Instructions to use kslote/sft-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kslote/sft-output with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kslote/sft-output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
round-bird/georgia-sports-llama3-sft
Browse files- README.md +2 -2
- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
- final/adapter_config.json +4 -4
- final/adapter_model.safetensors +1 -1
- final/training_args.bin +1 -1
- training_args.bin +1 -1
README.md
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model_name: sft-output
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tags:
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- generated_from_trainer
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- sft
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- trl
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licence: license
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---
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### Framework versions
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- TRL: 1.
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- Transformers: 5.0.0
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.8.4
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model_name: sft-output
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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### Framework versions
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- TRL: 1.1.0
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- Transformers: 5.0.0
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.8.4
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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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"k_proj",
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"gate_proj",
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"down_proj",
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"v_proj",
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"
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"up_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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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"down_proj",
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"k_proj",
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"o_proj",
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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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final/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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"k_proj",
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"v_proj",
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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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"q_proj",
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"down_proj",
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"k_proj",
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"gate_proj",
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"v_proj",
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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final/adapter_model.safetensors
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final/training_args.bin
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training_args.bin
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