Request for step-by-step SFT (single & multi-task) pipeline guidance for MFTCoder

Hello MFTCoder authors 👋,

First, thank you for releasing MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning and sharing the code. I’m a 3rd year PhD student currently attempting to reproduce your SFT experiments (both single-task and multi-task baselines) using the repo.

I’ve successfully set up the environment (conda, CUDA 12.x; multi-GPU available) and explored the repo (e.g., build_model.py, atorch_trainer.py). However, I’m still unclear on the exact SFT pipelines. Could you please clarify or provide a minimal set of example scripts/configs?

What I’m Hoping To Clarify

1. Data Loading & Formats
• Expected JSON/JSONL schema per task (fields for input/output, roles, label masking).
• Where task IDs / TASK2ID are defined and how they map to datasets.

2. SFT-Single (SFT-S-*)
• One concrete command (e.g., CodeLlama-13B-Python + QLoRA) to fine-tune on a single task (e.g., text-to-code or code completion).
• Example config/flags for:
• optimizer, LR schedule
• max sequence length
• gradient accumulation
• PEFT settings (LoRA/QLoRA)

3. SFT-Mixed (Multi-task)
• How to specify multiple datasets in one run (CLI flags vs config file).
• Task sampling/mixing policy: uniform vs size-based?
• How to switch between:
• sample-count weighted loss
• valid-token weighted loss
• Any recommendations on per-task batch sizes or temperature scaling.

4. Loss Functions
• Confirmation that SFT experiments used cross-entropy with weighted loss.
• Whether focal loss or FAMO were excluded in the official SFT baseline results.
• The exact flag names to enable:
• weighted by valid tokens
• weighted by samples

5. Evaluation
• Commands to evaluate on:
• HumanEval / HumanEval-X
• MBPP
• CodeFuseEval
• pass@k evaluation protocol, execution-based scoring, and seeds for reproducibility.

6. Reproducibility
• Example run logs or expected training curves.
• Early stopping criteria and typical step counts.
• Any specific branches (e.g., mftcoder_accelerate vs mftcoder_atorch) that contain the canonical SFT scripts.

Beginne mit dem Vergleich der Branches mftcoder_accelerate und mftcoder_atorch. Lies anschließend build_model.py und atorch_trainer.py, um die bestehenden SFT-Einstiegspunkte nachzuverfolgen. Dokumentiere die angeforderten Datenschemata, Befehle oder Konfigurationen für Single-Task und Multi-Task, Optionen für Loss und Evaluation sowie Hinweise zur Reproduzierbarkeit; als erledigt gilt die Aufgabe, wenn ein Neuling beide SFT-Varianten ausführen und evaluieren kann.

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