FineTuning - insureai360.com

Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU

“> Unsloth: kernel-level gains on a single GPU Unsloth’s published benchmarks show 2x training speed for Llama 3.1 8B and Llama 3.3 70B. The setup used…

I Tried Fine-Tuning a Robot AI Model on Colab. Here Is What Worked

model sounds expensive, fragile, and hard to verify. I wanted a smaller test: can I run a real OpenVLA LoRA fine-tune in Colab, prove that the…

Fine-Tuning Qwen3 with LoRA Using NVIDIA NeMo AutoModel: A Complete Single-GPU Google Colab Workflow Tutorial

In this tutorial, we build an end-to-end NVIDIA NeMo AutoModel workflow in Google Colab and use a single GPU to explore the same configuration-driven training architecture…

RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each

, I have written quite a lot about RAG, starting with the Hitchhiker’s Guide to RAG with ChatGPT API and LangChain, and then exploring various topics…

Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics

banner(“STEP 3 — Building the analysis DataFrame”) def process_example(ex): traj = normalize_trajectory(ex.get(“trajectory”)) rc = role_counts(traj) nf, add, dele, _files, _exts = parse_patch(ex.get(“model_patch”)) meta = normalize_metadata(ex.get(“metadata”)) full_text…

Poetiq’s Meta-System Automatically Builds a Model-Agnostic Harness That Improved Every LLM Tested on LiveCodeBench Pro Without Fine-Tuning

Poetiq has just published some very interesting results showing its Meta-System reached a new state-of-the-art on LiveCodeBench Pro (LCB Pro), a competitive coding benchmark, by automatically…

A Coding Implementation to Parsing, Analyzing, Visualizing, and Fine-Tuning Agent Reasoning Traces Using the lambda/hermes-agent-reasoning-traces Dataset

In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading…

smol-audio: A Colab-Friendly Notebook Collection for Fine-Tuning Whisper, Parakeet, Voxtral, Granite Speech, and Audio Flamingo 3

Audio AI has had a breakout year. Automatic speech recognition has gotten dramatically better with models like OpenAI’s Whisper variants, NVIDIA’s Parakeet, and Mistral’s Voxtral. Audio…

A Coding Implementation on Microsoft’s OpenMementos with Trace Structure Analysis, Context Compression, and Fine-Tuning Data Preparation

In this tutorial, we work with Microsoft’s OpenMementos dataset and explore how reasoning traces are structured through blocks and mementos in a practical, Colab-ready workflow. We…

A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning

import subprocess, sys, os, shutil, glob def pip_install(args): subprocess.run([sys.executable, “-m”, “pip”, “install”, “-q”, *args], check=True) pip_install([“huggingface_hub>=0.26,<1.0”]) pip_install([ “-U”, “transformers>=4.49,<4.57”, “accelerate>=0.33.0”, “bitsandbytes>=0.43.0”, “peft>=0.11.0”, “datasets>=2.20.0,<3.0”, “sentence-transformers>=3.0.0,<4.0”, “faiss-cpu”, ])…

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