Model save

README.md

@@ -1,68 +1,55 @@
 ---
+library_name: peft
+license: apache-2.0
 base_model: Qwen/Qwen2-0.5B-Instruct
-library_name: transformers
-model_name: Qwen2-0.5B-GRPO-test
 tags:
-- generated_from_trainer
 - trl
 - grpo
-licence: license
+- generated_from_trainer
+model-index:
+- name: Qwen2-0.5B-GRPO-test
+  results: []
 ---
 
-# Model Card for Qwen2-0.5B-GRPO-test
-
-This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-
-## Quick start
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-```python
-from transformers import pipeline
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/icl-grok/NuminaMath-TIR/runs/r1d9umv1)
+# Qwen2-0.5B-GRPO-test
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="jbrinkma/Qwen2-0.5B-GRPO-test", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on an unknown dataset.
 
-## Training procedure
+## Model description
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/icl-grok/NuminaMath-TIR/runs/r1d9umv1) 
+More information needed
 
+## Intended uses & limitations
 
-This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
+More information needed
 
-### Framework versions
+## Training and evaluation data
 
-- TRL: 0.19.0
-- Transformers: 4.52.4
-- Pytorch: 2.7.1
-- Datasets: 3.6.0
-- Tokenizers: 0.21.2
+More information needed
 
-## Citations
+## Training procedure
 
-Cite GRPO as:
+### Training hyperparameters
 
-```bibtex
-@article{zhihong2024deepseekmath,
-    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
-    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
-    year         = 2024,
-    eprint       = {arXiv:2402.03300},
-}
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 128
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 1
 
-```
+### Framework versions
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+- PEFT 0.15.2
+- Transformers 4.52.4
+- Pytorch 2.7.1+cu126
+- Datasets 3.6.0
+- Tokenizers 0.21.2

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