diff --git a/README.md b/README.md
index 261682cf..67456b45 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ Marker converts PDF, EPUB, and MOBI to markdown.  It's 10x faster than nougat, m
 - Removes headers/footers/other artifacts
 - Converts most equations to latex
 - Formats code blocks and tables
-- Support for multiple languages (although most testing is done in English).  See `settings.py` for a language list.
+- Support for multiple languages (although most testing is done in English).  See `settings.py` for a language list, or to add your own.
 - Works on GPU, CPU, or MPS
 
 ## How it works
@@ -15,7 +15,7 @@ Marker is a pipeline of deep learning models:
 
 - Extract text, OCR if necessary (heuristics, tesseract)
 - Detect page layout ([layout segmenter](https://huggingface.co/vikp/layout_segmenter), [column detector](https://huggingface.co/vikp/column_detector))
-- Clean and format each block (heuristics, [nougat](https://huggingface.co/facebook/nougat-base))
+- Clean and format each block (heuristics, [texify](https://huggingface.co/vikp/texify))
 - Combine blocks and postprocess complete text (heuristics, [pdf_postprocessor](https://huggingface.co/vikp/pdf_postprocessor_t5))
 
 Relying on autoregressive forward passes to generate text is slow and prone to hallucination/repetition.  From the nougat paper: `We observed [repetition] in 1.5% of pages in the test set, but the frequency increases for out-of-domain documents.`  In my anecdotal testing, repetitions happen on 5%+ of out-of-domain (non-arXiv) pages.  
@@ -48,10 +48,10 @@ See [below](#benchmarks) for detailed speed and accuracy benchmarks, and instruc
 
 PDF is a tricky format, so marker will not always work perfectly.  Here are some known limitations that are on the roadmap to address:
 
-- Marker will convert fewer equations to latex than nougat.  This is because it has to first detect equations, then convert them without hallucation.
+- Marker will not convert 100% of equations to LaTeX.  This is because it has to first detect equations, then convert them.
 - Whitespace and indentations are not always respected.
 - Not all lines/spans will be joined properly.
-- Languages similar to English (Spanish, French, German, Russian, etc) have the best support. There is provisional support for Chinese, Japanese, Korean, and Hindi, but it may not work as well.
+- Languages similar to English (Spanish, French, German, Russian, etc) have the best support. There is provisional support for Chinese, Japanese, Korean, and Hindi, but it may not work as well.  You can add other languages by adding them to the `TESSERACT_LANGUAGES` and `SPELLCHECK_LANGUAGES` settings in `settings.py`.
 - This works best on digital PDFs that won't require a lot of OCR.  It's optimized for speed, and limited OCR is used to fix errors.
 
 # Installation
@@ -88,17 +88,16 @@ First, clone the repo:
 - Install python requirements
   - `poetry install`
   - `poetry shell` to activate your poetry venv
-- On ARM macs (M1+), make sure to set the `TORCH_DEVICE` setting to `mps` (more details below) for a speedup
 
 # Usage
 
-First, some configuration:
+First, some configuration.  Note that settings can be overridden with env vars, or in a `local.env` file in the root `marker` folder.
 
-- Set your torch device in the `local.env` file.  For example, `TORCH_DEVICE=cuda` or `TORCH_DEVICE=mps`.  `cpu` is the default.
+- Your torch device will be automatically detected, but you can manually set it also.  For example, `TORCH_DEVICE=cuda` or `TORCH_DEVICE=mps`. `cpu` is the default.
   - If using GPU, set `INFERENCE_RAM` to your GPU VRAM (per GPU).  For example, if you have 16 GB of VRAM, set `INFERENCE_RAM=16`.
   - Depending on your document types, marker's average memory usage per task can vary slightly.  You can configure `VRAM_PER_TASK` to adjust this if you notice tasks failing with GPU out of memory errors.
 - Inspect the other settings in `marker/settings.py`.  You can override any settings in the `local.env` file, or by setting environment variables.
-  - By default, the final editor model is off.  Turn it on with `ENABLE_EDITOR_MODEL`.
+  - By default, the final editor model is off.  Turn it on with `ENABLE_EDITOR_MODEL=true`.
   - By default, marker will use ocrmypdf for OCR, which is slower than base tesseract, but higher quality.  You can change this with the `OCR_ENGINE` setting.
 
 ## Convert a single file
@@ -148,6 +147,8 @@ MIN_LENGTH=10000 METADATA_FILE=../pdf_meta.json NUM_DEVICES=4 NUM_WORKERS=15 bas
 - `NUM_WORKERS` is the number of parallel processes to run on each GPU.  Per-GPU parallelism will not increase beyond `INFERENCE_RAM / VRAM_PER_TASK`.
 - `MIN_LENGTH` is the minimum number of characters that need to be extracted from a pdf before it will be considered for processing.  If you're processing a lot of pdfs, I recommend setting this to avoid OCRing pdfs that are mostly images. (slows everything down)
 
+Note that the env variables above are specific to this script, and cannot be set in `local.env`.
+
 # Benchmarks
 
 Benchmarking PDF extraction quality is hard.  I've created a test set by finding books and scientific papers that have a pdf version and a latex source.  I convert the latex to text, and compare the reference to the output of text extraction methods.
@@ -203,7 +204,6 @@ I'm building a version that can be used commercially, by stripping out the depen
 Here are the non-commercial/restrictive dependencies:
 
 - LayoutLMv3: CC BY-NC-SA 4.0 .  [Source](https://huggingface.co/microsoft/layoutlmv3-base)
-- Nougat: CC-BY-NC . [Source](https://github.com/facebookresearch/nougat)
 - PyMuPDF - GPL . [Source](https://pymupdf.readthedocs.io/en/latest/about.html#license-and-copyright)
 
