Adapting AIM LLMs For Specific Use Cases Through Fine-Tuning in AMD AI Workbench

2026-06-03 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Novice, long

Summary

AMD AI Workbench v1.1.9 enables users to fine-tune pre-trained Large Language Models (LLMs) without code and deploy them using AMD Inference Microservices (AIMs). This process, validated on AMD Instinct MI300X GPUs, involves uploading a dataset, selecting a base model like "meta-llama/Llama-3.2-1B-Instruct", configuring training parameters, running the supervised fine-tuning job, and deploying the resulting model as an AIM. The article demonstrates specializing a model to answer questions about the AMD enterprise AI reference stack documentation, inducing topic classification, refusal behavior for off-topic queries, and a rigid Markdown output format. It also highlights the risk of overconfidence and hallucinations if fine-tuning data consists solely of positive examples.

Key takeaway

For AI Engineers adapting LLMs for specific enterprise use cases on AMD hardware, AMD AI Workbench provides a streamlined, low-code GUI for supervised fine-tuning and deployment via AIMs. You can specialize models like "meta-llama/Llama-3.2-1B-Instruct" to your domain, but you must carefully curate training datasets and rigorously evaluate fine-tuned models for potential overconfidence or hallucinations, particularly if using only positive examples.

Key insights

Fine-tuning adapts pre-trained LLMs for specific tasks, but core knowledge largely stems from pre-training.

Principles

• Fine-tuning complements methods like RAG or prompting.
• Supervised Fine-tuning (SFT) trains LLMs on positive input-output examples.
• Fine-tuning does not remove biases and can induce overconfidence.

Method

The AMD AI Workbench GUI workflow for SFT involves uploading a JSONL dataset, selecting a certified base model, configuring hyperparameters (batch size, epochs, learning rate multiplier), initiating training, and deploying the fine-tuned model via AIMs.

In practice

• Apply SFT to improve LLM performance on specific tasks or enable smaller LLMs for complex tasks.
• Utilize AMD AI Workbench's "Compare mode" to evaluate fine-tuned models against base versions.

Topics

• AMD AI Workbench
• LLM Fine-tuning
• Supervised Fine-tuning
• AMD Inference Microservices
• Llama-3.2-1B-Instruct
• Model Deployment

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.