Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
| #!/bin/bash | |
| ### steps #### | |
| # verify the system has a cuda-capable gpu | |
| # download and install the nvidia cuda toolkit and cudnn | |
| # setup environmental variables | |
| # verify the installation | |
| ### | |
| ### to verify your gpu is cuda enable check |
| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| """ | |
| citation: | |
| @misc{brown2025grpodemo, | |
| title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models}, | |
| author={Brown, William}, |
| #!/usr/bin/env python3 | |
| """ | |
| Ollama Model Manager | |
| This script provides two modes of operation: | |
| - export: Exports an installed Ollama model to a tarball. | |
| - import: Imports an Ollama model from a tarball. | |
| The export mode reads a manifest file located at: | |
| <base_path>/manifests/registry.ollama.ai/library/<model_name>/<model_size> |