 Other dependencies/datasets are openly licensed (doclaynet, byt5), or used in a way that is compatible with commercial usage (ghostscript).
diff --git a/marker/cleaners/equations.py b/marker/cleaners/equations.py
index e2cf325d..1d1cd353 100644
--- a/marker/cleaners/equations.py
+++ b/marker/cleaners/equations.py
@@ -4,30 +4,26 @@
 from typing import List
 
 import torch
-from nougat import NougatModel
-from nougat.postprocessing import markdown_compatible
-from nougat.utils.checkpoint import get_checkpoint
+from texify.inference import batch_inference
+from texify.model.model import load_model
+from texify.model.processor import load_processor
 import re
 from PIL import Image, ImageDraw
-from nougat.utils.dataset import ImageDataset
 
 from marker.bbox import should_merge_blocks, merge_boxes
-from marker.debug.data import dump_nougat_debug_data
+from marker.debug.data import dump_equation_debug_data
 from marker.settings import settings
 from marker.schema import Page, Span, Line, Block, BlockType
-from nougat.utils.device import move_to_device
 import os
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+processor = load_processor()
 
-def load_nougat_model():
-    ckpt = get_checkpoint(None, model_tag=settings.NOUGAT_MODEL_NAME)
-    nougat_model = NougatModel.from_pretrained(ckpt)
-    if settings.TORCH_DEVICE != "cpu":
-        move_to_device(nougat_model, bf16=settings.CUDA, cuda=settings.CUDA)
-    nougat_model.eval()
-    return nougat_model
+
+def load_texify_model():
+    texify_model = load_model(checkpoint=settings.TEXIFY_MODEL_NAME, device=settings.TORCH_DEVICE_MODEL, dtype=settings.TEXIFY_DTYPE)
+    return texify_model
 
 
 def mask_bbox(png_image, bbox, selected_bboxes):
@@ -54,76 +50,48 @@ def mask_bbox(png_image, bbox, selected_bboxes):
     return result
 
 
-def get_nougat_image(page, bbox, selected_bboxes):
-    pix = page.get_pixmap(dpi=settings.NOUGAT_DPI, clip=bbox)
-    png = pix.pil_tobytes(format="BMP")
+def get_masked_image(page, bbox, selected_bboxes):
+    pix = page.get_pixmap(dpi=settings.TEXIFY_DPI, clip=bbox)
+    png = pix.pil_tobytes(format="PNG")
     png_image = Image.open(io.BytesIO(png))
     png_image = mask_bbox(png_image, bbox, selected_bboxes)
     png_image = png_image.convert("RGB")
-
-    img_out = io.BytesIO()
-    png_image.save(img_out, format="BMP")
-    return img_out
-
-
-def replace_latex_fences(text):
-    # Replace block equations: \[ ... \] with $$...$$
-    text = re.sub(r'\\\[(.*?)\\\]', r'$$\1$$', text)
-
-    # Replace inline math: \( ... \) with $...$
-    text = re.sub(r'\\\((.*?)\\\)', r'$\1$', text)
-
-    return text
+    return png_image
 
 
-def get_nougat_text_batched(images, reformat_region_lens, nougat_model, batch_size):
+def get_latex_batched(images, reformat_region_lens, texify_model, batch_size):
     if len(images) == 0:
         return []
 
     predictions = [""] * len(images)
 
-    dataset = ImageDataset(
-        images,
-        partial(nougat_model.encoder.prepare_input, random_padding=False),
-    )
-
-    dataloader = torch.utils.data.DataLoader(
-        dataset,
-        batch_size=batch_size,
-        pin_memory=True,
-        shuffle=False,
-    )
-
-    for idx, sample in enumerate(dataloader):
+    for i in range(0, len(images), batch_size):
         # Dynamically set max length to save inference time
-        min_idx = idx * batch_size
+        min_idx = i
         max_idx = min(min_idx + batch_size, len(images))
         max_length = max(reformat_region_lens[min_idx:max_idx])
-        max_length = min(max_length, settings.NOUGAT_MODEL_MAX)
-        max_length += settings.NOUGAT_TOKEN_BUFFER
-
-        nougat_model.config.max_length = max_length
-        model_output = nougat_model.inference(image_tensors=sample, early_stopping=False)
-        for j, output in enumerate(model_output["predictions"]):
-            disclaimer = ""
-            token_count = get_total_nougat_tokens(output, nougat_model)
+        max_length = min(max_length, settings.TEXIFY_MODEL_MAX)
+        max_length += settings.TEXIFY_TOKEN_BUFFER
+
+        model_output = batch_inference(images[min_idx:max_idx], texify_model, processor, max_tokens=max_length)
+
+        for j, output in enumerate(model_output):
+            token_count = get_total_texify_tokens(output)
             if token_count >= max_length - 1:
-                disclaimer = "[TRUNCATED]"
+                output = ""
 
-            image_idx = idx * batch_size + j
-            predictions[image_idx] = (
-                replace_latex_fences(markdown_compatible(output)) + disclaimer
-            )
+            image_idx = i + j
+            predictions[image_idx] = output
     return predictions
 
 
-def get_total_nougat_tokens(text, nougat_model):
-    tokenizer = nougat_model.decoder.tokenizer
+def get_total_texify_tokens(text):
+    tokenizer = processor.tokenizer
     tokens = tokenizer(text)
     return len(tokens["input_ids"])
 
