vLLM server for optimized batch inference
@@ -45,7 +98,6 @@ This launches a Docker container with optimized inference settings. Configure vi
 - `VLLM_API_BASE`: Server URL (default: `http://localhost:8000/v1`)
 - `VLLM_MODEL_NAME`: Model name for the server (default: `chandra`)
 - `VLLM_GPUS`: GPU device IDs (default: `0`)
-- `HF_TOKEN`: HuggingFace token for model access
 
 ### Configuration
 
diff --git a/chandra/model/__init__.py b/chandra/model/__init__.py
index bd014bc..0b4b9e8 100644
--- a/chandra/model/__init__.py
+++ b/chandra/model/__init__.py
@@ -3,7 +3,7 @@ from typing import List
 from chandra.model.hf import load_model, generate_hf
 from chandra.model.schema import BatchInputItem, BatchOutputItem
 from chandra.model.vllm import generate_vllm
-from chandra.output import parse_markdown, parse_html, parse_chunks
+from chandra.output import parse_markdown, parse_html, parse_chunks, extract_images
 
 
 class InferenceManager:
@@ -19,6 +19,14 @@ class InferenceManager:
     def generate(
         self, batch: List[BatchInputItem], max_output_tokens=None, **kwargs
     ) -> List[BatchOutputItem]:
+        output_kwargs = {}
+        if "include_images" in kwargs:
+            output_kwargs["include_images"] = kwargs.pop("include_images")
+        if "include_headers_footers" in kwargs:
+            output_kwargs["include_headers_footers"] = kwargs.pop(
+                "include_headers_footers"
+            )
+
         if self.method == "vllm":
             results = generate_vllm(
                 batch, max_output_tokens=max_output_tokens, **kwargs
@@ -30,14 +38,16 @@ class InferenceManager:
 
         output = []
         for result, input_item in zip(results, batch):
+            chunks = parse_chunks(result.raw, input_item.image)
             output.append(
                 BatchOutputItem(
-                    markdown=parse_markdown(result.raw),
-                    html=parse_html(result.raw),
-                    chunks=parse_chunks(result.raw, input_item.image),
+                    markdown=parse_markdown(result.raw, **output_kwargs),
+                    html=parse_html(result.raw, **output_kwargs),
+                    chunks=chunks,
                     raw=result.raw,
                     page_box=[0, 0, input_item.image.width, input_item.image.height],
                     token_count=result.token_count,
+                    images=extract_images(result.raw, chunks, input_item.image),
                 )
             )
         return output
diff --git a/chandra/model/hf.py b/chandra/model/hf.py
index ff78467..52988c3 100644
--- a/chandra/model/hf.py
+++ b/chandra/model/hf.py
@@ -1,7 +1,7 @@
 from typing import List
 
 from qwen_vl_utils import process_vision_info
-from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLProcessor
+from transformers import Qwen3VLForConditionalGeneration, Qwen3VLProcessor
 
 from chandra.model.schema import BatchInputItem, GenerationResult
 from chandra.model.util import scale_to_fit
@@ -31,7 +31,7 @@ def generate_hf(
     inputs = inputs.to("cuda")
 
     # Inference: Generation of the output
-    generated_ids = model.generate_hf(**inputs, max_new_tokens=max_output_tokens)
+    generated_ids = model.generate(**inputs, max_new_tokens=max_output_tokens)
     generated_ids_trimmed = [
         out_ids[len(in_ids) :]
         for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
@@ -65,13 +65,13 @@ def process_batch_element(item: BatchInputItem, processor):
 
