Instructions to use Trelis/Llama-2-13b-chat-hf-stanford-nil-policy-ft-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Trelis/Llama-2-13b-chat-hf-stanford-nil-policy-ft-adapters with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf") model = PeftModel.from_pretrained(base_model, "Trelis/Llama-2-13b-chat-hf-stanford-nil-policy-ft-adapters") - Notebooks
- Google Colab
- Kaggle
File size: 778 Bytes
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"auto_mapping": null,
"base_model_name_or_path": "meta-llama/Llama-2-13b-chat-hf",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"lora_alpha": 32,
"lora_dropout": 0.1,
"modules_to_save": null,
"peft_type": "LORA",
"r": 8,
"rank_pattern": {},
"revision": null,
"target_modules": [
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"task_type": "CAUSAL_LM"
} |