Instructions to use ashik0001/test_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ashik0001/test_training with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-11B-Vision-Instruct") model = PeftModel.from_pretrained(base_model, "ashik0001/test_training") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ashik0001/test_training: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/ashik0001/test_training/resolve/main/README.md
- Command line
-
hf download hf://ashik0001/test_training/README.md
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curl -L -o README.md https://huggingface.co/ashik0001/test_training/resolve/main/README.md
1.25 kB
metadata
base_model: unsloth/Llama-3.2-11B-Vision-Instruct
library_name: peft
license: llama3.2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: test_training
results: []
test_training
This model is a fine-tuned version of unsloth/Llama-3.2-11B-Vision-Instruct 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
Training results
Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0