Instructions to use eja67/MBIAS_adapter_fseed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eja67/MBIAS_adapter_fseed with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eja67/MBIAS_adapter_fseed", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 25
Browse files- README.md +1 -1
- adapter_config.json +4 -4
- adapter_model.safetensors +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/eja67-university-of-cambridge/part-ii-project/runs/
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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/eja67-university-of-cambridge/part-ii-project/runs/knch2cvh)
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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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"o_proj",
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"down_proj",
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"k_proj",
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"q_proj",
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"v_proj",
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"up_proj",
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"
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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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"v_proj",
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"o_proj",
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"down_proj",
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"gate_proj",
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"up_proj",
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"q_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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version https://git-lfs.github.com/spec/v1
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