Instructions to use Shahradmz/Qwen2-0.5B-Instruct_continual_data_debug_CPPO_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shahradmz/Qwen2-0.5B-Instruct_continual_data_debug_CPPO_1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Shahradmz/Qwen2-0.5B-Instruct_continual_data_debug_CPPO_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +1 -1
- all_results.json +1 -1
- eval_results.json +1 -1
README.md
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## Training procedure
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[<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/AIFGen-ppo-continual-test/runs/
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This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593).
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## Training procedure
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[<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/AIFGen-ppo-continual-test/runs/3pa7x10a)
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This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593).
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all_results.json
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{
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"dataset": 1,
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"eval_score": 4.
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}
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{
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"dataset": 1,
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"eval_score": 4.5389204025268555
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}
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eval_results.json
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{
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"dataset": 1,
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"eval_score": 4.
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}
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{
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"dataset": 1,
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"eval_score": 4.5389204025268555
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}
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