The problem

PDF-to-markdown tools like MinerU, Docling, and Marker do great text extraction — then collapse your document structure. Every heading becomes # or ##. TOCs break. RAG chunking breaks. Navigation breaks.

md-reheader fixes it. A 0.6B-parameter Qwen3 fine-tune reads the document and predicts the correct H1–H6 level for every heading in a single forward pass.

Quick start

CLI

pip install md-reheader
rehead --input flat.md --output fixed.md

Auto-detects CUDA. Use --cpu or --gpu to override. Omit --output to stream to stdout — pipe-friendly for integration with other CLIs.

rehead -i flat.md | tee fixed.md               # pipe
rehead -i flat.md --gpu -o out/fixed.md        # creates nested dirs
rehead -i flat.md --force -o existing.md       # overwrite
rehead --help                                   # all flags

Python API

from md_reheader.inference.predict import load_model, reheader_document

model, tokenizer = load_model("joelbarmettler/md-reheader")

flat = open("document.md").read()
fixed = reheader_document(flat, model, tokenizer)

The package handles preprocessing (flattening + body stripping) and postprocessing (applying predicted levels back to the original document) automatically.

Direct transformers usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("joelbarmettler/md-reheader")
model = AutoModelForCausalLM.from_pretrained(
    "joelbarmettler/md-reheader",
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a markdown document structure expert. Given a markdown document with incorrect or flattened heading levels, output each heading with its correct markdown prefix (# for level 1, ## for level 2, etc.), one per line."},
    {"role": "user", "content": "# Introduction\n\nSome text...\n\n# Background\n\nMore text...\n\n# Methods"},
]

input_text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,
)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=4096, do_sample=False)

generated = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

Important: pass enable_thinking=False to apply_chat_template. Without it, the model enters a repetition loop because training used the non-thinking chat format.

Self-host with vLLM

pip install vllm
vllm serve joelbarmettler/md-reheader --dtype bfloat16 --max-model-len 8192

Higher throughput than raw transformers and drop-in OpenAI-compatible clients. On <10 GB cards add --enforce-eager --gpu-memory-utilization 0.70 to skip CUDA-graph allocations.

Remote inference (any OpenAI-compatible endpoint)

Once a server is running, use md-reheader as a thin client — no local weights needed.

rehead -i flat.md -o fixed.md --endpoint http://localhost:8000/v1
rehead -i flat.md -o fixed.md --endpoint https://api.example.com/v1 --api-key sk-xxx
# or set MD_REHEADER_API_KEY in the environment

from md_reheader.inference.remote import reheader_document_remote

fixed = reheader_document_remote(
    open("flat.md").read(),
    endpoint="http://localhost:8000/v1",
    model="joelbarmettler/md-reheader",
)

Identical output to local inference. Preprocessing (flatten + strip) happens client-side; the server just runs the chat completion with chat_template_kwargs={"enable_thinking": false} to match training.

How it works

flat markdown  ──►  flatten headings to #  ──►  strip body to 128+128 tokens
                                                         │
                                                         ▼
        restored markdown  ◄──  apply predicted levels  ◄── Qwen3-0.6B (fine-tuned)

1. Extract headings with markdown-it-py — correctly skips code blocks.
2. Flatten every heading to # — the model ignores input levels.
3. Strip each section's body to its first 128 + last 128 tokens — preserves structural cues, kills context bloat.
4. Qwen3-0.6B predicts the correct # prefix per heading.
5. Levels get mapped back to the original document.

Evaluation

Benchmarked on 7,321 held-out documents from GitHub markdown and Wikipedia.

Per-level accuracy

H1–H3 land in the 77–85% band; H5/H6 drop but still beat baselines. Most deep-level errors are off-by-one — the relative structure survives.

By document depth

By source

Speed

Documents longer than ~8k tokens (after stripping) are truncated from the tail.

Limitations

• Deep nesting (H5/H6) — accuracy drops to 45–50%. Relative structure is preserved; absolute depth gets compressed by 1–2 levels.
• Ambiguous structure — heading levels are subjective. The model learns common conventions; it can't resolve genuine ambiguity.
• Long documents — >8k tokens (after stripping) get truncated. Headings past the cutoff retain their input levels.
• English-centric — trained primarily on English content.

Reproducing training

git clone https://github.com/joelbarmettlerUZH/md-reheader.git
cd md-reheader

uv sync --extra train    # install training dependencies
make download            # download raw data (~150k documents)
make prepare             # strip, flatten, oversample, format
make train               # train on 2x GPU with Axolotl
make eval                # evaluate on test set

The model is a fine-tune of Qwen/Qwen3-0.6B trained on ~197k markdown documents:

• codeparrot/github-code — ~105k markdown files from GitHub repositories
• euirim/goodwiki — ~45k Wikipedia articles
• Deep documents (depth 4+) oversampled 2–8× for class balance

Trained with Axolotl on 2× RTX 4090 using DDP, BF16, 8k sequence length with sample packing.

License

Code and model weights: Apache 2.0. Training data includes Wikipedia content (CC BY-SA 4.0) and GitHub repositories (various open-source licenses).

About

Built and maintained by Joel Barmettler at Souverana — an AI consultancy that helps teams design, fine-tune, evaluate, and ship production LLM systems.

Need a small, self-hosted model that does one job well — like this one? Let's talk → souverana.ch

Citation

@software{barmettler2026mdreheader,
  author = {Barmettler, Joel},
  title  = {md-reheader: Restoring Heading Hierarchy in Markdown Documents},
  year   = {2026},
  url    = {https://github.com/joelbarmettlerUZH/md-reheader}
}