 
 def load_model():
-    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model = Qwen3VLForConditionalGeneration.from_pretrained(
         settings.MODEL_CHECKPOINT,
         dtype=settings.TORCH_DTYPE,
         device_map="auto",
         attn_implementation=settings.TORCH_ATTN_IMPLEMENTATION,
     ).to(settings.TORCH_DEVICE_MODEL)
     model = model.eval()
-    processor = Qwen2_5_VLProcessor.from_pretrained(settings.MODEL_CHECKPOINT)
+    processor = Qwen3VLProcessor.from_pretrained(settings.MODEL_CHECKPOINT)
     model.processor = processor
     return model
diff --git a/chandra/model/schema.py b/chandra/model/schema.py
index 0ad512b..623a958 100644
--- a/chandra/model/schema.py
+++ b/chandra/model/schema.py
@@ -26,3 +26,4 @@ class BatchOutputItem:
     raw: str
     page_box: List[int]
     token_count: int
+    images: dict
diff --git a/chandra/output.py b/chandra/output.py
index c7a4208..6ff0b6e 100644
--- a/chandra/output.py
+++ b/chandra/output.py
@@ -1,6 +1,8 @@
+import hashlib
 import json
 import re
 from dataclasses import dataclass, asdict
+from functools import lru_cache
 
 import six
 from PIL import Image
@@ -8,18 +10,59 @@ from bs4 import BeautifulSoup, NavigableString
 from markdownify import MarkdownConverter, re_whitespace
 
 
-def parse_html(html: str, include_headers_footers: bool = False):
+@lru_cache
+def _hash_html(html: str):
+    return hashlib.md5(html.encode("utf-8")).hexdigest()
+
+
+def get_image_name(html: str, div_idx: int, image_idx: int):
+    html_hash = _hash_html(html)
+    return f"{html_hash}_{div_idx}_img{image_idx}.webp"
+
+
+def extract_images(html: str, chunks: dict, image: Image.Image):
+    image_idx = 0
+    images = {}
+    div_idx = 0
+    for idx, chunk in enumerate(chunks):
+        div_idx += 1
+        if chunk["label"] in ["Image", "Figure"]:
+            img = chunk["content"].find("img")
+            if not img:
+                continue
+            bbox = chunk["bbox"]
+            image = image.crop(bbox)
+            img_name = get_image_name(html, div_idx, image_idx)
+            images[img_name] = image
+    return images
+
+
+def parse_html(
+    html: str, include_headers_footers: bool = False, include_images: bool = True
+):
     soup = BeautifulSoup(html, "html.parser")
     top_level_divs = soup.find_all("div", recursive=False)
     out_html = ""
+    image_idx = 0
+    div_idx = 0
     for div in top_level_divs:
+        div_idx += 1
         label = div.get("data-label")
 
         # Skip headers and footers if not included
         if label and not include_headers_footers:
             if label in ["Page-Header", "Page-Footer"]:
                 continue
+        if label and not include_images:
+            if label in ["Image", "Figure"]:
+                continue
 
+        if label in ["Image", "Figure"]:
+            img = div.find("img")
+            img_src = get_image_name(html, div_idx, image_idx)
+            if img:
+                img["src"] = img_src
+                image_idx += 1
         content = str(div.decode_contents())
         out_html += content
     return out_html
@@ -125,8 +168,10 @@ class Markdownify(MarkdownConverter):
         return text
 
 
-def parse_markdown(html: str, include_headers_footers: bool = False):
-    html = parse_html(html, include_headers_footers)
+def parse_markdown(
+    html: str, include_headers_footers: bool = False, include_images: bool = True
+):
+    html = parse_html(html, include_headers_footers, include_images)
 
     md_cls = Markdownify(
         heading_style="ATX",
diff --git a/chandra_app.py b/chandra_app.py
index 0f39660..368d4cc 100644
--- a/chandra_app.py
+++ b/chandra_app.py
@@ -1,6 +1,9 @@
 import pypdfium2 as pdfium
 import streamlit as st
 from PIL import Image
+import base64
+from io import BytesIO
+import re
 
 from chandra.model import InferenceManager
 from chandra.util import draw_layout
@@ -27,6 +30,26 @@ def page_counter(pdf_file):
     return doc_len
 
