Instructions to use deshanksuman/finetuned-Qwen2.5-3B-Instruct-WSD-Advanced-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deshanksuman/finetuned-Qwen2.5-3B-Instruct-WSD-Advanced-reasoning with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("deshanksuman/finetuned-Qwen2.5-3B-Instruct-WSD-Advanced-reasoning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
base_model:
- Qwen/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- llama
- trl
license: apache-2.0
language:
- en
datasets:
- deshanksuman/Reasoning_WSD_dataset
Uploaded model
- Developed by: deshanksuman
- License: apache-2.0
- Finetuned from model : Qwen/Qwen2.5-3B-Instruct
Dataset
Fews Training data arranged in the format of Instruction, Input and output deshanksuman/Reasoning_WSD_dataset
Hyperparameter for Training
- per_device_train_batch_size=4,
- gradient_accumulation_steps=8,
- warmup_steps=50,
- num_train_epochs=2,
- learning_rate=2e-4,
- fp16=not torch.cuda.is_bf16_supported(),
- bf16=torch.cuda.is_bf16_supported(),
- logging_steps=10,
- optim="adamw_torch",
- weight_decay=0.01,
- lr_scheduler_type="linear",
- seed=3407
This is developed by Deshan Sumanathilaka https://sumanathilaka.github.io
Acknowledgement
We acknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government.