Instructions to use 0x1202/0bc6a6b4-4105-42ea-83b7-74358aea94fe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use 0x1202/0bc6a6b4-4105-42ea-83b7-74358aea94fe with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "0x1202/0bc6a6b4-4105-42ea-83b7-74358aea94fe") - Notebooks
- Google Colab
- Kaggle
File size: 739 Bytes
36b04a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "NousResearch/Hermes-2-Pro-Mistral-7B",
"bias": "none",
"fan_in_fan_out": null,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 128,
"lora_dropout": 0.05,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 64,
"rank_pattern": {},
"revision": null,
"target_modules": [
"o_proj",
"up_proj",
"q_proj",
"v_proj",
"gate_proj",
"k_proj",
"down_proj"
],
"task_type": "CAUSAL_LM",
"use_dora": false,
"use_rslora": false
} |