Instructions to use crssbl/es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crssbl/es with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("crssbl/es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| library_name: transformers | |
| tags: | |
| - reinforcement-learning | |
| - grpo | |
| - math | |
| - trajectories | |
| # ES RL Model and Trajectories | |
| This repository contains the step-20 Hugging Face export for | |
| `qwen35-4b-math-grpo-dapo400-e15-seed1`, the corresponding RL trajectories, | |
| and the latest evaluation outputs. | |
| ## Contents | |
| - `models/qwen35-4b-math-grpo-dapo400-e15-seed1-step20/`: Hugging Face model export with 4 safetensors shards, tokenizer files, and generation/config metadata. | |
| - `models/qwen35-4b-docvqa-grpo-four-gpu-from-step40-seed42-step120/`: DocVQA Hugging Face model export with 4 safetensors shards, tokenizer files, and generation/config metadata. | |
| - `trajectories/qwen35-4b-math-grpo-dapo400-e15-seed1/raw/`: raw train and validation trajectory JSONL files. | |
| - `trajectories/qwen35-4b-math-grpo-dapo400-e15-seed1/exports/attempt2_step001_020/`: exported train/validation trajectory JSONL files for steps 1-20. | |
| - `trajectories/qwen35-4b-docvqa-grpo-four-gpu-from-step40-seed42/trace2skill_collection_step80/`: DocVQA trace2skill trajectory collection outputs, including RL rollouts and sample-only trajectories. | |
| - `eval/qwen35-4b-math-grpo-dapo400-e15-seed1/step20_eval_50x4096_rerun_20260805/`: evaluation summary and per-sample outputs. | |
| - `eval/qwen35-4b-docvqa-grpo-four-gpu-from-step40-seed42/`: DocVQA post-eval outputs, trace2skill sample-only eval outputs, and RL curve CSV. | |
| ## Evaluation | |
| Evaluation was run with 4 samples per item, temperature 1.0, top-p 1.0, | |
| top-k 40, presence penalty 2.0, and `paper_react_cli` mode. | |
| | Dataset | Records | Mean Score | Max@4 | | |
| | --- | ---: | ---: | ---: | | |
| | DAPO100 | 400 | 0.6875 | 0.8300 | | |
| | AIME2026 | 120 | 0.5833 | 0.7667 | | |
| | DocVQA post-eval | 400 | 0.4522 | 0.5147 | | |
| DocVQA trace2skill sample-only evaluation used 8 samples per item on 50 evolve | |
| items and reached mean score 0.4338 with max@8 0.7585. | |
| The full optimizer state, Megatron/FSDP training checkpoint files, tool | |
| workspaces, server logs, and cache directories are not included in this repo. | |