nebuly-ai/optimate

★ 8,347PythonAudience · dataComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((OptiMate))
    What it does
      AI model optimization
      Archived project
    Tools
      Speedster inference
      Nos GPU cluster
      ChatLLaMA fine-tuning
    Requirements
      Python
      PyTorch
      GPU hardware
    Status
      No longer maintained
      Reference only

mindmap root((OptiMate)) What it does AI model optimization Archived project Tools Speedster inference Nos GPU cluster ChatLLaMA fine-tuning Requirements Python PyTorch GPU hardware Status No longer maintained Reference only

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Things people build with this

USE CASE 1

Speed up AI model inference on existing GPU hardware using Speedster's hardware-aware optimization techniques.

USE CASE 2

Reduce Kubernetes GPU cluster costs by dynamically partitioning GPU resources with the Nos manager.

USE CASE 3

Fine-tune a large language model on limited data and hardware using ChatLLaMA's RLHF approach.

USE CASE 4

Browse archived reference code for GPU inference optimization and LLM fine-tuning techniques, even though the repo is no longer maintained.

Tech stack

PythonPyTorchKubernetesCUDA

Getting it running

Difficulty · hard Time to first run · 1h+

Repository is no longer maintained, treat as archived reference code only, with no active support, updates, or guaranteed compatibility with current libraries.

In plain English

OptiMate is a collection of open-source tools from Nebuly AI aimed at making AI models cheaper and faster to run. The repository is now in legacy status and no longer actively maintained, though the code remains available. Nebuly has shifted its focus to a different product, a platform for understanding how users interact with AI-based products at scale. While it was active, the repository contained three main tools. Speedster was designed to speed up AI model inference by applying optimization techniques that match the model to the specific hardware it runs on, whether GPUs or CPUs. The goal was to reduce the compute cost of running predictions. Nos focused on reducing infrastructure costs by managing a Kubernetes GPU cluster more efficiently through dynamic partitioning and flexible resource allocation. ChatLLaMA was a tool for fine-tuning large language models with less data and hardware, using techniques including reinforcement learning from human feedback. Because the repository is no longer maintained, anyone looking at it today should treat it as an archived snapshot rather than a supported project. The README points to external documentation for Nebuly's current commercial platform if you are looking for an actively supported solution. The source code in the git history is still accessible for reference.

Copy-paste prompts

Prompt 1

Using Speedster from nebuly-ai/optimate, optimize my PyTorch ResNet-50 model for faster inference on an NVIDIA T4 GPU, show me the full optimization pipeline and expected speedup.

Prompt 2

Show me how to configure the Nos GPU cluster manager on Kubernetes to reduce idle GPU time across 4 A100 nodes in a shared ML training environment.

Prompt 3

Walk me through using ChatLLaMA to fine-tune a LLaMA model on a custom instruction dataset with reinforcement learning from human feedback on a single GPU.

Prompt 4

I found code in the nebuly-ai/optimate repo I want to use, how do I adapt Speedster's optimization pipeline to work with a HuggingFace transformers model?

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