Instructions to use NTQuoc/OpenRS-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTQuoc/OpenRS-GRPO with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NTQuoc/OpenRS-GRPO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +2 -4
- all_results.json +4 -4
- step_metrics.csv +21 -21
- train_results.json +4 -4
- trainer_state.json +122 -122
- training_metrics.txt +6 -6
README.md
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---
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base_model:
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datasets: knoveleng/open-rs
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library_name: transformers
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model_name: OpenRS-GRPO
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tags:
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- generated_from_trainer
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- open-r1
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- trl
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- grpo
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licence: license
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# Model Card for OpenRS-GRPO
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This model is a fine-tuned version of [
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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---
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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library_name: transformers
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model_name: OpenRS-GRPO
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tags:
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- generated_from_trainer
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- trl
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- grpo
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licence: license
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# Model Card for OpenRS-GRPO
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+
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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all_results.json
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step_metrics.csv
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training_metrics.txt
CHANGED
|
@@ -1,6 +1,6 @@
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|
| 1 |
-
total_size_before (MB):
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| 2 |
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total_size_after (MB):
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|
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| 1 |
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total_size_before (MB): 3424.75
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total_time (seconds): 16314.67
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| 5 |
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ram_consump (MB): 3476.35
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disk_storage (MB): 180.81
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