Instructions to use care2achieve/codegen-350M-text2sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use care2achieve/codegen-350M-text2sql-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi") model = PeftModel.from_pretrained(base_model, "care2achieve/codegen-350M-text2sql-lora") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- README.md +1 -1
- adapter_config.json +5 -3
- adapter_model.safetensors +2 -2
README.md
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LoRA adapter for **text2sql** on [Salesforce/codegen-350M-multi](https://huggingface.co/Salesforce/codegen-350M-multi).
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- Checkpoint version: `
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- Task: `text2sql`
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## Usage
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LoRA adapter for **text2sql** on [Salesforce/codegen-350M-multi](https://huggingface.co/Salesforce/codegen-350M-multi).
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- Checkpoint version: `v4`
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- Task: `text2sql`
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## Usage
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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": 0.05,
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"lora_ga_config": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r":
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"out_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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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"lora_ga_config": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"fc_out",
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"qkv_proj",
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"out_proj",
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"fc_in"
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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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