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description: A step-by-step workflow for fine-tuning and deploying large language models using cloud GPU infrastructure. The process covers launching a GPU instance on...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Fine-tuning and Deploying LLM with Unsloth, SGLang and Runpod

**[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 3 min read · 2 upvotes · 0 comments

## Summary

A step-by-step workflow for fine-tuning and deploying large language models using cloud GPU infrastructure. The process covers launching a GPU instance on RunPod, loading a 20B parameter model with Unsloth's memory optimizations, applying LoRA adapters for efficient training, running supervised fine-tuning, exporting the merged model checkpoint, serving it with SGLang's OpenAI-compatible API, and making inference requests. The entire pipeline runs on on-demand GPU compute, enabling practical iteration from training to production deployment.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.dailydoseofds.com/p/fine-tuning-and-deploying-llm-with>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning), [#gpu](https://daily.dev/tags/gpu)

[View this post on daily.dev](https://daily.dev/posts/fine-tuning-and-deploying-llm-with-unsloth-sglang-and-runpod-ifo5tb1tx)

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