 
+def pil_image_to_base64(pil_image: Image.Image, format: str = "PNG") -> str:
+    """Convert PIL image to base64 data URL."""
+    buffered = BytesIO()
+    pil_image.save(buffered, format=format)
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    return f"data:image/{format.lower()};base64,{img_str}"
+
+
+def embed_images_in_markdown(markdown: str, images: dict) -> str:
+    """Replace image filenames in markdown with base64 data URLs."""
+    for img_name, pil_image in images.items():
+        # Convert PIL image to base64 data URL
+        data_url = pil_image_to_base64(pil_image, format="PNG")
+        # Replace the image reference in markdown
+        # Pattern matches: ![...](img_name) or ![...](img_name "title")
+        pattern = rf'(!\[.*?\])\({re.escape(img_name)}(?:\s+"[^"]*")?\)'
+        markdown = re.sub(pattern, rf"\1({data_url})", markdown)
+    return markdown
+
+
 def ocr_layout(
     img: Image.Image,
     model=None,
@@ -55,7 +78,7 @@ model_mode = st.sidebar.selectbox(
     "Model Mode",
     ["None", "hf", "vllm"],
     index=0,
-    help="Select how to run inference: hf loads the model in memory using huggingface transformers, vllm connects to a running vLLM server."
+    help="Select how to run inference: hf loads the model in memory using huggingface transformers, vllm connects to a running vLLM server.",
 )
 
 # Only load model if a mode is selected
@@ -99,10 +122,15 @@ if run_ocr:
             model,
         )
 
+        # Embed images as base64 data URLs in the markdown
+        markdown_with_images = embed_images_in_markdown(result.markdown, result.images)
+
         with col1:
-            html_tab, text_tab, layout_tab = st.tabs(["HTML", "HTML as text", "Layout Image"])
+            html_tab, text_tab, layout_tab = st.tabs(
+                ["HTML", "HTML as text", "Layout Image"]
+            )
             with html_tab:
-                st.markdown(result.markdown, unsafe_allow_html=True)
+                st.markdown(markdown_with_images, unsafe_allow_html=True)
                 st.download_button(
                     label="Download Markdown",
                     data=result.markdown,
@@ -114,7 +142,11 @@ if run_ocr:
 
             if layout_image:
                 with layout_tab:
-                    st.image(layout_image, caption="Detected Layout", use_container_width=True)
+                    st.image(
+                        layout_image,
+                        caption="Detected Layout",
+                        use_container_width=True,
+                    )
                     st.text_area(result.raw)
 
