Instructions to use i0445/islm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use i0445/islm with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "i0445/islm") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model": "Qwen/Qwen3-4B-Instruct-2507", | |
| "data_dir": "data/dataset_v2", | |
| "train_examples": 1288, | |
| "epochs": 1.0, | |
| "max_steps": 800, | |
| "optimizer_steps": 800, | |
| "per_device_batch_size": 1, | |
| "grad_accum": 8, | |
| "max_seq_len": 1024, | |
| "learning_rate": 0.0002, | |
| "lora_r": 32, | |
| "lora_alpha": 64, | |
| "lora_dropout": 0.05, | |
| "seed": 0, | |
| "qlora": true, | |
| "device": "cuda", | |
| "lr_scheduler_type": "cosine", | |
| "warmup_ratio": 0.03, | |
| "weight_decay": 0.001, | |
| "max_grad_norm": 0.3, | |
| "optim": "paged_adamw_32bit", | |
| "final_train_loss": 0.3792 | |
| } |