jinaai
/

ReaderLM-v2

@@ -17,12 +17,22 @@ library_name: transformers
 <b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
 </p>
-[Blog](https://jina.ai/news/readerlm-v2-frontier-small-language-model-for-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)
 # ReaderLM-v2
 `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.
 ## Model Overview
 - **Model Type**: Autoregressive, decoder-only transformer
@@ -36,16 +46,6 @@ library_name: transformers
 - **Intermediate Size**: 8960
 - **Supported Languages**: English, Chinese, Japanese, Korean, French, Spanish, Portuguese, German, Italian, Russian, Vietnamese, Thai, Arabic, and more (29 total)
-## What's New in `ReaderLM-v2`
-`ReaderLM-v2` represents a significant leap forward from its predecessor, with several key improvements:
-- **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.
-- **JSON Output**: Introduces direct HTML-to-JSON generation using predefined schemas, eliminating the need for intermediate markdown conversion.
-- **Longer Context Handling**: Handles up to 512K tokens combined input and output length, with improved performance on long-form content.
-- **Multilingual Support**: Comprehensive support across 29 languages for broader applications.
-- **Enhanced Stability**: Greatly alleviates degeneration issues after generating long sequences through contrastive loss during training.
 ---
 # Usage
@@ -65,7 +65,7 @@ You can try it without an API key at a lower rate limit. For higher rate limits,
 ## On Google Colab
-The easiest way to experience `ReaderLM-v2` is through 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.
 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.

 <b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
 </p>
+[Blog](https://jina.ai/news/readerlm-v2-frontier-small-language-model-for-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!)]
 # ReaderLM-v2
 `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.
+## What's New in `ReaderLM-v2`
+`ReaderLM-v2` represents a significant leap forward from its predecessor, with several key improvements:
+- **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.
+- **JSON Output**: Introduces direct HTML-to-JSON generation using predefined schemas, eliminating the need for intermediate markdown conversion.
+- **Longer Context Handling**: Handles up to 512K tokens combined input and output length, with improved performance on long-form content.
+- **Multilingual Support**: Comprehensive support across 29 languages for broader applications.
+- **Enhanced Stability**: Greatly alleviates degeneration issues after generating long sequences through contrastive loss during training.
 ## Model Overview
 - **Model Type**: Autoregressive, decoder-only transformer
 - **Intermediate Size**: 8960
 - **Supported Languages**: English, Chinese, Japanese, Korean, French, Spanish, Portuguese, German, Italian, Russian, Vietnamese, Thai, Arabic, and more (29 total)
 ---
 # Usage
 ## On Google Colab
+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.
 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.