 
-def find_page_equation_regions(pnum, page, block_types, nougat_model):
+def find_page_equation_regions(pnum, page, block_types):
     i = 0
     equation_boxes = [b.bbox for b in block_types[pnum] if b.block_type == "Formula"]
     reformatted_blocks = set()
@@ -151,7 +119,7 @@ def find_page_equation_regions(pnum, page, block_types, nougat_model):
                 prev_block = page.blocks[j]
                 prev_bbox = prev_block.bbox
                 prelim_block_text = prev_block.prelim_text + " " + block_text
-                if get_total_nougat_tokens(prelim_block_text, nougat_model) >= settings.NOUGAT_MODEL_MAX:
+                if get_total_texify_tokens(prelim_block_text) >= settings.TEXIFY_MODEL_MAX:
                     break
 
                 block_text = prelim_block_text
@@ -166,7 +134,7 @@ def find_page_equation_regions(pnum, page, block_types, nougat_model):
                     equation_boxes)) and i + 1 < len(page.blocks):
                 bbox = merge_boxes(bbox, next_bbox)
                 prelim_block_text = block_text + " " + next_block.prelim_text
-                if get_total_nougat_tokens(prelim_block_text, nougat_model) >= settings.NOUGAT_MODEL_MAX:
+                if get_total_texify_tokens(prelim_block_text) >= settings.TEXIFY_MODEL_MAX:
                     break
 
                 block_text = prelim_block_text
@@ -176,9 +144,9 @@ def find_page_equation_regions(pnum, page, block_types, nougat_model):
                     next_block = page.blocks[i + 1]
                     next_bbox = next_block.bbox
 
-        total_tokens = get_total_nougat_tokens(block_text, nougat_model)
+        total_tokens = get_total_texify_tokens(block_text)
         ordered_blocks = sorted(([sb[0] for sb in selected_blocks]))
-        if total_tokens < settings.NOUGAT_MODEL_MAX:
+        if total_tokens < settings.TEXIFY_MODEL_MAX:
             # Get indices of all blocks to merge
             reformat_regions.append(ordered_blocks)
             block_lens.append(total_tokens)
@@ -206,7 +174,7 @@ def get_bboxes_for_region(page, region):
     return bboxes, merged_box
 
 
-def replace_blocks_with_nougat_predictions(page_blocks: Page, merged_boxes, reformat_regions, predictions, pnum, nougat_model):
+def replace_blocks_with_latex(page_blocks: Page, merged_boxes, reformat_regions, predictions, pnum):
     new_blocks = []
     converted_spans = []
     current_region = 0
@@ -221,13 +189,12 @@ def replace_blocks_with_nougat_predictions(page_blocks: Page, merged_boxes, refo
             continue
 
         orig_block_text = " ".join([page_blocks.blocks[i].prelim_text for i in reformat_regions[current_region]])
-        nougat_text = predictions[current_region]
+        latex_text = predictions[current_region]
         conditions = [
-            len(nougat_text) > 0,
-            not any([word in nougat_text for word in settings.NOUGAT_HALLUCINATION_WORDS]),
-            get_total_nougat_tokens(nougat_text, nougat_model) < settings.NOUGAT_MODEL_MAX,  # Make sure we didn't run to the token max
-            len(nougat_text) > len(orig_block_text) * .8,
-            len(nougat_text.strip()) > 0
+            len(latex_text) > 0,
+            get_total_texify_tokens(latex_text) < settings.TEXIFY_MODEL_MAX,  # Make sure we didn't run to the overall token max
+            len(latex_text) > len(orig_block_text) * .8,
+            len(latex_text.strip()) > 0
         ]
 
         idx = reformat_regions[current_region][-1] + 1
@@ -241,7 +208,7 @@ def replace_blocks_with_nougat_predictions(page_blocks: Page, merged_boxes, refo
             block_line = Line(
                 spans=[
                     Span(
-                        text=nougat_text,
+                        text=latex_text,
                         bbox=merged_boxes[current_region],
                         span_id=f"{pnum}_{idx}_fixeq",
                         font="Latex",
@@ -261,7 +228,7 @@ def replace_blocks_with_nougat_predictions(page_blocks: Page, merged_boxes, refo
     return new_blocks, success_count, fail_count, converted_spans
 
 
-def replace_equations(doc, blocks: List[Page], block_types: List[List[BlockType]], nougat_model, batch_size=settings.NOUGAT_BATCH_SIZE):
+def replace_equations(doc, blocks: List[Page], block_types: List[List[BlockType]], texify_model, batch_size=settings.TEXIFY_BATCH_SIZE):
     unsuccessful_ocr = 0
     successful_ocr = 0
 
@@ -269,7 +236,7 @@ def replace_equations(doc, blocks: List[Page], block_types: List[List[BlockType]
     reformat_regions = []
     reformat_region_lens = []
     for pnum, page in enumerate(blocks):
-        regions, region_lens = find_page_equation_regions(pnum, page, block_types, nougat_model)
+        regions, region_lens = find_page_equation_regions(pnum, page, block_types)
         reformat_regions.append(regions)
         reformat_region_lens.append(region_lens)
 
@@ -283,12 +250,12 @@ def replace_equations(doc, blocks: List[Page], block_types: List[List[BlockType]
         page_obj = doc[page_idx]
         for reformat_region in reformat_regions_page:
             bboxes, merged_box = get_bboxes_for_region(blocks[page_idx], reformat_region)
-            png_image = get_nougat_image(page_obj, merged_box, bboxes)
+            png_image = get_masked_image(page_obj, merged_box, bboxes)
             images.append(png_image)
             merged_boxes.append(merged_box)
 
     # Make batched predictions
-    predictions = get_nougat_text_batched(images, flat_reformat_region_lens, nougat_model, batch_size)
+    predictions = get_latex_batched(images, flat_reformat_region_lens, texify_model, batch_size)
 
