Instructions to use nrishabh/llama3-8b-instruct-qlora-minute with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nrishabh/llama3-8b-instruct-qlora-minute with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LoftQ/Meta-Llama-3-8B-Instruct-4bit-64rank") model = PeftModel.from_pretrained(base_model, "nrishabh/llama3-8b-instruct-qlora-minute") - Notebooks
- Google Colab
- Kaggle
llama3-8b-instruct-qlora-minute
This model is a fine-tuned version of LoftQ/Meta-Llama-3-8B-Instruct-4bit-64rank on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0351
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5186 | 1.0 | 6 | 2.0351 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
- Downloads last month
- 5
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for nrishabh/llama3-8b-instruct-qlora-minute
Base model
LoftQ/Meta-Llama-3-8B-Instruct-4bit-64rank