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NEW

Stable-Baselines3 vs QLoRA: When to Train an Agent or Fine-Tune an LLM

Before we get into it, the short version: Stable Baselines3 trains reinforcement learning agents through trial and error in simulated environments. QLoRA fine-tunes large language models using labeled input-output pairs to reshape text generation behavior. The two address fundamentally different…
Thumbnail Image of Tutorial Stable-Baselines3 vs QLoRA: When to Train an Agent or Fine-Tune an LLM
NEW

When nangcuctv needs custom LLMs: Fine‑tune with MindsDB v26.0.0

Relying on a general-purpose LLM for lesson generation or quiz creation means you are running the same model as every other education platform: same capabilities, same generic voice, same cost per token. Generic large language models handle broad knowledge tasks well, but they do not understand…
Thumbnail Image of Tutorial When nangcuctv needs custom LLMs: Fine‑tune with MindsDB v26.0.0

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NEW

6 Fine-Tuning LLM Frameworks Ranked by Speed, Memory, and GPU Needs

Six training frameworks are covered here: Axolotl, DeepSpeed, Megatron-LM, PyTorch Lightning Fabric, Torchtune, and Unsloth. Available benchmarks don't line up across them. Speed comparisons come from reported throughput tests; memory figures reflect documented VRAM ranges for LoRA, QLoRA, and full…
Thumbnail Image of Tutorial 6 Fine-Tuning LLM Frameworks Ranked by Speed, Memory, and GPU Needs
NEW

Skip ML Theory: Ship AI Apps with One API Call and a Rollback Script

When you run an AI agent behind a production API, every millisecond you add compounds. Users don't care whether your agent consulted three models or made six tool calls. They want the result. Exposing that complexity at the API boundary leaves you with brittle clients and debugging sessions at 2…
Thumbnail Image of Tutorial Skip ML Theory: Ship AI Apps with One API Call and a Rollback Script
NEW

Fine-Tuning Llama 3 on a Single GPU: Memory vs Accuracy Tradeoffs

The short version: You hit the same wall every time: a fine-tuning job that should work crashes with an out-of-memory error, or accuracy drops because the only config that fits uses aggressive memory cuts. The memory-vs-accuracy question determines whether your single-GPU workflow succeeds, or…
Thumbnail Image of Tutorial Fine-Tuning Llama 3 on a Single GPU: Memory vs Accuracy Tradeoffs

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