Sold by: Jina AI
Deployed on AWS
Small Language Models for Cleaning and Converting HTML to Markdown
Overview
Jina Reader-LM 1.5 b is a small language model that converts 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.
Highlights
- Jina Reader-LM 1.5b is designed to efficiently convert noisy HTML into clean markdown, showcasing a novel approach to web content extraction that is both cost-effective and scalable.
- Jina Reader-LM 1.5b has been optimized for long context support, handling up to 256K tokens, which is crucial for dealing with the intricacies of modern HTML, including inline CSS and scripts.
- Jina Reader-LM 1.5b outperforms larger language models in the HTML-to-markdown conversion task, despite being significantly smaller in size, which is a testament to their specialized training and design for this specific task.
Details
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Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.xlarge instance type, batch mode | $2.70 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $4.50 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $4.14 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $45.00 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $7.20 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $12.60 |
ml.g4dn.16xlarge Inference (Batch) | Model inference on the ml.g4dn.16xlarge instance type, batch mode | $26.10 |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $32.40 |
ml.g4dn.8xlarge Inference (Batch) | Model inference on the ml.g4dn.8xlarge instance type, batch mode | $13.68 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $20.25 |
Dimensions summary
You pay by the hour for the GPU instance that runs this HTML-to-Markdown model. Charges are per host hour (HostHrs) and scale with the instance size you pick. Two deployment modes exist. Batch mode processes grouped jobs on p2, p3, and g4dn instance families. Real-time mode serves live requests and adds g5 instances alongside p2, p3, and g4dn options. Within each family, larger instances carry higher hourly rates. Software usage is billed separately from the AWS infrastructure fees you pay for the underlying compute. You choose the mode and instance that fit your workload.
Top-of-mind questions for buyers
One HostHrs unit equals one hour that a chosen GPU instance runs the model. Billing counts each active hour of the deployed instance. Larger instance types in a family run more hardware per hour, so they carry higher hourly rates. You pay only while the instance runs.
Batch mode processes grouped HTML-to-Markdown jobs and runs on p2, p3, and g4dn instance families. Real-time mode serves live requests and adds g5 instances alongside those families. Each meters host hours the same way. You pick the mode that matches your workload, and billing follows the instance you run.
Software charges accrue per host hour only while the instance runs. Stopped instances do not accrue model usage charges. Underlying AWS infrastructure fees are billed separately and may still apply to stored resources. The model licence meters running time, so idle time without a running instance does not add usage cost.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Reader-LM 1.5b
Additional details
Inputs
- Summary
The model accepts JSON inputs. Inputs must be in the following format.
{ "model": "reader-lm-1.5b", "prompt": "<html><head><title>Minimal Bullet Points</title></head><body><ul><li>hello</li><li>jina.ai</li></ul></body></html>", "stream": false }
- Input MIME type
- text/csv
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-reader-1500m-inference-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-reader-batch-input.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
model | It should be a fixed value: "reader-lm-1.5b". | Type: FreeText | Yes |
prompt | HTML content. | Type: FreeText | Yes |
stream | Whether to stream back partial progress. | Default value: false
Type: FreeText
Limitations: boolean | No |
Resources
Vendor resources
Support
Vendor support
We provide support for this model package through our enterprise support channel.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Small Language Models for Cleaning and Converting HTML to Markdown
1.5B parameter language model that converts raw HTML into beautifully formatted markdown or JSON
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