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README.md
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README.md
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---
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pipeline_tag: text-generation
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language:
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- multilingual
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inference: false
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license: cc-by-nc-4.0
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library_name: transformers
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---
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<br><br>
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<p align="center">
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<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
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</p>
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<p align="center">
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<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
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</p>
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[Blog](https://jina.ai/news/readerlm-v2-frontier-small-language-model-for-html-to-markdown-and-json) | [Colab](https://colab.research.google.com/drive/1FfPjZwkMSocOLsEYH45B3B4NxDryKLGI?usp=sharing) | [AWS](https://aws.amazon.com/marketplace/pp/prodview-jwfct4j4rvxk2?sr=0-21&ref_=beagle&applicationId=AWSMPContessa) | [Arxiv (soon!)]
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# ReaderLM-v2
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# ReaderLM-v2
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ReaderLM-v2
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`ReaderLM-v2` is a 1.5B parameter language model that converts raw HTML into beautifully formatted markdown or JSON with superior accuracy and improved longer context handling. Supporting multiple languages (29 in total), `ReaderLM-v2` is specialized for tasks involving HTML parsing, transformation, and text extraction.
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## What's New in `ReaderLM-v2`
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`ReaderLM-v2` represents a significant leap forward from its predecessor, with several key improvements:
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- **Better Markdown Generation**: Thanks to its new training paradigm and higher-quality training data, the model excels at generating complex elements like code fences, nested lists, tables, and LaTeX equations.
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- **JSON Output**: Introduces direct HTML-to-JSON generation using predefined schemas, eliminating the need for intermediate markdown conversion.
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- **Longer Context Handling**: Handles up to 512K tokens combined input and output length, with improved performance on long-form content.
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- **Multilingual Support**: Comprehensive support across 29 languages for broader applications.
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- **Enhanced Stability**: Greatly alleviates degeneration issues after generating long sequences through contrastive loss during training.
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## Model Overview
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- **Model Type**: Autoregressive, decoder-only transformer
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- **Parameter Count**: 1.54B
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- **Context Window**: Up to 512K tokens (combined input and output)
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- **Hidden Size**: 1536
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- **Number of Layers**: 28
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- **Query Heads**: 12
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- **KV Heads**: 2
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- **Head Size**: 128
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- **Intermediate Size**: 8960
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- **Supported Languages**: English, Chinese, Japanese, Korean, French, Spanish, Portuguese, German, Italian, Russian, Vietnamese, Thai, Arabic, and more (29 total)
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---
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# Usage
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Below, you will find instructions and examples for using `ReaderLM-v2` locally using the Hugging Face Transformers library.
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For a more hands-on experience in a hosted environment, see the [Google Colab Notebook](https://colab.research.google.com/drive/1FfPjZwkMSocOLsEYH45B3B4NxDryKLGI?usp=sharing).
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## Via Reader API
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`ReaderLM-v2` is now fully integrated with [Reader API](https://jina.ai/reader/). To use it, simply specify `x-engine: readerlm-v2` in your request headers and enable response streaming with `-H 'Accept: text/event-stream'`:
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```bash
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curl https://r.jina.ai/https://news.ycombinator.com/ -H 'x-engine: readerlm-v2' -H 'Accept: text/event-stream'
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```
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You can try it without an API key at a lower rate limit. For higher rate limits, you can purchase an API key. Please note that ReaderLM-v2 requests consume 3x the normal token count from your API key allocation. This is currently an experimental feature, and we're working with the GCP team to improve GPU efficiency.
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## On Google Colab
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You can try `ReaderLM-v2` via our [Colab notebook](https://colab.research.google.com/drive/1FfPjZwkMSocOLsEYH45B3B4NxDryKLGI?usp=sharing), which demonstrates HTML-to-markdown conversion, JSON extraction, and instruction-following using the HackerNews frontpage as an example. The notebook is optimized for Colab's free T4 GPU tier and requires `vllm` and `triton` for acceleration and running.
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Note that the free T4 GPU has limitations—it doesn't support bfloat16 or flash attention 2, leading to higher memory usage and slower processing of longer inputs. Nevertheless, ReaderLM-v2 successfully processes large documents under these constraints, achieving processing speeds of 67 tokens/s input and 36 tokens/s output. For production use, we recommend an RTX 3090/4090 for optimal performance.
