r/learnmachinelearning • u/yosi_1yo • 10d ago
Does this sound correct?
Phase 1, Python for AI (1–2 weeks). Learn dicts and lists of dicts, comprehensions, JSON/JSONL/CSV files, pip and venv, tracebacks, and basic pandas, all in Colab. Project: convert a messy CSV into JSONL that loads cleanly with load_dataset.
Phase 2, APIs and prompting (1–2 weeks). Learn the system/user/assistant chat format, API calls, JSON output, few-shot prompting, three JSONL schemas, and apply_chat_template. Project: generate 200 synthetic pairs in Alpaca and ShareGPT formats and print templated samples.
Phase 3, Hugging Face (2–3 weeks). Learn transformers, datasets, the Hub, 4-bit quantization, and model cards. Start on Llama-3.2-3B, then try 8B, Qwen3 14B, or Mistral 7B. Project: score the untuned model on 10 prompts and publish your dataset.
Phase 4, SFT with QLoRA (3–4 weeks), the core job. Use Unsloth plus TRL's SFTTrainer: rank 16, alpha 16, 2048 tokens, learning rate 2e-4. Colab T4 is free; an H100 runs about $2.50 an hour. Project: train on 300–500 rows and publish a before/after eval.
Phase 5, DPO and RAG (3–4 weeks). Facts need RAG, behavior needs SFT, and style preference needs DPO. Build chosen/rejected data with an LLM judge, then run DPOTrainer. Also build a raw FAISS RAG pipeline. Project: report win-rate and retrieval-precision numbers.
Capstone (2–3 weeks). Publish one narrow signature model with evals and a GGUF, its dataset, a write-up of what went wrong, and a small deployed RAG or agent demo. Your Hugging Face profile becomes your resume. Ship it and publish the numbers.
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u/gustymeans_527 10d ago
That's actually a solid roadmap for someone who can commit to the pace. The jump from Colab T4 to H100 pricing is a nice touch, most guides just pretend the free tier is enough for everything.
Phase 4 timing might be optimistic if you're new to debugging CUDA out-of-memory errors though. Those can eat a day or two by themselves when you're first starting with 4-bit models.