 with col2:
diff --git a/chandra_cli.py b/chandra_cli.py
new file mode 100755
index 0000000..800d0d4
--- /dev/null
+++ b/chandra_cli.py
@@ -0,0 +1,285 @@
+import json
+from pathlib import Path
+from typing import List
+
+import click
+
+from chandra.input import load_file
+from chandra.model import InferenceManager
+from chandra.model.schema import BatchInputItem
+
+
+def get_supported_files(input_path: Path) -> List[Path]:
+    """Get list of supported image/PDF files from path."""
+    supported_extensions = {
+        ".pdf",
+        ".png",
+        ".jpg",
+        ".jpeg",
+        ".gif",
+        ".webp",
+        ".tiff",
+        ".bmp",
+    }
+
+    if input_path.is_file():
+        if input_path.suffix.lower() in supported_extensions:
+            return [input_path]
+        else:
+            raise click.BadParameter(f"Unsupported file type: {input_path.suffix}")
+
+    elif input_path.is_dir():
+        files = []
+        for ext in supported_extensions:
+            files.extend(input_path.glob(f"*{ext}"))
+            files.extend(input_path.glob(f"*{ext.upper()}"))
+        return sorted(files)
+
+    else:
+        raise click.BadParameter(f"Path does not exist: {input_path}")
+
+
+def save_merged_output(
+    output_dir: Path,
+    file_name: str,
+    results: List,
+    save_images: bool = True,
+    save_html: bool = True,
+    paginate_output: bool = False,
+):
+    """Save merged OCR results for all pages to output directory."""
+    # Create subfolder for this file
+    safe_name = Path(file_name).stem
+    file_output_dir = output_dir / safe_name
+    file_output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Merge all pages
+    all_markdown = []
+    all_html = []
+    all_metadata = []
+    total_tokens = 0
+    total_chunks = 0
+    total_images = 0
+
+    # Process each page result
+    for page_num, result in enumerate(results):
+        # Add page separator for multi-page documents
+        if page_num > 0 and paginate_output:
+            all_markdown.append(f"\n\n{page_num}" + "-" * 48 + "\n\n")
+            all_html.append(f"\n\n<!-- Page {page_num + 1} -->\n\n")
+
+        all_markdown.append(result.markdown)
+        all_html.append(result.html)
+
+        # Accumulate metadata
+        total_tokens += result.token_count
+        total_chunks += len(result.chunks)
+        total_images += len(result.images)
+
+        page_metadata = {
+            "page_num": page_num,
+            "page_box": result.page_box,
+            "token_count": result.token_count,
+            "num_chunks": len(result.chunks),
+            "num_images": len(result.images),
+        }
+        all_metadata.append(page_metadata)
+
+        # Save extracted images if requested
+        if save_images and result.images:
+            images_dir = file_output_dir / "images"
+            images_dir.mkdir(exist_ok=True)
+
+            for img_name, pil_image in result.images.items():
+                img_path = images_dir / img_name
+                pil_image.save(img_path)
+
+    # Save merged markdown
+    markdown_path = file_output_dir / f"{safe_name}.md"
+    with open(markdown_path, "w", encoding="utf-8") as f:
+        f.write("".join(all_markdown))
+
+    # Save merged HTML if requested
+    if save_html:
+        html_path = file_output_dir / f"{safe_name}.html"
+        with open(html_path, "w", encoding="utf-8") as f:
+            f.write("".join(all_html))
+
+    # Save combined metadata
+    metadata = {
+        "file_name": file_name,
+        "num_pages": len(results),
+        "total_token_count": total_tokens,
+        "total_chunks": total_chunks,
+        "total_images": total_images,
+        "pages": all_metadata,
+    }
+    metadata_path = file_output_dir / f"{safe_name}_metadata.json"
+    with open(metadata_path, "w", encoding="utf-8") as f:
+        json.dump(metadata, f, indent=2)
+
+    click.echo(f"  Saved: {markdown_path} ({len(results)} page(s))")
+
+
+@click.command()
+@click.argument("input_path", type=click.Path(exists=True, path_type=Path))
+@click.argument("output_path", type=click.Path(path_type=Path))
+@click.option(
+    "--method",
+    type=click.Choice(["hf", "vllm"], case_sensitive=False),
+    default="vllm",
+    help="Inference method: 'hf' for local model, 'vllm' for vLLM server.",
+)
+@click.option(
+    "--page-range",
+    type=str,
+    default=None,
+    help="Page range for PDFs (e.g., '1-5,7,9-12'). Only applicable to PDF files.",
+)
+@click.option(
+    "--max-output-tokens",
+    type=int,
+    default=None,
+    help="Maximum number of output tokens per page.",
+)
+@click.option(
+    "--max-workers",
+    type=int,
+    default=None,
+    help="Maximum number of parallel workers for vLLM inference.",
+)
+@click.option(
+    "--max-retries",
+    type=int,
+    default=None,
+    help="Maximum number of retries for vLLM inference.",
+)
+@click.option(
