Instructions to use bihungba1101/task-1-formatting-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bihungba1101/task-1-formatting-lite with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bihungba1101/task-1-formatting-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1
Browse files- README.md +1 -1
- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
- training_args.bin +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/ielts-science/huggingface/runs/
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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).
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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/ielts-science/huggingface/runs/7af7s9gq)
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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).
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"gate_proj",
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"q_proj",
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"o_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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training_args.bin
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