Trained by Jina AI.

[Blog](https://jina.ai/news/reader-lm-small-language-models-for-cleaning-and-converting-html-to-markdown) | [Colab](https://colab.research.google.com/drive/1wXWyj5hOxEHY6WeHbOwEzYAC0WB1I5uA) # Intro Jina Reader-LM is a series of models that convert HTML content to Markdown content, which is useful for content conversion tasks. The model is trained on a curated collection of HTML content and its corresponding Markdown content. # Models | Name | Context Length | Download | |-----------------|-------------------|-----------------------------------------------------------------------| | reader-lm-0.5b | 256K | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-0.5b) | | reader-lm-1.5b | 256K | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-1.5b) | | | # Quick Start ## On Google Colab The easiest way to experience reader-lm is by running [our Colab notebook](https://colab.research.google.com/drive/1wXWyj5hOxEHY6WeHbOwEzYAC0WB1I5uA), where we demonstrate how to use reader-lm-1.5b to convert the HackerNews website into markdown. The notebook is optimized to run smoothly on Google Colab’s free T4 GPU tier. You can also load reader-lm-0.5b or change the URL to any website and explore the output. Note that the input (i.e., the prompt) to the model is the raw HTML—no prefix instruction is required. ## Local To use this model, you need to install `transformers`: ```bash pip install transformers<=4.43.4 ``` Then, you can use the model as follows: ```python # pip install transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "jinaai/reader-lm-0.5b" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) # example html content html_content = "

Hello, world!

" messages = [{"role": "user", "content": html_content}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08) print(tokenizer.decode(outputs[0])) ``` ## AWS Sagemaker & Azure Marketplace [AWS 0.5b](https://aws.amazon.com/marketplace/pp/prodview-nli7b6dueo424?sr=0-1&ref_=beagle&applicationId=AWSMPContessa) [AWS 1.5b](https://aws.amazon.com/marketplace/pp/prodview-ms27ixcwq3wjk?sr=0-2&ref_=beagle&applicationId=AWSMPContessa) [Azure 0.5b](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.reader-lm-500m) [Azure 1.5b](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.reader-lm-1500m)