+    "--include-images/--no-images",
+    default=True,
+    help="Include images in output.",
+)
+@click.option(
+    "--include-headers-footers/--no-headers-footers",
+    default=False,
+    help="Include page headers and footers in output.",
+)
+@click.option(
+    "--save-html/--no-html",
+    default=True,
+    help="Save HTML output files.",
+)
+@click.option(
+    "--batch-size",
+    type=int,
+    default=1,
+    help="Number of pages to process in a batch.",
+)
+@click.option(
+    "--paginate_output",
+    is_flag=True,
+    default=False,
+)
+def main(
+    input_path: Path,
+    output_path: Path,
+    method: str,
+    page_range: str,
+    max_output_tokens: int,
+    max_workers: int,
+    max_retries: int,
+    include_images: bool,
+    include_headers_footers: bool,
+    save_html: bool,
+    batch_size: int,
+    paginate_output: bool,
+):
+    click.echo("Chandra CLI - Starting OCR processing")
+    click.echo(f"Input: {input_path}")
+    click.echo(f"Output: {output_path}")
+    click.echo(f"Method: {method}")
+
+    # Create output directory
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    # Load model
+    click.echo(f"\nLoading model with method '{method}'...")
+    model = InferenceManager(method=method)
+    click.echo("Model loaded successfully.")
+
+    # Get files to process
+    files_to_process = get_supported_files(input_path)
+    click.echo(f"\nFound {len(files_to_process)} file(s) to process.")
+
+    if not files_to_process:
+        click.echo("No supported files found. Exiting.")
+        return
+
+    # Process each file
+    for file_idx, file_path in enumerate(files_to_process, 1):
+        click.echo(
+            f"\n[{file_idx}/{len(files_to_process)}] Processing: {file_path.name}"
+        )
+
+        try:
+            # Load images from file
+            config = {"page_range": page_range} if page_range else {}
+            images = load_file(str(file_path), config)
+            click.echo(f"  Loaded {len(images)} page(s)")
+
+            # Accumulate all results for this document
+            all_results = []
+
+            # Process pages in batches
+            for batch_start in range(0, len(images), batch_size):
+                batch_end = min(batch_start + batch_size, len(images))
+                batch_images = images[batch_start:batch_end]
+
+                # Create batch input items
+                batch = [
+                    BatchInputItem(image=img, prompt_type="ocr_layout")
+                    for img in batch_images
+                ]
+
+                # Run inference
+                click.echo(f"  Processing pages {batch_start + 1}-{batch_end}...")
+
+                # Build kwargs for generate
+                generate_kwargs = {
+                    "include_images": include_images,
+                    "include_headers_footers": include_headers_footers,
+                }
+
+                if max_output_tokens is not None:
+                    generate_kwargs["max_output_tokens"] = max_output_tokens
+
+                if method == "vllm":
+                    if max_workers is not None:
+                        generate_kwargs["max_workers"] = max_workers
+                    if max_retries is not None:
+                        generate_kwargs["max_retries"] = max_retries
+
+                results = model.generate(batch, **generate_kwargs)
+                all_results.extend(results)
+
+            # Save merged output for all pages
+            save_merged_output(
+                output_path,
+                file_path.name,
+                all_results,
+                save_images=include_images,
+                save_html=save_html,
+                paginate_output=paginate_output,
+            )
+
+            click.echo(f"  Completed: {file_path.name}")
+
+        except Exception as e:
+            click.echo(f"  Error processing {file_path.name}: {e}", err=True)
+            continue
+
+    click.echo(f"\nProcessing complete. Results saved to: {output_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pyproject.toml b/pyproject.toml
index c38712f..1d15eb0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,14 @@
 [project]
-name = "chandra"
+name = "chandra-ocr"
 version = "0.1.0"
-description = "Add your description here"
+description = "OCR model that converts documents to markdown, HTML, or JSON."
 readme = "README.md"
 requires-python = ">=3.10"
+license = {text = "Apache-2.0"}
+authors = [
+    {name = "Datalab", email = "hi@datalab.to"}
+]
+keywords = ["ocr", "pdf", "markdown", "layout"]
 dependencies = [
     "beautifulsoup4>=4.14.2",
     "filetype>=1.2.0",
@@ -15,17 +20,20 @@ dependencies = [
     "pypdfium2>=4.30.0",
     "python-dotenv>=1.1.1",
     "qwen-vl-utils>=0.0.14",
-    "streamlit>=1.50.0",
-    "surya-ocr>=0.17.0",
     "torch>=2.8.0",
     "torchvision>=0.23.0",
     "transformers>=4.57.1",
+    "streamlit>=1.50.0"
 ]
 