     # Replace blocks with predictions
     page_start = 0
@@ -296,13 +263,12 @@ def replace_equations(doc, blocks: List[Page], block_types: List[List[BlockType]
     for page_idx, reformat_regions_page in enumerate(reformat_regions):
         page_predictions = predictions[page_start:page_start + len(reformat_regions_page)]
         page_boxes = merged_boxes[page_start:page_start + len(reformat_regions_page)]
-        new_page_blocks, success_count, fail_count, converted_span = replace_blocks_with_nougat_predictions(
+        new_page_blocks, success_count, fail_count, converted_span = replace_blocks_with_latex(
             blocks[page_idx],
             page_boxes,
             reformat_regions_page,
             page_predictions,
-            page_idx,
-            nougat_model
+            page_idx
         )
         converted_spans.extend(converted_span)
         blocks[page_idx].blocks = new_page_blocks
@@ -311,6 +277,6 @@ def replace_equations(doc, blocks: List[Page], block_types: List[List[BlockType]
         unsuccessful_ocr += fail_count
 
     # If debug mode is on, dump out conversions for comparison
-    dump_nougat_debug_data(doc, images, converted_spans)
+    dump_equation_debug_data(doc, images, converted_spans)
 
     return blocks, {"successful_ocr": successful_ocr, "unsuccessful_ocr": unsuccessful_ocr, "equations": eq_count}
\ No newline at end of file
diff --git a/marker/convert.py b/marker/convert.py
index fa6317de..fef10116 100644
--- a/marker/convert.py
+++ b/marker/convert.py
@@ -102,7 +102,7 @@ def convert_single_pdf(
         return "", out_meta
 
     # Unpack models from list
-    nougat_model, layoutlm_model, order_model, edit_model = model_lst
+    texify_model, layoutlm_model, order_model, edit_model = model_lst
 
     block_types = detect_document_block_types(
         doc,
@@ -146,8 +146,8 @@ def convert_single_pdf(
         doc,
         blocks,
         block_types,
-        nougat_model,
-        batch_size=int(settings.NOUGAT_BATCH_SIZE * parallel_factor)
+        texify_model,
+        batch_size=int(settings.TEXIFY_BATCH_SIZE * parallel_factor)
     )
     out_meta["block_stats"]["equations"] = eq_stats
 
diff --git a/marker/debug/data.py b/marker/debug/data.py
index 97418d74..4258fea7 100644
--- a/marker/debug/data.py
+++ b/marker/debug/data.py
@@ -10,7 +10,7 @@
 import io
 
 
-def dump_nougat_debug_data(doc, images, converted_spans):
+def dump_equation_debug_data(doc, images, converted_spans):
     if not settings.DEBUG_DATA_FOLDER or settings.DEBUG_LEVEL == 0:
         return
 
@@ -55,7 +55,7 @@ def dump_bbox_debug_data(doc, blocks: List[Page]):
     for idx, page_blocks in enumerate(blocks):
         page = doc[idx]
 
-        pix = page.get_pixmap(dpi=settings.NOUGAT_DPI, annots=False, clip=page_blocks.bbox)
+        pix = page.get_pixmap(dpi=settings.TEXIFY_DPI, annots=False, clip=page_blocks.bbox)
         png = pix.pil_tobytes(format="PNG")
         png_image = Image.open(io.BytesIO(png))
         width, height = png_image.size
diff --git a/marker/models.py b/marker/models.py
index 95569f52..e8a1ee65 100644
--- a/marker/models.py
+++ b/marker/models.py
@@ -1,4 +1,4 @@
-from marker.cleaners.equations import load_nougat_model
+from marker.cleaners.equations import load_texify_model
 from marker.ordering import load_ordering_model
 from marker.postprocessors.editor import load_editing_model
 from marker.segmentation import load_layout_model
@@ -8,6 +8,6 @@ def load_all_models():
     edit = load_editing_model()
     order = load_ordering_model()
     layout = load_layout_model()
-    nougat = load_nougat_model()
-    model_lst = [nougat, layout, order, edit]
+    texify = load_texify_model()
+    model_lst = [texify, layout, order, edit]
     return model_lst
diff --git a/marker/ordering.py b/marker/ordering.py
index ea2371e5..1718c522 100644
--- a/marker/ordering.py
+++ b/marker/ordering.py
@@ -19,7 +19,7 @@ def load_ordering_model():
     model = LayoutLMv3ForSequenceClassification.from_pretrained(
         settings.ORDERER_MODEL_NAME,
         torch_dtype=settings.MODEL_DTYPE,
-    ).to(settings.TORCH_DEVICE)
+    ).to(settings.TORCH_DEVICE_MODEL)
     model.eval()
     return model
 
@@ -65,12 +65,11 @@ def batch_inference(rgb_images, bboxes, words, model):
         max_length=128
     )
 
