Instructions to use Atipico1/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Atipico1/output with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Atipico1/output") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: Qwen/Qwen2-1.5B-Instruct
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: output
results: []
output
This model is a fine-tuned version of Qwen/Qwen2-1.5B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0250
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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0812 | 1.0 | 1250 | 1.0450 |
| 1.012 | 2.0 | 2500 | 1.0273 |
| 1.0175 | 3.0 | 3750 | 1.0250 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1