Instructions to use NTQuoc/OpenRS-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTQuoc/OpenRS-GRPO with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NTQuoc/OpenRS-GRPO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1
Browse files- adapter_config.json +7 -7
- adapter_model.safetensors +1 -1
- training_args.bin +1 -1
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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"in_proj_b",
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"gate_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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"down_proj",
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"o_proj",
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"in_proj_b",
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"in_proj_z",
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"gate_proj",
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"in_proj_qkv",
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"in_proj_a",
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"q_proj",
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"up_proj",
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"v_proj",
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"k_proj",
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"out_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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size 10871936
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version https://git-lfs.github.com/spec/v1
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oid sha256:4f1ee7db502f5af1755583f34ca75513e448f95a195e1cdc28ce8d3dcf96eb01
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size 10871936
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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size 7672
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version https://git-lfs.github.com/spec/v1
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oid sha256:ba29384550b2f1dcf9f1820c90e20bcb227e77949a061d68c1072b35be93c1ae
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size 7672
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