|
--- |
|
pipeline_tag: text-generation |
|
language: |
|
- multilingual |
|
inference: false |
|
license: cc-by-nc-4.0 |
|
library_name: transformers |
|
--- |
|
|
|
<br><br> |
|
|
|
<p align="center"> |
|
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> |
|
</p> |
|
|
|
<p align="center"> |
|
<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> |
|
</p> |
|
|
|
[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) | |
|
| | |
|
|
|
# Get Started |
|
|
|
## 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-1.5b" |
|
|
|
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 = "<html><body><h1>Hello, world!</h1></body></html>" |
|
|
|
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) |
|
|
|
|
