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

[alvi75]

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.