End of training

README.md

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+---
+base_model: Qwen/Qwen2-0.5B-Instruct
+datasets: continual_data_debug_0
+library_name: transformers
+model_name: Qwen2-0.5B-Instruct_continual_data_debug_REWARD_0
+tags:
+- generated_from_trainer
+- trl
+- reward-trainer
+licence: license
+---
+
+# Model Card for Qwen2-0.5B-Instruct_continual_data_debug_REWARD_0
+
+This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [continual_data_debug_0](https://huggingface.co/datasets/continual_data_debug_0) dataset.
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+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="Shahradmz/Qwen2-0.5B-Instruct_continual_data_debug_REWARD_0", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<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/shahrad_m/huggingface/runs/76f337dj) 
+
+
+This model was trained with Reward.
+
+### Framework versions
+
+- TRL: 0.15.1
+- Transformers: 4.49.0
+- Pytorch: 2.3.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## Citations
+
+
+
+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 Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

all_results.json

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+{
+    "epoch": 1.0,
+    "eval_accuracy": 1.0,
+    "eval_loss": 0.42100343108177185,
+    "eval_runtime": 0.0672,
+    "eval_samples_per_second": 29.753,
+    "eval_steps_per_second": 14.876
+}

eval_results.json

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+{
+    "epoch": 1.0,
+    "eval_accuracy": 1.0,
+    "eval_loss": 0.42100343108177185,
+    "eval_runtime": 0.0672,
+    "eval_samples_per_second": 29.753,
+    "eval_steps_per_second": 14.876
+}