| --- |
| library_name: transformers |
| tags: |
| - policy-gradient |
| - adaptive-group-size |
| --- |
| |
| # Adaptive group-size policy checkpoints |
|
|
| Final fixed-group and adaptive policy checkpoints for GSM8K, mathematical |
| reasoning and code generation. This is a multi-model archive: choose a model |
| subdirectory from `checkpoints.json`, rather than loading the repository root. |
|
|
| | Family | Models | Final step | |
| |---|---:|---:| |
| | GSM8K / Qwen2.5-1.5B-Instruct | 21 | 500 | |
| | Math / Qwen3-8B-SFT496 | 6 | 300 | |
| | Math / Qwen2.5-Math-7B | 6 | 500 | |
| | Coding / DeepSeek-Coder-1.3B-Base | 6 | 500 | |
|
|
| The manifest lists planned models; a model is available after its folder has |
| been committed. Historical hybrid runs retain their original configuration |
| name. Base-model licenses remain applicable; no optimizer states or training |
| datasets are published here. |
|
|
| ```python |
| from pathlib import Path |
| from huggingface_hub import snapshot_download |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| subdir = "models/gsm8k/fixed2/seed42/step500" |
| root = snapshot_download("Gsj49/adaptiveG", allow_patterns=[subdir + "/*"]) |
| folder = Path(root) / subdir |
| tokenizer = AutoTokenizer.from_pretrained(folder) |
| model = AutoModelForCausalLM.from_pretrained(folder) |
| ``` |
|
|
