Instructions to use Tesslate/Gradience-RL-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tesslate/Gradience-RL-Math with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tesslate/Gradience-RL-Math", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download last-checkpoint/trainer_state.json from Tesslate/Gradience-RL-Math: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/Tesslate/Gradience-RL-Math/resolve/main/last-checkpoint/trainer_state.json
- Command line
-
hf download hf://Tesslate/Gradience-RL-Math/last-checkpoint/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/Tesslate/Gradience-RL-Math/resolve/main/last-checkpoint/trainer_state.json
1.11 kB
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 7.964256417199609e-05, | |
| "eval_steps": 500, | |
| "global_step": 10, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "completion_length": 815.3625, | |
| "epoch": 7.964256417199609e-05, | |
| "grad_norm": 0.1777159869670868, | |
| "kl": 0.0006033990764990449, | |
| "learning_rate": 6.25e-07, | |
| "loss": 0.0, | |
| "reward": 1.2572406262159348, | |
| "reward_std": 0.3052074507810175, | |
| "rewards/api_reward_func_with_db": 1.2572406262159348, | |
| "step": 10 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 800, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 10, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 0.0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |