Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
LHTM-OPT
By:
Latest Version:
v0.0.1
A Japanese LLM developed by alt inc., competitive in JGLUE and Rakuda leaderboards.
Product Overview
LHTM-Opt is an instruction-tuned Japanese Language Large Model developed by alt Inc., which has excellent Japanese knowledge and can be applied in various NLP tasks. alt Inc. is a venture firm with the mission of freeing humankind from non-creative/unproductive labor through the creation of P.A.I.® (Personal Artificial Intelligence) and AI clones. Lightweight and Deployable: With 7B model size, our LLM is designed to be lightweight, ensuring ease of deployment. Benchmark Excellence: LHTM-Opt obtained competitive scores on the JGLUE and Rakuda benchmarks, which are benchmarks for Japanese LLMs. These scores are a testament to our model's understanding, reasoning, and generation capabilities. Ideal for RAG Applications: LHTM-Opt can enhance question answering systems, content creation tools, and more by providing contextually relevant and coherent responses. Seamless Integration: Published on AWS Marketplace, our Japanese LLM is ready for immediate deployment.
Key Data
Version
By
Type
Model Package
Highlights
Key Features
- LHTM-Opt is lightweight and can be deployed with eases.
- LHTM-Opt obtained competitive scores on the JGLUE and Rakuda benchmarks, which are benchmarks for Japanese LLMs. Those scores indicated the ability of our model in Japanese language understanding and reasoning.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
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Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$1.20/hr
running on ml.p3.2xlarge
Model Batch Transform$1.20/hr
running on ml.p3.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$3.825/host/hr
running on ml.p3.2xlarge
SageMaker Batch Transform$3.825/host/hr
running on ml.p3.2xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.g4dn.4xlarge | $1.20 | |
ml.g4dn.16xlarge | $1.20 | |
ml.p3.16xlarge | $1.20 | |
ml.g5.xlarge | $1.20 | |
ml.g5.8xlarge | $1.20 | |
ml.g5.12xlarge | $1.20 | |
ml.g4dn.2xlarge | $1.20 | |
ml.g5.4xlarge | $1.20 | |
ml.g5.16xlarge | $1.20 | |
ml.p3.8xlarge | $1.20 | |
ml.p3.2xlarge Vendor Recommended | $1.20 | |
ml.g4dn.8xlarge | $1.20 | |
ml.g4dn.12xlarge | $1.20 | |
ml.g5.2xlarge | $1.20 | |
ml.g4dn.xlarge | $1.20 | |
ml.g5.48xlarge | $1.20 | |
ml.g5.24xlarge | $1.20 |
Usage Information
Model input and output details
Input
Summary
The model accepts JSON requests that specify the prompt and generation parameters. The prompt can be in Llama2 chat format for chatting.
Input MIME type
application/jsonSample input data
{
"prompt": "質問： 日本で一番高い山は何ですか？簡潔に教えてください。\n答え：",
"max_new_tokens": 50,
"top_p": 0.9,
"temperature": 0.2,
"top_k": 20,
"do_sample": true,
"repetition_penalty": 1.2,
"skip_prompt": true
}Output
Summary
The model outputs a completion of the provided prompt in JSON format.
Output MIME type
application/jsonSample output data
{
"message": "OK",
"text": "富士山（3776m）"
}Sample notebook
Additional Resources
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Support Information
AWS Infrastructure
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