-    if settings.CUDA:
-        encoding["pixel_values"] = encoding["pixel_values"].to(torch.bfloat16)
+    encoding["pixel_values"] = encoding["pixel_values"].to(model.dtype)
 
     with torch.inference_mode():
         for k in ["bbox", "input_ids", "pixel_values", "attention_mask"]:
-            encoding[k] = encoding[k].to(settings.TORCH_DEVICE)
+            encoding[k] = encoding[k].to(model.device)
         outputs = model(**encoding)
         logits = outputs.logits
 
diff --git a/marker/postprocessors/editor.py b/marker/postprocessors/editor.py
index 85f3b386..dd16b156 100644
--- a/marker/postprocessors/editor.py
+++ b/marker/postprocessors/editor.py
@@ -16,7 +16,7 @@ def load_editing_model():
     model = T5ForTokenClassification.from_pretrained(
             settings.EDITOR_MODEL_NAME,
             torch_dtype=settings.MODEL_DTYPE,
-        ).to(settings.TORCH_DEVICE)
+        ).to(settings.TORCH_DEVICE_MODEL)
     model.eval()
 
     model.config.label2id = {
diff --git a/marker/segmentation.py b/marker/segmentation.py
index d047114c..3aeadd0b 100644
--- a/marker/segmentation.py
+++ b/marker/segmentation.py
@@ -27,7 +27,7 @@ def load_layout_model():
     model = LayoutLMv3ForTokenClassification.from_pretrained(
         settings.LAYOUT_MODEL_NAME,
         torch_dtype=settings.MODEL_DTYPE,
-    ).to(settings.TORCH_DEVICE)
+    ).to(settings.TORCH_DEVICE_MODEL)
 
     model.config.id2label = {
         0: "Caption",
@@ -173,10 +173,9 @@ def predict_block_types(encodings, layoutlm_model, batch_size):
 
         model_in = {}
         for k in ["bbox", "input_ids", "attention_mask", "pixel_values"]:
-            model_in[k] = torch.stack([b[k] for b in batch]).to(settings.TORCH_DEVICE)
+            model_in[k] = torch.stack([b[k] for b in batch]).to(layoutlm_model.device)
 
-        if settings.CUDA:
-            model_in["pixel_values"] = model_in["pixel_values"].to(torch.bfloat16)
+        model_in["pixel_values"] = model_in["pixel_values"].to(layoutlm_model.dtype)
 
         with torch.inference_mode():
             outputs = layoutlm_model(**model_in)
diff --git a/marker/settings.py b/marker/settings.py
index 10c4ba61..ad3a9656 100644
--- a/marker/settings.py
+++ b/marker/settings.py
@@ -10,7 +10,22 @@
 
 class Settings(BaseSettings):
     # General
-    TORCH_DEVICE: str = "cpu"
+    TORCH_DEVICE: Optional[str] = None
+
+    @computed_field
+    @property
+    def TORCH_DEVICE_MODEL(self) -> str:
+        if self.TORCH_DEVICE is not None:
+            return self.TORCH_DEVICE
+
+        if torch.cuda.is_available():
+            return "cuda"
+
+        if torch.backends.mps.is_available():
+            return "mps"
+
+        return "cpu"
+
     INFERENCE_RAM: int = 40 # How much VRAM each GPU has (in GB).
     VRAM_PER_TASK: float = 2.5 # How much VRAM to allocate per task (in GB).  Peak marker VRAM usage is around 3GB, but avg across workers is lower.
     DEFAULT_LANG: str = "English" # Default language we assume files to be in, should be one of the keys in TESSERACT_LANGUAGES
@@ -59,28 +74,12 @@ class Settings(BaseSettings):
     OCR_PARALLEL_WORKERS: int = 2 # How many CPU workers to use for OCR
     OCR_ENGINE: str = "ocrmypdf" # Which OCR engine to use, either "tesseract" or "ocrmypdf".  Ocrmypdf is higher quality, but slower.
 
-    # Nougat model
-    NOUGAT_MODEL_MAX: int = 512 # Max inference length for nougat
-    NOUGAT_TOKEN_BUFFER: int = 256 # Number of tokens to buffer above max for nougat
-    NOUGAT_HALLUCINATION_WORDS: List[str] = [
-        "[MISSING_PAGE_POST]",
-        "## References\n",
-        "**Figure Captions**\n",
-        "Footnote",
-        "\par\par\par",
-        "## Chapter",
-        "Fig.",
-        "particle",
-        "[REPEATS]",
-        "[TRUNCATED]",
-        "### ",
-        "effective field strength",
-        "\Phi_{\rm eff}",
-        "\mathbf{\mathbf"
-    ]
-    NOUGAT_DPI: int = 96 # DPI to render images at, matches default settings for nougat
-    NOUGAT_MODEL_NAME: str = "0.1.0-small" # Name of the model to use
-    NOUGAT_BATCH_SIZE: int = 6 if TORCH_DEVICE == "cuda" else 1 # Batch size for nougat, don't batch on cpu
+    # Texify model
+    TEXIFY_MODEL_MAX: int = 384 # Max inference length for texify
+    TEXIFY_TOKEN_BUFFER: int = 256 # Number of tokens to buffer above max for texify
+    TEXIFY_DPI: int = 96 # DPI to render images at
+    TEXIFY_BATCH_SIZE: int = 1 if TORCH_DEVICE_MODEL == "cpu" else 6 # Batch size for texify, don't batch on cpu
+    TEXIFY_MODEL_NAME: str = "vikp/texify"
 
     # Layout model
     BAD_SPAN_TYPES: List[str] = ["Caption", "Footnote", "Page-footer", "Page-header", "Picture"]
@@ -119,7 +118,15 @@ def CUDA(self) -> bool:
     @computed_field
     @property
     def MODEL_DTYPE(self) -> torch.dtype:
-        return torch.bfloat16 if self.CUDA else torch.float32
+        if self.TORCH_DEVICE_MODEL == "cuda":
+            return torch.bfloat16
+        else:
+            return torch.float32
+
+    @computed_field
+    @property
+    def TEXIFY_DTYPE(self) -> torch.dtype:
+        return torch.float32 if self.TORCH_DEVICE_MODEL == "cpu" else torch.float16
 
     class Config:
         env_file = find_dotenv("local.env")
diff --git a/poetry.lock b/poetry.lock
index 3349f3eb..2d8f8bcb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,101 +1,5 @@
 # This file is automatically @generated by Poetry 1.5.0 and should not be changed by hand.
 