+[project.scripts]
+chandra = "chandra_cli:main"
+
 [tool.setuptools.packages.find]
 include = ["chandra*"]
 
 [dependency-groups]
 dev = [
     "pre-commit>=4.3.0",
+    "pytest>=8.4.2",
 ]
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..c73be09
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,4 @@
+[pytest]
+testpaths=tests
+filterwarnings =
+    ignore::Warning
\ No newline at end of file
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..3568958
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,10 @@
+import pytest
+from PIL import Image, ImageDraw
+
+
+@pytest.fixture(scope="session")
+def simple_text_image() -> Image.Image:
+    image = Image.new("RGB", (800, 600), "white")
+    draw = ImageDraw.Draw(image)
+    draw.text((50, 50), "Hello, World!", fill="black", font_size=32)
+    return image
diff --git a/tests/integration/test_image_inference.py b/tests/integration/test_image_inference.py
new file mode 100644
index 0000000..46e0c1f
--- /dev/null
+++ b/tests/integration/test_image_inference.py
@@ -0,0 +1,18 @@
+from chandra.model import InferenceManager, BatchInputItem
+
+
+def test_inference_image(simple_text_image):
+    manager = InferenceManager(method="hf")
+    batch = [
+        BatchInputItem(
+            image=simple_text_image,
+            prompt_type="ocr_layout",
+        )
+    ]
+    outputs = manager.generate(batch, max_output_tokens=128)
+    assert len(outputs) == 1
+    output = outputs[0]
+    assert "Hello, World!" in output.markdown
+
+    chunks = output.chunks
+    assert len(chunks) == 1
diff --git a/uv.lock b/uv.lock
index 3c8fc6a..f90f101 100644
--- a/uv.lock
+++ b/uv.lock
@@ -161,7 +161,7 @@ wheels = [
 ]
 
 [[package]]
-name = "chandra"
+name = "chandra-ocr"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
@@ -176,7 +176,6 @@ dependencies = [
     { name = "python-dotenv" },
     { name = "qwen-vl-utils" },
     { name = "streamlit" },
-    { name = "surya-ocr" },
     { name = "torch" },
     { name = "torchvision" },
     { name = "transformers" },
@@ -185,6 +184,7 @@ dependencies = [
 [package.dev-dependencies]
 dev = [
     { name = "pre-commit" },
+    { name = "pytest" },
 ]
 
 [package.metadata]
@@ -200,14 +200,16 @@ requires-dist = [
     { name = "python-dotenv", specifier = ">=1.1.1" },
     { name = "qwen-vl-utils", specifier = ">=0.0.14" },
     { name = "streamlit", specifier = ">=1.50.0" },
-    { name = "surya-ocr", specifier = ">=0.17.0" },
     { name = "torch", specifier = ">=2.8.0" },
     { name = "torchvision", specifier = ">=0.23.0" },
     { name = "transformers", specifier = ">=4.57.1" },
 ]
 
 [package.metadata.requires-dev]
-dev = [{ name = "pre-commit", specifier = ">=4.3.0" }]
+dev = [
+    { name = "pre-commit", specifier = ">=4.3.0" },
+    { name = "pytest", specifier = ">=8.4.2" },
+]
 
 [[package]]
 name = "charset-normalizer"
@@ -312,15 +314,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277 },
 ]
 
