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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 Core ML?

Core ML makes use of a machine learning algorithm tailored to a specific dataset to create a predictive model. This model facilitates predictions based on new incoming data, offering solutions for tasks that would be difficult or unfeasible to program by hand. For example, you could create a model that classifies images or detects specific objects within those images by analyzing their pixel data directly. After the model is developed, it is crucial to integrate it into your application and ensure it can be deployed on users' devices. Your application takes advantage of Core ML APIs and user data to enable predictions while also allowing for the model to be refined or retrained as needed. You can build and train your model using the Create ML application included with Xcode, which formats the models for Core ML, thus facilitating smooth integration into your app. Alternatively, other machine learning libraries can be utilized, and Core ML Tools can be employed to convert these models into the appropriate format for Core ML. Once the model is successfully deployed on a user's device, Core ML supports on-device retraining or fine-tuning, which improves its accuracy and overall performance. This capability not only enhances the model based on real-world feedback but also ensures that it remains relevant and effective in various applications over time. Continuous updates and adjustments can lead to significant advancements in the model's functionality.

Media

Media

Integrations Supported

Apple tvOS
Apple watchOS
ChatGLM
DeepSeek
Gemma
LLaVA
Llama
Llama 3
MLflow
Mistral AI
Mixtral 8x22B
Mixtral 8x7B
OpenAI
PaliGemma 2
Phi-2
Qwen
TensorBoard
TensorWave
Xcode
Yi-Large

Integrations Supported

Apple tvOS
Apple watchOS
ChatGLM
DeepSeek
Gemma
LLaVA
Llama
Llama 3
MLflow
Mistral AI
Mixtral 8x22B
Mixtral 8x7B
OpenAI
PaliGemma 2
Phi-2
Qwen
TensorBoard
TensorWave
Xcode
Yi-Large

API Availability

Has API

API Availability

Has API

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

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

Company Facts

Organization Name

hoshi-hiyouga

Company Website

github.com/hiyouga/LLaMA-Factory

Company Facts

Organization Name

Apple

Company Location

United States

Company Website

developer.apple.com/documentation/coreml

Categories and Features

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Popular Alternatives

Popular Alternatives

Create ML Reviews & Ratings

Create ML

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