Cool, let’s turn this into a concrete game plan for:

Base model: HuggingFaceTB/SmolLM-135M-Instruct Dataset: keivalya/MedQuad-MedicalQnADataset (or a trimmed variant)

No code, just what you’ll do at each step.

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Phase 0 – Define constraints

• Goal: make a tiny medical Q&A assistant that:

  • Answers medical questions more accurately.
  • Still writes normal English.
  • Is safe (no wild dosing advice etc.).

• Hardware assumption: 1 GPU with ~8–16 GB VRAM or a beefy CPU → use LoRA instead of full fine-tune.

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Phase 1 – Understand model & dataset

1. Model card (SmolLM-135M-Instruct):

   • Confirm: license (Apache 2.0), context length, chat format / expected prompts. (Hugging Face)
   • Note its “instruction” style (it’s already fine-tuned for following instructions).

2. Dataset card (keivalya/MedQuad-MedicalQnADataset or mlabonne/MedQuad-MedicalQnADataset):

   • Inspect columns: usually something like question, answer (names may vary). (Hugging Face)
   • Check size (~10k–100k samples).
   • Optionally consider a length-trimmed version like Laurent1/MedQuad-MedicalQnADataset_128tokens_max if you want shorter, cheaper training. (Hugging Face)

Outcome: you know exactly which columns represent the user’s question and the expected answer.

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Phase 2 – Decide your training format (how examples look)

You need to map each row into a single prompt + target answer.

For each MedQuad example:

• User side (input to model): Something like:

  • System-ish instruction: “You are a safe, helpful medical assistant…”
  • User question: the medical question from dataset.

• Assistant side (label to learn):

  • The ground truth medical answer from dataset.

You’ll:

• Adopt one consistent chat template for all examples.
• Ensure safety tone in system prompt (e.g., “do not give definitive treatment decisions; encourage consulting a doctor”).

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Phase 3 – Clean & split the data

Actions:

1. Filter noisy stuff:

   • Drop examples with missing/empty question or answer.
   • Optionally drop extremely long ones (e.g., > N characters/tokens).

2. Train / validation split:

   • ~80–90% → train.
   • ~10–20% → validation (never used for training).

3. (Optional but useful) Create a small “eval prompts” file by hand:

   • 20–50 medical questions that you care about (lab report interpretation, symptoms, edge cases).
   • You’ll use these later for before/after comparison.

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Phase 4 – Choose fine-tuning strategy (LoRA / PEFT)

You’ll go with:

• PEFT / LoRA on top of SmolLM-135M-Instruct:

  • Base model weights are frozen.
  • You train tiny adapter layers → light on memory, fast, reversible.

Decisions you’ll make (conceptually, no numbers yet is fine):

• Which layers to attach LoRA to (usually attention / feedforward modules).
• How many adapter dimensions (rank r).
• Whether to train embeddings / layernorms (likely no for a start).

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Phase 5 – Set basic training knobs

You’ll configure (at a high level):

• Max sequence length:

  • Big enough for question + answer; with MedQuad often 256–1024 tokens is okay.

• Batch size (effective):

  • Chosen based on VRAM; you can simulate big batch via gradient accumulation.

• Learning rate:

  • Small enough to not destroy the base model’s language knowledge.

• Epochs:

  • Start with 1–3 epochs over MedQuad.

• Logging / checkpoints:

  • Save model every X steps.
  • Track training loss and validation loss.

Core idea: You’ll watch validation loss to decide if you’re overfitting (loss starts going up).

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Phase 6 – Run a tiny sanity-check training

Before full training, you’ll:

1. Train on a very small subset (e.g., 100–500 examples) for a few hundred steps.

2. Check:

   • Does training loss go down?
   • Can the model now memorize a few training examples (it should reproduce them roughly)?
   • Does it still respond sensibly to a non-medical question?

If something is broken (wrong formatting, wrong labels, etc.), you catch it here instead of after a 3-hour run.

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Phase 7 – Full training run

Once the sanity check looks good:

1. Train on full MedQuad train split with chosen hyperparams.

2. Monitor:

   • Training loss.
   • Validation loss.

3. Possibly early stop if validation loss stops improving.

Result: a set of LoRA adapter weights that specialize SmolLM on MedQuad.

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Phase 8 – Evaluate: “Did it actually become more medical (and safe)?”

You’ll evaluate on three kinds of prompts.

1. In-domain medical questions:

   • Use held-out MedQuad validation questions.
   • Compare answers vs ground truth.
   • Optionally score correctness manually for a sample.

2. Out-of-domain / general questions:

   • Ask about random non-medical topics.
   • Make sure model hasn’t forgotten basic language ability.

3. Safety probes:

   • Risky questions: dosage, self-surgery, ignoring doctors, etc.

   • Check that the model:

     • Avoids giving specific dangerous instructions.
     • Encourages seeing a professional.
     • Uses cautious language.

If safety is weak:

• Add extra synthetic examples with “refusal + safe guidance” responses.
• Do another short SFT pass mainly on safety data.

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Phase 9 – Save, package, and (optionally) upload

You’ll:

1. Save:

   • The base model reference (SmolLM-135M-Instruct).
   • The LoRA adapter weights.
   • The training config (hyperparams, dataset used, commit hash).

2. For reuse:

   • Load base + LoRA for inference, or
   • (Optionally) merge LoRA into the base model and save a single merged checkpoint.

3. (Optional but nice) Push to Hugging Face:

   • Create a repo like yourname/SmolLM-135M-MedQuad-LoRA.

   • Document:

     • That it’s not a doctor.
     • That it was trained on MedQuad.
     • Intended use: research / experimentation, not clinical decisions.

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Phase 10 – Inference & iteration loop

Finally:

• Wire up a simple chat endpoint (FastAPI/Flask) that:

  • Wraps prompts with your chat template.
  • Applies system prompt (safe medical assistant).
  • Calls the fine-tuned model and streams responses.

• Use it interactively:

  • Note failure modes (hallucinations, unsafe suggestions).
  • Collect “bad” outputs → turn them into new training examples.
  • Do small, focused SFT passes to gradually improve behavior.

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If you want, next step I can:

• Turn this plan into exact libraries + components (e.g., “use transformers + trl + peft with SFTTrainer”), still concept-level, or
• Jump straight to actual training code for this SmolLM + MedQuad combo.