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What is Tinker?
Tinker is a groundbreaking training API designed specifically for researchers and developers, granting them extensive control over model fine-tuning while alleviating the intricacies associated with infrastructure management. It provides fundamental building blocks that enable users to construct custom training loops, implement various supervision methods, and develop reinforcement learning workflows. At present, Tinker supports LoRA fine-tuning on open-weight models from the LLama and Qwen families, catering to a spectrum of model sizes that range from compact versions to large mixture-of-experts setups. Users have the flexibility to craft Python scripts for data handling, loss function management, and algorithmic execution, while Tinker efficiently manages scheduling, resource allocation, distributed training, and failure recovery independently. The platform empowers users to download model weights at different checkpoints, freeing them from the responsibility of overseeing the computational environment. Offered as a managed service, Tinker runs training jobs on Thinking Machines’ proprietary GPU infrastructure, relieving users of the burdens associated with cluster orchestration and allowing them to concentrate on refining and enhancing their models. This harmonious combination of features positions Tinker as an indispensable resource for propelling advancements in machine learning research and development, ultimately fostering greater innovation within the field.
What is LLaMA-Factory?
LLaMA-Factory represents a cutting-edge open-source platform designed to streamline and enhance the fine-tuning process for over 100 Large Language Models (LLMs) and Vision-Language Models (VLMs). It offers diverse fine-tuning methods, including Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Prefix-Tuning, allowing users to customize models effortlessly. The platform has demonstrated impressive performance improvements; for instance, its LoRA tuning can achieve training speeds that are up to 3.7 times quicker, along with better Rouge scores in generating advertising text compared to traditional methods. Crafted with adaptability at its core, LLaMA-Factory's framework accommodates a wide range of model types and configurations. Users can easily incorporate their datasets and leverage the platform's tools for enhanced fine-tuning results. Detailed documentation and numerous examples are provided to help users navigate the fine-tuning process confidently. In addition to these features, the platform fosters collaboration and the exchange of techniques within the community, promoting an atmosphere of ongoing enhancement and innovation. Ultimately, LLaMA-Factory empowers users to push the boundaries of what is possible with model fine-tuning.
What is Akamas?
Businesses must deliver top-notch services while keeping costs low and maintaining agility in their operations. The landscape of contemporary applications, whether they are on-premises or cloud-based, and whether they utilize monolithic or microservices architectures, is intricate, featuring thousands of parameters and various instance types that need to be fine-tuned to find the perfect configuration that balances performance, resilience, and cost efficiency. By utilizing Akamas, organizations can clearly define their optimization goals and constraints, such as service level objectives (SLOs), which allows for effective optimization of their applications and IT infrastructures. Those who leverage Akamas can enjoy remarkable benefits, including a 60% decrease in infrastructure and cloud expenses without compromising application performance, a 30% increase in transactions per second with the same resources, a 70% reduction in response times while maintaining stability, and an astounding 80% decrease in tuning time. Furthermore, the AI-driven optimization capabilities offered by Akamas enable companies and digital enterprises to improve service quality, strengthen resilience, and achieve significant savings, fostering a more streamlined operational environment. This holistic approach not only enhances performance metrics but also positions organizations for future growth and adaptability in a competitive market.
Integrations Supported
Llama 3
Qwen
ChatGLM
Gemma
LLaVA
Llama
Llama 3.1
Llama 3.2
Llama 3.3
MLflow
Integrations Supported
Llama 3
Qwen
ChatGLM
Gemma
LLaVA
Llama
Llama 3.1
Llama 3.2
Llama 3.3
MLflow
Integrations Supported
Llama 3
Qwen
ChatGLM
Gemma
LLaVA
Llama
Llama 3.1
Llama 3.2
Llama 3.3
MLflow
API Availability
Has API
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Free
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Thinking Machines Lab
Company Location
United States
Company Website
thinkingmachines.ai/tinker/
Company Facts
Organization Name
hoshi-hiyouga
Company Website
github.com/hiyouga/LLaMA-Factory
Company Facts
Organization Name
Akamas
Date Founded
2019
Company Location
Italy
Company Website
akamas.io
Categories and Features
Categories and Features
Categories and Features
Application Performance Monitoring (APM)
Baseline Manager
Diagnostic Tools
Full Transaction Diagnostics
Performance Control
Resource Management
Root-Cause Diagnosis
Server Performance
Trace Individual Transactions