Is the Open Source AI Academy free?

Yes, 100% free. No sign-up, no credit card. All 18 modules, 31 lessons, and 73 quiz questions are accessible immediately. Your progress saves locally in your browser.

How is Infinity Open Source AI Academy different from vendor-specific open-weight tutorials?

Unlike generic or single-model tutorials, Infinity Tech Stack's Open Source AI Academy bridges global open weights (Meta Llama 4 & 3.3, Mistral Large 3/4, Google Gemma 4, OpenAI gpt-oss-120b/20b) and China Frontier Models (DeepSeek-V4 & R1, Qwen3/3.8, Kimi K3, GLM-5) with 18 comprehensive modules and simulated in-browser terminal labs for Ollama, ggml-org/llama.cpp, vLLM V1, SGLang, Unsloth, and GGUF quantization. It requires zero GPU hardware, no cloud subscriptions, and no sign-up.

Does the Open Source AI Academy cover China's latest frontier open models?

Yes. The academy includes a dedicated China Frontier Models module covering Qwen, DeepSeek, Z.ai GLM, Moonshot Kimi, InternLM, Baichuan, and Yi, plus a practical model-radar workflow for weekly tracking and gated production adoption.

Did Kimi K3 score higher than Claude?

On several benchmarks, yes. Kimi K3 (July 14, 2026: 2.8T open-weight MoE, 1M context) beat Claude Fable 5 on 6 of 14 shared benchmarks — including BrowseComp (91.2 vs 88.0), SWE Marathon (42.0 vs 35.0), and Terminal-Bench 2.1 (88.3 vs 84.6) — topped the Arena.ai Frontend Code Arena, and edged Claude Opus 4.8 on the Artificial Analysis Intelligence Index (57 vs 56) at about 3.3x lower pricing. Claude Fable 5 won the other 8 benchmarks and leads that index at 60. The academy's China Frontier Models module has a dedicated lesson on the full scoreboard.

What topics does the Open Source AI Academy cover?

The course covers Transformer architecture, Meta Llama 4, Mistral Large 3/4, OpenAI gpt-oss (120B/20B), DeepSeek-V4/R1, Qwen3/3.8, Google Gemma 4, Hugging Face Transformers v5, quantization (GGUF, AWQ, GPTQ, FP8, MXFP4, EXL2), Ollama, ggml-org/llama.cpp, vLLM V1, SGLang RadixAttention, Unsloth & PEFT LoRA/QLoRA fine-tuning, pre-training from scratch, and TRL RLHF/DPO/GRPO alignment, plus production MLOps with Docker and Kubernetes.

vLLM is the industry-standard open-source engine for high-throughput production LLM serving. Its V1 engine architecture pairs PagedAttention virtual memory paging with zero-overhead Automatic Prefix Caching, continuous batching, FP8/AWQ quantization, and native tool calling (--enable-auto-tool-choice) for >90% GPU utilization.

Do I need expensive hardware to run open-source models?

No. Many models can run on consumer GPUs (e.g., RTX 4090) or even CPUs via llama.cpp. The course covers deployment strategies for every budget, from Raspberry Pi edge devices to multi-GPU production clusters.

Does the Open Source AI Academy include hands-on labs?

Yes. Hands-on terminal labs are built into the lessons - ollama pull, run, and list models, quantize a model to Q4_K_M, and serve it with vLLM using real, vendor-verified commands, with objectives, hints, solutions, and XP rewards. No GPU or sign-up needed.

What training innovation made DeepSeek-R1's reasoning possible?

GRPO (no critic model, group-wise comparison) — GRPO generates multiple responses per prompt and optimizes using group-relative comparisons - no separate critic/value model needed.

The Alignment Stack: RLHF → DPO → GRPO — Training & Alignment

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The Alignment Stack: RLHF → DPO → GRPO

⚖️ Training & Alignment⏱ 12 min⭐ 150 BASE XP⌨ HANDS-ON LAB

Modern Post-Training Pipeline

Raw pre-trained models are "completion engines" - they continue text, not follow instructions. Alignment transforms them into useful assistants.

The Three Stages

Stage	Purpose	Technique
1. SFT	Instruction following, format, conversational style	Supervised Fine-Tuning on instruction datasets
2. Preference	Align with human values and preferences	DPO, KTO, SimPO (no reward model needed)
3. RL	Push beyond training data for reasoning	GRPO, RLVR (for math/code verification)

Technique Comparison

Method	Complexity	Memory	Best For
RLHF (PPO)	High (needs reward model + critic)	~4x model size	Classic, proven approach
DPO	Low (direct from preference pairs)	~2x model size	Simple, stable preference alignment
GRPO	Medium (group-wise comparison)	~2x model size	Reasoning, no critic needed

GRPO (popularized by DeepSeek-R1) generates multiple answers per prompt, compares them within the group, and optimizes accordingly - eliminating the separate "critic" model that PPO requires.

Runnable GRPO & DPO with Hugging Face TRL

from trl import GRPOTrainer, GRPOConfig, DPOTrainer, DPOConfig

# Verifiable reward function for math/code reasoning (GRPO)
def format_and_answer_reward(completions, **kwargs):
    return [1.0 if "<think>" in c and "</think>" in c else 0.0 for c in completions]

grpo_trainer = GRPOTrainer(
    model="Qwen/Qwen3-4B",
    reward_funcs=[format_and_answer_reward],
    args=GRPOConfig(output_dir="./grpo_out", learning_rate=5e-6, bf16=True, use_vllm=True),
    train_dataset=reasoning_dataset,
)
grpo_trainer.train()

💡 2026 Consensus: The era of one-size-fits-all alignment is over. Modern stacks are modular: SFT for format → DPO for preferences → GRPO for reasoning. Mix and match based on your use case.

⌨ HANDS-ON LABTrain Preference Adapter with TRL DPO

⭐ +200 XP

Run Direct Preference Optimization using Hugging Face TRL to align model responses with human preferences without a separate reward model.

1Invoke the TRL DPO CLI with a model path and preference dataset.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 1 — type "hint" if stuck

KNOWLEDGE CHECK

QUERY 1 // 2

What does DPO eliminate compared to RLHF?

The training data

The need for a separate reward model

The base model

The tokenizer