-[[package]]
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-version = "3.9.1"
-description = "Async http client/server framework (asyncio)"
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-]
-
-[package.dependencies]
-aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
-attrs = ">=17.3.0"
-frozenlist = ">=1.1.1"
-multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
-
-[package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
-
 [[package]]
 name = "aiosignal"
 version = "1.3.1"
@@ -111,28 +15,28 @@ files = [
 frozenlist = ">=1.1.0"
 
 [[package]]
-name = "albumentations"
-version = "1.3.1"
-description = "Fast image augmentation library and easy to use wrapper around other libraries"
+name = "altair"
+version = "5.2.0"
+description = "Vega-Altair: A declarative statistical visualization library for Python."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-numpy = ">=1.11.1"
-opencv-python-headless = ">=4.1.1"
-PyYAML = "*"
-qudida = ">=0.0.4"
-scikit-image = ">=0.16.1"
-scipy = ">=1.1.0"
+jinja2 = "*"
+jsonschema = ">=3.0"
+numpy = "*"
+packaging = "*"
+pandas = ">=0.25"
+toolz = "*"
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-develop = ["imgaug (>=0.4.0)", "pytest"]
-imgaug = ["imgaug (>=0.4.0)"]
-tests = ["pytest"]
+dev = ["anywidget", "geopandas", "hatch", "ipython", "m2r", "mypy", "pandas-stubs", "pyarrow (>=11)", "pytest", "pytest-cov", "ruff (>=0.1.3)", "types-jsonschema", "types-setuptools", "vega-datasets", "vegafusion[embed] (>=1.4.0)", "vl-convert-python (>=1.1.0)"]
+doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
 
 [[package]]
 name = "annotated-types"
@@ -285,17 +189,6 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
-[[package]]
-name = "async-timeout"
-version = "4.0.3"
-description = "Timeout context manager for asyncio programs"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
-    {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
-]
-
 [[package]]
 name = "attrs"
 version = "23.1.0"
@@ -378,6 +271,28 @@ webencodings = "*"
 [package.extras]
 css = ["tinycss2 (>=1.1.0,<1.3)"]
 
+[[package]]
+name = "blinker"
+version = "1.7.0"
+description = "Fast, simple object-to-object and broadcast signaling"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "blinker-1.7.0-py3-none-any.whl", hash = "sha256:c3f865d4d54db7abc53758a01601cf343fe55b84c1de4e3fa910e420b438d5b9"},
+    {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"},
+]
+
+[[package]]
+name = "cachetools"
+version = "5.3.2"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
+    {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
+]
+
 [[package]]
 name = "certifi"
 version = "2023.11.17"
@@ -639,49 +554,6 @@ ssh = ["bcrypt (>=3.1.5)"]
 test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
-[[package]]
-name = "datasets"
-version = "2.14.4"
-description = "HuggingFace community-driven open-source library of datasets"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"},
-    {file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"},
-]
-
-[package.dependencies]
-aiohttp = "*"
-dill = ">=0.3.0,<0.3.8"
-fsspec = {version = ">=2021.11.1", extras = ["http"]}
-huggingface-hub = ">=0.14.0,<1.0.0"
-multiprocess = "*"
-numpy = ">=1.17"
-packaging = "*"
-pandas = "*"
-Pillow = {version = ">=6.2.1", optional = true, markers = "extra == \"vision\""}
-pyarrow = ">=8.0.0"
-pyyaml = ">=5.1"
-requests = ">=2.19.0"
-tqdm = ">=4.62.1"
-xxhash = "*"
-
-[package.extras]
-apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
-audio = ["librosa", "soundfile (>=0.12.1)"]
-benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "black (>=23.1,<24.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "ruff (>=0.0.241)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
-docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
-jax = ["jax (>=0.2.8,!=0.3.2,<=0.3.25)", "jaxlib (>=0.1.65,<=0.3.25)"]
-metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
-quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
-s3 = ["s3fs"]
-tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
-tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
-torch = ["torch"]
-vision = ["Pillow (>=6.2.1)"]
-
 [[package]]
 name = "debugpy"
 version = "1.8.0"
@@ -762,20 +634,6 @@ files = [
 [package.dependencies]
 packaging = "*"
 
-[[package]]
-name = "dill"
-version = "0.3.7"
-description = "serialize all of Python"
-optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "dill-0.3.7.tar.gz", hash = "sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03"},
-]
-
-[package.extras]
-graph = ["objgraph (>=1.7.2)"]
-
 [[package]]
 name = "exceptiongroup"
 version = "1.2.0"
@@ -926,10 +784,6 @@ files = [
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 ]
 
-[package.dependencies]
-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
-
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -968,6 +822,37 @@ files = [
 [package.dependencies]
 wcwidth = ">=0.2.12,<0.3.0"
 
+[[package]]
+name = "gitdb"
+version = "4.0.11"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"},
+]
+
+[package.dependencies]
+smmap = ">=3.0.1,<6"
+
+[[package]]
+name = "gitpython"
+version = "3.1.40"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"},
+    {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"},
+]
+
+[package.dependencies]
+gitdb = ">=4.0.1,<5"
+
+[package.extras]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"]
+
 [[package]]
 name = "grpcio"
 version = "1.60.0"
@@ -1078,38 +963,6 @@ files = [
     {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
-[[package]]
-name = "imageio"
-version = "2.33.0"
-description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
-numpy = "*"
-pillow = ">=8.3.2"
-
-[package.extras]
-all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
-all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
-build = ["wheel"]
-dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
-docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
-ffmpeg = ["imageio-ffmpeg", "psutil"]
-fits = ["astropy"]
-full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
-gdal = ["gdal"]
-itk = ["itk"]
-linting = ["black", "flake8"]
-pillow-heif = ["pillow-heif"]
-pyav = ["av"]
-test = ["fsspec[github]", "pytest", "pytest-cov"]
-tifffile = ["tifffile"]
-
 [[package]]
 name = "img2pdf"
 version = "0.5.1"
@@ -1129,13 +982,13 @@ gui = ["tkinter"]
 