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## Local Usage
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To use `ReaderLM-v2` locally:
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1. Install the necessary dependencies:
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```bash
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pip install transformers
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```
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2. Load and run the model:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # or "cpu"
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tokenizer = AutoTokenizer.from_pretrained("jinaai/ReaderLM-v2")
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model = AutoModelForCausalLM.from_pretrained("jinaai/ReaderLM-v2").to(device)
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```
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3. (Optional) Pre-clean your HTML to remove scripts, styles, comments, to reduce the noise and length of the input:
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```python
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import re
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# Patterns
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SCRIPT_PATTERN = r"<[ ]*script.*?\/[ ]*script[ ]*>"
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STYLE_PATTERN = r"<[ ]*style.*?\/[ ]*style[ ]*>"
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META_PATTERN = r"<[ ]*meta.*?>"
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COMMENT_PATTERN = r"<[ ]*!--.*?--[ ]*>"
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LINK_PATTERN = r"<[ ]*link.*?>"
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BASE64_IMG_PATTERN = r'<img[^>]+src="data:image/[^;]+;base64,[^"]+"[^>]*>'
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SVG_PATTERN = r"(<svg[^>]*>)(.*?)(<\/svg>)"
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def replace_svg(html: str, new_content: str = "this is a placeholder") -> str:
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return re.sub(
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SVG_PATTERN,
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lambda match: f"{match.group(1)}{new_content}{match.group(3)}",
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html,
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flags=re.DOTALL,
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)
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def replace_base64_images(html: str, new_image_src: str = "#") -> str:
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return re.sub(BASE64_IMG_PATTERN, f'<img src="{new_image_src}"/>', html)
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def clean_html(html: str, clean_svg: bool = False, clean_base64: bool = False):
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html = re.sub(
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SCRIPT_PATTERN, "", html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL
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)
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html = re.sub(
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STYLE_PATTERN, "", html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL
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)
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html = re.sub(
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META_PATTERN, "", html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL
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)
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html = re.sub(
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COMMENT_PATTERN, "", html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL
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)
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html = re.sub(
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LINK_PATTERN, "", html, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL
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)
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if clean_svg:
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html = replace_svg(html)
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if clean_base64:
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html = replace_base64_images(html)
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return html
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```
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4. Create a prompt for the model:
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```python
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def create_prompt(
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text: str, tokenizer=None, instruction: str = None, schema: str = None
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) -> str:
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"""
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Create a prompt for the model with optional instruction and JSON schema.
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"""
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if not instruction:
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instruction = "Extract the main content from the given HTML and convert it to Markdown format."
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if schema:
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instruction = "Extract the specified information from a list of news threads and present it in a structured JSON format."
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prompt = f"{instruction}\n```html\n{text}\n```\nThe JSON schema is as follows:```json\n{schema}\n```"
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else:
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prompt = f"{instruction}\n```html\n{text}\n```"
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messages = [
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{
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"role": "user",
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"content": prompt,
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}
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]
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return tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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```
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### HTML to Markdown Example
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```python
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html = "<html><body><h1>Hello, world!</h1></body></html>"
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html = clean_html(html)
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input_prompt = create_prompt(html, tokenizer=tokenizer)
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inputs = tokenizer.encode(input_prompt, return_tensors="pt").to(device)
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outputs = model.generate(
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inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08
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)
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print(tokenizer.decode(outputs[0]))
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```
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### HTML to JSON Example
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```python
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schema = """
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{
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"type": "object",
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"properties": {
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"title": {
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"type": "string"
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},
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"author": {
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"type": "string"
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},
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"date": {
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"type": "string"
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},
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"content": {
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"type": "string"
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}
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},
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"required": ["title", "author", "date", "content"]
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}
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"""
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html = clean_html(html)
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input_prompt = create_prompt(html, schema=schema)
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inputs = tokenizer.encode(input_prompt, return_tensors="pt").to(device)
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outputs = model.generate(
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inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08
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)
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print(tokenizer.decode(outputs[0]))
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```
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## Model Performance
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ReaderLM-v2 has been extensively evaluated on various tasks:
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### Quantitative Evaluation
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For HTML-to-Markdown tasks, the model outperforms much larger models like Qwen2.5-32B-Instruct and Gemini2-flash-expr, achieving:
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- ROUGE-L: 0.84
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- Levenshtein Distance: 0.22
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- Jaro-Winkler Similarity: 0.82
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For HTML-to-JSON tasks, it shows competitive performance with:
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- F1 Score: 0.81
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- Precision: 0.82
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- Recall: 0.81
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- Pass-Rate: 0.98
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### Qualitative Evaluation
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The model excels in three key dimensions:
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- Content Integrity: 39/50
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- Structural Accuracy: 35/50
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- Format Compliance: 36/50
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These scores demonstrate strong performance in preserving semantic information, maintaining structural accuracy, and adhering to markdown syntax standards.
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## Training Details
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ReaderLM-v2 is built on Qwen2.5-1.5B-Instruction and trained using a sophisticated pipeline:
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1. Data Preparation: Created html-markdown-1m dataset with 1 million HTML documents
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2. Synthetic Data Generation: Three-step pipeline using Qwen2.5-32B-Instruction
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- Drafting: Initial markdown and JSON generation
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- Refinement: Content cleanup and structure alignment
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- Critique: Quality evaluation and filtering
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3. Training Process:
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- Long-context pretraining
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- Supervised fine-tuning
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- Direct preference optimization
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- Self-play reinforcement tuning
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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{
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"_name_or_path": "runs/qwen2.5-1.5b-step3-general",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 512768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 5000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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{
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"attn_implementation": "flash_attention_2",
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.08,
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"rope_theta": 5000000,
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"temperature": 0.65,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.46.2",
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"use_cache": true
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}
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
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|
|
@ -0,0 +1,210 @@
|
||||||
|
{
|
||||||
|
"add_bos_token": false,
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|object_ref_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|object_ref_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"content": "<|box_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151649": {
|
||||||
|
"content": "<|box_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151650": {
|
||||||
|
"content": "<|quad_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"attn_implementation": "flash_attention_2",
|
||||||
|
"bos_token": null,
|
||||||
|
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are an AI assistant developed by Jina AI.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"model_max_length": 512768,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"rope_theta": 5000000,
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null,
|
||||||
|
"use_cache": false
|
||||||
|
}
|
||||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue