Instructions to use Daksh1/simpleFinetuningTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Daksh1/simpleFinetuningTest with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Daksh1/simpleFinetuningTest", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload LlamaForCausalLM
Browse files- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
adapter_config.json
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha":
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"lora_bias": false,
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"lora_dropout": 0.1,
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"megatron_config": null,
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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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"k_proj",
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"gate_proj",
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"up_proj",
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"down_proj",
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"v_proj",
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"o_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 3,
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"lora_bias": false,
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"lora_dropout": 0.1,
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"megatron_config": null,
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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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"k_proj",
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"up_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"q_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 22573704
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version https://git-lfs.github.com/spec/v1
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oid sha256:27e013e7d0c8963c7224442e95dfe1ed17cd6221f99f4b1cdacbcc6caca81d5b
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size 22573704
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