Text Generation
PEFT
Safetensors
English
qwen3
lora
systematic-review
cochrane
title-abstract-screening
medical
conversational
Instructions to use deepcoder2024/Qwen3-1.7B-LoRA-Cochrane-Screening with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use deepcoder2024/Qwen3-1.7B-LoRA-Cochrane-Screening with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "deepcoder2024/Qwen3-1.7B-LoRA-Cochrane-Screening") - Notebooks
- Google Colab
- Kaggle
File size: 651 Bytes
1a3302b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"model_path": "Qwen/Qwen3-1.7B",
"train_file": "deepcoder2024/cochrane-screening-sft (train)",
"val_file": "deepcoder2024/cochrane-screening-sft (validation)",
"output_dir": "Qwen3-1.7B-LoRA-Cochrane-Screening",
"max_length": 2048,
"num_train_epochs": 1.0,
"learning_rate": 0.0002,
"per_device_train_batch_size": 2,
"per_device_eval_batch_size": 2,
"gradient_accumulation_steps": 8,
"warmup_ratio": 0.03,
"weight_decay": 0.01,
"logging_steps": 50,
"eval_steps": 2000,
"save_steps": 2000,
"save_total_limit": 2,
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"seed": 42,
"dataloader_num_workers": 2
}
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