I will deploy custom gpt using your preferred llm on your server
About this Gig
Are you looking to implement a customized GPT solution using your preferred LLM on your organization's server? Look no further! I specialize in deploying custom GPT models with local LLMs tailored to your specific business needs.
What This Gig Offers
- Custom GPT Model Setup: Ensuring seamless integration with your organization's existing infrastructure.
- Local LLM Models Deployment: GPT-J, GPT-Neo, Vicuna, LLaMA, or any custom LLM you prefer.
- Customization Based on Business Needs.
- Performance Optimization: Model Profiling, Quantization, Pruning, GPU Optimization, Batch & Parallel Processing.
- Security Measures Implementation: Access Control, Data Encryption, Firewall Configuration.
- Ongoing Maintenance and Support (After deployment 1 Month)
Why Choose Me?
- Experienced in AI/ML Systems: With a background in computer engineering and expertise in deploying and optimizing language models
- Customizable Solutions
- Security-Focused: I implement strong security measures to ensure that your data and model remain secure and compliant with industry standards.
Note: Send me a message before placing an order so we can discuss your specific needs, and I can offer you the best possible solution.
Device:
Server/Hosting
Operating system:
Linux/Unix
Also delivering:
Remote connection support
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Package deal
FAQ
What LLMs do you support for deployment?
I support a wide range of models, including but not limited to GPT-J, GPT-Neo, Vicuna, LLaMA, and many others. If you have a specific model in mind, feel free to ask!
Can you provide long-term maintenance?
Yes, I offer ongoing maintenance as an add-on service for as long as you need.
Is my data secure with a local LLM model?
Absolutely! I implement strict access control and encryption protocols to ensure that your data remains safe and private throughout the deployment and usage phases.
Do you offer a demo before full deployment?
Yes, I can provide a demo with limited functionalities to give you an idea of how the model will perform once fully deployed.

