Instructions to use archit11/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use archit11/checkpoints with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-4B-Base") model = PeftModel.from_pretrained(base_model, "archit11/checkpoints") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from archit11/checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/archit11/checkpoints/resolve/main/README.md
- Command line
-
hf download hf://archit11/checkpoints/README.md
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curl -L -o README.md https://huggingface.co/archit11/checkpoints/resolve/main/README.md
1.28 kB
metadata
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen3-4B-Base
tags:
- unsloth
- generated_from_trainer
model-index:
- name: checkpoints
results: []
checkpoints
This model is a fine-tuned version of unsloth/Qwen3-4B-Base on an unknown 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 10
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1