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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Choose a model from our extensive open-source library, launch the container, and seamlessly integrate the model API into your application. Whether you're working with image recognition or natural language processing, all our models come pre-trained and are conveniently packaged within a user-friendly API. Our diverse collection of models continues to expand, ensuring you have access to the latest innovations. We carefully select and refine the top models available from sources like HuggingFace and Github. You have the option to host the model on your own with ease or obtain your personal endpoint and API key with just a single click. Cargoship stays at the forefront of advancements in the AI field, relieving you of the burden of keeping up. With the Cargoship Model Store, you'll find a comprehensive selection tailored for every machine learning application. The website features interactive demos for you to explore, along with in-depth guidance that covers everything from the model's capabilities to implementation techniques. Regardless of your skill level, we’re committed to providing you with thorough instructions to ensure your success. Additionally, our support team is always available to assist you with any questions you may have.

Description

LLaMA-Factory is an innovative open-source platform aimed at simplifying and improving the fine-tuning process for more than 100 Large Language Models (LLMs) and Vision-Language Models (VLMs). It accommodates a variety of fine-tuning methods such as Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Prefix-Tuning, empowering users to personalize models with ease. The platform has shown remarkable performance enhancements; for example, its LoRA tuning achieves training speeds that are up to 3.7 times faster along with superior Rouge scores in advertising text generation tasks when compared to conventional techniques. Built with flexibility in mind, LLaMA-Factory's architecture supports an extensive array of model types and configurations. Users can seamlessly integrate their datasets and make use of the platform’s tools for optimized fine-tuning outcomes. Comprehensive documentation and a variety of examples are available to guide users through the fine-tuning process with confidence. Additionally, this platform encourages collaboration and sharing of techniques among the community, fostering an environment of continuous improvement and innovation.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

ChatGLM No 
DeepSeek No 
Docker Yes 
Gemma No 
LLaVA No 
Llama No 
Llama 3 No 
MLflow No 
Mistral AI No 
Mixtral 8x22B No 
Mixtral 8x7B No 
OpenAI No 
PaliGemma 2 No 
Phi-2 No 
Qwen No 
React Native Yes 
TensorBoard No 
TensorWave No 
Vue.js Yes 
Yi-Large No 

Integrations

ChatGLM Yes 
DeepSeek Yes 
Docker No 
Gemma Yes 
LLaVA Yes 
Llama Yes 
Llama 3 Yes 
MLflow Yes 
Mistral AI Yes 
Mixtral 8x22B Yes 
Mixtral 8x7B Yes 
OpenAI Yes 
PaliGemma 2 Yes 
Phi-2 Yes 
Qwen Yes 
React Native No 
TensorBoard Yes 
TensorWave Yes 
Vue.js No 
Yi-Large Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Cargoship

Website

cargoship.sh/

Vendor Details

Company Name

hoshi-hiyouga

Website

github.com/hiyouga/LLaMA-Factory

Product Features

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Alternatives

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