Text Generation
PEFT
Safetensors
English
sna-learning
lora
education
personalized
mnemonic
ml-concepts
conversational
Instructions to use Dev-the-dev91/sna-ml-adapter-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dev-the-dev91/sna-ml-adapter-v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "Dev-the-dev91/sna-ml-adapter-v2") - Notebooks
- Google Colab
- Kaggle
File size: 736 Bytes
5f36152 | 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 | {
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": null,
"bias": "none",
"corda_config": null,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"inference_mode": false,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 32,
"lora_bias": false,
"lora_dropout": 0,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 32,
"rank_pattern": {},
"revision": null,
"target_modules": "all-linear",
"task_type": "CAUSAL_LM",
"trainable_token_indices": null,
"use_dora": false,
"use_rslora": false
} |