 [[package]]
 name = "importlib-metadata"
-version = "7.0.0"
+version = "6.11.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "importlib_metadata-6.11.0.tar.gz", hash = "sha256:1231cf92d825c9e03cfc4da076a16de6422c863558229ea0b22b675657463443"},
 ]
 
 [package.dependencies]
@@ -1628,221 +1481,6 @@ files = [
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 ]
 
-[[package]]
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-description = "lazy_loader"
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-files = [
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-[package.extras]
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-test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
-
-[[package]]
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 [package.dependencies]
@@ -3186,47 +2583,47 @@ tests = ["pytest"]
 
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 python-dotenv = ">=0.21.0"
 
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 [[package]]
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 [[package]]
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-optional = false
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-
-[package.dependencies]
-Levenshtein = "0.23.0"
-
 [[package]]
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 version = "0.4.27"
@@ -3611,37 +2992,6 @@ files = [
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-optional = false
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-
 [[package]]
 name = "pytz"
 version = "2023.3.post1"
@@ -3887,23 +3237,6 @@ packaging = "*"
 [package.extras]
 test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
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-description = "QUick and DIrty Domain Adaptation"
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-scikit-learn = ">=0.19.1"
-typing-extensions = "*"
-
 [[package]]
 name = "rapidfuzz"
 version = "3.5.2"
@@ -4365,83 +3698,6 @@ files = [
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 ]
 
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-version = "0.18.5"
-description = "ruamel.yaml is a YAML parser/emitter that supports roundtrip preservation of comments, seq/map flow style, and map key order"
-optional = false
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 version = "0.4.1"
@@ -4561,54 +3817,6 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
 testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
 torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
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-packaging = ">=21"
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-tifffile = ">=2022.8.12"
-
-[package.extras]
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-data = ["pooch (>=1.6.0)"]
-developer = ["pre-commit", "tomli"]
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-
 [[package]]
 name = "scikit-learn"
 version = "1.3.2"
@@ -4698,20 +3906,6 @@ dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyl
 doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
-[[package]]
-name = "sconf"
-version = "0.2.5"
-description = "Simple config supporting CLI modification"
-optional = false
-python-versions = ">=3.6"
-files = [
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-
-[package.dependencies]
-munch = "*"
-"ruamel.yaml" = "*"
-
 [[package]]
 name = "send2trash"
 version = "1.8.2"
@@ -4728,60 +3922,6 @@ nativelib = ["pyobjc-framework-Cocoa", "pywin32"]
 objc = ["pyobjc-framework-Cocoa"]
 win32 = ["pywin32"]
 
-[[package]]
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-description = "SentencePiece python wrapper"
-optional = false
-python-versions = "*"
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-
 [[package]]
 name = "setuptools"
 version = "69.0.2"
@@ -4809,6 +3949,17 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "smmap"
+version = "5.0.1"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.0"
@@ -4850,6 +4001,61 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "streamlit"
+version = "1.29.0"
+description = "A faster way to build and share data apps"
+optional = false
+python-versions = ">=3.8, !=3.9.7"
+files = [
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+]
+
+[package.dependencies]
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+blinker = ">=1.0.0,<2"
+cachetools = ">=4.0,<6"
+click = ">=7.0,<9"
+gitpython = ">=3.0.7,<3.1.19 || >3.1.19,<4"
+importlib-metadata = ">=1.4,<7"
+numpy = ">=1.19.3,<2"
+packaging = ">=16.8,<24"
+pandas = ">=1.3.0,<3"
+pillow = ">=7.1.0,<11"
+protobuf = ">=3.20,<5"
+pyarrow = ">=6.0"
+pydeck = ">=0.8.0b4,<1"
+python-dateutil = ">=2.7.3,<3"
+requests = ">=2.27,<3"
+rich = ">=10.14.0,<14"
+tenacity = ">=8.1.0,<9"
+toml = ">=0.10.1,<2"
+tornado = ">=6.0.3,<7"
+typing-extensions = ">=4.3.0,<5"
+tzlocal = ">=1.1,<6"
+validators = ">=0.2,<1"
+watchdog = {version = ">=2.1.5", markers = "platform_system != \"Darwin\""}
+
+[package.extras]
+snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python (>=0.9.0)"]
+
+[[package]]
+name = "streamlit-drawable-canvas-jsretry"
+version = "0.9.3"
+description = "A Streamlit custom component for a free drawing canvas using Fabric.js.  A fork to enable retrying for bg images."
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.dependencies]
+numpy = "*"
+Pillow = "*"
+streamlit = ">=0.63"
+
 [[package]]
 name = "sympy"
 version = "1.12"
@@ -4878,6 +4084,20 @@ files = [
 [package.extras]
 widechars = ["wcwidth"]
 
