Instructions to use HaoxinranYu/data-filtering-challenge-2025-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HaoxinranYu/data-filtering-challenge-2025-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("data4elm/Llama-400M-12L") model = PeftModel.from_pretrained(base_model, "HaoxinranYu/data-filtering-challenge-2025-model") - Notebooks
- Google Colab
- Kaggle
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Download README.md from HaoxinranYu/data-filtering-challenge-2025-model: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/HaoxinranYu/data-filtering-challenge-2025-model/resolve/main/README.md
- Command line
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hf download hf://HaoxinranYu/data-filtering-challenge-2025-model/README.md
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curl -L -o README.md https://huggingface.co/HaoxinranYu/data-filtering-challenge-2025-model/resolve/main/README.md
1.3 kB
metadata
library_name: peft
license: apache-2.0
base_model: data4elm/Llama-400M-12L
tags:
- generated_from_trainer
datasets:
- customized
model-index:
- name: finetune
results: []
finetune
This model is a fine-tuned version of data4elm/Llama-400M-12L on the customized dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 192
- total_eval_batch_size: 64
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1.0
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.56.2
- Pytorch 2.8.0+cu128
- Datasets 2.14.6
- Tokenizers 0.22.1