Instructions to use Gsj49/verl_adaptiveg_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gsj49/verl_adaptiveg_checkpoints with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Gsj49/verl_adaptiveg_checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
VERL Adaptive-G checkpoints
Unified public checkpoint archive for verl_adaptiveG.
Directory convention:
<family>/<model>/<method>/<variant>/<rep>/global_step_<N>/
Each checkpoint directory is a complete Transformers model. Per-item
checksums and upload receipts are stored under metadata/items/.
| Checkpoint | Repository path | Upload status |
|---|---|---|
| Adaptive-G Qwen3-8B Math โ Common Sketch G32/B64 | math_rlvr/qwen3-8b/common_sketch/g32_b64_every25/rep4/global_step_200 |
complete |
| Adaptive-G Qwen2.5-Coder-7B โ Common Sketch G32/B64 | coding_rlvr/qwen2.5-coder-7b/common_sketch/g32_b64_every25/rep4/global_step_300 |
complete |
| Adaptive-G Qwen3-4B Code โ Common Sketch G32/B64 | coding_rlvr/qwen3-4b/common_sketch/g32_b64_every25/rep4/global_step_300 |
complete |
The archive contains model weights and tokenizer/configuration files. Optimizer and scheduler state are excluded unless a future item is explicitly marked as a resumable training checkpoint.
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support