+[[package]]
+name = "tenacity"
+version = "8.2.3"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+doc = ["reno", "sphinx", "tornado (>=4.5)"]
+
 [[package]]
 name = "terminado"
 version = "0.18.0"
@@ -4899,6 +4119,32 @@ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
 typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
+[[package]]
+name = "texify"
+version = "0.1.8"
+description = "OCR for latex images"
+optional = false
+python-versions = ">=3.9, !=2.7.*, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*, !=3.7.*, !=3.8.*"
+files = [
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+    {file = "texify-0.1.8.tar.gz", hash = "sha256:1ff7615b58b55381aed723cb24202b107a5ec12a1a4564fe1edd8d1315b3f0d1"},
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+
+[package.dependencies]
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+huggingface-hub = "0.19.4"
+numpy = ">=1.26.2,<2.0.0"
+Pillow = ">=10.1.0,<11.0.0"
+pydantic = ">=2.5.2,<3.0.0"
+pydantic-settings = ">=2.1.0,<3.0.0"
+pypdfium2 = ">=4.25.0,<5.0.0"
+python-dotenv = ">=1.0.0,<2.0.0"
+streamlit = ">=1.29.0,<2.0.0"
+streamlit-drawable-canvas-jsretry = ">=0.9.3,<0.10.0"
+tabulate = ">=0.9.0,<0.10.0"
+transformers = ">=4.36.2,<5.0.0"
+watchdog = ">=3.0.0,<4.0.0"
+
 [[package]]
 name = "thefuzz"
 version = "0.20.0"
@@ -4924,38 +4170,6 @@ files = [
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 ]
 
-[[package]]
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-version = "2023.9.26"
-description = "Read and write TIFF files"
-optional = false
-python-versions = ">=3.9"
-files = [
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-
-[package.dependencies]
-numpy = "*"
-
-[package.extras]
-all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
-
-[[package]]
-name = "timm"
-version = "0.5.4"
-description = "(Unofficial) PyTorch Image Models"
-optional = false
-python-versions = ">=3.6"
-files = [
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-
-[package.dependencies]
-torch = ">=1.4"
-torchvision = "*"
-
 [[package]]
 name = "tinycss2"
 version = "1.2.1"
@@ -5089,6 +4303,17 @@ dev = ["tokenizers[testing]"]
 docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
 testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
 
+[[package]]
+name = "toml"
+version = "0.10.2"
+description = "Python Library for Tom's Obvious, Minimal Language"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
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+    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -5100,33 +4325,44 @@ files = [
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 ]
 
+[[package]]
+name = "toolz"
+version = "0.12.0"
+description = "List processing tools and functional utilities"
+optional = false
+python-versions = ">=3.5"
+files = [
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+    {file = "toolz-0.12.0.tar.gz", hash = "sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194"},
+]
+
 [[package]]
 name = "torch"
-version = "2.1.1"
+version = "2.1.2"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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-ray = ["ray[tune]"]
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 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
@@ -5330,18 +4499,18 @@ serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
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 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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-vision = ["Pillow (<10.0.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
@@ -5392,15 +4561,32 @@ files = [
 
 [[package]]
 name = "tzdata"
-version = "2023.3"
+version = "2023.4"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
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+    {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
+    {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
+]
+
+[[package]]
+name = "tzlocal"
+version = "5.2"
+description = "tzinfo object for the local timezone"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
+tzdata = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"]
+
 [[package]]
 name = "uri-template"
 version = "1.3.0"
@@ -5431,6 +4617,67 @@ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "validators"
+version = "0.22.0"
+description = "Python Data Validation for Humans™"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
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+hooks = ["pre-commit (>=3.3.3)"]
+package = ["build (>=1.0.0)", "twine (>=4.0.2)"]
+runner = ["tox (>=4.11.1)"]
+sast = ["bandit[toml] (>=1.7.5)"]
+testing = ["pytest (>=7.4.0)"]
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+tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"]
+
+[[package]]
+name = "watchdog"
+version = "3.0.0"
+description = "Filesystem events monitoring"
+optional = false
+python-versions = ">=3.7"
+files = [
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-    {file = "yarl-1.9.3.tar.gz", hash = "sha256:4a14907b597ec55740f63e52d7fee0e9ee09d5b9d57a4f399a7423268e457b57"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
 [[package]]
 name = "zipp"
 version = "3.17.0"
@@ -5811,5 +4838,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.9,<3.13"
-content-hash = "7d8f07f7b4ab7e802386b76d1add6ade5560636df131e8a7123436817638ad7c"
+python-versions = ">=3.9,<3.13,!=3.9.7"
+content-hash = "f91c7dd6b1e0ce34ca55fd50d54791e95d323b17e0331eef988eb7cf61bed5e1"
diff --git a/pyproject.toml b/pyproject.toml
index 89df89b7..1ac7a5fb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "marker-pdf"
-version = "0.1.1"
+version = "0.1.3"
 description = "Convert PDF to markdown with high speed and accuracy."
 authors = ["Vik Paruchuri <github@vikas.sh>"]
 readme = "README.md"
@@ -19,7 +19,7 @@ include = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.9,<3.13"
+python = ">=3.9,<3.13,!=3.9.7"
 scikit-learn = "^1.3.2"
 Pillow = "^10.1.0"
 pytesseract = "^0.3.10"
@@ -27,7 +27,6 @@ PyMuPDF = "^1.23.5"
 pymupdf-fonts = "^1.0.5"
 pydantic = "^2.4.2"
 pydantic-settings = "^2.0.3"
-nougat-ocr = "^0.1.17"
 transformers = "^4.34.1"
 numpy = "^1.26.1"
 python-dotenv = "^1.0.0"
@@ -43,6 +42,7 @@ nltk = "^3.8.1"
 ocrmypdf = "^15.4.0"
 bitsandbytes = "^0.41.2.post2"
 grpcio = "^1.60.0"
+texify = "^0.1.8"
 
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"