Revisions
main contains the final step-98 model. Every saved training checkpoint is
also available as a Hugging Face revision.
Table with columns: Revision, Training step, Notes| Revision | Training step | Notes |
|---|
main | 98 | Final model; identical weights to checkpoint-98 |
checkpoint-10 | 10 | GRPO checkpoint |
checkpoint-20 | 20 | GRPO checkpoint |
checkpoint-30 | 30 | GRPO checkpoint |
checkpoint-40 | 40 | GRPO checkpoint |
checkpoint-50 | 50 | GRPO checkpoint |
checkpoint-60 | 60 | Highest Correct rate in the held-out checkpoint evaluation |
checkpoint-70 | 70 | Highest Branch Coverage in the held-out checkpoint evaluation |
checkpoint-80 | 80 | GRPO checkpoint |
checkpoint-90 | 90 | GRPO checkpoint |
checkpoint-98 | 98 | Final checkpoint |
Loading a Checkpoint
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "tomhu/RL4TG-Qwen2.5-3B-Coder7B-Offline-SFT-GRPO"
revision = "checkpoint-60"
tokenizer = AutoTokenizer.from_pretrained(repo_id, revision=revision)
model = AutoModelForCausalLM.from_pretrained(
repo_id,
revision=revision,
torch_dtype="auto",
device_map="auto",
)
Omit revision to load the final step-98 model from main.
GRPO Configuration
- Training samples: 1,582 Defects4J focal methods with eligible FAIL mutants
- Epochs: 2
- Global prompt batch: 32
- Rollouts per prompt: 8
- Sampling temperature: 1.0
- Learning rate: 1e-6
- Data shuffle seed: 42
- Maximum prompt and response lengths: 4,096 tokens each
- Checkpoint interval: 10 steps
This model is part of the RL4TG experimental model collection.