Hands-on tutorials, notebook walk-throughs, and step-by-step implementation guides.
Train Qwen3-VL-8B Vision with GRPO: multimodal policy optimization, two-tier visual reward functions, and 16-bit LoRA under 15GB VRAM.
Fine-tune Qwen3.5-4B Vision using Unsloth: 16-bit LoRA optimization, multi-modal patch projection, and Q4_K_M GGUF edge deployment on consumer GPUs.
Train FLUX.2 Klein LoRA on 8GB GPUs using Kohya_ss: compact 4B MMDiT flow matching, CLIP-L conditioning, latent caching, and ComfyUI deployment.
Train Qwen3.5-4B Vision with GRPO: multimodal policy optimization, two-tier visual reward functions, and 16-bit LoRA under 15GB VRAM.
Fine-tune OpenAI gpt-oss-20B MoE using Unsloth on 16GB GPUs: 4-bit QLoRA, Harmony chat templates, channel separation, and SFTTrainer pipeline.
Fine-tune Qwen3-VL-8B Vision on LaTeX OCR tasks with Unsloth: 4-bit NF4 QLoRA, multimodal token collation, and GGUF export under 8.5GB VRAM.
Fine-tune Qwen3.5-0.8B Vision on VQA tasks using Unsloth: BF16 LoRA optimization, multi-modal token alignment, and GGUF edge deployment under 3GB VRAM.
Fine-tune Qwen3.5-2B Vision on document VQA using Unsloth: 16-bit LoRA optimization, multi-modal OCR token alignment, and GGUF export under 5GB VRAM.
Fine-tune Qwen3-4B-Thinking on DeepSeek-R1 CoT traces with Unsloth: OpenMathReasoning alignment, thinking-mode toggles, and GGUF multi-quant export.
Fine-tune Qwen3-14B dual-mode reasoning and chat: 75/25 mixed-dataset distillation, 4-bit QLoRA, and GGUF multi-quant export on 12GB VRAM.
Train gpt-oss-20B MoE using GRPO: AST sandboxing, anti-reward hacking, 4-bit QLoRA, and execution benchmark reward engineering.
Master FLUX.1 Dev LoRA fine-tuning with Kohya_ss: MMDiT joint attention, Flow Matching velocity loss, text encoder freezing, and ComfyUI deployment.
Train high-fidelity SDXL LoRAs with Kohya_ss: latent diffusion objective, cross-attention projection tuning, multi-aspect bucketing, and ComfyUI deployment.
Train Qwen3-4B with GRPO on 16GB GPUs: two-stage format pre-tuning, 4-tier composite reward shaping, rsLoRA, and vLLM acceleration.
Train Qwen3-8B in native FP8 precision using GRPO reinforcement learning: memory-efficient policy updates, multi-tier reward functions, and 20GB VRAM execution.
Fine-tune Qwen3-4B-Instruct with Unsloth: response-only loss masking, 4-bit QLoRA, memory profiling under 8GB VRAM, and GGUF multi-quant export.
Train 20B Qwen-Image LoRA on 24GB GPUs using AI-Toolkit: uint3 quantization, Accuracy Recovery Adapters (ARA), Flow Matching, and ComfyUI deployment.
Fine-tune Qwen2.5-Coder-1.5B for tool calling using Unsloth: Hermes JSON schema formatting, 4-bit QLoRA, multi-tool dispatch, and edge deployment.
Master LTX-2 Character-Consistent Video LoRA training: in-context conditioning (IC-LoRA), paired dataset curation, YAML configs, and ComfyUI deployment.
Master FLUX.2-dev LoRA fine-tuning with AI-Toolkit: MMDiT flow matching, dataset curation, Prodigy adaptive optimization, and ComfyUI deployment.
Train Qwen-Image-Edit-2511 LoRA using AI-Toolkit: paired MMDiT datasets, qfloat8 quantization, Diff Output Preservation, and ComfyUI deployment.
Accelerate MoE LLM fine-tuning with Unsloth Faster MoE: torch._grouped_mm, Triton fused kernels, Split LoRA memory optimization, and benchmarks.
Train Llama 3.2 (3B) with GRPO and LoRA: multi-reward optimization, SFT format warm-up, cosine similarity scoring, and PyTorch deployment.
Master distributed LLM pretraining (13B–70B+) with DeepSpeed and Megatron-LM: 3D Parallelism (TP/PP/DP), ZeRO-3, FP8 TransformerEngine, and multi-node NCCL.
Train Llama 3.2 1B using hardware FP8 quantization and GRPO: 4-layer reward shaping, vLLM weight sharing, and 60% VRAM reduction.
Train Llama 3.1 8B with GRPO using Unsloth on a single 16GB GPU: 5-tier composite reward functions, 4-bit QLoRA, XML reasoning, and vLLM acceleration.
Train DeepSeek-R1 distilled Qwen3 (8B) using GRPO: multi-signal reward functions, group advantage normalization, and single-GPU fine-tuning.