-[[package]]
-name = "einops"
-version = "0.8.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e5/81/df4fbe24dff8ba3934af99044188e20a98ed441ad17a274539b74e82e126/einops-0.8.1.tar.gz", hash = "sha256:de5d960a7a761225532e0f1959e5315ebeafc0cd43394732f103ca44b9837e84", size = 54805 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/87/62/9773de14fe6c45c23649e98b83231fffd7b9892b6cf863251dc2afa73643/einops-0.8.1-py3-none-any.whl", hash = "sha256:919387eb55330f5757c6bea9165c5ff5cfe63a642682ea788a6d472576d81737", size = 64359 },
-]
-
 [[package]]
 name = "exceptiongroup"
 version = "1.3.0"
@@ -473,6 +466,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
 ]
 
+[[package]]
+name = "iniconfig"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484 },
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -1041,24 +1043,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cb/92/6aeef1836e66dfec7f7f160a4f06d7041be7f6ccfc47a2f0f5738b332245/openai-2.2.0-py3-none-any.whl", hash = "sha256:d222e63436e33f3134a3d7ce490dc2d2f146fa98036eb65cc225df3ce163916f", size = 998972 },
 ]
 
-[[package]]
-name = "opencv-python-headless"
-version = "4.11.0.86"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/36/2f/5b2b3ba52c864848885ba988f24b7f105052f68da9ab0e693cc7c25b0b30/opencv-python-headless-4.11.0.86.tar.gz", hash = "sha256:996eb282ca4b43ec6a3972414de0e2331f5d9cda2b41091a49739c19fb843798", size = 95177929 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/dc/53/2c50afa0b1e05ecdb4603818e85f7d174e683d874ef63a6abe3ac92220c8/opencv_python_headless-4.11.0.86-cp37-abi3-macosx_13_0_arm64.whl", hash = "sha256:48128188ade4a7e517237c8e1e11a9cdf5c282761473383e77beb875bb1e61ca", size = 37326460 },
-    { url = "https://files.pythonhosted.org/packages/3b/43/68555327df94bb9b59a1fd645f63fafb0762515344d2046698762fc19d58/opencv_python_headless-4.11.0.86-cp37-abi3-macosx_13_0_x86_64.whl", hash = "sha256:a66c1b286a9de872c343ee7c3553b084244299714ebb50fbdcd76f07ebbe6c81", size = 56723330 },
-    { url = "https://files.pythonhosted.org/packages/45/be/1438ce43ebe65317344a87e4b150865c5585f4c0db880a34cdae5ac46881/opencv_python_headless-4.11.0.86-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6efabcaa9df731f29e5ea9051776715b1bdd1845d7c9530065c7951d2a2899eb", size = 29487060 },
-    { url = "https://files.pythonhosted.org/packages/dd/5c/c139a7876099916879609372bfa513b7f1257f7f1a908b0bdc1c2328241b/opencv_python_headless-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0e0a27c19dd1f40ddff94976cfe43066fbbe9dfbb2ec1907d66c19caef42a57b", size = 49969856 },
-    { url = "https://files.pythonhosted.org/packages/95/dd/ed1191c9dc91abcc9f752b499b7928aacabf10567bb2c2535944d848af18/opencv_python_headless-4.11.0.86-cp37-abi3-win32.whl", hash = "sha256:f447d8acbb0b6f2808da71fddd29c1cdd448d2bc98f72d9bb78a7a898fc9621b", size = 29324425 },
-    { url = "https://files.pythonhosted.org/packages/86/8a/69176a64335aed183529207ba8bc3d329c2999d852b4f3818027203f50e6/opencv_python_headless-4.11.0.86-cp37-abi3-win_amd64.whl", hash = "sha256:6c304df9caa7a6a5710b91709dd4786bf20a74d57672b3c31f7033cc638174ca", size = 39402386 },
-]
-
 [[package]]
 name = "packaging"
 version = "25.0"
@@ -1198,6 +1182,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/73/cb/ac7874b3e5d58441674fb70742e6c374b28b0c7cb988d37d991cde47166c/platformdirs-4.5.0-py3-none-any.whl", hash = "sha256:e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3", size = 18651 },
 ]
 
+[[package]]
+name = "pluggy"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
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